Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

PubMed Central

Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

2014-01-01

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

Rani, R Ranjani; Ramyachitra, D

2016-12-01

Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Variable selection based near infrared spectroscopy quantitative and qualitative analysis on wheat wet gluten

NASA Astrophysics Data System (ADS)

Lü, Chengxu; Jiang, Xunpeng; Zhou, Xingfan; Zhang, Yinqiao; Zhang, Naiqian; Wei, Chongfeng; Mao, Wenhua

2017-10-01

Wet gluten is a useful quality indicator for wheat, and short wave near infrared spectroscopy (NIRS) is a high performance technique with the advantage of economic rapid and nondestructive test. To study the feasibility of short wave NIRS analyzing wet gluten directly from wheat seed, 54 representative wheat seed samples were collected and scanned by spectrometer. 8 spectral pretreatment method and genetic algorithm (GA) variable selection method were used to optimize analysis. Both quantitative and qualitative model of wet gluten were built by partial least squares regression and discriminate analysis. For quantitative analysis, normalization is the optimized pretreatment method, 17 wet gluten sensitive variables are selected by GA, and GA model performs a better result than that of all variable model, with R2V=0.88, and RMSEV=1.47. For qualitative analysis, automatic weighted least squares baseline is the optimized pretreatment method, all variable models perform better results than those of GA models. The correct classification rates of 3 class of <24%, 24-30%, >30% wet gluten content are 95.45, 84.52, and 90.00%, respectively. The short wave NIRS technique shows potential for both quantitative and qualitative analysis of wet gluten for wheat seed.

A Comparative Study of Probability Collectives Based Multi-agent Systems and Genetic Algorithms

NASA Technical Reports Server (NTRS)

Huang, Chien-Feng; Wolpert, David H.; Bieniawski, Stefan; Strauss, Charles E. M.

2005-01-01

We compare Genetic Algorithms (GA's) with Probability Collectives (PC), a new framework for distributed optimization and control. In contrast to GA's, PC-based methods do not update populations of solutions. Instead they update an explicitly parameterized probability distribution p over the space of solutions. That updating of p arises as the optimization of a functional of p. The functional is chosen so that any p that optimizes it should be p peaked about good solutions. The PC approach works in both continuous and discrete problems. It does not suffer from the resolution limitation of the finite bit length encoding of parameters into GA alleles. It also has deep connections with both game theory and statistical physics. We review the PC approach using its motivation as the information theoretic formulation of bounded rationality for multi-agent systems. It is then compared with GA's on a diverse set of problems. To handle high dimensional surfaces, in the PC method investigated here p is restricted to a product distribution. Each distribution in that product is controlled by a separate agent. The test functions were selected for their difficulty using either traditional gradient descent or genetic algorithms. On those functions the PC-based approach significantly outperforms traditional GA's in both rate of descent, trapping in false minima, and long term optimization.

Genetic algorithm for the optimization of features and neural networks in ECG signals classification

NASA Astrophysics Data System (ADS)

Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

2017-01-01

Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

An improved genetic algorithm for designing optimal temporal patterns of neural stimulation

NASA Astrophysics Data System (ADS)

Cassar, Isaac R.; Titus, Nathan D.; Grill, Warren M.

2017-12-01

Objective. Electrical neuromodulation therapies typically apply constant frequency stimulation, but non-regular temporal patterns of stimulation may be more effective and more efficient. However, the design space for temporal patterns is exceedingly large, and model-based optimization is required for pattern design. We designed and implemented a modified genetic algorithm (GA) intended for design optimal temporal patterns of electrical neuromodulation. Approach. We tested and modified standard GA methods for application to designing temporal patterns of neural stimulation. We evaluated each modification individually and all modifications collectively by comparing performance to the standard GA across three test functions and two biophysically-based models of neural stimulation. Main results. The proposed modifications of the GA significantly improved performance across the test functions and performed best when all were used collectively. The standard GA found patterns that outperformed fixed-frequency, clinically-standard patterns in biophysically-based models of neural stimulation, but the modified GA, in many fewer iterations, consistently converged to higher-scoring, non-regular patterns of stimulation. Significance. The proposed improvements to standard GA methodology reduced the number of iterations required for convergence and identified superior solutions.

Microwave synthesis of gibberellin acid 3 magnetic molecularly imprinted polymer beads for the trace analysis of gibberellin acids in plant samples by liquid chromatography-mass spectrometry detection.

PubMed

Zhang, Zhuomin; Tan, Wei; Hu, Yuling; Li, Gongke; Zan, Song

2012-02-21

In this study, novel GA3 magnetic molecularly imprinted polymer (mag-MIP) beads were synthesized by a microwave irradiation method, and the beads were applied for the trace analysis of gibberellin acids (GAs) in plant samples including rice and cucumber coupled with high performance liquid chromatography-mass spectrometry (HPLC-MS). The microwave synthetic procedure was optimized in detail. In particular, the interaction between GA3 and functional monomers was further studied for the selection of the optimal functional monomers during synthesis. It can be seen that the interaction between GA3 and acrylamide (AM) finally selected was stronger than that between GA3 and other functional monomers. GA3 mag-MIP beads were characterized by a series of physical tests. GA3 mag-MIP beads had a porous and homogeneous surface morphology with stable chemical, thermal and magnetic properties. Moreover, GA3 mag-MIP beads demonstrated selective and specific absorption behavior for the target compounds during unsaturated extraction, which resulted in a higher extraction capacity (∼708.4 pmol for GA3) and selectivity than GA3 mag-non-imprinted polymer beads. Finally, an analytical method of GA3 mag-AM-MIP bead extraction coupled with HPLC-MS detection was established and applied for the determination of trace GA1, GA3, GA4 and GA7 in rice and cucumber samples. It was satisfactory that GA4 could be actually found to be 121.5 ± 1.4 μg kg(-1) in real rice samples by this novel analytical method. The recoveries of spiked rice and cucumber samples were found to be 76.0-109.1% and 79.9-93.6% with RSDs of 2.8-8.8% and 3.1-7.7% (n = 3), respectively. The proposed method is efficient and applicable for the trace analysis of GAs in complicated plant samples.

Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms.

PubMed

Chiang, Tzu-An; Che, Z H; Cui, Zhihua

2014-01-01

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did.

Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

PubMed Central

Chiang, Tzu-An; Che, Z. H.

2014-01-01

This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V Max method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026

An Adaptive Niching Genetic Algorithm using a niche size equalization mechanism

NASA Astrophysics Data System (ADS)

Nagata, Yuichi

Niching GAs have been widely investigated to apply genetic algorithms (GAs) to multimodal function optimization problems. In this paper, we suggest a new niching GA that attempts to form niches, each consisting of an equal number of individuals. The proposed GA can be applied also to combinatorial optimization problems by defining a distance metric in the search space. We apply the proposed GA to the job-shop scheduling problem (JSP) and demonstrate that the proposed niching method enhances the ability to maintain niches and improve the performance of GAs.

Optimization of a Boiling Water Reactor Loading Pattern Using an Improved Genetic Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kobayashi, Yoko; Aiyoshi, Eitaro

2003-08-15

A search method based on genetic algorithms (GA) using deterministic operators has been developed to generate optimized boiling water reactor (BWR) loading patterns (LPs). The search method uses an Improved GA operator, that is, crossover, mutation, and selection. The handling of the encoding technique and constraint conditions is designed so that the GA reflects the peculiar characteristics of the BWR. In addition, some strategies such as elitism and self-reproduction are effectively used to improve the search speed. LP evaluations were performed with a three-dimensional diffusion code that coupled neutronic and thermal-hydraulic models. Strong axial heterogeneities and three-dimensional-dependent constraints have alwaysmore » necessitated the use of three-dimensional core simulators for BWRs, so that an optimization method is required for computational efficiency. The proposed algorithm is demonstrated by successfully generating LPs for an actual BWR plant applying the Haling technique. In test calculations, candidates that shuffled fresh and burned fuel assemblies within a reasonable computation time were obtained.« less

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem.

PubMed

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA.

Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem

PubMed Central

Yue, Yi-xiang; Zhang, Tong; Yue, Qun-xing

2015-01-01

Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. PMID:26167171

Study on the Electronic Transport Properties of Zigzag GaN Nanotubes

NASA Astrophysics Data System (ADS)

Li, Enling; Wang, Xiqiang; Hou, Liping; Zhao, Danna; Dai, Yuanbin; Wang, Xuewen

2011-02-01

The electronic transport properties of zigzag GaN nanotubes (n, 0) (4 <= n <= 9) have been calculated using the density functional theory and non-equilibrium Green's functions method. Firstly, the density functional theory (DFT) is used to optimize and calculate the electronic structure of GaNNTs (n, 0) (4<=n<=9). Secondly, DFT and non-equilibrium Green function (NEGF) method are also used to predict the electronic transport properties of GaNNTs two-probe system. The results showed: there is a corresponding relation between the electronic transport properties and the valley of state density of each GaNNT. In addition, the volt-ampere curve of GaNNT is approximately linear.

A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem

PubMed Central

Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun

2014-01-01

A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031

Load forecast method of electric vehicle charging station using SVR based on GA-PSO

NASA Astrophysics Data System (ADS)

Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan

2017-06-01

This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.

Application of GA-SVM method with parameter optimization for landslide development prediction

NASA Astrophysics Data System (ADS)

Li, X. Z.; Kong, J. M.

2013-10-01

Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle

NASA Astrophysics Data System (ADS)

Wang, Aimeng; Guo, Jiayu

2017-12-01

A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.

Genetic algorithm optimized triply compensated pulses in NMR spectroscopy

NASA Astrophysics Data System (ADS)

Manu, V. S.; Veglia, Gianluigi

2015-11-01

Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature's evolutionary processes. The newly designed π and π / 2 pulses belong to the 'type A' (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U-13C, 15N NAVL peptide as well as U-13C, 15N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences.

Analysis of light extraction efficiency enhancement for thin-film-flip-chip InGaN quantum wells light-emitting diodes with GaN micro-domes.

PubMed

Zhao, Peng; Zhao, Hongping

2012-09-10

The enhancement of light extraction efficiency for thin-film flip-chip (TFFC) InGaN quantum wells (QWs) light-emitting diodes (LEDs) with GaN micro-domes on n-GaN layer was studied. The light extraction efficiency of TFFC InGaN QWs LEDs with GaN micro-domes were calculated and compared to that of the conventional TFFC InGaN QWs LEDs with flat surface. The three dimensional finite difference time domain (3D-FDTD) method was used to calculate the light extraction efficiency for the InGaN QWs LEDs emitting at 460nm and 550 nm, respectively. The effects of the GaN micro-dome feature size and the p-GaN layer thickness on the light extraction efficiency were studied systematically. Studies indicate that the p-GaN layer thickness is critical for optimizing the TFFC LED light extraction efficiency. Significant enhancement of the light extraction efficiency (2.5-2.7 times for λ(peak) = 460nm and 2.7-2.8 times for λ(peak) = 550nm) is achievable from TFFC InGaN QWs LEDs with optimized GaN micro-dome diameter and height.

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation.

PubMed

Cheema, Jitender Jit Singh; Sankpal, Narendra V; Tambe, Sanjeev S; Kulkarni, Bhaskar D

2002-01-01

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

Experimental design for estimating unknown groundwater pumping using genetic algorithm and reduced order model

NASA Astrophysics Data System (ADS)

Ushijima, Timothy T.; Yeh, William W.-G.

2013-10-01

An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.

Interleaved segment correction achieves higher improvement factors in using genetic algorithm to optimize light focusing through scattering media

NASA Astrophysics Data System (ADS)

Li, Runze; Peng, Tong; Liang, Yansheng; Yang, Yanlong; Yao, Baoli; Yu, Xianghua; Min, Junwei; Lei, Ming; Yan, Shaohui; Zhang, Chunmin; Ye, Tong

2017-10-01

Focusing and imaging through scattering media has been proved possible with high resolution wavefront shaping. A completely scrambled scattering field can be corrected by applying a correction phase mask on a phase only spatial light modulator (SLM) and thereby the focusing quality can be improved. The correction phase is often found by global searching algorithms, among which Genetic Algorithm (GA) stands out for its parallel optimization process and high performance in noisy environment. However, the convergence of GA slows down gradually with the progression of optimization, causing the improvement factor of optimization to reach a plateau eventually. In this report, we propose an interleaved segment correction (ISC) method that can significantly boost the improvement factor with the same number of iterations comparing with the conventional all segment correction method. In the ISC method, all the phase segments are divided into a number of interleaved groups; GA optimization procedures are performed individually and sequentially among each group of segments. The final correction phase mask is formed by applying correction phases of all interleaved groups together on the SLM. The ISC method has been proved significantly useful in practice because of its ability to achieve better improvement factors when noise is present in the system. We have also demonstrated that the imaging quality is improved as better correction phases are found and applied on the SLM. Additionally, the ISC method lowers the demand of dynamic ranges of detection devices. The proposed method holds potential in applications, such as high-resolution imaging in deep tissue.

Elimination of Bimodal Size in InAs/GaAs Quantum Dots for Preparation of 1.3-μm Quantum Dot Lasers

NASA Astrophysics Data System (ADS)

Su, Xiang-Bin; Ding, Ying; Ma, Ben; Zhang, Ke-Lu; Chen, Ze-Sheng; Li, Jing-Lun; Cui, Xiao-Ran; Xu, Ying-Qiang; Ni, Hai-Qiao; Niu, Zhi-Chuan

2018-02-01

The device characteristics of semiconductor quantum dot lasers have been improved with progress in active layer structures. Self-assembly formed InAs quantum dots grown on GaAs had been intensively promoted in order to achieve quantum dot lasers with superior device performances. In the process of growing high-density InAs/GaAs quantum dots, bimodal size occurs due to large mismatch and other factors. The bimodal size in the InAs/GaAs quantum dot system is eliminated by the method of high-temperature annealing and optimized the in situ annealing temperature. The annealing temperature is taken as the key optimization parameters, and the optimal annealing temperature of 680 °C was obtained. In this process, quantum dot growth temperature, InAs deposition, and arsenic (As) pressure are optimized to improve quantum dot quality and emission wavelength. A 1.3-μm high-performance F-P quantum dot laser with a threshold current density of 110 A/cm2 was demonstrated.

Elimination of Bimodal Size in InAs/GaAs Quantum Dots for Preparation of 1.3-μm Quantum Dot Lasers.

PubMed

Su, Xiang-Bin; Ding, Ying; Ma, Ben; Zhang, Ke-Lu; Chen, Ze-Sheng; Li, Jing-Lun; Cui, Xiao-Ran; Xu, Ying-Qiang; Ni, Hai-Qiao; Niu, Zhi-Chuan

2018-02-21

The device characteristics of semiconductor quantum dot lasers have been improved with progress in active layer structures. Self-assembly formed InAs quantum dots grown on GaAs had been intensively promoted in order to achieve quantum dot lasers with superior device performances. In the process of growing high-density InAs/GaAs quantum dots, bimodal size occurs due to large mismatch and other factors. The bimodal size in the InAs/GaAs quantum dot system is eliminated by the method of high-temperature annealing and optimized the in situ annealing temperature. The annealing temperature is taken as the key optimization parameters, and the optimal annealing temperature of 680 °C was obtained. In this process, quantum dot growth temperature, InAs deposition, and arsenic (As) pressure are optimized to improve quantum dot quality and emission wavelength. A 1.3-μm high-performance F-P quantum dot laser with a threshold current density of 110 A/cm 2 was demonstrated.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimization of solar cells for air mass zero operation and study of solar cells at high temperatures, phase 4

NASA Technical Reports Server (NTRS)

Hovel, H. J.; Woodall, J. M.

1980-01-01

The Pd contact to GaAs was studied using backscattering, Auger analysis, and sheet resistance measurements. Several metallurgical phases were present at low temperatures, but PdGa was the dominant phase in samples annealed at 500 C. Ti/Pd/Ag contacts appeared to have the lowest contact resistance. Etchback epitaxy (EBE) was compared to saturated melt epitaxy (SME) method of growing liquid phase epitaxial layers. The SME method resulted in a lower density of Ga microdroplets in the grown layer, although the best solar cells were made by the EBE method. Photoluminescence was developed as a tool for contactless analysis of GaAs cells. Efficiencies of over 8 percent were measured at 250 C.

Optimization of Boiling Water Reactor Loading Pattern Using Two-Stage Genetic Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kobayashi, Yoko; Aiyoshi, Eitaro

2002-10-15

A new two-stage optimization method based on genetic algorithms (GAs) using an if-then heuristic rule was developed to generate optimized boiling water reactor (BWR) loading patterns (LPs). In the first stage, the LP is optimized using an improved GA operator. In the second stage, an exposure-dependent control rod pattern (CRP) is sought using GA with an if-then heuristic rule. The procedure of the improved GA is based on deterministic operators that consist of crossover, mutation, and selection. The handling of the encoding technique and constraint conditions by that GA reflects the peculiar characteristics of the BWR. In addition, strategies suchmore » as elitism and self-reproduction are effectively used in order to improve the search speed. The LP evaluations were performed with a three-dimensional diffusion code that coupled neutronic and thermal-hydraulic models. Strong axial heterogeneities and constraints dependent on three dimensions have always necessitated the use of three-dimensional core simulators for BWRs, so that optimization of computational efficiency is required. The proposed algorithm is demonstrated by successfully generating LPs for an actual BWR plant in two phases. One phase is only LP optimization applying the Haling technique. The other phase is an LP optimization that considers the CRP during reactor operation. In test calculations, candidates that shuffled fresh and burned fuel assemblies within a reasonable computation time were obtained.« less

Study on Multi-stage Logistics System Design Problem with Inventory Considering Demand Change by Hybrid Genetic Algorithm

NASA Astrophysics Data System (ADS)

Inoue, Hisaki; Gen, Mitsuo

The logistics model used in this study is 3-stage model employed by an automobile company, which aims to solve traffic problems at a total minimum cost. Recently, research on the metaheuristics method has advanced as an approximate means for solving optimization problems like this model. These problems can be solved using various methods such as the genetic algorithm (GA), simulated annealing, and tabu search. GA is superior in robustness and adjustability toward a change in the structure of these problems. However, GA has a disadvantage in that it has a slightly inefficient search performance because it carries out a multi-point search. A hybrid GA that combines another method is attracting considerable attention since it can compensate for a fault to a partial solution that early convergence gives a bad influence on a result. In this study, we propose a novel hybrid random key-based GA(h-rkGA) that combines local search and parameter tuning of crossover rate and mutation rate; h-rkGA is an improved version of the random key-based GA (rk-GA). We attempted comparative experiments with spanning tree-based GA, priority based GA and random key-based GA. Further, we attempted comparative experiments with “h-GA by only local search” and “h-GA by only parameter tuning”. We reported the effectiveness of the proposed method on the basis of the results of these experiments.

Parallel computation of GA search for the artery shape determinants with CFD

NASA Astrophysics Data System (ADS)

Himeno, M.; Noda, S.; Fukasaku, K.; Himeno, R.

2010-06-01

We studied which factors play important role to determine the shape of arteries at the carotid artery bifurcation by performing multi-objective optimization with computation fluid dynamics (CFD) and the genetic algorithm (GA). To perform it, the most difficult problem is how to reduce turn-around time of the GA optimization with 3D unsteady computation of blood flow. We devised two levels of parallel computation method with the following features: level 1: parallel CFD computation with appropriate number of cores; level 2: parallel jobs generated by "master", which finds quickly available job cue and dispatches jobs, to reduce turn-around time. As a result, the turn-around time of one GA trial, which would have taken 462 days with one core, was reduced to less than two days on RIKEN supercomputer system, RICC, with 8192 cores. We performed a multi-objective optimization to minimize the maximum mean WSS and to minimize the sum of circumference for four different shapes and obtained a set of trade-off solutions for each shape. In addition, we found that the carotid bulb has the feature of the minimum local mean WSS and minimum local radius. We confirmed that our method is effective for examining determinants of artery shapes.

Genetic algorithm with maximum-minimum crossover (GA-MMC) applied in optimization of radiation pattern control of phased-array radars for rocket tracking systems.

PubMed

Silva, Leonardo W T; Barros, Vitor F; Silva, Sandro G

2014-08-18

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.

Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) Applied in Optimization of Radiation Pattern Control of Phased-Array Radars for Rocket Tracking Systems

PubMed Central

Silva, Leonardo W. T.; Barros, Vitor F.; Silva, Sandro G.

2014-01-01

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence. PMID:25196013

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Arab, Mohammad M.; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

2017-01-01

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation. PMID:29163583

Optimal sensor placement for leak location in water distribution networks using genetic algorithms.

PubMed

Casillas, Myrna V; Puig, Vicenç; Garza-Castañón, Luis E; Rosich, Albert

2013-11-04

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach.

Optimal Sensor Placement for Leak Location in Water Distribution Networks Using Genetic Algorithms

PubMed Central

Casillas, Myrna V.; Puig, Vicenç; Garza-Castañón, Luis E.; Rosich, Albert

2013-01-01

This paper proposes a new sensor placement approach for leak location in water distribution networks (WDNs). The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the large size and non-linear integer nature of the resulting optimization problem, genetic algorithms (GAs) are used as the solution approach. The obtained results are compared with a semi-exhaustive search method with higher computational effort, proving that GA allows one to find near-optimal solutions with less computational load. Moreover, three ways of increasing the robustness of the GA-based sensor placement method have been proposed using a time horizon analysis, a distance-based scoring and considering different leaks sizes. A great advantage of the proposed methodology is that it does not depend on the isolation method chosen by the user, as long as it is based on leak sensitivity analysis. Experiments in two networks allow us to evaluate the performance of the proposed approach. PMID:24193099

A comparison of GaAs and Si hybrid solar power systems

NASA Technical Reports Server (NTRS)

Heinbockel, J. H.; Roberts, A. S., Jr.

1977-01-01

Five different hybrid solar power systems using silicon solar cells to produce thermal and electric power are modeled and compared with a hybrid system using a GaAs cell. Among the indices determined are capital cost per unit electric power plus mechanical power, annual cost per unit electric energy, and annual cost per unit electric plus mechanical work. Current costs are taken to be $35,000/sq m for GaAs cells with an efficiency of 15% and $1000/sq m for Si cells with an efficiency of 10%. It is shown that hybrid systems can be competitive with existing methods of practical energy conversion. Limiting values for annual costs of Si and GaAs cells are calculated to be 10.3 cents/kWh and 6.8 cents/kWh, respectively. Results for both systems indicate that for a given flow rate there is an optimal operating condition for minimum cost photovoltaic output. For Si cell costs of $50/sq m optimal performance can be achieved at concentrations of about 10; for GaAs cells costing 1000/sq m, optimal performance can be obtained at concentrations of around 100. High concentration hybrid systems offer a distinct cost advantage over flat systems.

Gallium arsenide-gallium nitride wafer fusion and the n-aluminum gallium arsenide/p-gallium arsenide/n-gallium nitride double heterojunction bipolar transistor

NASA Astrophysics Data System (ADS)

Estrada, Sarah M.

This dissertation describes the n-AlGaAs/p-GaAs/n-GaN heterojunction bipolar transistor (HBT), the first transistor formed via wafer fusion. The fusion process was developed as a way to combine lattice-mismatched materials for high-performance electronic devices, not obtainable via conventional all-epitaxial formation methods. Despite the many challenges of wafer fusion, successful transistors were demonstrated and improved, via the optimization of material structure and fusion process conditions. Thus, this project demonstrated the integration of disparate device materials, chosen for their optimal electronic properties, unrestricted by the conventional (and very limiting) requirement of lattice-matching. By combining an AlGaAs-GaAs emitter-base with a GaN collector, the HBT benefited from the high breakdown voltage of GaN, and from the high emitter injection efficiency and low base transit time of AlGaAs-GaAs. Because the GaAs-GaN lattice mismatch precluded an all-epitaxial formation of the HBT, the GaAs-GaN heterostructure was formed via fusion. This project began with the development of a fusion process that formed mechanically robust and electrically active GaAs-GaN heterojunctions. During the correlation of device electrical performance with a systematic variation of fusion conditions over a wide range (500--750°C, 0.5--2hours), a mid-range fusion temperature was found to induce optimal HBT electrical performance. Transmission electron microscopy (TEM) and secondary ion mass spectrometry (SIMS) were used to assess possible reasons for the variations observed in device electrical performance. Fusion process conditions were correlated with electrical (I-V), structural (TEM), and chemical (SIMS) analyses of the resulting heterojunctions, in order to investigate the trade-off between increased interfacial disorder (TEM) with low fusion temperature and increased diffusion (SIMS) with high fusion temperature. The best do device results (IC ˜ 2.9 kA/cm2 and beta ˜ 3.5, at VCE = 20V and IB = 10mA) were obtained with an HBT formed via fusion at 600°C for 1 hour, with an optimized base-collector design. This was quite an improvement, as compared to an HBT with a simpler base-collector structure, also fused at 600°C for 1 hour (IC ˜ 0.83 kA/cm2 and beta ˜ 0.89, at VCE = 20V and IB = 10mA). Fused AlGaAs-GaAs-GaAs HBTs were compared to fused AlGaAs-GaAs-GaN HBTs, demonstrating that the use of a wider bandgap collector (Eg,GaN > Eg,GaAs) did indeed improve HBT performance at high applied voltages, as desired for high-power applications.

Simulation and optimization performance of GaAs/GaAs0.5Sb0.5/GaSb mechanically stacked tandem solar cells

NASA Astrophysics Data System (ADS)

Tayubi, Y. R.; Suhandi, A.; Samsudin, A.; Arifin, P.; Supriyatman

2018-05-01

Different approaches have been made in order to reach higher solar cells efficiencies. Concepts for multilayer solar cells have been developed. This can be realised if multiple individual single junction solar cells with different suitably chosen band gaps are connected in series in multi-junction solar cells. In our work, we have simulated and optimized solar cells based on the system mechanically stacked using computer simulation and predict their maximum performance. The structures of solar cells are based on the single junction GaAs, GaAs0.5Sb0.5 and GaSb cells. We have simulated each cell individually and extracted their optimal parameters (layer thickness, carrier concentration, the recombination velocity, etc), also, we calculated the efficiency of each cells optimized by separation of the solar spectrum in bands where the cell is sensible for the absorption. The optimal values of conversion efficiency have obtained for the three individual solar cells and the GaAs/GaAs0.5Sb0.5/GaSb tandem solar cells, that are: η = 19,76% for GaAs solar cell, η = 8,42% for GaAs0,5Sb0,5 solar cell, η = 4, 84% for GaSb solar cell and η = 33,02% for GaAs/GaAs0.5Sb0.5/GaSb tandem solar cell.

Heuristic rules embedded genetic algorithm for in-core fuel management optimization

NASA Astrophysics Data System (ADS)

Alim, Fatih

The objective of this study was to develop a unique methodology and a practical tool for designing loading pattern (LP) and burnable poison (BP) pattern for a given Pressurized Water Reactor (PWR) core. Because of the large number of possible combinations for the fuel assembly (FA) loading in the core, the design of the core configuration is a complex optimization problem. It requires finding an optimal FA arrangement and BP placement in order to achieve maximum cycle length while satisfying the safety constraints. Genetic Algorithms (GA) have been already used to solve this problem for LP optimization for both PWR and Boiling Water Reactor (BWR). The GA, which is a stochastic method works with a group of solutions and uses random variables to make decisions. Based on the theories of evaluation, the GA involves natural selection and reproduction of the individuals in the population for the next generation. The GA works by creating an initial population, evaluating it, and then improving the population by using the evaluation operators. To solve this optimization problem, a LP optimization package, GARCO (Genetic Algorithm Reactor Code Optimization) code is developed in the framework of this thesis. This code is applicable for all types of PWR cores having different geometries and structures with an unlimited number of FA types in the inventory. To reach this goal, an innovative GA is developed by modifying the classical representation of the genotype. To obtain the best result in a shorter time, not only the representation is changed but also the algorithm is changed to use in-core fuel management heuristics rules. The improved GA code was tested to demonstrate and verify the advantages of the new enhancements. The developed methodology is explained in this thesis and preliminary results are shown for the VVER-1000 reactor hexagonal geometry core and the TMI-1 PWR. The improved GA code was tested to verify the advantages of new enhancements. The core physics code used for VVER in this research is Moby-Dick, which was developed to analyze the VVER by SKODA Inc. The SIMULATE-3 code, which is an advanced two-group nodal code, is used to analyze the TMI-1.

Strategies for global optimization in photonics design.

PubMed

Vukovic, Ana; Sewell, Phillip; Benson, Trevor M

2010-10-01

This paper reports on two important issues that arise in the context of the global optimization of photonic components where large problem spaces must be investigated. The first is the implementation of a fast simulation method and associated matrix solver for assessing particular designs and the second, the strategies that a designer can adopt to control the size of the problem design space to reduce runtimes without compromising the convergence of the global optimization tool. For this study an analytical simulation method based on Mie scattering and a fast matrix solver exploiting the fast multipole method are combined with genetic algorithms (GAs). The impact of the approximations of the simulation method on the accuracy and runtime of individual design assessments and the consequent effects on the GA are also examined. An investigation of optimization strategies for controlling the design space size is conducted on two illustrative examples, namely, 60° and 90° waveguide bends based on photonic microstructures, and their effectiveness is analyzed in terms of a GA's ability to converge to the best solution within an acceptable timeframe. Finally, the paper describes some particular optimized solutions found in the course of this work.

GaN nanostructure design for optimal dislocation filtering

NASA Astrophysics Data System (ADS)

Liang, Zhiwen; Colby, Robert; Wildeson, Isaac H.; Ewoldt, David A.; Sands, Timothy D.; Stach, Eric A.; García, R. Edwin

2010-10-01

The effect of image forces in GaN pyramidal nanorod structures is investigated to develop dislocation-free light emitting diodes (LEDs). A model based on the eigenstrain method and nonlocal stress is developed to demonstrate that the pyramidal nanorod efficiently ejects dislocations out of the structure. Two possible regimes of filtering behavior are found: (1) cap-dominated and (2) base-dominated. The cap-dominated regime is shown to be the more effective filtering mechanism. Optimal ranges of fabrication parameters that favor a dislocation-free LED are predicted and corroborated by resorting to available experimental evidence. The filtering probability is summarized as a function of practical processing parameters: the nanorod radius and height. The results suggest an optimal nanorod geometry with a radius of ˜50b (26 nm) and a height of ˜125b (65 nm), in which b is the magnitude of the Burgers vector for the GaN system studied. A filtering probability of greater than 95% is predicted for the optimal geometry.

Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm.

PubMed

Peng, Jiansheng; Meng, Fanmei; Ai, Yuncan

2013-06-01

The artificial neural network (ANN) and genetic algorithm (GA) were combined to optimize the fermentation process for enhancing production of marine bacteriocin 1701 in a 5-L-stirred-tank. Fermentation time, pH value, dissolved oxygen level, temperature and turbidity were used to construct a "5-10-1" ANN topology to identify the nonlinear relationship between fermentation parameters and the antibiotic effects (shown as in inhibition diameters) of bacteriocin 1701. The predicted values by the trained ANN model were coincided with the observed ones (the coefficient of R(2) was greater than 0.95). As the fermentation time was brought in as one of the ANN input nodes, fermentation parameters could be optimized by stages through GA, and an optimal fermentation process control trajectory was created. The production of marine bacteriocin 1701 was significantly improved by 26% under the guidance of fermentation control trajectory that was optimized by using of combined ANN-GA method. Copyright © 2013 Elsevier Ltd. All rights reserved.

Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA

NASA Astrophysics Data System (ADS)

Rong, Youmin; Zhang, Zhen; Zhang, Guojun; Yue, Chen; Gu, Yafei; Huang, Yu; Wang, Chunming; Shao, Xinyu

2015-04-01

The laser brazing (LB) is widely used in the automotive industry due to the advantages of high speed, small heat affected zone, high quality of welding seam, and low heat input. Welding parameters play a significant role in determining the bead geometry and hence quality of the weld joint. This paper addresses the optimization of the seam shape in LB process with welding crimping butt of 0.8 mm thickness using back propagation neural network (BPNN) and genetic algorithm (GA). A 3-factor, 5-level welding experiment is conducted by Taguchi L25 orthogonal array through the statistical design method. Then, the input parameters are considered here including welding speed, wire speed rate, and gap with 5 levels. The output results are efficient connection length of left side and right side, top width (WT) and bottom width (WB) of the weld bead. The experiment results are embed into the BPNN network to establish relationship between the input and output variables. The predicted results of the BPNN are fed to GA algorithm that optimizes the process parameters subjected to the objectives. Then, the effects of welding speed (WS), wire feed rate (WF), and gap (GAP) on the sum values of bead geometry is discussed. Eventually, the confirmation experiments are carried out to demonstrate the optimal values were effective and reliable. On the whole, the proposed hybrid method, BPNN-GA, can be used to guide the actual work and improve the efficiency and stability of LB process.

Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

NASA Astrophysics Data System (ADS)

Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan

2015-03-01

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.

Design Optimization of a Hybrid Electric Vehicle Powertrain

NASA Astrophysics Data System (ADS)

Mangun, Firdause; Idres, Moumen; Abdullah, Kassim

2017-03-01

This paper presents an optimization work on hybrid electric vehicle (HEV) powertrain using Genetic Algorithm (GA) method. It focused on optimization of the parameters of powertrain components including supercapacitors to obtain maximum fuel economy. Vehicle modelling is based on Quasi-Static-Simulation (QSS) backward-facing approach. A combined city (FTP-75)-highway (HWFET) drive cycle is utilized for the design process. Seeking global optimum solution, GA was executed with different initial settings to obtain sets of optimal parameters. Starting from a benchmark HEV, optimization results in a smaller engine (2 l instead of 3 l) and a larger battery (15.66 kWh instead of 2.01 kWh). This leads to a reduction of 38.3% in fuel consumption and 30.5% in equivalent fuel consumption. Optimized parameters are also compared with actual values for HEV in the market.

Combinatorial Multiobjective Optimization Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Crossley, William A.; Martin. Eric T.

2002-01-01

The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

Investigation on high-efficiency Ga0.51In0.49P/In0.01Ga0.99As/Ge triple-junction solar cells for space applications

NASA Astrophysics Data System (ADS)

Zhang, Lei; Niu, Pingjuan; Li, Yuqiang; Song, Minghui; Zhang, Jianxin; Ning, Pingfan; Chen, Peizhuan

2017-12-01

Ga0.51In0.49P/In0.01Ga0.99As/Ge triple-junction solar cells for space applications were grown on 4 inch Ge substrates by metal organic chemical vapor deposition methods. The triple-junction solar cells were obtained by optimizing the subcell structure, showing a high open-circuit voltage of 2.77 V and a high conversion efficiency of 31% with 30.15 cm2 area under the AM0 spectrum at 25 °C. In addition, the In0.01Ga0.99As middle subcell structure was focused by optimizing in order to improve the anti radiation ability of triple-junction solar cells, and the remaining factor of conversion efficiency for middle subcell structure was enhanced from 84% to 92%. Finally, the remaining factor of external quantum efficiency for triple-junction solar cells was increased from 80% to 85.5%.

Study of gain and photoresponse characteristics for back-illuminated separate absorption and multiplication GaN avalanche photodiodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Xiaodong; Pan, Ming; Hou, Liwei

2014-01-07

The gain and photoresponse characteristics have been numerically studied for back-illuminated separate absorption and multiplication (SAM) GaN avalanche photodiodes (APDs). The parameters of fundamental models are calibrated by simultaneously comparing the simulated dark and light current characteristics with the experimental results. Effects of environmental temperatures and device dimensions on gain characteristics have been investigated, and a method to achieve the optimum thickness of charge layer is obtained. The dependence of gain characteristics and breakdown voltage on the doping concentration of the charge layer is also studied in detail to get the optimal charge layer. The bias-dependent spectral responsivity and quantummore » efficiency are then presented to study the photoresponse mechanisms inside SAM GaN APDs. It is found the responsivity peak red-shifts at first due to the Franz-Keldysh effect and then blue-shifts due to the reach-through effect of the absorption layer. Finally, a new SAM GaN/AlGaN heterojunction APD structure is proposed for optimizing SAM GaN APDs.« less

a Comparison of Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization in Optimal First-Order Design of Indoor Tls Networks

NASA Astrophysics Data System (ADS)

Jia, F.; Lichti, D.

2017-09-01

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn't guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

Ultrasound assisted extraction of Maxilon Red GRL dye from water samples using cobalt ferrite nanoparticles loaded on activated carbon as sorbent: Optimization and modeling.

PubMed

Mehrabi, Fatemeh; Vafaei, Azam; Ghaedi, Mehrorang; Ghaedi, Abdol Mohammad; Alipanahpour Dil, Ebrahim; Asfaram, Arash

2017-09-01

In this research, a selective, simple and rapid ultrasound assisted dispersive solid-phase micro-microextraction (UA-DSPME) was developed using cobalt ferrite nanoparticles loaded on activated carbon (CoFe 2 O 4 -NPs-AC) as an efficient sorbent for the preconcentration and determination of Maxilon Red GRL (MR-GRL) dye. The properties of sorbent are characterized by X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Vibrating sample magnetometers (VSM), Fourier transform infrared spectroscopy (FTIR), Particle size distribution (PSD) and Scanning Electron Microscope (SEM) techniques. The factors affecting on the determination of MR-GRL dye were investigated and optimized by central composite design (CCD) and artificial neural networks based on genetic algorithm (ANN-GA). CCD and ANN-GA were used for optimization. Using ANN-GA, optimum conditions were set at 6.70, 1.2mg, 5.5min and 174μL for pH, sorbent amount, sonication time and volume of eluent, respectively. Under the optimized conditions obtained from ANN-GA, the method exhibited a linear dynamic range of 30-3000ngmL -1 with a detection limit of 5.70ngmL -1 . The preconcentration factor and enrichment factor were 57.47 and 93.54, respectively with relative standard deviations (RSDs) less than 4.0% (N=6). The interference effect of some ions and dyes was also investigated and the results show a good selectivity for this method. Finally, the method was successfully applied to the preconcentration and determination of Maxilon Red GRL in water and wastewater samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Theoretical research on bandgap of H-saturated Ga1-xAlxN nanowires

NASA Astrophysics Data System (ADS)

Xia, Sihao; Liu, Lei; Kong, Yike; Wang, Honggang; Wang, Meishan

2017-01-01

Based on first-principles plane-wave ultra-soft pseudopotential method, bandgaps of Ga1-xAlxN nanowires with different diameters and different Al constituents are calculated. After the optimization of the model, the bandgaps are achieved. According to the results, the bandgap of Ga1-xAlxN decreases with increasing diameter and finally, closed to that of the bulk. In addition, with increasing Al constituent, the bandgaps of Ga1-xAlxN nanowires increase. However, the amount of the increase is lower than that of the bulk Ga1-xAlxN with the increase of Al constituent.

Optimization of the defects and the nonradiative lifetime of GaAs/AlGaAs double heterostructures

NASA Astrophysics Data System (ADS)

Cevher, Z.; Folkes, P. A.; Hier, H. S.; VanMil, B. L.; Connelly, B. C.; Beck, W. A.; Ren, Y. H.

2018-04-01

We used Raman scattering and time-resolved photoluminescence spectroscopy to investigate the molecular-beam-epitaxy (MBE) growth parameters that optimize the structural defects and therefore the internal radiative quantum efficiency of MBE-grown GaAs/AlGaAs double heterostructures (DH). The DH structures were grown at two different temperatures and three different As/Ga flux ratios to determine the conditions for an optimized structure with the longest nonradiative minority carrier lifetime. Raman scattering measurements show an improvement in the lattice disorder in the AlGaAs and GaAs layers as the As/Ga flux ratio is reduced from 40 to 15 and as the growth temperature is increased from 550 to 595 °C. The optimized structure is obtained with the As/Ga flux ratio equal to 15 and the substrate temperature 595 °C. This is consistent with the fact that the optimized structure has the longest minority carrier lifetime. Moreover, our Raman studies reveal that incorporation of a distributed Bragg reflector layer between the substrate and DH structures significantly reduces the defect density in the subsequent epitaxial layers.

Optimized power simulation of AlGaN/GaN HEMT for continuous wave and pulse applications

NASA Astrophysics Data System (ADS)

Tiwat, Pongthavornkamol; Lei, Pang; Xinhua, Wang; Sen, Huang; Guoguo, Liu; Tingting, Yuan; Xinyu, Liu

2015-07-01

An optimized modeling method of 8 × 100 μm AlGaN/GaN-based high electron mobility transistor (HEMT) for accurate continuous wave (CW) and pulsed power simulations is proposed. Since the self-heating effect can occur during the continuous operation, the power gain from the continuous operation significantly decreases when compared to a pulsed power operation. This paper extracts power performances of different device models from different quiescent biases of pulsed current-voltage (I-V) measurements and compared them in order to determine the most suitable device model for CW and pulse RF microwave power amplifier design. The simulated output power and gain results of the models at Vgs = -3.5 V, Vds = 30 V with a frequency of 9.6 GHz are presented. Project supported by the National Natural Science Foundation of China (No. 61204086).

Curie temperatures of cubic (Ga, Mn)N diluted magnetic semiconductors from the RKKY spin model.

PubMed

Zhu, Li-Fang; Liu, Bang-Gui

2009-11-04

We explore how much the RKKY spin interaction can contribute to the high-temperature ferromagnetism in cubic (Ga, Mn)N diluted magnetic semiconductors. The usual coupling constant is used and effective carriers are considered independent of doped magnetic atoms, as is shown experimentally. Our Monte Carlo simulated results show that maximal Curie temperature is reached at the optimal carrier concentration for a given Mn concentration, equaling 373 K for 5% Mn and 703 K for 8% Mn. Because such a Monte Carlo method does not overestimate transition temperatures, these calculations indicate that the RKKY spin interaction alone can yield high-enough Curie temperatures in cubic (Ga, Mn)N under optimized conditions.

Mid and long-term optimize scheduling of cascade hydro-power stations based on modified GA-POA method

NASA Astrophysics Data System (ADS)

Li, Jiqing; Yang, Xiong

2018-06-01

In this paper, to explore the efficiency and rationality of the cascade combined generation, a cascade combined optimal model with the maximum generating capacity is established, and solving the model by the modified GA-POA method. It provides a useful reference for the joint development of cascade hydro-power stations in large river basins. The typical annual runoff data are selected to calculate the difference between the calculated results under different representative years. The results show that the cascade operation of cascaded hydro-power stations can significantly increase the overall power generation of cascade and ease the flood risk caused by concentration of flood season.

Use of genetic algorithm for the selection of EEG features

NASA Astrophysics Data System (ADS)

Asvestas, P.; Korda, A.; Kostopoulos, S.; Karanasiou, I.; Ouzounoglou, A.; Sidiropoulos, K.; Ventouras, E.; Matsopoulos, G.

2015-09-01

Genetic Algorithm (GA) is a popular optimization technique that can detect the global optimum of a multivariable function containing several local optima. GA has been widely used in the field of biomedical informatics, especially in the context of designing decision support systems that classify biomedical signals or images into classes of interest. The aim of this paper is to present a methodology, based on GA, for the selection of the optimal subset of features that can be used for the efficient classification of Event Related Potentials (ERPs), which are recorded during the observation of correct or incorrect actions. In our experiment, ERP recordings were acquired from sixteen (16) healthy volunteers who observed correct or incorrect actions of other subjects. The brain electrical activity was recorded at 47 locations on the scalp. The GA was formulated as a combinatorial optimizer for the selection of the combination of electrodes that maximizes the performance of the Fuzzy C Means (FCM) classification algorithm. In particular, during the evolution of the GA, for each candidate combination of electrodes, the well-known (Σ, Φ, Ω) features were calculated and were evaluated by means of the FCM method. The proposed methodology provided a combination of 8 electrodes, with classification accuracy 93.8%. Thus, GA can be the basis for the selection of features that discriminate ERP recordings of observations of correct or incorrect actions.

Using genetic algorithms to determine near-optimal pricing, investment and operating strategies in the electric power industry

NASA Astrophysics Data System (ADS)

Wu, Dongjun

Network industries have technologies characterized by a spatial hierarchy, the "network," with capital-intensive interconnections and time-dependent, capacity-limited flows of products and services through the network to customers. This dissertation studies service pricing, investment and business operating strategies for the electric power network. First-best solutions for a variety of pricing and investment problems have been studied. The evaluation of genetic algorithms (GA, which are methods based on the idea of natural evolution) as a primary means of solving complicated network problems, both w.r.t. pricing: as well as w.r.t. investment and other operating decisions, has been conducted. New constraint-handling techniques in GAs have been studied and tested. The actual application of such constraint-handling techniques in solving practical non-linear optimization problems has been tested on several complex network design problems with encouraging initial results. Genetic algorithms provide solutions that are feasible and close to optimal when the optimal solution is know; in some instances, the near-optimal solutions for small problems by the proposed GA approach can only be tested by pushing the limits of currently available non-linear optimization software. The performance is far better than several commercially available GA programs, which are generally inadequate in solving any of the problems studied in this dissertation, primarily because of their poor handling of constraints. Genetic algorithms, if carefully designed, seem very promising in solving difficult problems which are intractable by traditional analytic methods.

Lamb Wave Damage Quantification Using GA-Based LS-SVM.

PubMed

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-06-12

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification.

Lamb Wave Damage Quantification Using GA-Based LS-SVM

PubMed Central

Sun, Fuqiang; Wang, Ning; He, Jingjing; Guan, Xuefei; Yang, Jinsong

2017-01-01

Lamb waves have been reported to be an efficient tool for non-destructive evaluations (NDE) for various application scenarios. However, accurate and reliable damage quantification using the Lamb wave method is still a practical challenge, due to the complex underlying mechanism of Lamb wave propagation and damage detection. This paper presents a Lamb wave damage quantification method using a least square support vector machine (LS-SVM) and a genetic algorithm (GA). Three damage sensitive features, namely, normalized amplitude, phase change, and correlation coefficient, were proposed to describe changes of Lamb wave characteristics caused by damage. In view of commonly used data-driven methods, the GA-based LS-SVM model using the proposed three damage sensitive features was implemented to evaluate the crack size. The GA method was adopted to optimize the model parameters. The results of GA-based LS-SVM were validated using coupon test data and lap joint component test data with naturally developed fatigue cracks. Cases of different loading and manufacturer were also included to further verify the robustness of the proposed method for crack quantification. PMID:28773003

Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model

PubMed Central

Fei, Ze-yuan; Xu, Yi-feng; Wang, Jie; Fan, Bing-feng; Ma, Xue-jin; Wang, Gang

2018-01-01

Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality. PMID:29515883

Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model.

PubMed

Li, Jian; Fei, Ze-Yuan; Xu, Yi-Feng; Wang, Jie; Fan, Bing-Feng; Ma, Xue-Jin; Wang, Gang

2018-02-01

Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.

Study on the optimization of the deposition rate of planetary GaN-MOCVD films based on CFD simulation and the corresponding surface model

NASA Astrophysics Data System (ADS)

Li, Jian; Fei, Ze-yuan; Xu, Yi-feng; Wang, Jie; Fan, Bing-feng; Ma, Xue-jin; Wang, Gang

2018-02-01

Metal-organic chemical vapour deposition (MOCVD) is a key technique for fabricating GaN thin film structures for light-emitting and semiconductor laser diodes. Film uniformity is an important index to measure equipment performance and chip processes. This paper introduces a method to improve the quality of thin films by optimizing the rotation speed of different substrates of a model consisting of a planetary with seven 6-inch wafers for the planetary GaN-MOCVD. A numerical solution to the transient state at low pressure is obtained using computational fluid dynamics. To evaluate the role of the different zone speeds on the growth uniformity, single factor analysis is introduced. The results show that the growth rate and uniformity are strongly related to the rotational speed. Next, a response surface model was constructed by using the variables and the corresponding simulation results. The optimized combination of the matching of different speeds is also proposed as a useful reference for applications in industry, obtained by a response surface model and genetic algorithm with a balance between the growth rate and the growth uniformity. This method can save time, and the optimization can obtain the most uniform and highest thin film quality.

Optimization of the Nonradiative Lifetime of Molecular-Beam-Epitaxy (MBE)-Grown Undoped GaAs/AlGaAs Double Heterostructures (DH)

DTIC Science & Technology

2013-09-01

Optimization of the Nonradiative Lifetime of Molecular- Beam-Epitaxy (MBE)-Grown Undoped GaAs/AlGaAs Double Heterostructures (DH) by P...it to the originator. Army Research Laboratory Adelphi, MD 20783-1197 ARL-TR-6660 September 2013 Optimization of the Nonradiative ...REPORT TYPE Final 3. DATES COVERED (From - To) FY2013 4. TITLE AND SUBTITLE Optimization of the Nonradiative Lifetime of Molecular-Beam-Epitaxy

Assessment of gene order computing methods for Alzheimer's disease

PubMed Central

2013-01-01

Background Computational genomics of Alzheimer disease (AD), the most common form of senile dementia, is a nascent field in AD research. The field includes AD gene clustering by computing gene order which generates higher quality gene clustering patterns than most other clustering methods. However, there are few available gene order computing methods such as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). Further, their performance in gene order computation using AD microarray data is not known. We thus set forth to evaluate the performances of current gene order computing methods with different distance formulas, and to identify additional features associated with gene order computation. Methods Using different distance formulas- Pearson distance and Euclidean distance, the squared Euclidean distance, and other conditions, gene orders were calculated by ACO and GA (including standard GA and improved GA) methods, respectively. The qualities of the gene orders were compared, and new features from the calculated gene orders were identified. Results Compared to the GA methods tested in this study, ACO fits the AD microarray data the best when calculating gene order. In addition, the following features were revealed: different distance formulas generated a different quality of gene order, and the commonly used Pearson distance was not the best distance formula when used with both GA and ACO methods for AD microarray data. Conclusion Compared with Pearson distance and Euclidean distance, the squared Euclidean distance generated the best quality gene order computed by GA and ACO methods. PMID:23369541

Thermodynamic assessment and binary nucleation modeling of Sn-seeded InGaAs nanowires

NASA Astrophysics Data System (ADS)

Ghasemi, Masoomeh; Selleby, Malin; Johansson, Jonas

2017-11-01

We have performed a thermodynamic assessment of the As-Ga-In-Sn system based on the CALculation of PHAse Diagram (CALPHAD) method. This system is part of a comprehensive thermodynamic database that we are developing for nanowire materials. Specifically, the As-Ga-In-Sn can be used in modeling the growth of GaAs, InAs, and InxGa1-xAs nanowires assisted by Sn liquid seeds. In this work, the As-Sn binary, the As-Ga-Sn, As-In-Sn, and Ga-In-Sn ternary systems have been thermodynamically assessed using the CALPHAD method. We show the relevant phase diagrams and property diagrams. They all show good agreement with experimental data. Using our optimized description we have modeled the nucleation of InxGa1-xAs in the zinc blende phase from a Sn-based quaternary liquid alloy using binary nucleation modeling. We have linked the composition of the solid nucleus to the composition of the liquid phase. Eventually, we have predicted the critical size of the nucleus that forms from InAs and GaAs pairs under various conditions. We believe that our modeling can guide future experimental realization of Sn-seeded InxGa1-xAs nanowires.

[Mutual Effect on Determination of Gibberellins and Glyphosate in Groundwater by Spectrophotometry].

PubMed

Zhang, Li; Chen, Liang; Liu, Fei

2015-04-01

In the present study, a spectrophotometry method for the simultaneous determination of gibberellins (GA3) and glyphosate in groundwater was established and optimized. In addition, the mutual effect on simultaneous determination of GA3 and glyphosate was studied. Based on the experiment, good linearity (R2 > 0.99) was obtained for GA3 in the range of 0-20 and 0-100 µg and for glyphosate in the range of 0-8 and 5-15 µg. The method's detection limit (MDL) of GA3 and glyphosate was 0.48 and 0.82 µg, respectively; and the recovery rates of 15 to 150 µg GA3 and 3 to 10 µg glyphosate in all samples at a spiked level were 71.3% ± 1.9% and 98.4% ± 8.1%, respectively. No obvious influence of glyphosate (0-100 mg · L(-1)) on the recovery rates of GA3 was observed, but the presence of glyphosate could cause slight determination precision decrease of GA3. Meanwhile, adding 2 mg · L(-1) GA3 can increase the recovery rate of glyphosate.

Hybrid intelligent optimization methods for engineering problems

NASA Astrophysics Data System (ADS)

Pehlivanoglu, Yasin Volkan

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles.

A method of network topology optimization design considering application process characteristic

NASA Astrophysics Data System (ADS)

Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo

2018-03-01

Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

A graph decomposition-based approach for water distribution network optimization

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.; Deuerlein, Jochen W.

2013-04-01

A novel optimization approach for water distribution network design is proposed in this paper. Using graph theory algorithms, a full water network is first decomposed into different subnetworks based on the connectivity of the network's components. The original whole network is simplified to a directed augmented tree, in which the subnetworks are substituted by augmented nodes and directed links are created to connect them. Differential evolution (DE) is then employed to optimize each subnetwork based on the sequence specified by the assigned directed links in the augmented tree. Rather than optimizing the original network as a whole, the subnetworks are sequentially optimized by the DE algorithm. A solution choice table is established for each subnetwork (except for the subnetwork that includes a supply node) and the optimal solution of the original whole network is finally obtained by use of the solution choice tables. Furthermore, a preconditioning algorithm is applied to the subnetworks to produce an approximately optimal solution for the original whole network. This solution specifies promising regions for the final optimization algorithm to further optimize the subnetworks. Five water network case studies are used to demonstrate the effectiveness of the proposed optimization method. A standard DE algorithm (SDE) and a genetic algorithm (GA) are applied to each case study without network decomposition to enable a comparison with the proposed method. The results show that the proposed method consistently outperforms the SDE and GA (both with tuned parameters) in terms of both the solution quality and efficiency.

Electron-nuclear spin dynamics of Ga centers in GaAsN dilute nitride semiconductors probed by pump-probe spectroscopy

NASA Astrophysics Data System (ADS)

Sandoval-Santana, J. C.; Ibarra-Sierra, V. G.; Azaizia, S.; Carrère, H.; Bakaleinikov, L. A.; Kalevich, V. K.; Ivchenko, E. L.; Marie, X.; Amand, T.; Balocchi, A.; Kunold, A.

2018-03-01

We propose an experimental procedure to track the evolution of electronic and nuclear spins in Ga2+ centers in GaAsN dilute semiconductors. The method is based on a pump-probe scheme that enables to monitor the time evolution of the three components of the electronic and nuclear spin variables. In contrast to other characterization methods, as nuclear magnetic resonance, this one only needs moderate magnetic fields (B≈ 10 mT), and does not require microwave irradiation. Specifically, we carry out a series of tests for different experimental conditions in order to optimize the procedure for maximum sensitivity in the measurement of the circular degree of polarization. Based on previous experimental results and the theoretical calculations presented here, we estimate that the method could yield a time resolution of about 10ps.

Optimization of the ANFIS using a genetic algorithm for physical work rate classification.

PubMed

Habibi, Ehsanollah; Salehi, Mina; Yadegarfar, Ghasem; Taheri, Ali

2018-03-13

Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.

Design and simulation of GaN based Schottky betavoltaic nuclear micro-battery.

PubMed

San, Haisheng; Yao, Shulin; Wang, Xiang; Cheng, Zaijun; Chen, Xuyuan

2013-10-01

The current paper presents a theoretical analysis of Ni-63 nuclear micro-battery based on a wide-band gap semiconductor GaN thin-film covered with thin Ni/Au films to form Schottky barrier for carrier separation. The total energy deposition in GaN was calculated using Monte Carlo methods by taking into account the full beta spectral energy, which provided an optimal design on Schottky barrier width. The calculated results show that an 8 μm thick Schottky barrier can collect about 95% of the incident beta particle energy. Considering the actual limitations of current GaN growth technique, a Fe-doped compensation technique by MOCVD method can be used to realize the n-type GaN with a carrier concentration of 1×10(15) cm(-3), by which a GaN based Schottky betavoltaic micro-battery can achieve an energy conversion efficiency of 2.25% based on the theoretical calculations of semiconductor device physics. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comprehensive growth and characterization study on highly n-doped InGaAs as a contact layer for quantum cascade laser applications

NASA Astrophysics Data System (ADS)

Demir, Ilkay; Altuntas, Ismail; Bulut, Baris; Ezzedini, Maher; Ergun, Yuksel; Elagoz, Sezai

2018-05-01

We present growth and characterization studies of highly n-doped InGaAs epilayers on InP substrate by metal organic vapor phase epitaxy to use as an n-contact layer in quantum cascade laser applications. We have introduced quasi two-dimensional electrons between 10 s pulsed growth n-doped InGaAs epilayers to improve both carrier concentration and mobility of structure by applying pulsed growth and doping methods towards increasing the Si dopant concentration in InGaAs. Additionally, the V/III ratio optimization under fixed group III source flow has been investigated with this new method to understand the effects on both crystalline quality and electrical properties of n-InGaAs epilayers. Finally, we have obtained high crystalline quality of n-InGaAs epilayers grown by 10 s pulsed as a contact layer with 2.8 × 1019 cm‑3 carrier concentration and 1530 cm2 V‑1 s‑1 mobility.

Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

NASA Astrophysics Data System (ADS)

Rahmalia, Dinita

2017-08-01

Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

Design optimization of GaAs betavoltaic batteries

NASA Astrophysics Data System (ADS)

Chen, Haiyanag; Jiang, Lan; Chen, Xuyuan

2011-06-01

GaAs junctions are designed and fabricated for betavoltaic batteries. The design is optimized according to the characteristics of GaAs interface states and the diffusion length in the depletion region of GaAs carriers. Under an illumination of 10 mCi cm-2 63Ni, the open circuit voltage of the optimized batteries is about ~0.3 V. It is found that the GaAs interface states induce depletion layers on P-type GaAs surfaces. The depletion layer along the P+PN+ junction edge isolates the perimeter surface from the bulk junction, which tends to significantly reduce the battery dark current and leads to a high open circuit voltage. The short circuit current density of the optimized junction is about 28 nA cm-2, which indicates a carrier diffusion length of less than 1 µm. The overall results show that multi-layer P+PN+ junctions are the preferred structures for GaAs betavoltaic battery design.

Photoluminescence and Band Alignment of Strained GaAsSb/GaAs QW Structures Grown by MBE on GaAs

PubMed Central

Sadofyev, Yuri G.; Samal, Nigamananda

2010-01-01

An in-depth optimization of growth conditions and investigation of optical properties including discussions on band alignment of GaAsSb/GaAs quantum well (QW) on GaAs by molecular beam epitaxy (MBE) are reported. Optimal MBE growth temperature of GaAsSb QW is found to be 470 ± 10 °C. GaAsSb/GaAs QW with Sb content ~0.36 has a weak type-II band alignment with valence band offset ratio QV ~1.06. A full width at half maximum (FWHM) of ~60 meV in room temperature (RT) photoluminescence (PL) indicates fluctuation in electrostatic potential to be less than 20 meV. Samples grown under optimal conditions do not exhibit any blue shift of peak in RT PL spectra under varying excitation.

Implementation of atomic layer deposition-based AlON gate dielectrics in AlGaN/GaN MOS structure and its physical and electrical properties

NASA Astrophysics Data System (ADS)

Nozaki, Mikito; Watanabe, Kenta; Yamada, Takahiro; Shih, Hong-An; Nakazawa, Satoshi; Anda, Yoshiharu; Ueda, Tetsuzo; Yoshigoe, Akitaka; Hosoi, Takuji; Shimura, Takayoshi; Watanabe, Heiji

2018-06-01

Alumina incorporating nitrogen (aluminum oxynitride; AlON) for immunity against charge injection was grown on a AlGaN/GaN substrate through the repeated atomic layer deposition (ALD) of AlN layers and in situ oxidation in ozone (O3) ambient under optimized conditions. The nitrogen distribution was uniform in the depth direction, the composition was controllable over a wide range (0.5–32%), and the thickness could be precisely controlled. Physical analysis based on synchrotron radiation X-ray photoelectron spectroscopy (SR-XPS) revealed that harmful intermixing at the insulator/AlGaN interface causing Ga out-diffusion in the gate stack was effectively suppressed by this method. AlON/AlGaN/GaN MOS capacitors were fabricated, and they had excellent electrical properties and immunity against electrical stressing as a result of the improved interface stability.

Optimal groundwater remediation design of pump and treat systems via a simulation-optimization approach and firefly algorithm

NASA Astrophysics Data System (ADS)

Javad Kazemzadeh-Parsi, Mohammad; Daneshmand, Farhang; Ahmadfard, Mohammad Amin; Adamowski, Jan; Martel, Richard

2015-01-01

In the present study, an optimization approach based on the firefly algorithm (FA) is combined with a finite element simulation method (FEM) to determine the optimum design of pump and treat remediation systems. Three multi-objective functions in which pumping rate and clean-up time are design variables are considered and the proposed FA-FEM model is used to minimize operating costs, total pumping volumes and total pumping rates in three scenarios while meeting water quality requirements. The groundwater lift and contaminant concentration are also minimized through the optimization process. The obtained results show the applicability of the FA in conjunction with the FEM for the optimal design of groundwater remediation systems. The performance of the FA is also compared with the genetic algorithm (GA) and the FA is found to have a better convergence rate than the GA.

Combined Approach for Government E-Tendering Using GA and TOPSIS with Intuitionistic Fuzzy Information.

PubMed

Wang, Yan; Xi, Chengyu; Zhang, Shuai; Zhang, Wenyu; Yu, Dejian

2015-01-01

As E-government continues to develop with ever-increasing speed, the requirement to enhance traditional government systems and affairs with electronic methods that are more effective and efficient is becoming critical. As a new product of information technology, E-tendering is becoming an inevitable reality owing to its efficiency, fairness, transparency, and accountability. Thus, developing and promoting government E-tendering (GeT) is imperative. This paper presents a hybrid approach combining genetic algorithm (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to enable GeT to search for the optimal tenderer efficiently and fairly under circumstances where the attributes of the tenderers are expressed as fuzzy number intuitionistic fuzzy sets (FNIFSs). GA is applied to obtain the optimal weights of evaluation criteria of tenderers automatically. TOPSIS is employed to search for the optimal tenderer. A prototype system is built and validated with an illustrative example from GeT to verify the feasibility and availability of the proposed approach.

Combined Approach for Government E-Tendering Using GA and TOPSIS with Intuitionistic Fuzzy Information

PubMed Central

Zhang, Wenyu; Yu, Dejian

2015-01-01

As E-government continues to develop with ever-increasing speed, the requirement to enhance traditional government systems and affairs with electronic methods that are more effective and efficient is becoming critical. As a new product of information technology, E-tendering is becoming an inevitable reality owing to its efficiency, fairness, transparency, and accountability. Thus, developing and promoting government E-tendering (GeT) is imperative. This paper presents a hybrid approach combining genetic algorithm (GA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to enable GeT to search for the optimal tenderer efficiently and fairly under circumstances where the attributes of the tenderers are expressed as fuzzy number intuitionistic fuzzy sets (FNIFSs). GA is applied to obtain the optimal weights of evaluation criteria of tenderers automatically. TOPSIS is employed to search for the optimal tenderer. A prototype system is built and validated with an illustrative example from GeT to verify the feasibility and availability of the proposed approach. PMID:26147468

Welded joints integrity analysis and optimization for fiber laser welding of dissimilar materials

NASA Astrophysics Data System (ADS)

Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Liu, Wei

2016-11-01

Dissimilar materials welded joints provide many advantages in power, automotive, chemical, and spacecraft industries. The weld bead integrity which is determined by process parameters plays a significant role in the welding quality during the fiber laser welding (FLW) of dissimilar materials. In this paper, an optimization method by taking the integrity of the weld bead and weld area into consideration is proposed for FLW of dissimilar materials, the low carbon steel and stainless steel. The relationships between the weld bead integrity and process parameters are developed by the genetic algorithm optimized back propagation neural network (GA-BPNN). The particle swarm optimization (PSO) algorithm is taken for optimizing the predicted outputs from GA-BPNN for the objective. Through the optimization process, the desired weld bead with good integrity and minimum weld area are obtained and the corresponding microstructure and microhardness are excellent. The mechanical properties of the optimized joints are greatly improved compared with that of the un-optimized welded joints. Moreover, the effects of significant factors are analyzed based on the statistical approach and the laser power (LP) is identified as the most significant factor on the weld bead integrity and weld area. The results indicate that the proposed method is effective for improving the reliability and stability of welded joints in the practical production.

Multi-objective shape optimization of plate structure under stress criteria based on sub-structured mixed FEM and genetic algorithms

NASA Astrophysics Data System (ADS)

Garambois, Pierre; Besset, Sebastien; Jézéquel, Louis

2015-07-01

This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.

Application of genetic algorithm in the evaluation of the profile error of archimedes helicoid surface

NASA Astrophysics Data System (ADS)

Zhu, Lianqing; Chen, Yunfang; Chen, Qingshan; Meng, Hao

2011-05-01

According to minimum zone condition, a method for evaluating the profile error of Archimedes helicoid surface based on Genetic Algorithm (GA) is proposed. The mathematic model of the surface is provided and the unknown parameters in the equation of surface are acquired through least square method. Principle of GA is explained. Then, the profile error of Archimedes Helicoid surface is obtained through GA optimization method. To validate the proposed method, the profile error of an Archimedes helicoid surface, Archimedes Cylindrical worm (ZA worm) surface, is evaluated. The results show that the proposed method is capable of correctly evaluating the profile error of Archimedes helicoid surface and satisfy the evaluation standard of the Minimum Zone Method. It can be applied to deal with the measured data of profile error of complex surface obtained by three coordinate measurement machines (CMM).

Epitaxial GaN layers formed on langasite substrates by the plasma-assisted MBE method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lobanov, D. N., E-mail: dima@ipmras.ru; Novikov, A. V.; Yunin, P. A.

2016-11-15

In this publication, the results of development of the technology of the epitaxial growth of GaN on single-crystal langasite substrates La{sub 3}Ga{sub 5}SiO{sub 14} (0001) by the plasma-assisted molecular-beam epitaxy (PA MBE) method are reported. An investigation of the effect of the growth temperature at the initial stage of deposition on the crystal quality and morphology of the obtained GaN layer is performed. It is demonstrated that the optimal temperature for deposition of the initial GaN layer onto the langasite substrate is about ~520°C. A decrease in the growth temperature to this value allows the suppression of oxygen diffusion frommore » langasite into the growing layer and a decrease in the dislocation density in the main GaN layer upon its subsequent high-temperature deposition (~700°C). Further lowering of the growth temperature of the nucleation layer leads to sharp degradation of the GaN/LGS layer crystal quality. As a result of the performed research, an epitaxial GaN/LGS layer with a dislocation density of ~10{sup 11} cm{sup –2} and low surface roughness (<2 nm) is obtained.« less

Good manufacturing practice production of [68Ga]Ga-ABY-025 for HER2 specific breast cancer imaging

PubMed Central

Velikyan, Irina; Wennborg, Anders; Feldwisch, Joachim; Lindman, Henrik; Carlsson, Jörgen; Sörensen, Jens

2016-01-01

Therapies targeting human epidermal growth factor receptor type 2 (HER2) have revolutionized breast cancer treatment, but require invasive biopsies and rigorous histopathology for optimal patient stratification. A non-invasive and quantitative diagnostic method such as positron emission tomography (PET) for the pre-therapeutic determination of the presence and density of the HER2 would significantly improve patient management efficacy and treatment cost. The essential part of the PET methodology is the production of the radiopharmaceutical in compliance with good manufacturing practice (GMP). The use of generator produced positron emitting 68Ga radionuclide would provide worldwide accessibility of the agent. GMP compliant, reliable and highly reproducible production of [68Ga]Ga-ABY-025 with control over the product peptide concentration and amount of radioactivity was accomplished within one hour. Two radiopharmaceuticals were developed differing in the total peptide content and were validated independently. The specific radioactivity could be kept similar throughout the study, and it was 6-fold higher for the low peptide content radiopharmaceutical. Intrapatient comparison of the two peptide doses allowed imaging optimization. The high peptide content decreased the uptake in healthy tissue, in particular liver, improving image contrast. The later imaging time points enhanced the contrast. The combination of high peptide content radiopharmaceutical and whole-body imaging at 2 hours post injection appeared to be optimal for routine clinical use. PMID:27186441

Calibration of a biome-biogeochemical cycles model for modeling the net primary production of teak forests through inverse modeling of remotely sensed data

NASA Astrophysics Data System (ADS)

Imvitthaya, Chomchid; Honda, Kiyoshi; Lertlum, Surat; Tangtham, Nipon

2011-01-01

In this paper, we present the results of a net primary production (NPP) modeling of teak (Tectona grandis Lin F.), an important species in tropical deciduous forests. The biome-biogeochemical cycles or Biome-BGC model was calibrated to estimate net NPP through the inverse modeling approach. A genetic algorithm (GA) was linked with Biome-BGC to determine the optimal ecophysiological model parameters. The Biome-BGC was calibrated by adjusting the ecophysiological model parameters to fit the simulated LAI to the satellite LAI (SPOT-Vegetation), and the best fitness confirmed the high accuracy of generated ecophysioligical parameter from GA. The modeled NPP, using optimized parameters from GA as input data, was evaluated using daily NPP derived by the MODIS satellite and the annual field data in northern Thailand. The results showed that NPP obtained using the optimized ecophysiological parameters were more accurate than those obtained using default literature parameterization. This improvement occurred mainly because the model's optimized parameters reduced the bias by reducing systematic underestimation in the model. These Biome-BGC results can be effectively applied in teak forests in tropical areas. The study proposes a more effective method of using GA to determine ecophysiological parameters at the site level and represents a first step toward the analysis of the carbon budget of teak plantations at the regional scale.

Finding optimal vaccination strategies for pandemic influenza using genetic algorithms.

PubMed

Patel, Rajan; Longini, Ira M; Halloran, M Elizabeth

2005-05-21

In the event of pandemic influenza, only limited supplies of vaccine may be available. We use stochastic epidemic simulations, genetic algorithms (GA), and random mutation hill climbing (RMHC) to find optimal vaccine distributions to minimize the number of illnesses or deaths in the population, given limited quantities of vaccine. Due to the non-linearity, complexity and stochasticity of the epidemic process, it is not possible to solve for optimal vaccine distributions mathematically. However, we use GA and RMHC to find near optimal vaccine distributions. We model an influenza pandemic that has age-specific illness attack rates similar to the Asian pandemic in 1957-1958 caused by influenza A(H2N2), as well as a distribution similar to the Hong Kong pandemic in 1968-1969 caused by influenza A(H3N2). We find the optimal vaccine distributions given that the number of doses is limited over the range of 10-90% of the population. While GA and RMHC work well in finding optimal vaccine distributions, GA is significantly more efficient than RMHC. We show that the optimal vaccine distribution found by GA and RMHC is up to 84% more effective than random mass vaccination in the mid range of vaccine availability. GA is generalizable to the optimization of stochastic model parameters for other infectious diseases and population structures.

Data mining-based coefficient of influence factors optimization of test paper reliability

NASA Astrophysics Data System (ADS)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

Genetic Algorithm Optimization of Phononic Bandgap Structures

DTIC Science & Technology

2006-09-01

a GA with a computational finite element method for solving the acoustic wave equation, and find optimal designs for both metal-matrix composite...systems consisting of Ti/SiC, and H2O-filled porous ceramic media, by maximizing the relative acoustic bandgap for these media. The term acoustic here...stress minimization, global optimization, phonon bandgap, genetic algorithm, periodic elastic media, inhomogeneity, inclusion, porous media, acoustic

Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

NASA Astrophysics Data System (ADS)

Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

Optimization of Ammonium Sulfate Concentration for Purification of Colorectal Cancer Vaccine Candidate Recombinant Protein GA733-FcK Isolated from Plants.

PubMed

Park, Se-Ra; Lim, Chae-Yeon; Kim, Deuk-Su; Ko, Kisung

2015-01-01

A protein purification procedure is required to obtain high-value recombinant injectable vaccine proteins produced in plants as a bioreactor. However, existing purification procedures for plant-derived recombinant proteins are often not optimized and are inefficient, with low recovery rates. In our previous study, we used 25-30% ammonium sulfate to precipitate total soluble proteins (TSPs) in purification process for recombinant proteins from plant leaf biomass which has not been optimized. Thus, the objective in this study is to optimize the conditions for plant-derived protein purification procedures. Various ammonium sulfate concentrations (15-80%) were compared to determine their effects on TSPs yield. With 50% ammonium sulfate, the yield of precipitated TSP was the highest, and that of the plant-derived colorectal cancer-specific surface glycoprotein GA733 fused to the Fc fragment of human IgG tagged with endoplasmic reticulum retention signal KDEL (GA733(P)-FcK) protein significantly increased 1.8-fold. SDS-PAGE analysis showed that the purity of GA733(P)-FcK protein band appeared to be similar to that of an equal dose of mammalian-derived GA733-Fc (GA733(M)-Fc). The binding activity of purified GA733(P)-FcK to anti-GA733 mAb was as efficient as the native GA733(M)-Fc. Thus, the purification process was effectively optimized for obtaining a high yield of plant-derived antigenic protein with good quality. In conclusion, the purification recovery rate of large quantities of recombinant protein from plant expression systems can be enhanced via optimization of ammonium sulfate concentration during downstream processes, thereby offering a promising solution for production of recombinant GA733-Fc protein in plants.

Energy and Process Optimization Assessment, Fort Stewart, GA

DTIC Science & Technology

2006-04-01

ER D C/ CE R L TR -0 6 -8 Energy and Process Optimization Assessment Fort Stewart, GA John L. Vavrin, Alexander M. Zhivov, William T...distribution is unlimited. ERDC/CERL TR-06-8 April 2006 Energy and Process Optimization Assessment Fort Stewart, GA John L. Vavrin, Alexander...U.S. Army Corps of Engineers Washington, DC 20314-1000 Under Work Unit 33143 ERDC/CERL TR-06-8 ii Abstract: An Energy and Process Optimization

Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT

NASA Astrophysics Data System (ADS)

Wang, Qingrui; Liu, Ruimin; Men, Cong; Guo, Lijia

2018-05-01

The genetic algorithm (GA) was combined with the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model to obtain an optimized land use pattern for controlling non-point source (NPS) pollution. The performance of the combination was evaluated. The effect of the optimized land use pattern on the NPS pollution control was estimated by the Soil and Water Assessment Tool (SWAT) model and an assistant map was drawn to support the land use plan for the future. The Xiangxi River watershed was selected as the study area. Two scenarios were used to simulate the land use change. Under the historical trend scenario (Markov chain prediction), the forest area decreased by 2035.06 ha, and was mainly converted into paddy and dryland area. In contrast, under the optimized scenario (genetic algorithm (GA) prediction), up to 3370 ha of dryland area was converted into forest area. Spatially, the conversion of paddy and dryland into forest occurred mainly in the northwest and southeast of the watershed, where the slope land occupied a large proportion. The organic and inorganic phosphorus loads decreased by 3.6% and 3.7%, respectively, in the optimized scenario compared to those in the historical trend scenario. GA showed a better performance in optimized land use prediction. A comparison of the land use patterns in 2010 under the real situation and in 2020 under the optimized situation showed that Shennongjia and Shuiyuesi should convert 1201.76 ha and 1115.33 ha of dryland into forest areas, respectively, which represented the greatest changes in all regions in the watershed. The results of this study indicated that GA and the CLUE-S model can be used to optimize the land use patterns in the future and that SWAT can be used to evaluate the effect of land use optimization on non-point source pollution control. These methods may provide support for land use plan of an area.

Genetic Algorithm (GA)-Based Inclinometer Layout Optimization.

PubMed

Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

2015-04-17

This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors.

Genetic Algorithm (GA)-Based Inclinometer Layout Optimization

PubMed Central

Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo

2015-01-01

This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500

Improvements of the Vis-NIRS Model in the Prediction of Soil Organic Matter Content Using Spectral Pretreatments, Sample Selection, and Wavelength Optimization

NASA Astrophysics Data System (ADS)

Lin, Z. D.; Wang, Y. B.; Wang, R. J.; Wang, L. S.; Lu, C. P.; Zhang, Z. Y.; Song, L. T.; Liu, Y.

2017-07-01

A total of 130 topsoil samples collected from Guoyang County, Anhui Province, China, were used to establish a Vis-NIR model for the prediction of organic matter content (OMC) in lime concretion black soils. Different spectral pretreatments were applied for minimizing the irrelevant and useless information of the spectra and increasing the spectra correlation with the measured values. Subsequently, the Kennard-Stone (KS) method and sample set partitioning based on joint x-y distances (SPXY) were used to select the training set. Successive projection algorithm (SPA) and genetic algorithm (GA) were then applied for wavelength optimization. Finally, the principal component regression (PCR) model was constructed, in which the optimal number of principal components was determined using the leave-one-out cross validation technique. The results show that the combination of the Savitzky-Golay (SG) filter for smoothing and multiplicative scatter correction (MSC) can eliminate the effect of noise and baseline drift; the SPXY method is preferable to KS in the sample selection; both the SPA and the GA can significantly reduce the number of wavelength variables and favorably increase the accuracy, especially GA, which greatly improved the prediction accuracy of soil OMC with Rcc, RMSEP, and RPD up to 0.9316, 0.2142, and 2.3195, respectively.

Genetic Optimization and Simulation of a Piezoelectric Pipe-Crawling Inspection Robot

NASA Technical Reports Server (NTRS)

Hollinger, Geoffrey A.; Briscoe, Jeri M.

2004-01-01

Using the DarwinZk development software, a genetic algorithm (GA) was used to design and optimize a pipe-crawling robot for parameters such as mass, power consumption, and joint extension to further the research of the Miniature Inspection Systems Technology (MIST) team. In an attempt to improve on existing designs, a new robot was developed, the piezo robot. The final proposed design uses piezoelectric expansion actuators to move the robot with a 'chimneying' method employed by mountain climbers and greatly improves on previous designs in load bearing ability, pipe traversing specifications, and field usability. This research shows the advantages of GA assisted design in the field of robotics.

GaAs/AlOx high-contrast grating mirrors for mid-infrared VCSELs

NASA Astrophysics Data System (ADS)

Almuneau, G.; Laaroussi, Y.; Chevallier, C.; Genty, F.; Fressengeas, N. s.; Cerutti, L.; Gauthier-Lafaye, Olivier

2015-02-01

Mid-infrared Vertical cavity surface emitting lasers (MIR-VCSEL) are very attractive compact sources for spectroscopic measurements above 2μm, relevant for molecules sensing in various application domains. A long-standing issue for long wavelength VCSEL is the large structure thickness affecting the laser properties, added for the MIR to the tricky technological implementation of the antimonide alloys system. In this paper, we propose a new geometry for MIR-VCSEL including both a lateral confinement by an oxide aperture, and a high-contrast sub-wavelength grating mirror (HCG mirror) formed by the high contrast combination AIOx/GaAs in place of GaSb/A|AsSb top Bragg reflector. In addition to drastically simplifying the vertical stack, HCG mirror allows to control through its design the beam properties. The robust design of the HCG has been ensured by an original method of optimization based on particle swarm optimization algorithm combined with an anti-optimization one, thus allowing large error tolerance for the nano-fabrication. Oxide-based electro-optical confinement has been adapted to mid-infrared lasers, byusing a metamorphic approach with (Al) GaAs layer directly epitaxially grown on the GaSb-based VCSEL bottom structure. This approach combines the advantages of the will-controlled oxidation of AlAs layer and the efficient gain media of Sb-based for mid-infrared emission. We finally present the results obtained on electrically pumped mid-IR-VCSELs structures, for which we included oxide aperturing for lateral confinement and HCG as high reflectivity output mirrors, both based on AlxOy/GaAs heterostructures.

Optimal laser wavelength for efficient laser power converter operation over temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Höhn, O., E-mail: oliver.hoehn@ise.fraunhofer.de; Walker, A. W.; Bett, A. W.

2016-06-13

A temperature dependent modeling study is conducted on a GaAs laser power converter to identify the optimal incident laser wavelength for optical power transmission. Furthermore, the respective temperature dependent maximal conversion efficiencies in the radiative limit as well as in a practically achievable limit are presented. The model is based on the transfer matrix method coupled to a two-diode model, and is calibrated to experimental data of a GaAs photovoltaic device over laser irradiance and temperature. Since the laser wavelength does not strongly influence the open circuit voltage of the laser power converter, the optimal laser wavelength is determined tomore » be in the range where the external quantum efficiency is maximal, but weighted by the photon flux of the laser.« less

Low-thrust trajectory optimization in a full ephemeris model

NASA Astrophysics Data System (ADS)

Cai, Xing-Shan; Chen, Yang; Li, Jun-Feng

2014-10-01

The low-thrust trajectory optimization with complicated constraints must be considered in practical engineering. In most literature, this problem is simplified into a two-body model in which the spacecraft is subject to the gravitational force at the center of mass and the spacecraft's own electric propulsion only, and the gravity assist (GA) is modeled as an instantaneous velocity increment. This paper presents a method to solve the fuel-optimal problem of low-thrust trajectory with complicated constraints in a full ephemeris model, which is closer to practical engineering conditions. First, it introduces various perturbations, including a third body's gravity, the nonspherical perturbation and the solar radiation pressure in a dynamic equation. Second, it builds two types of equivalent inner constraints to describe the GA. At the same time, the present paper applies a series of techniques, such as a homotopic approach, to enhance the possibility of convergence of the global optimal solution.

Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

NASA Astrophysics Data System (ADS)

Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

A guided search genetic algorithm using mined rules for optimal affective product design

NASA Astrophysics Data System (ADS)

Fung, Chris K. Y.; Kwong, C. K.; Chan, Kit Yan; Jiang, H.

2014-08-01

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.

Enhanced light extraction from a GaN-based green light-emitting diode with hemicylindrical linear grating structure.

PubMed

Jin, Yuanhao; Yang, Fenglei; Li, Qunqing; Zhu, Zhendong; Zhu, Jun; Fan, Shoushan

2012-07-02

Significant enhancement in the light output from GaN-based green light-emitting diodes (LEDs) was achieved with a hemicylindrical grating structure on the top layer of the diodes. The grating structure was first optimized by the finite-difference time-domain (FDTD) method, which showed that the profile of the grating structure was critical for light extraction efficiency. It was found that the transmission efficiency of the 530 nm light emitted from the inside of the GaN LED increased for incidence angles between 23.58° and 60°. Such a structure was fabricated by electron-beam lithography and an etching method. The light output power from the LED was increased approximately 4.7 times compared with that from a conventional LED. The structure optimization is the key to the great increase in transmission efficiency. Furthermore, the light emitted from the edge of the LED units could be collected and extracted by the grating structures in adjacent LED units, thus enhancing the performance of the whole LED chip.

Enhanced nonlinearity interval mapping scheme for high-performance simulation-optimization of watershed-scale BMP placement

NASA Astrophysics Data System (ADS)

Zou, Rui; Riverson, John; Liu, Yong; Murphy, Ryan; Sim, Youn

2015-03-01

Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)—each with multiple Total Maximum Daily Loads (TMDL) targets—were selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of 67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of 67.7 million—marginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.

Evaluation of the Possible Utilization of 68Ga-DOTATOC in Diagnosis of Adenocarcinoma Breast Cancer

PubMed Central

Zolghadri, Samaneh; Naderi, Mojdeh; Yousefnia, Hassan; Alirezapour, Behrouz; Beiki, Davood

2018-01-01

Objective(s): Studies have indicated advantageous properties of [DOTA-DPhe1, Tyr3] octreotide (DOTATOC) in tumor models and labeling with gallium. Breast cancer is the second leading cause of cancer mortality in women, and most of these cancers are often an adenocarcinoma. Due to the importance of target to non-target ratios in the efficacy of diagnosis, the pharmacokinetic of 68Ga-DOTATOC in an adenocarcinoma breast cancer animal model was studied in this research, and the optimized time for imaging was determined. Methods: 68Ga was obtained from 68Ge/68Ga generator. The complex was prepared at optimized conditions. Radiochemical purity of the complex was checked using both HPLC and ITLC methods. Biodistribution of the complex was studied in BALB/c mice bearing adenocarcinoma breast cancer. Also, PET/CT imaging was performed up to 120 min post injection. Results: The complex was produced with radiochemical purity of greater than 98% and specific activity of about 40 GBq/mM at optimized conditions. Biodistribution of the complex was studied in BALB/c mice bearing adenocarcinoma breast cancer indicated fast blood clearance and significant uptake in the tumor. Significant tumor: blood and tumor:muscle uptake ratios were observed even at early times post-injection. PET/CT images were also confirmed the considerable accumulation of the tracer in the tumor. Conclusion: Generally, the results proved the possible application of the radiolabelled complex for the detection of the adenocarcinoma breast cancer and according to the pharmacokenitic data, the suitable time for imaging was determined as at least 30 min after injection. PMID:29333466

Application of the gravity search algorithm to multi-reservoir operation optimization

NASA Astrophysics Data System (ADS)

Bozorg-Haddad, Omid; Janbaz, Mahdieh; Loáiciga, Hugo A.

2016-12-01

Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems.

Homogeneous transparent conductive ZnO:Ga by ALD for large LED wafers

NASA Astrophysics Data System (ADS)

Szabó, Zoltán; Baji, Zsófia; Basa, Péter; Czigány, Zsolt; Bársony, István; Wang, Hsin-Ying; Volk, János

2016-08-01

Highly conductive and uniform Ga doped ZnO (GZO) films were prepared by atomic layer deposition (ALD) as transparent conductive layers for InGaN/GaN LEDs. The optimal Ga doping concentration was found to be 3 at%. Even for 4" wafers, the TCO layer shows excellent homogeneity of film resistivity (0.8 %) according to Eddy current and spectroscopic ellipsometry mapping. This makes ALD a favourable technique over concurrent methods like MBE and PLD where the up-scaling is problematic. In agreement with previous studies, it was found that by an annealing treatment the quality of the GZO/p-GaN interface can be improved, although it causes the degradation of TCO conductivity. Therefore, a two-step ALD deposition technique was proposed and demonstrated: a "buffer layer" deposited and annealed first was followed by a second deposition step to maintain the high conductivity of the top layer.

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

NASA Technical Reports Server (NTRS)

Patrick, Nicholas J. M.; Sheridan, Thomas B.

1996-01-01

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance vary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are: (1) determining what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major u.s. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort-topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimality with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider unmodelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented.

Structures of Aln (n= 27, 28, 29, and 30) clusters with double-tetrahedron structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, W.; Lu, W. C.; Sun, J.

2008-01-31

Global search for lowest-energy structures of neutral aluminum clusters Al{sub n} (n = 27, 28, 29 and 30) was performed using a genetic algorithm (GA) coupled with a tight-binding (TB) method. Structural candidates obtained from our GA search were further optimized with first-principles calculations. It is found that the medium-sized aluminum clusters Al{sub 27} to Al{sub 30} favor double-tetrahedron structures.

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm

PubMed Central

Yan, Li; Xie, Hong; Chen, Changjun

2017-01-01

Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%. PMID:28850100

Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm.

PubMed

Yan, Li; Tan, Junxiang; Liu, Hua; Xie, Hong; Chen, Changjun

2017-08-29

Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%.

An improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling

NASA Astrophysics Data System (ADS)

Dao, Son Duy; Abhary, Kazem; Marian, Romeo

2017-06-01

Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial, NP-hard problem, for which no polynomial time algorithm is known to produce an optimal result on a random graph. In this paper, the further development of Genetic Algorithm (GA) for this integrated optimization is presented. Because of the dynamic nature of the problem, the size of its solution is variable. To deal with this variability and find an optimal solution to the problem, GA with new features in chromosome encoding, crossover, mutation, selection as well as algorithm structure is developed herein. With the proposed structure, the proposed GA is able to "learn" from its experience. Robustness of the proposed GA is demonstrated by a complex numerical example in which performance of the proposed GA is compared with those of three commercial optimization solvers.

Application of Shuffled Frog Leaping Algorithm and Genetic Algorithm for the Optimization of Urban Stormwater Drainage

NASA Astrophysics Data System (ADS)

Kumar, S.; Kaushal, D. R.; Gosain, A. K.

2017-12-01

Urban hydrology will have an increasing role to play in the sustainability of human settlements. Expansion of urban areas brings significant changes in physical characteristics of landuse. Problems with administration of urban flooding have their roots in concentration of population within a relatively small area. As watersheds are urbanized, infiltration decreases, pattern of surface runoff is changed generating high peak flows, large runoff volumes from urban areas. Conceptual rainfall-runoff models have become a foremost tool for predicting surface runoff and flood forecasting. Manual calibration is often time consuming and tedious because of the involved subjectivity, which makes automatic approach more preferable. The calibration of parameters usually includes numerous criteria for evaluating the performances with respect to the observed data. Moreover, derivation of objective function assosciat6ed with the calibration of model parameters is quite challenging. Various studies dealing with optimization methods has steered the embracement of evolution based optimization algorithms. In this paper, a systematic comparison of two evolutionary approaches to multi-objective optimization namely shuffled frog leaping algorithm (SFLA) and genetic algorithms (GA) is done. SFLA is a cooperative search metaphor, stimulated by natural memetics based on the population while, GA is based on principle of survival of the fittest and natural evolution. SFLA and GA has been employed for optimizing the major parameters i.e. width, imperviousness, Manning's coefficient and depression storage for the highly urbanized catchment of Delhi, India. The study summarizes the auto-tuning of a widely used storm water management model (SWMM), by internal coupling of SWMM with SFLA and GA separately. The values of statistical parameters such as, Nash-Sutcliffe efficiency (NSE) and Percent Bias (PBIAS) were found to lie within the acceptable limit, indicating reasonably good model performance. Overall, this study proved promising for assessing risk in urban drainage systems and should prove useful to improve integrity of the urban system, its reliability and provides guidance for inundation preparedness.Keywords: Hydrologic model, SWMM, Urbanization, SFLA and GA.

Optimization of the highly strained InGaAs/GaAs quantum well lasers grown by MOVPE

NASA Astrophysics Data System (ADS)

Su, Y. K.; Chen, W. C.; Wan, C. T.; Yu, H. C.; Chuang, R. W.; Tsai, M. C.; Cheng, K. Y.; Hu, C.; Tsau, Seth

2008-07-01

In this article, we study the highly compressive-strained InGaAs/GaAs quantum wells and the broad-area lasers grown by MOVPE. Several epitaxial parameters were optimized, including the growth temperature, pressure and group V to group III (V/III) ratio. Grown with the optimized epitaxial parameters, the highly strained In 0.39Ga 0.61As/GaAs lasers could be continuously operated at 1.22 μm and their threshold current density Jth was 140 A/cm 2. To the best of our knowledge, the demonstrated InGaAs QW laser has the lowest threshold current per quantum well (Jth/QW) of 46.7 A/cm 2. The fitted characteristic temperature ( T0) was 146.2 K, indicating the good electron confinement ability. Furthermore, by lowering the growth temperature down to 475 °C and the TBAs/III ratio to 5, the emission wavelength of the In 0.42Ga 0.58As/GaAs quantum wells was as long as 1245 nm and FWHM was 43 meV.

Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm.

PubMed

Ma, Changxi; Hao, Wei; Pan, Fuquan; Xiang, Wang

2018-01-01

Route optimization of hazardous materials transportation is one of the basic steps in ensuring the safety of hazardous materials transportation. The optimization scheme may be a security risk if road screening is not completed before the distribution route is optimized. For road screening issues of hazardous materials transportation, a road screening algorithm of hazardous materials transportation is built based on genetic algorithm and Levenberg-Marquardt neural network (GA-LM-NN) by analyzing 15 attributes data of each road network section. A multi-objective robust optimization model with adjustable robustness is constructed for the hazardous materials transportation problem of single distribution center to minimize transportation risk and time. A multi-objective genetic algorithm is designed to solve the problem according to the characteristics of the model. The algorithm uses an improved strategy to complete the selection operation, applies partial matching cross shift and single ortho swap methods to complete the crossover and mutation operation, and employs an exclusive method to construct Pareto optimal solutions. Studies show that the sets of hazardous materials transportation road can be found quickly through the proposed road screening algorithm based on GA-LM-NN, whereas the distribution route Pareto solutions with different levels of robustness can be found rapidly through the proposed multi-objective robust optimization model and algorithm.

Clinical Endpoints for the Study of Geographic Atrophy Secondary to Age-Related Macular Degeneration

PubMed Central

Sadda, SriniVas R.; Chakravarthy, Usha; Birch, David G.; Staurenghi, Giovanni; Henry, Erin C.; Brittain, Christopher

2017-01-01

Purpose To summarize the recent literature describing the application of modern technologies in the study of patients with geographic atrophy (GA) secondary to age-related macular degeneration (AMD). Methods Review of the literature describing the terms and definitions used to describe GA, imaging modalities used to capture and measure GA, and the tests of visual function and functional deficits that occur in patients with GA. Results In this paper we describe the evolution of the definitions used to describe GA. We compare imaging modalities used in the characterization of GA, report on the sensitivity and specificity of the techniques where data exist, and describe the correlations between these various modes of capturing the presence of GA. We review the functional tests that have been used in patients with GA, and critically examine their ability to detect and quantify visual deficits. Conclusion Ophthalmologists and retina specialists now have a wide range of assessments available for the functional and anatomic characterization of GA in patients with AMD. To date, studies have been limited by their unimodal approach and we recommend that future studies of GA use multimodal imaging. We also suggest strategies for the optimal functional testing of patients with GA. PMID:27652913

Genetic learning in rule-based and neural systems

NASA Technical Reports Server (NTRS)

Smith, Robert E.

1993-01-01

The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.

An optimized BP neural network based on genetic algorithm for static decoupling of a six-axis force/torque sensor

NASA Astrophysics Data System (ADS)

Fu, Liyue; Song, Aiguo

2018-02-01

In order to improve the measurement precision of 6-axis force/torque sensor for robot, BP decoupling algorithm optimized by GA (GA-BP algorithm) is proposed in this paper. The weights and thresholds of a BP neural network with 6-10-6 topology are optimized by GA to develop decouple a six-axis force/torque sensor. By comparison with other traditional decoupling algorithm, calculating the pseudo-inverse matrix of calibration and classical BP algorithm, the decoupling results validate the good decoupling performance of GA-BP algorithm and the coupling errors are reduced.

Characterization and Optimization Design of the Polymer-Based Capacitive Micro-Arrayed Ultrasonic Transducer

NASA Astrophysics Data System (ADS)

Chiou, De-Yi; Chen, Mu-Yueh; Chang, Ming-Wei; Deng, Hsu-Cheng

2007-11-01

This study constructs an electromechanical finite element model of the polymer-based capacitive micro-arrayed ultrasonic transducer (P-CMUT). The electrostatic-structural coupled-field simulations are performed to investigate the operational characteristics, such as collapse voltage and resonant frequency. The numerical results are found to be in good agreement with experimental observations. The study of influence of each defined parameter on the collapse voltage and resonant frequency are also presented. To solve some conflict problems in diversely physical fields, an integrated design method is developed to optimize the geometric parameters of the P-CMUT. The optimization search routine conducted using the genetic algorithm (GA) is connected with the commercial FEM software ANSYS to obtain the best design variable using multi-objective functions. The results show that the optimal parameter values satisfy the conflicting objectives, namely to minimize the collapse voltage while simultaneously maintaining a customized frequency. Overall, the present result indicates that the combined FEM/GA optimization scheme provides an efficient and versatile approach of optimization design of the P-CMUT.

Optimization of polarization compensating interlayers for InGaN/GaN MQW solar cells

NASA Astrophysics Data System (ADS)

Saini, Basant; Sharma, Sugandha; Kaur, Ravinder; Pal, Suchandan; Kapoor, Avinashi

2018-05-01

Optimization of polarization compensating interlayer (PCI) is performed numerically to improve the photovoltaic properties of InGaN/GaN multiple quantum well solar cell (MQWSC). Simulations are performed to investigate the effect of change in thickness and composition of PCI on the performance of cell. Short circuit current density is increased as we increase the thickness of the PCI. Changing the constitution of PCI not only mitigates the negative effects of polarization-induced electric fields but also reduces the high potential barrier existing at the QW/p-GaN hetero-interface. This claim is validated by the performance shown by the cell containing optimized PCI, as it shows an improved efficiency of 1.54 % under AM1.5G illumination.

A flatter gallium profile for high-efficiency Cu(In,Ga)(Se,S)2 solar cell and improved robustness against sulfur-gradient variation

NASA Astrophysics Data System (ADS)

Huang, Chien-Yao; Lee, Wen-Chin; Lin, Albert

2016-09-01

Co-optimization of the gallium and sulfur profiles in penternary Cu(In,Ga)(Se,S)2 thin film solar cell and its impacts on device performance and variability are investigated in this work. An absorber formation method to modulate the gallium profiling under low sulfur-incorporation is disclosed, which solves the problem of Ga-segregation in selenization. Flatter Ga-profiles, which lack of experimental investigations to date, are explored and an optimal Ga-profile achieving 17.1% conversion efficiency on a 30 cm × 30 cm sub-module without anti-reflection coating is presented. Flatter Ga-profile gives rise to the higher Voc × Jsc by improved bandgap matching to solar spectrum, which is hard to be achieved by the case of Ga-accumulation. However, voltage-induced carrier collection loss is found, as evident from the measured voltage-dependent photocurrent characteristics based on a small-signal circuit model. The simulation results reveal that the loss is attributed to the synergistic effect of the detrimental gallium and sulfur gradients, which can deteriorate the carrier collection especially in quasi-neutral region (QNR). Furthermore, the underlying physics is presented, and it provides a clear physical picture to the empirical trends of device performance, I-V characteristics, and voltage-dependent photocurrent, which cannot be explained by the standard solar circuit model. The parameter "FGa" and front sulfur-gradient are found to play critical roles on the trade-off between space charge region (SCR) recombination and QNR carrier collection. The co-optimized gallium and sulfur gradients are investigated, and the corresponding process modification for further efficiency-enhancement is proposed. In addition, the performance impact of sulfur-gradient variation is studied, and a gallium design for suppressing the sulfur-induced variability is proposed. Device performances of varied Ga-profiles with front sulfur-gradients are simulated based on a compact device model. Finally, an exploratory path toward 20% high-efficiency Ga-profile with robustness against sulfur-induced performance variability is presented.

Lyophilized Kit for the Preparation of the PET Perfusion Agent [(68)Ga]-MAA.

PubMed

Amor-Coarasa, Alejandro; Milera, Andrew; Carvajal, Denny; Gulec, Seza; McGoron, Anthony J

2014-01-01

Rapid developments in the field of medical imaging have opened new avenues for the use of positron emitting labeled microparticles. The radioisotope used in our research was (68)Ga, which is easy to obtain from a generator and has good nuclear properties for PET imaging. Methods. Commercially available macroaggregated albumin (MAA) microparticles were suspended in sterile saline, centrifuged to remove the free albumin and stannous chloride, relyophilized, and stored for later labeling with (68)Ga. Labeling was performed at different temperatures and times. (68)Ga purification settings were also tested and optimized. Labeling yield and purity of relyophilized MAA microparticles were compared with those that were not relyophilized. Results. MAA particles kept their original size distribution after relyophilization. Labeling yield was 98% at 75°C when a (68)Ga purification system was used, compared to 80% with unpurified (68)Ga. Radiochemical purity was over 97% up to 4 hours after the labeling. The relyophilized MAA and labeling method eliminate the need for centrifugation purification of the final product and simplify the labeling process. Animal experiments demonstrated the high in vivo stability of the obtained PET agent with more than 95% of the activity remaining in the lungs after 4 hours.

Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method

ERIC Educational Resources Information Center

Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen

2008-01-01

In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…

Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft

NASA Technical Reports Server (NTRS)

Patrick, Nicholas J. M.; Sheridan, Thomas B.

1996-01-01

Most decisions made in the cockpit are related to safety, and have therefore been proceduralized in order to reduce risk. There are very few which are made on the basis of a value metric such as economic cost. One which can be shown to be value based, however, is the selection of a flight profile. Fuel consumption and flight time both have a substantial effect on aircraft operating cost, but they cannot be minimized simultaneously. In addition, winds, turbulence, and performance x,ary widely with altitude and time. These factors make it important and difficult for pilots to (a) evaluate the outcomes associated with a particular trajectory before it is flown and (b) decide among possible trajectories. The two elements of this problem considered here are (1) determining, what constitutes optimality, and (2) finding optimal trajectories. Pilots and dispatchers from major U.S. airlines were surveyed to determine which attributes of the outcome of a flight they considered the most important. Avoiding turbulence-for passenger comfort topped the list of items which were not safety related. Pilots' decision making about the selection of flight profile on the basis of flight time, fuel burn, and exposure to turbulence was then observed. Of the several behavioral and prescriptive decision models invoked to explain the pilots' choices, utility maximization is shown to best reproduce the pilots' decisions. After considering more traditional methods for optimizing trajectories, a novel method is developed using a genetic algorithm (GA) operating on a discrete representation of the trajectory search space. The representation is a sequence of command altitudes, and was chosen to be compatible with the constraints imposed by Air Traffic Control, and with the training given to pilots. Since trajectory evaluation for the GA is performed holistically, a wide class of objective functions can be optimized easily. Also, using the GA it is possible to compare the costs associated with different airspace design and air traffic management policies. A decision aid is proposed which would combine the pilot's notion of optimility with the GA-based optimization, provide the pilot with a number of alternative pareto-optimal trajectories, and allow him to consider un-modelled attributes and constraints in choosing among them. A solution to the problem of displaying alternatives in a multi-attribute decision space is also presented.

Optimization of selenylation modification for garlic polysaccharide based on immune-enhancing activity.

PubMed

Gao, Zhenzhen; Chen, Jin; Qiu, Shulei; Li, Youying; Wang, Deyun; Liu, Cui; Li, Xiuping; Hou, Ranran; Yue, Chanjuan; Liu, Jie; Li, Hongquan; Hu, Yuanliang

2016-01-20

Garlic polysaccharide (GPS) was modified in selenylation respectively by nitric acid-sodium selenite (NA-SS), glacial acetic acid-selenous acid (GA-SA), glacial acetic acid-sodium selenite (GA-SS) and selenium oxychloride (SOC) methods each under nine modification conditions of L9(3(4)) orthogonal design and each to obtain nine selenizing GPSs (sGPSs). Their structures were identified, yields and selenium contents were determined, selenium yields were calculated, and the immune-enhancing activities of four sGPSs with higher selenium yields were compared taking unmodified GPS as control. The results showed that among four methods the selenylation efficiency of NA-SS method were the highest, the activity of sGPS5 was the strongest and significantly stronger than that of unmodified GPS. This indicates that selenylation modification can significantly enhance the immune-enhancing activity of GPS, NA-SS method is the best method and the optimal conditions are 0.8:1 weight ratio of sodium selenite to GPS, reaction temperature of 70 °C and reaction time of 10h. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effect of Selenization Processes on CIGS Solar Cell Performance.

PubMed

Wu, C H; Wu, P W; Chen, J H; Kao, J Y; Hsu, C Y

2018-07-01

Cu(In, Ga)Se2 (CIGS) films were fabricated by a two-step process method using sputtering from Cu0.7Ga0.3 and In targets. The metallic precursor structures of In/CuGa/In were prepared, and CuGa film was adjusted to the thicknesses of 150, 200, 250 and 300 nm, in order to optimize the CIGS film. After selenization, three independent CIGS (112), CIGS (220/204) and CIGS (312/116) began to crystallize at ~280 °C and phase peaks continued growing until 560 °C. Experimental results showed that with a single stage selenization method, the excessive stoichiometry of the CIGS films was obtained. Using three sequential stages for the selenization process, with a annealing time of 20 min, the stoichiometry of the CIGS absorbers with the Cu/(In + Ga) and Ga/(In + Ga) showed atomic ratios of 0.94 and 0.34, respectively. The intensity of the (112) XRD diffraction peak became stronger, indicating an improvement in the crystallinity. Raman spectra of CIGS absorbers showed a main peak (174 cm-1) and two weak signals (212 and 231 cm-1). TEM image for electron diffraction pattern showed that the grains were randomly oriented. CIGS solar cell device prepared with a proper selenization, a maximum efficiency of 12.45% was obtained.

Simulation of thermal management in AlGaN/GaN HEMTs with integrated diamond heat spreaders

NASA Astrophysics Data System (ADS)

Wang, A.; Tadjer, M. J.; Calle, F.

2013-05-01

We investigated the impact of diamond heat spreading layers on the performance of AlGaN/GaN high-electron-mobility-transistors (HEMTs). A finite element method was used to simulate the thermal and electrical characteristics of the devices under dc and pulsed operation conditions. The results show that the device performance can be improved significantly by optimized heat spreading, an effect strongly dependent on the lateral thermal conductivity of the initial several micrometers of diamond deposition. Of crucial importance is the proximity of the diamond layer to the heat source, which makes this method advantageous over other thermal management procedures, especially for the device in pulsed operation. In this case, the self-heating effect can be suppressed, and it is not affected by either the substrate or its thermal boundary resistance at the GaN/substrate at wider pulses. The device with a 5 µm diamond layer can present 10.5% improvement of drain current, and the self-heating effect can be neglected for a 100 ns pulse width at 1 V gate and 20 V drain voltage.

The Secondary Development of ABAQUS by using Python and the Application of the Advanced GA

NASA Astrophysics Data System (ADS)

Luo, Lilong; Zhao, Meiying

Realizing the secondary development of the ABAQUS based on the manual of ABAQUS. In order to overcome the prematurity and the worse convergence of the Simple Genetic Algorithm (SGA), a new strategy how to improve the efficiency of the SGA has been put forward. In the new GA, the selection probability and the mutation probability are self-adaptive. Taking the stability of the composite laminates as the target, the optimized laminates sequences and radius of the hatch are analyzed with the help of ABAQUS. Compared with the SGA, the new GA method shows a good consistency, fast convergence and practical feasibility.

Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Zhang, Qigui; Deng, Kai

2016-12-01

As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

Hybrid feedforward and feedback controller design for nuclear steam generators over wide range operation using genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Y.; Edwards, R.M.; Lee, K.Y.

1997-03-01

In this paper, a simplified model with a lower order is first developed for a nuclear steam generator system and verified against some realistic environments. Based on this simplified model, a hybrid multi-input and multi-out (MIMO) control system, consisting of feedforward control (FFC) and feedback control (FBC), is designed for wide range conditions by using the genetic algorithm (GA) technique. The FFC control, obtained by the GA optimization method, injects an a priori command input into the system to achieve an optimal performance for the designed system, while the GA-based FBC control provides the necessary compensation for any disturbances ormore » uncertainties in a real steam generator. The FBC control is an optimal design of a PI-based control system which would be more acceptable for industrial practices and power plant control system upgrades. The designed hybrid MIMO FFC/FBC control system is first applied to the simplified model and then to a more complicated model with a higher order which is used as a substitute of the real system to test the efficacy of the designed control system. Results from computer simulations show that the designed GA-based hybrid MIMO FFC/FBC control can achieve good responses and robust performances. Hence, it can be considered as a viable alternative to the current control system upgrade.« less

Design and optimization of ARC less InGaP/GaAs single-/multi-junction solar cells with tunnel junction and back surface field layers

NASA Astrophysics Data System (ADS)

Chee, Kuan W. A.; Hu, Yuning

2018-07-01

There has always been an inexorable interest in the solar industry in boosting the photovoltaic conversion efficiency. This paper presents a theoretical and numerical simulation study of the effects of key design parameters on the photoelectric performance of single junction (InGaP- or GaAs-based) and dual junction (InGaP/GaAs) inorganic solar cells. The influence of base layer thickness, base doping concentration, junction temperature, back surface field layer composition and thickness, and tunnel junction material, were correlated with open circuit voltage, short-circuit current, fill factor and power conversion efficiency performance. The InGaP/GaAs dual junction solar cell was optimized with the tunnel junction and back surface field designs, yielding a short-circuit current density of 20.71 mAcm-2 , open-circuit voltage of 2.44 V and fill factor of 88.6%, and guaranteeing an optimal power conversion efficiency of at least 32.4% under 1 sun AM0 illumination even without an anti-reflective coating.

New type side weir discharge coefficient simulation using three novel hybrid adaptive neuro-fuzzy inference systems

NASA Astrophysics Data System (ADS)

Bonakdari, Hossein; Zaji, Amir Hossein

2018-03-01

In many hydraulic structures, side weirs have a critical role. Accurately predicting the discharge coefficient is one of the most important stages in the side weir design process. In the present paper, a new high efficient side weir is investigated. To simulate the discharge coefficient of these side weirs, three novel soft computing methods are used. The process includes modeling the discharge coefficient with the hybrid Adaptive Neuro-Fuzzy Interface System (ANFIS) and three optimization algorithms, namely Differential Evaluation (ANFIS-DE), Genetic Algorithm (ANFIS-GA) and Particle Swarm Optimization (ANFIS-PSO). In addition, sensitivity analysis is done to find the most efficient input variables for modeling the discharge coefficient of these types of side weirs. According to the results, the ANFIS method has higher performance when using simpler input variables. In addition, the ANFIS-DE with RMSE of 0.077 has higher performance than the ANFIS-GA and ANFIS-PSO methods with RMSE of 0.079 and 0.096, respectively.

Differences in optoelectronic properties between H-saturated and unsaturated GaN nanowires with DFT method

NASA Astrophysics Data System (ADS)

Diao, Yu; Liu, Lei; Xia, Sihao; Kong, Yike

2017-05-01

To investigate the influences of dangling bonds on GaN nanowires surface, the differences in optoelectronic properties between H-saturated and unsaturated GaN nanowires are researched through first-principles study. The GaN nanowires along the [0001] growth direction with diameters of 3.7, 7.5 and 9.5 Å are considered. According to the results, H-saturated GaN nanowires are more stable than the unsaturated ones. With increasing nanowire diameter, unsaturated GaN nanowires become more stable, while the stability of H-saturated GaN nanowires has little change. After geometry optimization, the atomic displacements of unsaturated and H-saturated models are almost reversed. In (0001) crystal plane, Ga atoms tend to move inwards and N atoms tend to move outwards slightly for the unsaturated nanowires, while Ga atoms tend to move outwards and N atoms tend to move inwards slightly for the H-saturated nanowires. Besides, with increasing nanowire diameter, the conduction band minimum of H-saturated nanowire moves to the lower energy side, while that of the unsaturated nanowire changes slightly. The bandgaps of H-saturated nanowires are approaching to bulk GaN as the diameter increases. Absorption curves and reflectivity curves of the unsaturated and H-saturated nanowires exhibit the same trend with the change of energy except the H-saturated models which show larger variations. Through all the calculated results above, we can better understand the effects of dangling bonds on the optoelectronic properties of GaN nanowires and select more proper calculation models and methods for other calculations.

Preclinical Study of 68Ga-DOTATOC: Biodistribution Assessment in Syrian Rats and Evaluation of Absorbed Dose in Human Organs

PubMed Central

Naderi, Mojdeh; Zolghadri, Samaneh; Yousefnia, Hassan; Ramazani, Ali; Jalilian, Amir Reza

2016-01-01

Objective(s): Gallium-68 DOTA-DPhe1-Tyr3-Octreotide (68Ga-DOTATOC) has been applied by several European centers for the treatment of a variety of human malignancies. Nevertheless, definitive dosimetric data are yet unavailable. According to the Society of Nuclear Medicine and Molecular Imaging, researchers are investigating the safety and efficacy of this radiotracer to meet Food and Drug Administration requirements. The aim of this study was to introduce the optimized procedure for 68Ga-DOTATOC preparation, using a novel germanium-68 (68Ge)/68Ga generator in Iran and evaluate the absorbed doses in numerous organs with high accuracy. Methods: The optimized conditions for preparing the radiolabeled complex were determined via several experiments by changing the ligand concentration, pH, temperature and incubation time. Radiochemical purity of the complex was assessed, using high-performance liquid chromatography and instant thin-layer chromatography. The absorbed dose of human organs was evaluated, based on biodistribution studies on Syrian rats via Radiation Absorbed Dose Assessment Resource Method. Results: 68Ga-DOTATOC was prepared with radiochemical purity of >98% and specific activity of 39.6 MBq/nmol. The complex demonstrated great stability at room temperature and in human serum at 37°C at least two hours after preparation. Significant uptake was observed in somatostatin receptor-positive tissues such as pancreatic and adrenal tissues (12.83 %ID/g and 0.91 %ID/g, respectively). Dose estimations in human organs showed that the pancreas, kidneys and adrenal glands received the maximum absorbed doses (0.105, 0.074 and 0.010 mGy/MBq, respectively). Also, the effective absorbed dose was estimated at 0.026 mSv/MBq for 68Ga-DOTATOC. Conclusion: The obtained results showed that 68Ga-DOTATOC can be considered as an effective agent for clinical PET imaging in Iran. PMID:27904870

Delta-doping optimization for high quality p-type GaN

NASA Astrophysics Data System (ADS)

Bayram, C.; Pau, J. L.; McClintock, R.; Razeghi, M.

2008-10-01

Delta (δ -) doping is studied in order to achieve high quality p-type GaN. Atomic force microscopy, x-ray diffraction, photoluminescence, and Hall measurements are performed on the samples to optimize the δ-doping characteristics. The effect of annealing on the electrical, optical, and structural quality is also investigated for different δ-doping parameters. Optimized pulsing conditions result in layers with hole concentrations near 1018 cm-3 and superior crystal quality compared to conventional p-GaN. This material improvement is achieved thanks to the reduction in the Mg activation energy and self-compensation effects in δ-doped p-GaN.

Optimization of ELISA Conditions to Quantify Colorectal Cancer Antigen-Antibody Complex Protein (GA733-FcK) Expressed in Transgenic Plant

PubMed Central

Ahn, Junsik; Lee, Kyung Jin

2014-01-01

The purpose of this study is to optimize ELISA conditions to quantify the colorectal cancer antigen GA733 linked to the Fc antibody fragment fused to KDEL, an ER retention motif (GA733-FcK) expressed in transgenic plant. Variable conditions of capture antibody, blocking buffer, and detection antibody for ELISA were optimized with application of leaf extracts from transgenic plant expressing GA733-FcK. In detection antibody, anti-EpCAM/CD362 IgG recognizing the GA733 did not detect any GA733-FcK whereas anti-human Fc IgG recognizing the human Fc existed in plant leaf extracts. For blocking buffer conditions, 3% BSA buffer clearly blocked the plate, compared to the 5% skim-milk buffer. For capture antibody, monoclonal antibody (MAb) CO17-1A was applied to coat the plate with different amounts (1, 0.5, and 0.25 μg/well). Among the amounts of the capture antibody, 1 and 0.5 μg/well (capture antibody) showed similar absorbance, whereas 0.25 μg/well of the capture antibody showed significantly less absorbance. Taken together, the optimized conditions to quantify plant-derived GA733-FcK were 0.5 μg/well of MAb CO17-1A per well for the capture antibody, 3% BSA for blocking buffer, and anti-human Fc conjugated HRP. To confirm the optimized ELISA conditions, correlation analysis was conducted between the quantified amount of GA733-FcK in ELISA and its protein density values of different leaf samples in Western blot. The co-efficient value R2 between the ELISA quantified value and protein density was 0.85 (p<0.01), which indicates that the optimized ELISA conditions feasibly provides quantitative information of GA733-FcK expression in transgenic plant. PMID:24555929

Long-term evaluation of TiO2-based 68Ge/68Ga generators and optimized automation of [68Ga]DOTATOC radiosynthesis.

PubMed

Lin, Mai; Ranganathan, David; Mori, Tetsuya; Hagooly, Aviv; Rossin, Raffaella; Welch, Michael J; Lapi, Suzanne E

2012-10-01

Interest in using (68)Ga is rapidly increasing for clinical PET applications due to its favorable imaging characteristics and increased accessibility. The focus of this study was to provide our long-term evaluations of the two TiO(2)-based (68)Ge/(68)Ga generators and develop an optimized automation strategy to synthesize [(68)Ga]DOTATOC by using HEPES as a buffer system. This data will be useful in standardizing the evaluation of (68)Ge/(68)Ga generators and automation strategies to comply with regulatory issues for clinical use. Copyright © 2012 Elsevier Ltd. All rights reserved.

On the physical operation and optimization of the p-GaN gate in normally-off GaN HEMT devices

NASA Astrophysics Data System (ADS)

Efthymiou, L.; Longobardi, G.; Camuso, G.; Chien, T.; Chen, M.; Udrea, F.

2017-03-01

In this study, an investigation is undertaken to determine the effect of gate design parameters on the on-state characteristics (threshold voltage and gate turn-on voltage) of pGaN/AlGaN/GaN high electron mobility transistors (HEMTs). Design parameters considered are pGaN doping and gate metal work function. The analysis considers the effects of variations on these parameters using a TCAD model matched with experimental results. A better understanding of the underlying physics governing the operation of these devices is achieved with a view to enable better optimization of such gate designs.

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data

PubMed Central

Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

2016-01-01

The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew’s Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions. PMID:26861308

Heterointerface study of InAs/GaSb nanoridge heterostructures grown by metal organic chemical vapor deposition on V-grooved Si (0 0 1) substrates

NASA Astrophysics Data System (ADS)

Lai, Billy; Li, Qiang; Lau, Kei May

2018-02-01

InAs/GaSb nanoridge heterostructures were grown on V-grooved (0 0 1) Si by metal organic chemical vapor deposition. Combining the aspect ratio trapping process and a low temperature GaAs buffer, we demonstrated high quality GaSb nanoridge templates for InAs/GaSb heterostructure growth. Two different interfaces, a transitional GaAsSb and an InSb-like interface, were investigated when growing these heterostructures. A 500 °C growth temperature in conjunction with a GaAsSb interface was determined to produce the optimal interface, properly compensating for the tensile strain accumulated when growing InAs on GaSb. Without the need for a complicated switching sequence, this GaAsSb-like interface utilized at the optimized temperature is the initial step towards InAs/GaSb type II superlattice and other device structures integrated onto Si.

A Genetic Algorithm for Flow Shop Scheduling with Assembly Operations to Minimize Makespan

NASA Astrophysics Data System (ADS)

Bhongade, A. S.; Khodke, P. M.

2014-04-01

Manufacturing systems, in which, several parts are processed through machining workstations and later assembled to form final products, is common. Though scheduling of such problems are solved using heuristics, available solution approaches can provide solution for only moderate sized problems due to large computation time required. In this work, scheduling approach is developed for such flow-shop manufacturing system having machining workstations followed by assembly workstations. The initial schedule is generated using Disjunctive method and genetic algorithm (GA) is applied further for generating schedule for large sized problems. GA is found to give near optimal solution based on the deviation of makespan from lower bound. The lower bound of makespan of such problem is estimated and percent deviation of makespan from lower bounds is used as a performance measure to evaluate the schedules. Computational experiments are conducted on problems developed using fractional factorial orthogonal array, varying the number of parts per product, number of products, and number of workstations (ranging upto 1,520 number of operations). A statistical analysis indicated the significance of all the three factors considered. It is concluded that GA method can obtain optimal makespan.

Multi-objective Optimization of Pulsed Gas Metal Arc Welding Process Using Neuro NSGA-II

NASA Astrophysics Data System (ADS)

Pal, Kamal; Pal, Surjya K.

2018-05-01

Weld quality is a critical issue in fabrication industries where products are custom-designed. Multi-objective optimization results number of solutions in the pareto-optimal front. Mathematical regression model based optimization methods are often found to be inadequate for highly non-linear arc welding processes. Thus, various global evolutionary approaches like artificial neural network, genetic algorithm (GA) have been developed. The present work attempts with elitist non-dominated sorting GA (NSGA-II) for optimization of pulsed gas metal arc welding process using back propagation neural network (BPNN) based weld quality feature models. The primary objective to maintain butt joint weld quality is the maximization of tensile strength with minimum plate distortion. BPNN has been used to compute the fitness of each solution after adequate training, whereas NSGA-II algorithm generates the optimum solutions for two conflicting objectives. Welding experiments have been conducted on low carbon steel using response surface methodology. The pareto-optimal front with three ranked solutions after 20th generations was considered as the best without further improvement. The joint strength as well as transverse shrinkage was found to be drastically improved over the design of experimental results as per validated pareto-optimal solutions obtained.

Synthesis design of artificial magnetic metamaterials using a genetic algorithm.

PubMed

Chen, P Y; Chen, C H; Wang, H; Tsai, J H; Ni, W X

2008-08-18

In this article, we present a genetic algorithm (GA) as one branch of artificial intelligence (AI) for the optimization-design of the artificial magnetic metamaterial whose structure is automatically generated by computer through the filling element methodology. A representative design example, metamaterials with permeability of negative unity, is investigated and the optimized structures found by the GA are presented. It is also demonstrated that our approach is effective for the synthesis of functional magnetic and electric metamaterials with optimal structures. This GA-based optimization-design technique shows great versatility and applicability in the design of functional metamaterials.

Genetics algorithm optimization of DWT-DCT based image Watermarking

NASA Astrophysics Data System (ADS)

Budiman, Gelar; Novamizanti, Ledya; Iwut, Iwan

2017-01-01

Data hiding in an image content is mandatory for setting the ownership of the image. Two dimensions discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed as transform method in this paper. First, the host image in RGB color space is converted to selected color space. We also can select the layer where the watermark is embedded. Next, 2D-DWT transforms the selected layer obtaining 4 subband. We select only one subband. And then block-based 2D-DCT transforms the selected subband. Binary-based watermark is embedded on the AC coefficients of each block after zigzag movement and range based pixel selection. Delta parameter replacing pixels in each range represents embedded bit. +Delta represents bit “1” and -delta represents bit “0”. Several parameters to be optimized by Genetics Algorithm (GA) are selected color space, layer, selected subband of DWT decomposition, block size, embedding range, and delta. The result of simulation performs that GA is able to determine the exact parameters obtaining optimum imperceptibility and robustness, in any watermarked image condition, either it is not attacked or attacked. DWT process in DCT based image watermarking optimized by GA has improved the performance of image watermarking. By five attacks: JPEG 50%, resize 50%, histogram equalization, salt-pepper and additive noise with variance 0.01, robustness in the proposed method has reached perfect watermark quality with BER=0. And the watermarked image quality by PSNR parameter is also increased about 5 dB than the watermarked image quality from previous method.

Optimizing the Long-Term Operating Plan of Railway Marshalling Station for Capacity Utilization Analysis

PubMed Central

Zhou, Wenliang; Yang, Xia; Deng, Lianbo

2014-01-01

Not only is the operating plan the basis of organizing marshalling station's operation, but it is also used to analyze in detail the capacity utilization of each facility in marshalling station. In this paper, a long-term operating plan is optimized mainly for capacity utilization analysis. Firstly, a model is developed to minimize railcars' average staying time with the constraints of minimum time intervals, marshalling track capacity, and so forth. Secondly, an algorithm is designed to solve this model based on genetic algorithm (GA) and simulation method. It divides the plan of whole planning horizon into many subplans, and optimizes them with GA one by one in order to obtain a satisfactory plan with less computing time. Finally, some numeric examples are constructed to analyze (1) the convergence of the algorithm, (2) the effect of some algorithm parameters, and (3) the influence of arrival train flow on the algorithm. PMID:25525614

GA-optimization for rapid prototype system demonstration

NASA Technical Reports Server (NTRS)

Kim, Jinwoo; Zeigler, Bernard P.

1994-01-01

An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was developed to solve complicated engineering problems which require optimization of a large number of parameters with high precision. High level GAs search for few parameters which are much more sensitive to the system performance. Low level GAs search in more detail and employ a greater number of parameters for further optimization. Therefore, the complexity of the search is decreased and the computing resources are used more efficiently.

Indexed triangle strips optimization for real-time visualization using genetic algorithm: preliminary study

NASA Astrophysics Data System (ADS)

Tanaka, Kiyoshi; Takano, Shuichi; Sugimura, Tatsuo

2000-10-01

In this work we focus on the indexed triangle strips that is an extended representation of triangle strips to improve the efficiency for geometrical transformation of vertices, and present a method to construct optimum indexed triangle strips using Genetic Algorithm (GA) for real-time visualization. The main objective of this work is how to optimally construct indexed triangle strips by improving the ratio that reuses the data stored in the cash memory and simultaneously reducing the total index numbers with GA. Simulation results verify that the average index numbers and cache miss ratio per polygon cold be small, and consequently the total visualization time required for the optimum solution obtained by this scheme could be remarkably reduced.

Test Scheduling for Core-Based SOCs Using Genetic Algorithm Based Heuristic Approach

NASA Astrophysics Data System (ADS)

Giri, Chandan; Sarkar, Soumojit; Chattopadhyay, Santanu

This paper presents a Genetic algorithm (GA) based solution to co-optimize test scheduling and wrapper design for core based SOCs. Core testing solutions are generated as a set of wrapper configurations, represented as rectangles with width equal to the number of TAM (Test Access Mechanism) channels and height equal to the corresponding testing time. A locally optimal best-fit heuristic based bin packing algorithm has been used to determine placement of rectangles minimizing the overall test times, whereas, GA has been utilized to generate the sequence of rectangles to be considered for placement. Experimental result on ITC'02 benchmark SOCs shows that the proposed method provides better solutions compared to the recent works reported in the literature.

Growth of quantum three-dimensional structure of InGaAs emitting at 1 μm applicable for a broadband near-infrared light source

NASA Astrophysics Data System (ADS)

Ozaki, Nobuhiko; Kanehira, Shingo; Hayashi, Yuma; Ohkouchi, Shunsuke; Ikeda, Naoki; Sugimoto, Yoshimasa; Hogg, Richard A.

2017-11-01

We obtained a high-intensity and broadband emission centered at 1 μm from InGaAs quantum three-dimensional (3D) structures grown on a GaAs substrate using molecular beam epitaxy. An InGaAs thin layer grown on GaAs with a thickness close to the critical layer thickness is normally affected by strain as a result of the lattice mismatch and introduced misfit dislocations. However, under certain growth conditions for the In concentration and growth temperature, the growth mode of the InGaAs layer can be transformed from two-dimensional to 3D growth. We found the optimal conditions to obtain a broadband emission from 3D structures with a high intensity and controlled center wavelength at 1 μm. This method offers an alternative approach for fabricating a broadband near-infrared light source for telecommunication and medical imaging systems such as for optical coherence tomography.

Development and Optimization of Targeted Nanoscale Iron Delivery Methods for Treatment of NAPL Source Zones

DTIC Science & Technology

2011-04-01

27 III.1.2.3. Gum Arabic Emulsion ……………………………………………. 29 III.1.2.4. Reactivity Studies GA...GA Gum Arabic HLB Hydrophobic Lipophilic Balance IFT Interfacial Tension MISER Michigan Soil-Vapor Extraction Remediation model mRNIP Modified...trichloroethylene (TCE) (99.9%) were supplied by Fischer Scientific. Sodium borohydride (NaBH4) (98+%) and Gum Arabic were supplied by Acros Organics. Purified water

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA.

PubMed

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method.

Layout Design of Human-Machine Interaction Interface of Cabin Based on Cognitive Ergonomics and GA-ACA

PubMed Central

Deng, Li; Wang, Guohua; Yu, Suihuai

2016-01-01

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout design, cognitive ergonomics and GA-ACA (genetic algorithm and ant colony algorithm) were introduced into the layout design of human-machine interaction interface. First, from the perspective of cognitive psychology, according to the information processing process, the cognitive model of human-machine interaction interface was established. Then, the human cognitive characteristics were analyzed, and the layout principles of human-machine interaction interface were summarized as the constraints in layout design. Again, the expression form of fitness function, pheromone, and heuristic information for the layout optimization of cabin was studied. The layout design model of human-machine interaction interface was established based on GA-ACA. At last, a layout design system was developed based on this model. For validation, the human-machine interaction interface layout design of drilling rig control room was taken as an example, and the optimization result showed the feasibility and effectiveness of the proposed method. PMID:26884745

Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

PubMed

Cakar, Tarik; Koker, Rasit

2015-01-01

A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system.

Formation mechanism of thermally optimized Ga-doped MgZnO transparent conducting electrodes for GaN-based light-emitting diodes

NASA Astrophysics Data System (ADS)

Jang, Seon-Ho; Jo, Yong-Ryun; Lee, Young-Woong; Kim, Sei-Min; Kim, Bong-Joong; Bae, Jae-Hyun; An, Huei-Chun; Jang, Ja-Soon

2015-05-01

We report a highly transparent conducting electrode (TCE) scheme of MgxZn1-xO:Ga/Au/NiOx which was deposited on p-GaN by e-beam for GaN-based light emitting diodes (LEDs). The optical and electrical properties of the electrode were optimized by thermal annealing at 500°C for 1 minute in N2 + O2 (5:3) ambient. The light transmittance at the optimal condition increased up to 84-97% from the UV-A to yellow region. The specific contact resistance decreased to 4.3(±0.3) × 10-5 Ωcm2. The improved properties of the electrode were attributed to the directionally elongated crystalline nanostructures formed in the MgxZn1-xO:Ga layer which is compositionally uniform. Interestingly, the Au alloy nano-clusters created in the MgxZn1-xO:Ga layer during annealing at 500°C may also enhance the properties of the electrode by acting as a conducting bridge and a nano-sized mirror. Based on studies of the external quantum efficiency of blue LED devices, the proposed electrode scheme combined with an optimized annealing treatment suggests a potential alternative to ITO. [Figure not available: see fulltext.

RBT-GA: a novel metaheuristic for solving the Multiple Sequence Alignment problem.

PubMed

Taheri, Javid; Zomaya, Albert Y

2009-07-07

Multiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics. MSA is mainly focused on discovering biologically meaningful relationships among different sequences or proteins in order to investigate the underlying main characteristics/functions. This information is also used to generate phylogenetic trees. This paper presents a novel approach, namely RBT-GA, to solve the MSA problem using a hybrid solution methodology combining the Rubber Band Technique (RBT) and the Genetic Algorithm (GA) metaheuristic. RBT is inspired by the behavior of an elastic Rubber Band (RB) on a plate with several poles, which is analogues to locations in the input sequences that could potentially be biologically related. A GA attempts to mimic the evolutionary processes of life in order to locate optimal solutions in an often very complex landscape. RBT-GA is a population based optimization algorithm designed to find the optimal alignment for a set of input protein sequences. In this novel technique, each alignment answer is modeled as a chromosome consisting of several poles in the RBT framework. These poles resemble locations in the input sequences that are most likely to be correlated and/or biologically related. A GA-based optimization process improves these chromosomes gradually yielding a set of mostly optimal answers for the MSA problem. RBT-GA is tested with one of the well-known benchmarks suites (BALiBASE 2.0) in this area. The obtained results show that the superiority of the proposed technique even in the case of formidable sequences.

Electrochemical removal of hydrogen atoms in Mg-doped GaN epitaxial layers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, June Key, E-mail: junekey@jnu.ac.kr, E-mail: hskim7@jbnu.ac.kr; Hyeon, Gil Yong; Tawfik, Wael Z.

2015-05-14

Hydrogen atoms inside of an Mg-doped GaN epitaxial layer were effectively removed by the electrochemical potentiostatic activation (EPA) method. The role of hydrogen was investigated in terms of the device performance of light-emitting diodes (LEDs). The effect of the main process parameters for EPA such as solution type, voltage, and time was studied and optimized for application to LED fabrication. In optimized conditions, the light output of 385-nm LEDs was improved by about 26% at 30 mA, which was caused by the reduction of the hydrogen concentration by ∼35%. Further removal of hydrogen seems to be involved in the breaking ofmore » Ga-H bonds that passivate the nitrogen vacancies. An EPA process with high voltage breaks not only Mg-H bonds that generate hole carriers but also Ga-H bonds that generate electron carriers, thus causing compensation that impedes the practical increase of hole concentration, regardless of the drastic removal of hydrogen atoms. A decrease in hydrogen concentration affects the current-voltage characteristics, reducing the reverse current by about one order and altering the forward current behavior in the low voltage region.« less

Electrochemical removal of hydrogen atoms in Mg-doped GaN epitaxial layers

NASA Astrophysics Data System (ADS)

Lee, June Key; Hyeon, Gil Yong; Tawfik, Wael Z.; Choi, Hee Seok; Ryu, Sang-Wan; Jeong, Tak; Jung, Eunjin; Kim, Hyunsoo

2015-05-01

Hydrogen atoms inside of an Mg-doped GaN epitaxial layer were effectively removed by the electrochemical potentiostatic activation (EPA) method. The role of hydrogen was investigated in terms of the device performance of light-emitting diodes (LEDs). The effect of the main process parameters for EPA such as solution type, voltage, and time was studied and optimized for application to LED fabrication. In optimized conditions, the light output of 385-nm LEDs was improved by about 26% at 30 mA, which was caused by the reduction of the hydrogen concentration by ˜35%. Further removal of hydrogen seems to be involved in the breaking of Ga-H bonds that passivate the nitrogen vacancies. An EPA process with high voltage breaks not only Mg-H bonds that generate hole carriers but also Ga-H bonds that generate electron carriers, thus causing compensation that impedes the practical increase of hole concentration, regardless of the drastic removal of hydrogen atoms. A decrease in hydrogen concentration affects the current-voltage characteristics, reducing the reverse current by about one order and altering the forward current behavior in the low voltage region.

A hybrid feature selection method using multiclass SVM for diagnosis of erythemato-squamous disease

NASA Astrophysics Data System (ADS)

Maryam, Setiawan, Noor Akhmad; Wahyunggoro, Oyas

2017-08-01

The diagnosis of erythemato-squamous disease is a complex problem and difficult to detect in dermatology. Besides that, it is a major cause of skin cancer. Data mining implementation in the medical field helps expert to diagnose precisely, accurately, and inexpensively. In this research, we use data mining technique to developed a diagnosis model based on multiclass SVM with a novel hybrid feature selection method to diagnose erythemato-squamous disease. Our hybrid feature selection method, named ChiGA (Chi Square and Genetic Algorithm), uses the advantages from filter and wrapper methods to select the optimal feature subset from original feature. Chi square used as filter method to remove redundant features and GA as wrapper method to select the ideal feature subset with SVM used as classifier. Experiment performed with 10 fold cross validation on erythemato-squamous diseases dataset taken from University of California Irvine (UCI) machine learning database. The experimental result shows that the proposed model based multiclass SVM with Chi Square and GA can give an optimum feature subset. There are 18 optimum features with 99.18% accuracy.

Influence of double- and triple-layer antireflection coatings on the formation of photocurrents in multijunction III–V solar cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musalinov, S. B.; Anzulevich, A. P.; Bychkov, I. V.

2017-01-15

The results of simulation by the transfer-matrix method of TiO{sub 2}/SiO{sub 2} double-layer and TiO{sub 2}/Si{sub 3}N{sub 4}/SiO{sub 2} triple-layer antireflection coatings for multijunction InGaP/GaAs/Ge heterostructure solar cells are presented. The TiO{sub 2}/SiO{sub 2} double-layer antireflection coating is experimentally developed and optimized. The experimental spectral dependences of the external quantum yield of the InGaP/GaAs/Ge heterostructure solar cell and optical characteristics of antireflection coatings, obtained in the simulation, are used to determine the photogenerated current densities of each subcell in the InGaP/GaAs/Ge solar cell under AM1.5D irradiation conditions (1000 W/m{sup 2}) and for the case of zero reflection loss. It ismore » shown in the simulation that the optimized TiO{sub 2}/Si{sub 3}N{sub 4}/SiO{sub 2} triple-layer antireflection coating provides a 2.3 mA/cm{sup 2} gain in the photocurrent density for the Ge subcell under AM1.5D conditions in comparison with the TiO{sub 2}/SiO{sub 2} double-layer antireflection coating under consideration. This thereby provides an increase in the fill factor of the current–voltage curve and in the output electric power of the multijunction solar cell.« less

Radiation hardness of Ga0.5In0.5 P/GaAs tandem solar cells

NASA Technical Reports Server (NTRS)

Kurtz, Sarah R.; Olson, J. M.; Bertness, K. A.; Friedman, D. J.; Kibbler, A.; Cavicchi, B. T.; Krut, D. D.

1991-01-01

The radiation hardness of a two-junction monolithic Ga sub 0.5 In sub 0.5 P/GaAs cell with tunnel junction interconnect was investigated. Related single junction cells were also studied to identify the origins of the radiation losses. The optimal design of the cell is discussed. The air mass efficiency of an optimized tandem cell after irradiation with 10(exp 15) cm (-2) 1 MeV electrons is estimated to be 20 percent using currently available technology.

System OptimizatIon of the Glow Discharge Optical Spectroscopy Technique Used for Impurity Profiling of ION Implanted Gallium Arsenide.

DTIC Science & Technology

1980-12-01

AFIT/GEO/EE/80D-1 I -’ SYSTEM OPTIMIZATION OF THE GLOW DISCHARGE OPTICAL SPECTROSCOPY TECHNIQUE USED FOR IMPURITY PROFILING OF ION IMPLANTED GALLIUM ...EE/80D-1 (\\) SYSTEM OPTIMIZATION OF THE GLOW DISCHARGE OPTICAL SPECTROSCOPY TECHNIQUE USED FOR IMPURITY PROFILING OF ION IMPLANTED GALLIUM ARSENIDE...semiconductors, specifically annealed and unan- nealed ion implanted gallium arsenide (GaAs). Methods to improve the sensitivity of the GDOS system have

Optimum design of bolted composite lap joints under mechanical and thermal loading

NASA Astrophysics Data System (ADS)

Kradinov, Vladimir Yurievich

A new approach is developed for the analysis and design of mechanically fastened composite lap joints under mechanical and thermal loading. Based on the combined complex potential and variational formulation, the solution method satisfies the equilibrium equations exactly while the boundary conditions are satisfied by minimizing the total potential. This approach is capable of modeling finite laminate planform dimensions, uniform and variable laminate thickness, laminate lay-up, interaction among bolts, bolt torque, bolt flexibility, bolt size, bolt-hole clearance and interference, insert dimensions and insert material properties. Comparing to the finite element analysis, the robustness of the method does not decrease when modeling the interaction of many bolts; also, the method is more suitable for parametric study and design optimization. The Genetic Algorithm (GA), a powerful optimization technique for multiple extrema functions in multiple dimensions search spaces, is applied in conjunction with the complex potential and variational formulation to achieve optimum designs of bolted composite lap joints. The objective of the optimization is to acquire such a design that ensures the highest strength of the joint. The fitness function for the GA optimization is based on the average stress failure criterion predicting net-section, shear-out, and bearing failure modes in bolted lap joints. The criterion accounts for the stress distribution in the thickness direction at the bolt location by applying an approach utilizing a beam on an elastic foundation formulation.

A maximum power point prediction method for group control of photovoltaic water pumping systems based on parameter identification

NASA Astrophysics Data System (ADS)

Chen, B.; Su, J. H.; Guo, L.; Chen, J.

2017-06-01

This paper puts forward a maximum power estimation method based on the photovoltaic array (PVA) model to solve the optimization problems about group control of the PV water pumping systems (PVWPS) at the maximum power point (MPP). This method uses the improved genetic algorithm (GA) for model parameters estimation and identification in view of multi P-V characteristic curves of a PVA model, and then corrects the identification results through least square method. On this basis, the irradiation level and operating temperature under any condition are able to estimate so an accurate PVA model is established and the MPP none-disturbance estimation is achieved. The simulation adopts the proposed GA to determine parameters, and the results verify the accuracy and practicability of the methods.

High-efficiency thin-film GaAs solar cells, phase2

NASA Technical Reports Server (NTRS)

Yeh, Y. C. M.

1981-01-01

Thin GaAs epi-layers with good crystallographic quality were grown using a (100) Si-substrate on which a thin Ge epi-interlayer was grown by CVD from germane. Both antireflection-coated metal oxide semiconductor (AMOS) and n(+)/p homojunction structures were studied. The AMOS cells were fabricated on undoped-GaAs epi-layers deposited on bulk poly-Ge substrates using organo-metallic CVD film-growth, with the best achieved AM1 conversion efficiency being 9.1%. Both p-type and n(+)-type GaAs growth were optimized using 50 ppm dimethyl zinc and 1% hydrogen sulfide, respectively. A direct GaAs deposition method in fabricating ultra-thin top layer, epitaxial n(+)/p shallow homojunction solar cells on (100) GaAs substrates (without anodic thinning) was developed to produce large area (1 sq/cm) cells, with 19.4% AM1 conversion efficiency achieved. Additionally, an AM1 conversion efficiency of 18.4% (17.5% with 5% grid coverage) was achieved for a single crystal GaAs n(+)/p cell grown by OM-CVD on a Ge wafer.

Optimization of a Tube Hydroforming Process

NASA Astrophysics Data System (ADS)

Abedrabbo, Nader; Zafar, Naeem; Averill, Ron; Pourboghrat, Farhang; Sidhu, Ranny

2004-06-01

An approach is presented to optimize a tube hydroforming process using a Genetic Algorithm (GA) search method. The goal of the study is to maximize formability by identifying the optimal internal hydraulic pressure and feed rate while satisfying the forming limit diagram (FLD). The optimization software HEEDS is used in combination with the nonlinear structural finite element code LS-DYNA to carry out the investigation. In particular, a sub-region of a circular tube blank is formed into a square die. Compared to the best results of a manual optimization procedure, a 55% increase in expansion was achieved when using the pressure and feed profiles identified by the automated optimization procedure.

Optimization of Blended Wing Body Composite Panels Using Both NASTRAN and Genetic Algorithm

NASA Technical Reports Server (NTRS)

Lovejoy, Andrew E.

2006-01-01

The blended wing body (BWB) is a concept that has been investigated for improving the performance of transport aircraft. A trade study was conducted by evaluating four regions from a BWB design characterized by three fuselage bays and a 400,000 lb. gross take-off weight (GTW). This report describes the structural optimization of these regions via computational analysis and compares them to the baseline designs of the same construction. The identified regions were simplified for use in the optimization. The regions were represented by flat panels having appropriate classical boundary conditions and uniform force resultants along the panel edges. Panel-edge tractions and internal pressure values applied during the study were those determined by nonlinear NASTRAN analyses. Only one load case was considered in the optimization analysis for each panel region. Optimization was accomplished using both NASTRAN solution 200 and Genetic Algorithm (GA), with constraints imposed on stress, buckling, and minimum thicknesses. The NASTRAN optimization analyses often resulted in infeasible solutions due to violation of the constraints, whereas the GA enforced satisfaction of the constraints and, therefore, always ensured a feasible solution. However, both optimization methods encountered difficulties when the number of design variables was increased. In general, the optimized panels weighed less than the comparable baseline panels.

Growth optimization toward low angle incidence microchannel epitaxy of GaN using ammonia-based metal-organic molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Lin, Chia-Hung; Abe, Ryota; Uchiyama, Shota; Maruyama, Takahiro; Naritsuka, Shigeya

2012-08-01

Growth optimization toward low angle incidence microchannel epitaxy (LAIMCE) of GaN was accomplished using ammonia-based metal-organic molecular beam epitaxy (NH3-based MOMBE). Firstly, the [NH3]/[trimethylgallium (TMG)] ratio (R) dependence of selective GaN growth was studied. The growth temperature was set at 860 °C while R was varied from 5 to 200 with precursors being supplied parallel to the openings cut in the SiO2 mask. The selectivity of the growth was superior for all R, because TMG and NH3 preferably decompose on the GaN film. The formation of {112¯0}GaN or {112¯2}GaN sidewalls and (0001)GaN surface were observed by the change in R. The intersurface diffusion of Ga adatoms was also changed by a change in R. Ga adatoms migrate from the sidewalls to the top at R lower than 50, whereas the migration weakened with R greater than 100. Secondly, LAIMCE was optimized by changing the growth temperature. Consequently, 6 μm wide lateral overgrowth in the direction of precursor incidence was achieved with no pit after etching by H3PO4, which was six times wider than that in the opposite direction.

Optimization of GaAs Nanowire Pin Junction Array Solar Cells by Using AlGaAs/GaAs Heterojunctions

NASA Astrophysics Data System (ADS)

Wu, Yao; Yan, Xin; Wei, Wei; Zhang, Jinnan; Zhang, Xia; Ren, Xiaomin

2018-04-01

We optimized the performance of GaAs nanowire pin junction array solar cells by introducing AlGaAs/GaAs heterejunctions. AlGaAs is used for the p type top segment for axial junctions and the p type outer shell for radial junctions. The AlGaAs not only serves as passivation layers for GaAs nanowires but also confines the optical generation in the active regions, reducing the recombination loss in heavily doped regions and the minority carrier recombination at the top contact. The results show that the conversion efficiency of GaAs nanowires can be greatly enhanced by using AlGaAs for the p segment instead of GaAs. A maximum efficiency enhancement of 8.42% has been achieved in this study. And for axial nanowire, by using AlGaAs for the top p segment, a relatively long top segment can be employed without degenerating device performance, which could facilitate the fabrication and contacting of nanowire array solar cells. While for radial nanowires, AlGaAs/GaAs nanowires show better tolerance to p-shell thickness and surface condition.

Optimization of GaAs Nanowire Pin Junction Array Solar Cells by Using AlGaAs/GaAs Heterojunctions.

PubMed

Wu, Yao; Yan, Xin; Wei, Wei; Zhang, Jinnan; Zhang, Xia; Ren, Xiaomin

2018-04-25

We optimized the performance of GaAs nanowire pin junction array solar cells by introducing AlGaAs/GaAs heterejunctions. AlGaAs is used for the p type top segment for axial junctions and the p type outer shell for radial junctions. The AlGaAs not only serves as passivation layers for GaAs nanowires but also confines the optical generation in the active regions, reducing the recombination loss in heavily doped regions and the minority carrier recombination at the top contact. The results show that the conversion efficiency of GaAs nanowires can be greatly enhanced by using AlGaAs for the p segment instead of GaAs. A maximum efficiency enhancement of 8.42% has been achieved in this study. And for axial nanowire, by using AlGaAs for the top p segment, a relatively long top segment can be employed without degenerating device performance, which could facilitate the fabrication and contacting of nanowire array solar cells. While for radial nanowires, AlGaAs/GaAs nanowires show better tolerance to p-shell thickness and surface condition.

Lyophilized Kit for the Preparation of the PET Perfusion Agent [68Ga]-MAA

PubMed Central

Amor-Coarasa, Alejandro; Milera, Andrew; Gulec, Seza; McGoron, Anthony J.

2014-01-01

Rapid developments in the field of medical imaging have opened new avenues for the use of positron emitting labeled microparticles. The radioisotope used in our research was 68Ga, which is easy to obtain from a generator and has good nuclear properties for PET imaging. Methods. Commercially available macroaggregated albumin (MAA) microparticles were suspended in sterile saline, centrifuged to remove the free albumin and stannous chloride, relyophilized, and stored for later labeling with 68Ga. Labeling was performed at different temperatures and times. 68Ga purification settings were also tested and optimized. Labeling yield and purity of relyophilized MAA microparticles were compared with those that were not relyophilized. Results. MAA particles kept their original size distribution after relyophilization. Labeling yield was 98% at 75°C when a 68Ga purification system was used, compared to 80% with unpurified 68Ga. Radiochemical purity was over 97% up to 4 hours after the labeling. The relyophilized MAA and labeling method eliminate the need for centrifugation purification of the final product and simplify the labeling process. Animal experiments demonstrated the high in vivo stability of the obtained PET agent with more than 95% of the activity remaining in the lungs after 4 hours. PMID:24800071

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm.

PubMed

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene x gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene x gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.

Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm

PubMed Central

Namkung, Junghyun; Nam, Jin-Wu; Park, Taesung

2007-01-01

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms. PMID:18466570

Direct evaluation of influence of electron damage on the subcell performance in triple-junction solar cells using photoluminescence decays.

PubMed

Tex, David M; Nakamura, Tetsuya; Imaizumi, Mitsuru; Ohshima, Takeshi; Kanemitsu, Yoshihiko

2017-05-16

Tandem solar cells are suited for space applications due to their high performance, but also have to be designed in such a way to minimize influence of degradation by the high energy particle flux in space. The analysis of the subcell performance is crucial to understand the device physics and achieve optimized designs of tandem solar cells. Here, the radiation-induced damage of inverted grown InGaP/GaAs/InGaAs triple-junction solar cells for various electron fluences are characterized using conventional current-voltage (I-V) measurements and time-resolved photoluminescence (PL). The conversion efficiencies of the entire device before and after damage are measured with I-V curves and compared with the efficiencies predicted from the time-resolved method. Using the time-resolved data the change in the carrier dynamics in the subcells can be discussed. Our optical method allows to predict the absolute electrical conversion efficiency of the device with an accuracy of better than 5%. While both InGaP and GaAs subcells suffered from significant material degradation, the performance loss of the total device can be completely ascribed to the damage in the GaAs subcell. This points out the importance of high internal electric fields at the operating point.

Comparison of gestational age at birth based on last menstrual period and ultrasound during the first trimester.

PubMed

Hoffman, Caroline S; Messer, Lynne C; Mendola, Pauline; Savitz, David A; Herring, Amy H; Hartmann, Katherine E

2008-11-01

Reported last menstrual period (LMP) is commonly used to estimate gestational age (GA) but may be unreliable. Ultrasound in the first trimester is generally considered a highly accurate method of pregnancy dating. The authors compared first trimester report of LMP and first trimester ultrasound for estimating GA at birth and examined whether disagreement between estimates varied by maternal and infant characteristics. Analyses included 1867 singleton livebirths to women enrolled in a prospective pregnancy cohort. The authors computed the difference between LMP and ultrasound GA estimates (GA difference) and examined the proportion of births within categories of GA difference stratified by maternal and infant characteristics. The proportion of births classified as preterm, term and post-term by pregnancy dating methods was also examined. LMP-based estimates were 0.8 days (standard deviation = 8.0, median = 0) longer on average than ultrasound estimates. LMP classified more births as post-term than ultrasound (4.0% vs. 0.7%). GA difference was greater among young women, non-Hispanic Black and Hispanic women, women of non-optimal body weight and mothers of low-birthweight infants. Results indicate first trimester report of LMP reasonably approximates gestational age obtained from first trimester ultrasound, but the degree of discrepancy between estimates varies by important maternal characteristics.

A Simulation-Optimization Model for the Management of Seawater Intrusion

NASA Astrophysics Data System (ADS)

Stanko, Z.; Nishikawa, T.

2012-12-01

Seawater intrusion is a common problem in coastal aquifers where excessive groundwater pumping can lead to chloride contamination of a freshwater resource. Simulation-optimization techniques have been developed to determine optimal management strategies while mitigating seawater intrusion. The simulation models are often density-independent groundwater-flow models that may assume a sharp interface and/or use equivalent freshwater heads. The optimization methods are often linear-programming (LP) based techniques that that require simplifications of the real-world system. However, seawater intrusion is a highly nonlinear, density-dependent flow and transport problem, which requires the use of nonlinear-programming (NLP) or global-optimization (GO) techniques. NLP approaches are difficult because of the need for gradient information; therefore, we have chosen a GO technique for this study. Specifically, we have coupled a multi-objective genetic algorithm (GA) with a density-dependent groundwater-flow and transport model to simulate and identify strategies that optimally manage seawater intrusion. GA is a heuristic approach, often chosen when seeking optimal solutions to highly complex and nonlinear problems where LP or NLP methods cannot be applied. The GA utilized in this study is the Epsilon-Nondominated Sorted Genetic Algorithm II (ɛ-NSGAII), which can approximate a pareto-optimal front between competing objectives. This algorithm has several key features: real and/or binary variable capabilities; an efficient sorting scheme; preservation and diversity of good solutions; dynamic population sizing; constraint handling; parallelizable implementation; and user controlled precision for each objective. The simulation model is SEAWAT, the USGS model that couples MODFLOW with MT3DMS for variable-density flow and transport. ɛ-NSGAII and SEAWAT were efficiently linked together through a C-Fortran interface. The simulation-optimization model was first tested by using a published density-independent flow model test case that was originally solved using a sequential LP method with the USGS's Ground-Water Management Process (GWM). For the problem formulation, the objective is to maximize net groundwater extraction, subject to head and head-gradient constraints. The decision variables are pumping rates at fixed wells and the system's state is represented with freshwater hydraulic head. The results of the proposed algorithm were similar to the published results (within 1%); discrepancies may be attributed to differences in the simulators and inherent differences between LP and GA. The GWM test case was then extended to a density-dependent flow and transport version. As formulated, the optimization problem is infeasible because of the density effects on hydraulic head. Therefore, the sum of the squared constraint violation (SSC) was used as a second objective. The result is a pareto curve showing optimal pumping rates versus the SSC. Analysis of this curve indicates that a similar net-extraction rate to the test case can be obtained with a minor violation in vertical head-gradient constraints. This study shows that a coupled ɛ-NSGAII/SEAWAT model can be used for the management of groundwater seawater intrusion. In the future, the proposed methodology will be applied to a real-world seawater intrusion and resource management problem for Santa Barbara, CA.

Formation of redispersible polyelectrolyte complex nanoparticles from gallic acid-chitosan conjugate and gum arabic.

PubMed

Hu, Qiaobin; Wang, Taoran; Zhou, Mingyong; Xue, Jingyi; Luo, Yangchao

2016-11-01

Polyelectrolyte complex (PEC) nanoparticles between chitosan (CS) and biomacromolecules offer better physicochemical properties as delivery vehicles for nutrients than other CS-based nanoparticles. Our major objective was to fabricate PEC nanoparticles between water soluble gallic acid-chitosan conjugate (GA-CS) and gum arabic. The optimal fabrication method, physicochemical characteristics and stability were investigated. Furthermore, we also evaluated the effects of nano spray drying technology on the morphology and redispersibility of nanoparticle powders using Buchi B-90 Nano Spray Dryer. Results showed that the mass ratio between GA-CS and gum arabic and the preparation pH had significant contributions in determining the particle size and count rate of the nanoparticles, with the ratio of 3:1 and pH 5.0 being the optimal conditions that resulted in 112.2nm and 122.9kcps. The polyethylene glycol (PEG) played a vital role in forming the well-separated spray dried nanoparticles. The most homogeneous nanoparticles with the smoothest surface were obtained when the mass ratio of GA-CS and PEG was 1:0.5. In addition, the GA-CS/gum arabic spray dried nanoparticles exhibited excellent water-redispersibiliy compared to native CS/gum arabic nanoparticles. Our results demonstrated GA-CS/gum arabic nanoparticles were successfully fabricated with promising physicochemical properties and great potential for their applications in food and pharmaceutical industries. Copyright © 2016 Elsevier B.V. All rights reserved.

Case Study on Optimal Routing in Logistics Network by Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Wang, Xiaoguang; Lin, Lin; Gen, Mitsuo; Shiota, Mitsushige

Recently, research on logistics caught more and more attention. One of the important issues on logistics system is to find optimal delivery routes with the least cost for products delivery. Numerous models have been developed for that reason. However, due to the diversity and complexity of practical problem, the existing models are usually not very satisfying to find the solution efficiently and convinently. In this paper, we treat a real-world logistics case with a company named ABC Co. ltd., in Kitakyusyu Japan. Firstly, based on the natures of this conveyance routing problem, as an extension of transportation problem (TP) and fixed charge transportation problem (fcTP) we formulate the problem as a minimum cost flow (MCF) model. Due to the complexity of fcTP, we proposed a priority-based genetic algorithm (pGA) approach to find the most acceptable solution to this problem. In this pGA approach, a two-stage path decoding method is adopted to develop delivery paths from a chromosome. We also apply the pGA approach to this problem, and compare our results with the current logistics network situation, and calculate the improvement of logistics cost to help the management to make decisions. Finally, in order to check the effectiveness of the proposed method, the results acquired are compared with those come from the two methods/ software, such as LINDO and CPLEX.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid.

PubMed

Arefi-Oskoui, Samira; Khataee, Alireza; Vatanpour, Vahid

2017-07-10

In this research, MgAl-CO 3 2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

Determination of 200 °C Isothermal Section of Al-Ag-Ga Phase Diagram by Microanalysis, X-ray Diffraction, Hardness and Electrical Conductivity Measurements

NASA Astrophysics Data System (ADS)

Premović, Milena; Tomović, Milica; Minić, Duško; Manasijević, Dragan; Živković, Dragana; Ćosović, Vladan; Grković, Vladan; Đorđević, Aleksandar

2017-04-01

Ternary Al-Ag-Ga system at 200 °C was experimentally and thermodynamically assessed. Isothermal section was extrapolated using optimized thermodynamic parameters for constitutive binary systems. Microstructure and phase composition of the selected alloy samples were analyzed using light microscopy, scanning electron microscopy combined with energy-dispersive spectrometry and x-ray powder diffraction technique. The obtained experimental results were found to be in a close agreement with the predicted phase equilibria. Hardness and electrical conductivity of the alloy samples from four vertical sections Al-Ag80Ga20, Al-Ag60Ga40, Ag-Al80Ga20 and Ag-Al60Ga40 of the ternary Al-Ag-Ga system at 200 °C were experimentally determined using Brinell method and eddy current measurements. Additionally, hardness of the individual phases present in the microstructure of the studied alloy samples was determined using Vickers microhardness test. Based on experimentally obtained results, isolines of Brinell hardness and electrical conductivity were calculated for the alloys from isothermal section of the ternary Al-Ag-Ga system at 200 °C.

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

NASA Technical Reports Server (NTRS)

Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

2002-01-01

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

The optimization of Ga (1-x)Al (x)As-GaAs solar cells for air mass zero operation and a study of Ga (1-x)Al (x)As-GaAs solar cells at high temperatures, phase 1

NASA Technical Reports Server (NTRS)

Hovel, H. J.; Woodall, J. M.

1976-01-01

The three types of solar cells investigated were: (1) one consisting of a nGaAs substrate, a Zn doped pGaAs region, and a Zn doped Ga(1-x)Al(x)As layer, (2) one consisting of an nGaAs substrate, a Ge doped pGaAs region, and a pGa(1-x)Al(x)As upper layer, and (3) one consisting of an n+GaAs substrate, an nGa(1-x)Al(X)As region, a pGa(1-x)Bl(X) As region, and a pGa(1-y)Al(y)As upper layer. In all three cases, the upper alloy layer is thin and of high Al composition in order to obtain high spectral response over the widest possible range of photon energies. Spectral response, capacitance-voltage, current-voltage, diffusion length, sunlight (or the equivalent)-efficiency, and efficiency-temperature measurements were made as a function of device parameters in order to analyze and optimize the solar cell behavior.

Stochastic Set-Based Particle Swarm Optimization Based on Local Exploration for Solving the Carpool Service Problem.

PubMed

Chou, Sheng-Kai; Jiau, Ming-Kai; Huang, Shih-Chia

2016-08-01

The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP.

RBT-GA: a novel metaheuristic for solving the multiple sequence alignment problem

PubMed Central

Taheri, Javid; Zomaya, Albert Y

2009-01-01

Background Multiple Sequence Alignment (MSA) has always been an active area of research in Bioinformatics. MSA is mainly focused on discovering biologically meaningful relationships among different sequences or proteins in order to investigate the underlying main characteristics/functions. This information is also used to generate phylogenetic trees. Results This paper presents a novel approach, namely RBT-GA, to solve the MSA problem using a hybrid solution methodology combining the Rubber Band Technique (RBT) and the Genetic Algorithm (GA) metaheuristic. RBT is inspired by the behavior of an elastic Rubber Band (RB) on a plate with several poles, which is analogues to locations in the input sequences that could potentially be biologically related. A GA attempts to mimic the evolutionary processes of life in order to locate optimal solutions in an often very complex landscape. RBT-GA is a population based optimization algorithm designed to find the optimal alignment for a set of input protein sequences. In this novel technique, each alignment answer is modeled as a chromosome consisting of several poles in the RBT framework. These poles resemble locations in the input sequences that are most likely to be correlated and/or biologically related. A GA-based optimization process improves these chromosomes gradually yielding a set of mostly optimal answers for the MSA problem. Conclusion RBT-GA is tested with one of the well-known benchmarks suites (BALiBASE 2.0) in this area. The obtained results show that the superiority of the proposed technique even in the case of formidable sequences. PMID:19594869

Contact electrification induced interfacial reactions and direct electrochemical nanoimprint lithography in n-type gallium arsenate wafer† †Electronic supplementary information (ESI) available: Electrochemical measurements of the interfaces, optimization of the contact force and temperature of ECNL, XPS analysis, and more examples of ECNL on n-GaAs. See DOI: 10.1039/c6sc04091h Click here for additional data file.

PubMed Central

Zhang, Jie; Zhang, Lin; Wang, Wei; Han, Lianhuan; Jia, Jing-Chun; Tian, Zhao-Wu; Tian, Zhong-Qun

2017-01-01

Although metal assisted chemical etching (MacEtch) has emerged as a versatile micro-nanofabrication method for semiconductors, the chemical mechanism remains ambiguous in terms of both thermodynamics and kinetics. Here we demonstrate an innovative phenomenon, i.e., the contact electrification between platinum (Pt) and an n-type gallium arsenide (100) wafer (n-GaAs) can induce interfacial redox reactions. Because of their different work functions, when the Pt electrode comes into contact with n-GaAs, electrons will move from n-GaAs to Pt and form a contact electric field at the Pt/n-GaAs junction until their electron Fermi levels (E F) become equal. In the presence of an electrolyte, the potential of the Pt/electrolyte interface will shift due to the contact electricity and induce the spontaneous reduction of MnO4 – anions on the Pt surface. Because the equilibrium of contact electrification is disturbed, electrons will transfer from n-GaAs to Pt through the tunneling effect. Thus, the accumulated positive holes at the n-GaAs/electrolyte interface make n-GaAs dissolve anodically along the Pt/n-GaAs/electrolyte 3-phase interface. Based on this principle, we developed a direct electrochemical nanoimprint lithography method applicable to crystalline semiconductors. PMID:28451347

Sequential Optimization Methods for Augmentation of Marine Enzymes Production in Solid-State Fermentation: l-Glutaminase Production a Case Study.

PubMed

Sathish, T; Uppuluri, K B; Veera Bramha Chari, P; Kezia, D

There is an increased l-glutaminase market worldwide due to its relevant industrial applications. Salt tolerance l-glutaminases play a vital role in the increase of flavor of different types of foods like soya sauce and tofu. This chapter is presenting the economically viable l-glutaminases production in solid-state fermentation (SSF) by Aspergillus flavus MTCC 9972 as a case study. The enzyme production was improved following a three step optimization process. Initially mixture design (MD) (augmented simplex lattice design) was employed to optimize the solid substrate mixture. Such solid substrate mixture consisted of 59:41 of wheat bran and Bengal gram husk has given higher amounts of l-glutaminase. Glucose and l-glutamine were screened as a finest additional carbon and nitrogen sources for l-glutaminase production with help of Plackett-Burman Design (PBD). l-Glutamine also acting as a nitrogen source as well as inducer for secretion of l-glutaminase from A. flavus MTCC 9972. In the final step of optimization various environmental and nutritive parameters such as pH, temperature, moisture content, inoculum concentration, glucose, and l-glutamine levels were optimized through the use of hybrid feed forward neural networks (FFNNs) and genetic algorithm (GA). Through sequential optimization methods MD-PBD-FFNN-GA, the l-glutaminase production in SSF could be improved by 2.7-fold (453-1690U/g). © 2016 Elsevier Inc. All rights reserved.

SU-F-T-347: An Absolute Dose-Volume Constraint Based Deterministic Optimization Framework for Multi-Co60 Source Focused Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liang, B; Liu, B; Li, Y

2016-06-15

Purpose: Treatment plan optimization in multi-Co60 source focused radiotherapy with multiple isocenters is challenging, because dose distribution is normalized to maximum dose during optimization and evaluation. The objective functions are traditionally defined based on relative dosimetric distribution. This study presents an alternative absolute dose-volume constraint (ADC) based deterministic optimization framework (ADC-DOF). Methods: The initial isocenters are placed on the eroded target surface. Collimator size is chosen based on the area of 2D contour on corresponding axial slice. The isocenter spacing is determined by adjacent collimator sizes. The weights are optimized by minimizing the deviation from ADCs using the steepest descentmore » technique. An iterative procedure is developed to reduce the number of isocenters, where the isocenter with lowest weight is removed without affecting plan quality. The ADC-DOF is compared with the genetic algorithm (GA) using the same arbitrary shaped target (254cc), with a 15mm margin ring structure representing normal tissues. Results: For ADC-DOF, the ADCs imposed on target and ring are (D100>10Gy, D50,10, 0<12Gy, 15Gy and 20Gy) and (D40<10Gy). The resulting D100, 50, 10, 0 and D40 are (9.9Gy, 12.0Gy, 14.1Gy and 16.2Gy) and (10.2Gy). The objectives of GA are to maximize 50% isodose target coverage (TC) while minimize the dose delivered to the ring structure, which results in 97% TC and 47.2% average dose in ring structure. For ADC-DOF (GA) techniques, 20 out of 38 (10 out of 12) initial isocenters are used in the final plan, and the computation time is 8.7s (412.2s) on an i5 computer. Conclusion: We have developed a new optimization technique using ADC and deterministic optimization. Compared with GA, ADC-DOF uses more isocenters but is faster and more robust, and achieves a better conformity. For future work, we will focus on developing a more effective mechanism for initial isocenter determination.« less

Short-wavelength light beam in situ monitoring growth of InGaN/GaN green LEDs by MOCVD

PubMed Central

2012-01-01

In this paper, five-period InGaN/GaN multiple quantum well green light-emitting diodes (LEDs) were grown by metal organic chemical vapor deposition with 405-nm light beam in situ monitoring system. Based on the signal of 405-nm in situ monitoring system, the related information of growth rate, indium composition and interfacial quality of each InGaN/GaN QW were obtained, and thus, the growth conditions and structural parameters were optimized to grow high-quality InGaN/GaN green LED structure. Finally, a green LED with a wavelength of 509 nm was fabricated under the optimal parameters, which was also proved by ex situ characterization such as high-resolution X-ray diffraction, photoluminescence, and electroluminescence. The results demonstrated that short-wavelength in situ monitoring system was a quick and non-destroyed tool to provide the growth information on InGaN/GaN, which would accelerate the research and development of GaN-based green LEDs. PMID:22650991

Computational intelligence-based optimization of maximally stable extremal region segmentation for object detection

NASA Astrophysics Data System (ADS)

Davis, Jeremy E.; Bednar, Amy E.; Goodin, Christopher T.; Durst, Phillip J.; Anderson, Derek T.; Bethel, Cindy L.

2017-05-01

Particle swarm optimization (PSO) and genetic algorithms (GAs) are two optimization techniques from the field of computational intelligence (CI) for search problems where a direct solution can not easily be obtained. One such problem is finding an optimal set of parameters for the maximally stable extremal region (MSER) algorithm to detect areas of interest in imagery. Specifically, this paper describes the design of a GA and PSO for optimizing MSER parameters to detect stop signs in imagery produced via simulation for use in an autonomous vehicle navigation system. Several additions to the GA and PSO are required to successfully detect stop signs in simulated images. These additions are a primary focus of this paper and include: the identification of an appropriate fitness function, the creation of a variable mutation operator for the GA, an anytime algorithm modification to allow the GA to compute a solution quickly, the addition of an exponential velocity decay function to the PSO, the addition of an "execution best" omnipresent particle to the PSO, and the addition of an attractive force component to the PSO velocity update equation. Experimentation was performed with the GA using various combinations of selection, crossover, and mutation operators and experimentation was also performed with the PSO using various combinations of neighborhood topologies, swarm sizes, cognitive influence scalars, and social influence scalars. The results of both the GA and PSO optimized parameter sets are presented. This paper details the benefits and drawbacks of each algorithm in terms of detection accuracy, execution speed, and additions required to generate successful problem specific parameter sets.

Using a genetic algorithm to optimize a water-monitoring network for accuracy and cost effectiveness

NASA Astrophysics Data System (ADS)

Julich, R. J.

2004-05-01

The purpose of this project is to determine the optimal spatial distribution of water-monitoring wells to maximize important data collection and to minimize the cost of managing the network. We have employed a genetic algorithm (GA) towards this goal. The GA uses a simple fitness measure with two parts: the first part awards a maximal score to those combinations of hydraulic head observations whose net uncertainty is closest to the value representing all observations present, thereby maximizing accuracy; the second part applies a penalty function to minimize the number of observations, thereby minimizing the overall cost of the monitoring network. We used the linear statistical inference equation to calculate standard deviations on predictions from a numerical model generated for the 501-observation Death Valley Regional Flow System as the basis for our uncertainty calculations. We have organized the results to address the following three questions: 1) what is the optimal design strategy for a genetic algorithm to optimize this problem domain; 2) what is the consistency of solutions over several optimization runs; and 3) how do these results compare to what is known about the conceptual hydrogeology? Our results indicate the genetic algorithms are a more efficient and robust method for solving this class of optimization problems than have been traditional optimization approaches.

Optimization of the design of Gas Cherenkov Detectors for ICF diagnosis

NASA Astrophysics Data System (ADS)

Liu, Bin; Hu, Huasi; Han, Hetong; Lv, Huanwen; Li, Lan

2018-07-01

A design method, which combines a genetic algorithm (GA) with Monte-Carlo simulation, is established and applied to two different types of Cherenkov detectors, namely, Gas Cherenkov Detector (GCD) and Gamma Reaction History (GRH). For accelerating the optimization program, open Message Passing Interface (MPI) is used in the Geant4 simulation. Compared with the traditional optical ray-tracing method, the performances of these detectors have been improved with the optimization method. The efficiency for GCD system, with a threshold of 6.3 MeV, is enhanced by ∼20% and time response improved by ∼7.2%. For the GRH system, with threshold of 10 MeV, the efficiency is enhanced by ∼76% in comparison with previously published results.

Simulation and analysis of the absorption enhancement in p-i-n InGaN/GaN solar cell using photonic crystal light trapping structures

NASA Astrophysics Data System (ADS)

Gupta, Nikhil Deep; Janyani, Vijay

2016-10-01

The structure of p-i-n InGaN/GaN based solar cell having a photonic crystal (PhC)-based light trapping structure (LTS) at the top assisted by the planar metallic (aluminum) back reflector (BR) is proposed. We propose two different designs for efficiency enhancement: in one we keep the PhC structure etching depth extending from the top antireflective coating (ARC) of indium tin oxide (ITO) up to the p-GaN layer (which is beneath the ITO and above the active layer), whereas in the other design, the PhC LTS etching depth has been extended up to the InxGa1-xN absorbing layer, starting from the top ITO layer. The theoretical optical simulation studies and optimization of the required parameters of the structure, which help to investigate and demonstrate the effectiveness of the LTS in the efficiency enhancement of the structure, are presented. The work also demonstrates the Lambertian light trapping limits for the practical indium concentrations in a InxGa1-xN active layer cell. The paper also presents the comparison between the proposed designs and compares their results with that of a planar reference cell. The studies are carried out for various indium concentrations. The results indicate considerable enhancement in the efficiency due to the PhC LTS, mainly because of better coupling, low reflectance, and diffraction capability of the proposed LTS, although it is still under the Lambertian limits. The performance evaluation of the proposed structure with respect to the angle of incident light has also been done, indicating improved performance. The parameters have been optimized and calculated by means of rigorous coupled wave analysis (RCWA) method.

Redundancy allocation problem for k-out-of- n systems with a choice of redundancy strategies

NASA Astrophysics Data System (ADS)

Aghaei, Mahsa; Zeinal Hamadani, Ali; Abouei Ardakan, Mostafa

2017-03-01

To increase the reliability of a specific system, using redundant components is a common method which is called redundancy allocation problem (RAP). Some of the RAP studies have focused on k-out-of- n systems. However, all of these studies assumed predetermined active or standby strategies for each subsystem. In this paper, for the first time, we propose a k-out-of- n system with a choice of redundancy strategies. Therefore, a k-out-of- n series-parallel system is considered when the redundancy strategy can be chosen for each subsystem. In other words, in the proposed model, the redundancy strategy is considered as an additional decision variable and an exact method based on integer programming is used to obtain the optimal solution of the problem. As the optimization of RAP belongs to the NP-hard class of problems, a modified version of genetic algorithm (GA) is also developed. The exact method and the proposed GA are implemented on a well-known test problem and the results demonstrate the efficiency of the new approach compared with the previous studies.

The Study of an Optimal Robust Design and Adjustable Ordering Strategies in the HSCM

PubMed Central

Liao, Hung-Chang; Chen, Yan-Kwang; Wang, Ya-huei

2015-01-01

The purpose of this study was to establish a hospital supply chain management (HSCM) model in which three kinds of drugs in the same class and with the same indications were used in creating an optimal robust design and adjustable ordering strategies to deal with a drug shortage. The main assumption was that although each doctor has his/her own prescription pattern, when there is a shortage of a particular drug, the doctor may choose a similar drug with the same indications as a replacement. Four steps were used to construct and analyze the HSCM model. The computation technology used included a simulation, a neural network (NN), and a genetic algorithm (GA). The mathematical methods of the simulation and the NN were used to construct a relationship between the factor levels and performance, while the GA was used to obtain the optimal combination of factor levels from the NN. A sensitivity analysis was also used to assess the change in the optimal factor levels. Adjustable ordering strategies were also developed to prevent drug shortages. PMID:26451162

Rapid ultrasound-assisted magnetic microextraction of gallic acid from urine, plasma and water samples by HKUST-1-MOF-Fe3O4-GA-MIP-NPs: UV-vis detection and optimization study.

PubMed

Asfaram, Arash; Ghaedi, Mehrorang; Dashtian, Kheibar

2017-01-01

Magnetite (Fe 3 O 4 nanoparticles (NPs)) HKUST-1 metal organic framework (MOF) composite as a support was used for surface imprinting of gallic acid imprinted polymer (HKUST-1-MOF-Fe 3 O 4 -GA-MIP) using vinyltrimethoxysilane (VTMOS) as the cross-linker. Subsequently, HKUST-1-MOF-Fe 3 O 4 -NPs-GA-MIP characterized by FT-IR, XRD and FE-SEM analysis and applied for fast and selective and sensitive ultrasound assisted dispersive magnetic solid phase microextraction of gallic acid (GA) by UV-Vis (UA-DMSPME-UV-Vis) detection method. Plackett-Burman design (PBD) and central composite design (CCD) according to desirability function (DF) indicate the significant variables among the extraction factors vortex (mixing) time (min), sonication time (min), temperature (°C), eluent volume (L), pH and HKUST-1-MOF-Fe 3 O 4 -NPs-GA-MIP mass (mg) and their contribution on the response. Optimum conditions and values correspond to pH, HKUST-1-MOF-Fe 3 O 4 -NPs-GA-MIP mass, sonication time and the eluent volume were set as follow 3.0, 1.6mg, 4.0min and 180μL, respectively. The average recovery (ER%) of GA was 98.13% with desirability of 0.997, while the present method has best operational performance like wide linear range 8-6000ngmL -1 with a Limit of detection (LOD) of 1.377ngmL -1 , limit of quantification (LOQ) 4.591ngmL -1 and precision (<3.50% RSD). The recovery of GA in urine, human plasma and water samples within the range of 92.3-100.6% that strongly support high applicability of present method for real samples analysis, which candidate this method as promise for further application. Copyright © 2016. Published by Elsevier B.V.

Revealing the preferred interlayer orientations and stackings of two-dimensional bilayer gallium selenide crystals.

PubMed

Li, Xufan; Basile, Leonardo; Yoon, Mina; Ma, Cheng; Puretzky, Alexander A; Lee, Jaekwang; Idrobo, Juan C; Chi, Miaofang; Rouleau, Christopher M; Geohegan, David B; Xiao, Kai

2015-02-23

Characterizing and controlling the interlayer orientations and stacking orders of two-dimensional (2D) bilayer crystals and van der Waals (vdW) heterostructures is crucial to optimize their electrical and optoelectronic properties. The four polymorphs of layered gallium selenide (GaSe) crystals that result from different layer stackings provide an ideal platform to study the stacking configurations in 2D bilayer crystals. Through a controllable vapor-phase deposition method, bilayer GaSe crystals were selectively grown and their two preferred 0° or 60° interlayer rotations were investigated. The commensurate stacking configurations (AA' and AB stacking) in as-grown bilayer GaSe crystals are clearly observed at the atomic scale, and the Ga-terminated edge structure was identified using scanning transmission electron microscopy. Theoretical analysis reveals that the energies of the interlayer coupling are responsible for the preferred orientations among the bilayer GaSe crystals. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Resolving the multiple sequence alignment problem using biogeography-based optimization with multiple populations.

PubMed

Zemali, El-Amine; Boukra, Abdelmadjid

2015-08-01

The multiple sequence alignment (MSA) is one of the most challenging problems in bioinformatics, it involves discovering similarity between a set of protein or DNA sequences. This paper introduces a new method for the MSA problem called biogeography-based optimization with multiple populations (BBOMP). It is based on a recent metaheuristic inspired from the mathematics of biogeography named biogeography-based optimization (BBO). To improve the exploration ability of BBO, we have introduced a new concept allowing better exploration of the search space. It consists of manipulating multiple populations having each one its own parameters. These parameters are used to build up progressive alignments allowing more diversity. At each iteration, the best found solution is injected in each population. Moreover, to improve solution quality, six operators are defined. These operators are selected with a dynamic probability which changes according to the operators efficiency. In order to test proposed approach performance, we have considered a set of datasets from Balibase 2.0 and compared it with many recent algorithms such as GAPAM, MSA-GA, QEAMSA and RBT-GA. The results show that the proposed approach achieves better average score than the previously cited methods.

Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Kong, Wenwen; Liu, Fei; Zhang, Chu; Bao, Yidan; Yu, Jiajia; He, Yong

2014-01-01

Tomatoes are cultivated around the world and gray mold is one of its most prominent and destructive diseases. An early disease detection method can decrease losses caused by plant diseases and prevent the spread of diseases. The activity of peroxidase (POD) is very important indicator of disease stress for plants. The objective of this study is to examine the possibility of fast detection of POD activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging data. Five pre-treatment methods were investigated. Genetic algorithm-partial least squares (GA-PLS) was applied to select optimal wavelengths. A new fast learning neural algorithm named extreme learning machine (ELM) was employed as multivariate analytical tool in this study. 21 optimal wavelengths were selected by GA-PLS and used as inputs of three calibration models. The optimal prediction result was achieved by ELM model with selected wavelengths, and the r and RMSEP in validation were 0.8647 and 465.9880 respectively. The results indicated that hyperspectral imaging could be considered as a valuable tool for POD activity prediction. The selected wavelengths could be potential resources for instrument development.

Modeling of defect-tolerant thin multi-junction solar cells for space application

NASA Astrophysics Data System (ADS)

Mehrotra, A.; Alemu, A.; Freundlich, A.

2012-02-01

Using drift-diffusion model and considering experimental III-V material parameters, AM0 efficiencies of lattice-matched multijunction solar cells have been calculated and the effects of dislocations and radiation damage have been analyzed. Ultrathin multi-junction devices perform better in presence of dislocations or/and radiation harsh environment compared to conventional thick multijunction devices. Our results show that device design optimization of Ga0.51In0.49P/GaAs multijunction devices leads to an improvement in EOL efficiency from 4.8%, for the conventional thick device design, to 12.7%, for the EOL optimized thin devices. In addition, an optimized defect free lattice matched Ga0.51In0.49P/GaAs solar cell under 1016cm-2 1Mev equivalent electron fluence is shown to give an EOL efficiency of 12.7%; while a Ga0.51In0.49P/GaAs solar cell with 108 cm-2 dislocation density under 1016cm-2 electron fluence gives an EOL efficiency of 12.3%. The results suggest that by optimizing the device design, we can obtain nearly the same EOL efficiencies for high dislocation metamorphic solar cells and defect filtered metamorphic multijunction solar cells. The findings relax the need for thick or graded buffer used for defect filtering in metamorphic devices. It is found that device design optimization allows highly dislocated devices to be nearly as efficient as defect free devices for space applications.

Sensitive and selective determination of gallic acid in green tea samples based on an electrochemical platform of poly(melamine) film.

PubMed

Su, Ya-Ling; Cheng, Shu-Hua

2015-12-11

In this work, an electrochemical sensor coupled with an effective flow-injection amperometry (FIA) system is developed, targeting the determination of gallic acid (GA) in a mild neutral condition, in contrast to the existing electrochemical methods. The sensor is based on a thin electroactive poly(melamine) film immobilized on a pre-anodized screen-printed carbon electrode (SPCE*/PME). The characteristics of the sensing surface are well-characterized by field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS) and surface water contact angle experiments. The proposed assay exhibits a wide linear response to GA in both pH 3 and pH 7.0 phosphate buffer solutions (PBS) under the optimized flow-injection amperometry. The detection limit (S/N = 3) is 0.076 μM and 0.21 μM in the pH 3 and pH 7 solutions, respectively. A relative standard deviation (RSD) of 3.9% is obtained for 57 successive measurements of 50 μM GA in pH 7 solutions. Interference studies indicate that some inorganic salts, catechol, caffeine and ascorbic acid do not interfere with the GA assay. The interference effects from some orthodiphenolic compounds are also investigated. The proposed method and a conventional Folin-Ciocalteu method are applied to detect GA in green tea samples using the standard addition method, and satisfactory spiked recoveries are obtained. Copyright © 2015 Elsevier B.V. All rights reserved.

Epitaxial growth of GaN/AlN/InAlN heterostructures for HEMTs in horizontal MOCVD reactors with different designs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsatsulnikov, A. F., E-mail: andrew@beam.ioffe.ru; Lundin, W. V.; Sakharov, A. V.

2016-09-15

The epitaxial growth of InAlN layers and GaN/AlN/InAlN heterostructures for HEMTs in growth systems with horizontal reactors of the sizes 1 × 2', 3 × 2', and 6 × 2' is investigated. Studies of the structural properties of the grown InAlN layers and electrophysical parameters of the GaN/AlN/InAlN heterostructures show that the optimal quality of epitaxial growth is attained upon a compromise between the growth conditions for InGaN and AlGaN. A comparison of the epitaxial growth in different reactors shows that optimal conditions are realized in small-scale reactors which make possible the suppression of parasitic reactions in the gas phase.more » In addition, the size of the reactor should be sufficient to provide highly homogeneous heterostructure parameters over area for the subsequent fabrication of devices. The optimal compositions and thicknesses of the InAlN layer for attaining the highest conductance in GaN/AlN/InAlN transistor heterostructures.« less

Interpretation of magnetic anomalies using a genetic algorithm

NASA Astrophysics Data System (ADS)

Kaftan, İlknur

2017-08-01

A genetic algorithm (GA) is an artificial intelligence method used for optimization. We applied a GA to the inversion of magnetic anomalies over a thick dike. Inversion of nonlinear geophysical problems using a GA has advantages because it does not require model gradients or well-defined initial model parameters. The evolution process consists of selection, crossover, and mutation genetic operators that look for the best fit to the observed data and a solution consisting of plausible compact sources. The efficiency of a GA on both synthetic and real magnetic anomalies of dikes by estimating model parameters, such as depth to the top of the dike ( H), the half-width of the dike ( B), the distance from the origin to the reference point ( D), the dip of the thick dike ( δ), and the susceptibility contrast ( k), has been shown. For the synthetic anomaly case, it has been considered for both noise-free and noisy magnetic data. In the real case, the vertical magnetic anomaly from the Pima copper mine in Arizona, USA, and the vertical magnetic anomaly in the Bayburt-Sarıhan skarn zone in northeastern Turkey have been inverted and interpreted. We compared the estimated parameters with the results of conventional inversion methods used in previous studies. We can conclude that the GA method used in this study is a useful tool for evaluating magnetic anomalies for dike models.

Optimization Technology of the LHS-1 Strain for Degrading Gallnut Water Extract and Appraisal of Benzene Ring Derivatives from Fermented Gallnut Water Extract Pyrolysis by Py-GC/MS.

PubMed

Wang, Chengzhang; Li, Wenjun

2017-12-20

Gallnut water extract (GWE) enriches 80~90% of gallnut tannic acid (TA). In order to study the biodegradation of GWE into gallic acid (GA), the LHS-1 strain, a variant of Aspergillus niger , was chosen to determine the optimal degradation parameters for maximum production of GA by the response surface method. Pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) was first applied to appraise benzene ring derivatives of fermented GWE (FGWE) pyrolysis by comparison with the pyrolytic products of a tannic acid standard sample (TAS) and GWE. The results showed that optimum conditions were at 31 °C and pH of 5, with a 50-h incubation period and 0.1 g·L -1 of TA as substrate. The maximum yields of GA and tannase were 63~65 mg·mL -1 and 1.17 U·mL -1 , respectively. Over 20 kinds of compounds were identified as linear hydrocarbons and benzene ring derivatives based on GA and glucose. The key benzene ring derivatives were 3,4,5-trimethoxybenzoic acid methyl ester, 3-methoxy-1,2-benzenediol, and 4-hydroxy-3,5-dimethoxy-benzoic acid hydrazide.

Tuning Parameters in Heuristics by Using Design of Experiments Methods

NASA Technical Reports Server (NTRS)

Arin, Arif; Rabadi, Ghaith; Unal, Resit

2010-01-01

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.

[Model and analysis of spectropolarimetric BRDF of painted target based on GA-LM method].

PubMed

Chen, Chao; Zhao, Yong-Qiang; Luo, Li; Pan, Quan; Cheng, Yong-Mei; Wang, Kai

2010-03-01

Models based on microfacet were used to describe spectropolarimetric BRDF (short for bidirectional reflectance distribution function) with experimental data. And the spectropolarimetric BRDF values of targets were measured with the comparison to the standard whiteboard, which was considered as Lambert and had a uniform reflectance rate up to 98% at arbitrary angle of view. And then the relationships between measured spectropolarimetric BRDF values and the angles of view, as well as wavelengths which were in a range of 400-720 nm were analyzed in details. The initial value needed to be input to the LM optimization method was difficult to get and greatly impacted the results. Therefore, optimization approach which combines genetic algorithm and Levenberg-Marquardt (LM) was utilized aiming to retrieve parameters of nonlinear models, and the initial values were obtained using GA approach. Simulated experiments were used to test the efficiency of the adopted optimization method. And the simulated experiment ensures the optimization method to have a good performance and be able to retrieve the parameters of nonlinear model efficiently. The correctness of the models was validated by real outdoor sampled data. The parameters of DoP model retrieved are the refraction index of measured targets. The refraction index of the same color painted target but with different materials was also obtained. Conclusion has been drawn that the refraction index from these two targets are very near and this slight difference could be understood by the difference in the conditions of paint targets' surface, not the material of the targets.

Research on energy-saving optimal control of trains in a following operation under a fixed four-aspect autoblock system based on multi-dimension parallel GA

NASA Astrophysics Data System (ADS)

Lu, Qiheng; Feng, Xiaoyun

2013-03-01

After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model was created based on the dynamics equations of the trains in order to study the energy-saving optimal control strategy of trains in a following operation. Besides the safety and punctuality, the main aims of the model were the energy consumption and the time error. Based on this model, the static and dynamic speed restraints under a four-aspect fixed autoblock system were put forward. The multi-dimension parallel genetic algorithm (GA) and the external punishment function were adopted to solve this problem. By using the real number coding and the strategy of ramps divided into three parts, the convergence of GA was speeded up and the length of chromosomes was shortened. A vector of Gaussian random disturbance with zero mean was superposed to the mutation operator. The simulation result showed that the method could reduce the energy consumption effectively based on safety and punctuality.

Multi-probe-based resonance-frequency electrical impedance spectroscopy for detection of suspicious breast lesions: improving performance using partial ROC optimization

NASA Astrophysics Data System (ADS)

Lederman, Dror; Zheng, Bin; Wang, Xingwei; Wang, Xiao Hui; Gur, David

2011-03-01

We have developed a multi-probe resonance-frequency electrical impedance spectroscope (REIS) system to detect breast abnormalities. Based on assessing asymmetry in REIS signals acquired between left and right breasts, we developed several machine learning classifiers to classify younger women (i.e., under 50YO) into two groups of having high and low risk for developing breast cancer. In this study, we investigated a new method to optimize performance based on the area under a selected partial receiver operating characteristic (ROC) curve when optimizing an artificial neural network (ANN), and tested whether it could improve classification performance. From an ongoing prospective study, we selected a dataset of 174 cases for whom we have both REIS signals and diagnostic status verification. The dataset includes 66 "positive" cases recommended for biopsy due to detection of highly suspicious breast lesions and 108 "negative" cases determined by imaging based examinations. A set of REIS-based feature differences, extracted from the two breasts using a mirror-matched approach, was computed and constituted an initial feature pool. Using a leave-one-case-out cross-validation method, we applied a genetic algorithm (GA) to train the ANN with an optimal subset of features. Two optimization criteria were separately used in GA optimization, namely the area under the entire ROC curve (AUC) and the partial area under the ROC curve, up to a predetermined threshold (i.e., 90% specificity). The results showed that although the ANN optimized using the entire AUC yielded higher overall performance (AUC = 0.83 versus 0.76), the ANN optimized using the partial ROC area criterion achieved substantially higher operational performance (i.e., increasing sensitivity level from 28% to 48% at 95% specificity and/ or from 48% to 58% at 90% specificity).

Reconfiguration of Smart Distribution Network in the Presence of Renewable DG’s Using GWO Algorithm

NASA Astrophysics Data System (ADS)

Siavash, M.; Pfeifer, C.; Rahiminejad, A.; Vahidi, B.

2017-08-01

In this paper, the optimal reconfiguration of smart distribution system is performed with the aim of active power loss reduction and voltage stability improvement. The distribution network is considered equipped with wind turbines and solar cells as Renewable DG’s (RDG’s). Because of the presence of smart metering devices, the network state is known accurately at any moment. Based on the network conditions (the amount of load and generation of RDG’s), the optimal configuration of the network is obtained. The optimization problem is solved using a recently introduced method known as Grey Wolf Optimizer (GWO). The proposed approach is applied on 69-bus radial test system and the results of the GWO are compared to those of Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The results show the effectiveness of the proposed approach and the selected optimization method.

Optimal power flow with optimal placement TCSC device on 500 kV Java-Bali electrical power system using genetic Algorithm-Taguchi method

NASA Astrophysics Data System (ADS)

Apribowo, Chico Hermanu Brillianto; Ibrahim, Muhammad Hamka; Wicaksono, F. X. Rian

2018-02-01

The growing burden of the load and the complexity of the power system has had an impact on the need for optimization of power system operation. Optimal power flow (OPF) with optimal location placement and rating of thyristor controlled series capacitor (TCSC) is an effective solution used to determine the economic cost of operating the plant and regulate the power flow in the power system. The purpose of this study is to minimize the total cost of generation by placing the location and the optimal rating of TCSC using genetic algorithm-design of experiment techniques (GA-DOE). Simulation on Java-Bali system 500 kV with the amount of TCSC used by 5 compensator, the proposed method can reduce the generation cost by 0.89% compared to OPF without using TCSC.

Thermodynamic assessments and inter-relationships between systems involving Al, Am, Ga, Pu, and U

NASA Astrophysics Data System (ADS)

Perron, A.; Turchi, P. E. A.; Landa, A.; Oudot, B.; Ravat, B.; Delaunay, F.

2016-12-01

A newly developed self-consistent CALPHAD thermodynamic database involving Al, Am, Ga, Pu, and U is presented. A first optimization of the slightly characterized Am-Al and completely unknown Am-Ga phase diagrams is proposed. To this end, phase diagram features as crystal structures, stoichiometric compounds, solubility limits, and melting temperatures have been studied along the U-Al → Pu-Al → Am-Al, and U-Ga → Pu-Ga → Am-Ga series, and the thermodynamic assessments involving Al and Ga alloying are compared. In addition, two distinct optimizations of the Pu-Al phase diagram are proposed to account for the low temperature and Pu-rich region controversy. The previously assessed thermodynamics of the other binary systems (Am-Pu, Am-U, Pu-U, and Al-Ga) is also included in the database and is briefly described in the present work. Finally, predictions on phase stability of ternary and quaternary systems of interest are reported to check the consistency of the database.

Thermodynamic assessments and inter-relationships between systems involving Al, Am, Ga, Pu, and U

DOE PAGES

Perron, A.; Turchi, P. E. A.; Landa, A.; ...

2016-12-01

We present a newly developed self-consistent CALPHAD thermodynamic database involving Al, Am, Ga, Pu, and U. A first optimization of the slightly characterized Am-Al and completely unknown Am-Ga phase diagrams is proposed. To this end, phase diagram features as crystal structures, stoichiometric compounds, solubility limits, and melting temperatures have been studied along the U-Al → Pu-Al → Am-Al, and U-Ga → Pu-Ga → Am-Ga series, and the thermodynamic assessments involving Al and Ga alloying are compared. In addition, two distinct optimizations of the Pu-Al phase diagram are proposed to account for the low temperature and Pu-rich region controversy. We includedmore » the previously assessed thermodynamics of the other binary systems (Am-Pu, Am-U, Pu-U, and Al-Ga) in the database and is briefly described in the present work. In conclusion, predictions on phase stability of ternary and quaternary systems of interest are reported to check the consistency of the database.« less

The optimal location of piezoelectric actuators and sensors for vibration control of plates

NASA Astrophysics Data System (ADS)

Kumar, K. Ramesh; Narayanan, S.

2007-12-01

This paper considers the optimal placement of collocated piezoelectric actuator-sensor pairs on a thin plate using a model-based linear quadratic regulator (LQR) controller. LQR performance is taken as objective for finding the optimal location of sensor-actuator pairs. The problem is formulated using the finite element method (FEM) as multi-input-multi-output (MIMO) model control. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero-one optimization problem. A genetic algorithm (GA) is used to solve the zero-one optimization problem. Different classical control strategies like direct proportional feedback, constant-gain negative velocity feedback and the LQR optimal control scheme are applied to study the control effectiveness.

Design considerations of high-performance InGaAs/InP single-photon avalanche diodes for quantum key distribution.

PubMed

Ma, Jian; Bai, Bing; Wang, Liu-Jun; Tong, Cun-Zhu; Jin, Ge; Zhang, Jun; Pan, Jian-Wei

2016-09-20

InGaAs/InP single-photon avalanche diodes (SPADs) are widely used in practical applications requiring near-infrared photon counting such as quantum key distribution (QKD). Photon detection efficiency and dark count rate are the intrinsic parameters of InGaAs/InP SPADs, due to the fact that their performances cannot be improved using different quenching electronics given the same operation conditions. After modeling these parameters and developing a simulation platform for InGaAs/InP SPADs, we investigate the semiconductor structure design and optimization. The parameters of photon detection efficiency and dark count rate highly depend on the variables of absorption layer thickness, multiplication layer thickness, excess bias voltage, and temperature. By evaluating the decoy-state QKD performance, the variables for SPAD design and operation can be globally optimized. Such optimization from the perspective of specific applications can provide an effective approach to design high-performance InGaAs/InP SPADs.

Development and applications of various optimization algorithms for diesel engine combustion and emissions optimization

NASA Astrophysics Data System (ADS)

Ogren, Ryan M.

For this work, Hybrid PSO-GA and Artificial Bee Colony Optimization (ABC) algorithms are applied to the optimization of experimental diesel engine performance, to meet Environmental Protection Agency, off-road, diesel engine standards. This work is the first to apply ABC optimization to experimental engine testing. All trials were conducted at partial load on a four-cylinder, turbocharged, John Deere engine using neat-Biodiesel for PSO-GA and regular pump diesel for ABC. Key variables were altered throughout the experiments, including, fuel pressure, intake gas temperature, exhaust gas recirculation flow, fuel injection quantity for two injections, pilot injection timing and main injection timing. Both forms of optimization proved effective for optimizing engine operation. The PSO-GA hybrid was able to find a superior solution to that of ABC within fewer engine runs. Both solutions call for high exhaust gas recirculation to reduce oxide of nitrogen (NOx) emissions while also moving pilot and main fuel injections to near top dead center for improved tradeoffs between NOx and particulate matter.

Evaluation of the Possible Utilization of 68Ga-DOTATOC in Diagnosis of Adenocarcinoma Breast Cancer.

PubMed

Zolghadri, Samaneh; Naderi, Mojdeh; Yousefnia, Hassan; Alirezapour, Behrouz; Beiki, Davood

2018-01-01

Studies have indicated advantageous properties of [DOTA-DPhe 1 , Tyr 3 ] octreotide (DOTATOC) in tumor models and labeling with gallium. Breast cancer is the second leading cause of cancer mortality in women, and most of these cancers are often an adenocarcinoma. Due to the importance of target to non-target ratios in the efficacy of diagnosis, the pharmacokinetic of 68 Ga-DOTATOC in an adenocarcinoma breast cancer animal model was studied in this research, and the optimized time for imaging was determined. 68 Ga was obtained from 68 Ge/ 68 Ga generator. The complex was prepared at optimized conditions. Radiochemical purity of the complex was checked using both HPLC and ITLC methods. Biodistribution of the complex was studied in BALB/c mice bearing adenocarcinoma breast cancer. Also, PET/CT imaging was performed up to 120 min post injection. The complex was produced with radiochemical purity of greater than 98% and specific activity of about 40 GBq/mM at optimized conditions. Biodistribution of the complex was studied in BALB/c mice bearing adenocarcinoma breast cancer indicated fast blood clearance and significant uptake in the tumor. Significant tumor: blood and tumor:muscle uptake ratios were observed even at early times post-injection. PET/CT images were also confirmed the considerable accumulation of the tracer in the tumor. Generally, the results proved the possible application of the radiolabelled complex for the detection of the adenocarcinoma breast cancer and according to the pharmacokenitic data, the suitable time for imaging was determined as at least 30 min after injection.

Modeling and optimization of a double-well double-barrier GaN/AlGaN/GaN/AlGaN resonant tunneling diode

NASA Astrophysics Data System (ADS)

Liu, Yang; Gao, Bo; Gong, Min; Shi, Ruiying

2017-06-01

The influence of a GaN layer as a sub-quantum well for an AlGaN/GaN/AlGaN double barrier resonant tunneling diode (RTD) on device performance has been investigated by means of numerical simulation. The introduction of the GaN layer as the sub-quantum well turns the dominant transport mechanism of RTD from the 3D-2D model to the 2D-2D model and increases the energy difference between tunneling energy levels. It can also lower the effective height of the emitter barrier. Consequently, the peak current and peak-to-valley current difference of RTD have been increased. The optimal GaN sub-quantum well parameters are found through analyzing the electrical performance, energy band, and transmission coefficient of RTD with different widths and depths of the GaN sub-quantum well. The most pronounced electrical parameters, a peak current density of 5800 KA/cm2, a peak-to-valley current difference of 1.466 A, and a peak-to-valley current ratio of 6.35, could be achieved by designing RTD with the active region structure of GaN/Al0.2Ga0.8 N/GaN/Al0.2Ga0.8 N (3 nm/1.5 nm/1.5 nm/1.5 nm).

Modeling and Optimization of Sub-Wavelength Grating Nanostructures on Cu(In,Ga)Se2 Solar Cell

NASA Astrophysics Data System (ADS)

Kuo, Shou-Yi; Hsieh, Ming-Yang; Lai, Fang-I.; Liao, Yu-Kuang; Kao, Ming-Hsuan; Kuo, Hao-Chung

2012-10-01

In this study, an optical simulation of Cu(In,Ga)Se2 (CIGS) solar cells by the rigorous coupled-wave analysis (RCWA) method is carried out to investigate the effects of surface morphology on the light absorption and power conversion efficiencies. Various sub-wavelength grating (SWG) nanostructures of periodic ZnO:Al (AZO) on CIGS solar cells were discussed in detail. SWG nanostructures were used as efficient antireflection layers. From the simulation results, AZO structures with nipple arrays effectively suppress the Fresnel reflection compared with nanorod- and cone-shaped AZO structures. The optimized reflectance decreased from 8.44 to 3.02% and the efficiency increased from 14.92 to 16.11% accordingly. The remarkable enhancement in light harvesting is attributed to the gradient refractive index profile between the AZO nanostructures and air.

Optimal clustering of MGs based on droop controller for improving reliability using a hybrid of harmony search and genetic algorithms.

PubMed

Abedini, Mohammad; Moradi, Mohammad H; Hosseinian, S M

2016-03-01

This paper proposes a novel method to address reliability and technical problems of microgrids (MGs) based on designing a number of self-adequate autonomous sub-MGs via adopting MGs clustering thinking. In doing so, a multi-objective optimization problem is developed where power losses reduction, voltage profile improvement and reliability enhancement are considered as the objective functions. To solve the optimization problem a hybrid algorithm, named HS-GA, is provided, based on genetic and harmony search algorithms, and a load flow method is given to model different types of DGs as droop controller. The performance of the proposed method is evaluated in two case studies. The results provide support for the performance of the proposed method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Optimized alumina coagulants for water treatment

DOEpatents

Nyman, May D [Albuquerque, NM; Stewart, Thomas A [Albuquerque, NM

2012-02-21

Substitution of a single Ga-atom or single Ge-atom (GaAl.sub.12 and GeAl.sub.12 respectively) into the center of an aluminum Keggin polycation (Al.sub.13) produces an optimal water-treatment product for neutralization and coagulation of anionic contaminants in water. GaAl.sub.12 consistently shows .about.1 order of magnitude increase in pathogen reduction, compared to Al.sub.13. At a concentration of 2 ppm, GaAl.sub.12 performs equivalently to 40 ppm alum, removing .about.90% of the dissolved organic material. The substituted GaAl.sub.12 product also offers extended shelf-life and consistent performance. We also synthesized a related polyaluminum chloride compound made of pre-hydrolyzed dissolved alumina clusters of [GaO.sub.4Al.sub.12(OH).sub.24(H.sub.2O).sub.12].sup.7+.

Determination of foodborne pathogenic bacteria by multiplex PCR-microchip capillary electrophoresis with genetic algorithm-support vector regression optimization.

PubMed

Li, Yongxin; Li, Yuanqian; Zheng, Bo; Qu, Lingli; Li, Can

2009-06-08

A rapid and sensitive method based on microchip capillary electrophoresis with condition optimization of genetic algorithm-support vector regression (GA-SVR) was developed and applied to simultaneous analysis of multiplex PCR products of four foodborne pathogenic bacteria. Four pairs of oligonucleotide primers were designed to exclusively amplify the targeted gene of Vibrio parahemolyticus, Salmonella, Escherichia coli (E. coli) O157:H7, Shigella and the quadruplex PCR parameters were optimized. At the same time, GA-SVR was employed to optimize the separation conditions of DNA fragments in microchip capillary electrophoresis. The proposed method was applied to simultaneously detect the multiplex PCR products of four foodborne pathogenic bacteria under the optimal conditions within 8 min. The levels of detection were as low as 1.2 x 10(2) CFU mL(-1) of Vibrio parahemolyticus, 2.9 x 10(2) CFU mL(-1) of Salmonella, 8.7 x 10(1) CFU mL(-1) of E. coli O157:H7 and 5.2 x 10(1) CFU mL(-1) of Shigella, respectively. The relative standard deviation of migration time was in the range of 0.74-2.09%. The results demonstrated that the good resolution and less analytical time were achieved due to the application of the multivariate strategy. This study offers an efficient alternative to routine foodborne pathogenic bacteria detection in a fast, reliable, and sensitive way.

Randomly iterated search and statistical competency as powerful inversion tools for deformation source modeling: Application to volcano interferometric synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Shirzaei, M.; Walter, T. R.

2009-10-01

Modern geodetic techniques provide valuable and near real-time observations of volcanic activity. Characterizing the source of deformation based on these observations has become of major importance in related monitoring efforts. We investigate two random search approaches, simulated annealing (SA) and genetic algorithm (GA), and utilize them in an iterated manner. The iterated approach helps to prevent GA in general and SA in particular from getting trapped in local minima, and it also increases redundancy for exploring the search space. We apply a statistical competency test for estimating the confidence interval of the inversion source parameters, considering their internal interaction through the model, the effect of the model deficiency, and the observational error. Here, we present and test this new randomly iterated search and statistical competency (RISC) optimization method together with GA and SA for the modeling of data associated with volcanic deformations. Following synthetic and sensitivity tests, we apply the improved inversion techniques to two episodes of activity in the Campi Flegrei volcanic region in Italy, observed by the interferometric synthetic aperture radar technique. Inversion of these data allows derivation of deformation source parameters and their associated quality so that we can compare the two inversion methods. The RISC approach was found to be an efficient method in terms of computation time and search results and may be applied to other optimization problems in volcanic and tectonic environments.

Optimization design of multiphase pump impeller based on combined genetic algorithm and boundary vortex flux diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Jin-ya; Cai, Shu-jie; Li, Yong-jiang; Li, Yong-jiang; Zhang, Yong-xue

2017-12-01

A novel optimization design method for the multiphase pump impeller is proposed through combining the quasi-3D hydraulic design (Q3DHD), the boundary vortex flux (BVF) diagnosis, and the genetic algorithm (GA). The BVF diagnosis based on the Q3DHD is used to evaluate the objection function. Numerical simulations and hydraulic performance tests are carried out to compare the impeller designed only by the Q3DHD method and that optimized by the presented method. The comparisons of both the flow fields simulated under the same condition show that (1) the pressure distribution in the optimized impeller is more reasonable and the gas-liquid separation is more efficiently inhibited, (2) the scales of the gas pocket and the vortex decrease remarkably for the optimized impeller, (3) the unevenness of the BVF distributions near the shroud of the original impeller is effectively eliminated in the optimized impeller. The experimental results show that the differential pressure and the maximum efficiency of the optimized impeller are increased by 4% and 2.5%, respectively. Overall, the study indicates that the optimization design method proposed in this paper is feasible.

RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms

NASA Astrophysics Data System (ADS)

Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay

The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.

Significantly improved surface morphology of N-polar GaN film grown on SiC substrate by the optimization of V/III ratio

NASA Astrophysics Data System (ADS)

Deng, Gaoqiang; Zhang, Yuantao; Yu, Ye; Yan, Long; Li, Pengchong; Han, Xu; Chen, Liang; Zhao, Degang; Du, Guotong

2018-04-01

In this paper, N-polar GaN films with different V/III ratios were grown on vicinal C-face SiC substrates by metalorganic chemical vapor deposition. During the growth of N-polar GaN film, the V/III ratio was controlled by adjusting the molar flow rate of ammonia while keeping the trimethylgallium flow rate unchanged. The influence of the V/III ratio on the surface morphology of N-polar GaN film has been studied. We find that the surface root mean square roughness of N-polar GaN film over an area of 20 × 20 μm2 can be reduced from 8.13 to 2.78 nm by optimization of the V/III ratio. Then, using the same growth conditions, N-polar InGaN/GaN multiple quantum wells (MQWs) light-emitting diodes (LEDs) were grown on the rough and the smooth N-polar GaN templates, respectively. Compared with the LED grown on the rough N-polar GaN template, dramatically improved interface sharpness and luminescence uniformity of the InGaN/GaN MQWs are achieved for the LED grown on the smooth N-polar GaN template.

Self-consistent optimization of [111]-AlGaInAs/InP MQWs structures lasing at 1.55 μm by a genetic algorithm

NASA Astrophysics Data System (ADS)

Saidi, Hosni; Msahli, Melek; Ben Dhafer, Rania; Ridene, Said

2017-12-01

Band structure and optical gain properties of [111]-oriented AlGaInAs/AlGaInAs-delta-InGaAs multi-quantum wells, subjected to piezoelectric field, for the near-infrared lasers diodes applications was proposed and investigated in this paper. By using genetic algorithm based on optimization technique we demonstrate that the structural parameters can be conveniently optimized to achieve high-efficiency laser diode performance at room temperature. In this work, significant optical gain for the wished emission wavelength at 1.55 μm and low threshold injection current are the optimization target. The end result of this optimization is a laser diode based on InP substrate using quaternary compound material of AlGaInAs in both quantum wells and barriers with different composition. It has been shown that the transverse electric polarized optical gain which reaches 3500 cm-1 may be acquired for λ = 1.55 μm with a threshold carrier density Nth≈1.31018cm-3, which is very promising to serve as an alternative active region for high-efficiency near-infrared lasers. Finally, from the design presented here, we show that it is possible to apply this technique to a different III-V compound semiconductors and wavelength ranging from deep-ultra-violet to far infrared.

Crystal growth of device quality GaAs in space

NASA Technical Reports Server (NTRS)

Gatos, H. C.; Lagowski, J.

1979-01-01

The optimization of space processing of GaAs is described. The detailed compositional, structural, and electronic characterization of GaAs on a macro- and microscale and the relationships between growth parameters and the properties of GaAs are among the factors discussed. The key parameters limiting device performance are assessed.

Particle Swarm Optimization Toolbox

NASA Technical Reports Server (NTRS)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.

Fully automated GMP production of [68Ga]Ga-DO3A-VS-Cys40-Exendin-4 for clinical use

PubMed Central

Velikyan, Irina; Rosenstrom, Ulrika; Eriksson, Olof

2017-01-01

[68Ga]Ga-DO3A-VS-Cys40-Exendin-4/PET-CT targeting glucagon like peptide-1 receptor (GLP-1R) has previously demonstrated its potential clinical value for the detection of insulinomas. The production and accessibility of this radiopharmaceutical is one of the critical factors in realization of clinical trials and routine clinical examinations. Previously, the radiopharmaceutical was prepared manually, however larger scale of clinical trials and healthcare requires automation of the production process in order to limit the operator radiation dose as well as improve tracer manufacturing robustness and on-line documentation for enhanced good manufacturing practice (GMP) compliance. A method for 68Ga-labelling of DO3A-VS-Cys40-Exendin-4 on a commercially available synthesis platform was developed. Equipment such as 68Ge/68Ga generator, synthesis platform, and disposable cassettes for 68Ga-labelling used in the study was purchased from Eckert & Ziegler. DO3A-VS-Cys40-Exendin-4 was synthesized in-house. The parameters such as time, temperature, precursor concentration, radical scavenger, buffer concentration, pH, product purification step were investigated and optimised. Reproducible and GMP compliant automated production of [68Ga]Ga-DO3A-VS-Cys40-Exendin-4 was developed. Exendin-4 comprising methionine amino acid residue was prone to oxidation which was strongly influenced by the elevated temperature, radioactivity amount, and precursor concentration. The suppression of the oxidative radiolysis was achieved by addition of ethanol, dihydroxybenzoic acid and ascorbic acid to the reaction buffer as well as by optimizing heating temperature. The non-decay corrected radiochemical yield was 43±2% with radiochemical purity of over 90% wherein the individual impurity signals in HPLC chromatogram did not exceed 5%. Automated production and quality control methods were established for paving the pathway for broader clinical use of [68Ga]Ga-DO3A-VS-Cys40-Exendin-4. PMID:28721305

Genetic Algorithm for Optimization: Preprocessor and Algorithm

NASA Technical Reports Server (NTRS)

Sen, S. K.; Shaykhian, Gholam A.

2006-01-01

Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

Quantification of the fluorine containing drug 5-fluorouracil in cancer cells by GaF molecular absorption via high-resolution continuum source molecular absorption spectrometry

NASA Astrophysics Data System (ADS)

Krüger, Magnus; Huang, Mao-Dong; Becker-Roß, Helmut; Florek, Stefan; Ott, Ingo; Gust, Ronald

The development of high-resolution continuum source molecular absorption spectrometry made the quantification of fluorine feasible by measuring the molecular absorption as gallium monofluoride (GaF). Using this new technique, we developed on the example of 5-fluorouracil (5-FU) a graphite furnace method to quantify fluorine in organic molecules. The effect of 5-FU on the generation of the diatomic GaF molecule was investigated. The experimental conditions such as gallium nitrate amount, temperature program, interfering anions (represented as corresponding acids) and calibration for the determination of 5-FU in standard solution and in cellular matrix samples were investigated and optimized. The sample matrix showed no effect on the sensitivity of GaF molecular absorption. A simple calibration curve using an inorganic sodium fluoride solution can conveniently be used for the calibration. The described method is sensitive and the achievable limit of detection is 0.23 ng of 5-FU. In order to establish the concept of "fluorine as a probe in medicinal chemistry" an exemplary application was selected, in which the developed method was successfully demonstrated by performing cellular uptake studies of the 5-FU in human colon carcinoma cells.

Gallium nitride photocathodes for imaging photon counters

NASA Astrophysics Data System (ADS)

Siegmund, Oswald H. W.; Hull, Jeffrey S.; Tremsin, Anton S.; McPhate, Jason B.; Dabiran, Amir M.

2010-07-01

Gallium nitride opaque and semitransparent photocathodes provide high ultraviolet quantum efficiencies from 100 nm to a long wavelength cutoff at ~380 nm. P (Mg) doped GaN photocathode layers ~100 nm thick with a barrier layer of AlN (22 nm) on sapphire substrates also have low out of band response, and are highly robust. Opaque GaN photocathodes are relatively easy to optimize, and consistently provide high quantum efficiency (70% at 120 nm) provided the surface cleaning and activation (Cs) processes are well established. We have used two dimensional photon counting imaging microchannel plate detectors, with an active area of 25 mm diameter, to investigate the imaging characteristics of semitransparent GaN photocathodes. These can be produced with high (20%) efficiency, but the thickness and conductivity of the GaN must be carefully optimized. High spatial resolution of ~50 μm with low intrinsic background (~7 events sec-1 cm-2) and good image uniformity have been achieved. Selectively patterned deposited GaN photocathodes have also been used to allow quick diagnostics of optimization parameters. GaN photocathodes of both types show great promise for future detector applications in ultraviolet Astrophysical instruments.

Real coded genetic algorithm for fuzzy time series prediction

NASA Astrophysics Data System (ADS)

Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

2017-10-01

Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Constraints

NASA Technical Reports Server (NTRS)

Hinckley, David; Englander, Jacob; Hitt, Darren

2015-01-01

Single trial evaluations Trial creation by Phase-wise GA-style or DE-inspired recombination Bin repository structure requires an initialization period Non-exclusionary Kill Distance Population collapse mechanic Main loop Creation Probabilistic switch between GA and DE creation types Locally optimize Submit to repository Repeat.

Single-photon sources based on InAs/GaAs QDs for solar cell

NASA Astrophysics Data System (ADS)

Jia, Wei; Liu, Zhi; Wang, Xunchun

2013-08-01

We have grown InAs/GaAs quantum dots (QDs) by droplet epitaxy for application in single photon sources. This growth method enables the formation of QDs without strain, with emission wavelengths of around 1.3μm within the optimal detection range of cost effective silicon detector, and with reduced surface density of several tens to a few QDs per μm2 for easier isolation of single QDs. The optical properties of QDs were envisaged by exciton and biexciton emission peaks identified from power dependent and time-resolved micro-photoluminescence (μ-PL) measurements.

Near-field phase-change recording using a GaN laser diode

NASA Astrophysics Data System (ADS)

Kishima, Koichiro; Ichimura, Isao; Yamamoto, Kenji; Osato, Kiyoshi; Kuroda, Yuji; Iida, Atsushi; Saito, Kimihiro

2000-09-01

We developed a 1.5-Numerical-Aperture optical setup using a GaN blue-violet laser diode. We used a 1.0 mm-diameter super-hemispherical solid immersion lens, and optimized a phase-change disk structure including the cover layer by the method of MTF simulation. The disk surface was polished by tape burnishing technique. An eye-pattern of (1-7)-coded data at the linear density of 80 nm/bit was demonstrated on the phase-change disk below a 50 nm gap height, which was realized through our air-gap servo mechanism.

Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Junsheng; Peng, Yanfeng; Yang, Yu; Wu, Zhantao

2017-02-01

Enlightened by ASTFA method, adaptive sparsest narrow-band decomposition (ASNBD) method is proposed in this paper. In ASNBD method, an optimized filter must be established at first. The parameters of the filter are determined by solving a nonlinear optimization problem. A regulated differential operator is used as the objective function so that each component is constrained to be a local narrow-band signal. Afterwards, the signal is filtered by the optimized filter to generate an intrinsic narrow-band component (INBC). ASNBD is proposed aiming at solving the problems existed in ASTFA. Gauss-Newton type method, which is applied to solve the optimization problem in ASTFA, is irreplaceable and very sensitive to initial values. However, more appropriate optimization method such as genetic algorithm (GA) can be utilized to solve the optimization problem in ASNBD. Meanwhile, compared with ASTFA, the decomposition results generated by ASNBD have better physical meaning by constraining the components to be local narrow-band signals. Comparisons are made between ASNBD, ASTFA and EMD by analyzing simulation and experimental signals. The results indicate that ASNBD method is superior to the other two methods in generating more accurate components from noise signal, restraining the boundary effect, possessing better orthogonality and diagnosing rolling element bearing fault.

The prediction in computer color matching of dentistry based on GA+BP neural network.

PubMed

Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang

2015-01-01

Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

Temporal behavior of RHEED intensity oscillations during molecular beam epitaxial growth of GaAs and AlGaAs on (111)B GaAs substrates

NASA Astrophysics Data System (ADS)

Yen, Ming Y.; Haas, T. W.

1990-10-01

We present the temporal behavior of intensity oscillations in reflection high-energy electron diffraction (RHEED) during molecular beam epitaxial (MBE) growth of GaAs and A1GaAs on (1 1 1)B GaAs substrates. The RHEED intensity oscillations were examined as a function of growth parameters in order to provide the insight into the dynamic characteristics and to identify the optimal condition for the two-dimensional layer-by-layer growth. The most intense RHEED oscillation was found to occur within a very narrow temperature range which seems to optimize the surface migration kinetics of the arriving group III elements and the molecular dissodiative reaction of the group V elements. The appearance of an initial transient of the intensity upon commencement of the growth and its implications are described.

Alien Genetic Algorithm for Exploration of Search Space

NASA Astrophysics Data System (ADS)

Patel, Narendra; Padhiyar, Nitin

2010-10-01

Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.

The optimal thickness of a transmission-mode GaN photocathode

NASA Astrophysics Data System (ADS)

Wang, Xiao-Hui; Shi, Feng; Guo, Hui; Hu, Cang-Lu; Cheng, Hong-Chang; Chang, Ben-Kang; Ren, Ling; Du, Yu-Jie; Zhang, Jun-Ju

2012-08-01

A 150-nm-thick GaN photocathode with a Mg doping concentration of 1.6 × 1017 cm-3 is activated by Cs/O in an ultrahigh vacuum chamber, and a quantum efficiency (QE) curve of the negative electron affinity transmission-mode (t-mode) of the GaN photocathode is obtained. The maximum QE reaches 13.0% at 290 nm. According to the t-mode QE equation solved from the diffusion equation, the QE curve is fitted. From the fitting results, the electron escape probability is 0.32, the back-interface recombination velocity is 5 × 104 cm·s-1, and the electron diffusion length is 116 nm. Based on these parameters, the influence of GaN thickness on t-mode QE is simulated. The simulation shows that the optimal thickness of GaN is 90 nm, which is better than the 150-nm GaN.

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa; Gholami, Amin

2015-06-01

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

In situ passivation of GaAsP nanowires.

PubMed

Himwas, C; Collin, S; Rale, P; Chauvin, N; Patriarche, G; Oehler, F; Julien, F H; Travers, L; Harmand, J-C; Tchernycheva, M

2017-12-08

We report on the structural and optical properties of GaAsP nanowires (NWs) grown by molecular-beam epitaxy. By adjusting the alloy composition in the NWs, the transition energy was tuned to the optimal value required for tandem III-V/silicon solar cells. We discovered that an unintentional shell was also formed during the GaAsP NW growth. The NW surface was passivated by an in situ deposition of a radial Ga(As)P shell. Different shell compositions and thicknesses were investigated. We demonstrate that the optimal passivation conditions for GaAsP NWs (with a gap of 1.78 eV) are obtained with a 5 nm thick GaP shell. This passivation enhances the luminescence intensity of the NWs by 2 orders of magnitude and yields a longer luminescence decay. The luminescence dynamics changes from single exponential decay with a 4 ps characteristic time in non-passivated NWs to a bi-exponential decay with characteristic times of 85 and 540 ps in NWs with GaP shell passivation.

Self-consistent vertical transport calculations in AlxGa1-xN/GaN based resonant tunneling diode

NASA Astrophysics Data System (ADS)

Rached, A.; Bhouri, A.; Sakr, S.; Lazzari, J.-L.; Belmabrouk, H.

2016-03-01

The formation of two-dimensional electron gases (2DEGs) at AlxGa1-xN/GaN hexagonal double-barriers (DB) resonant tunneling diodes (RTD) is investigated by numerical self-consistent (SC) solutions of the coupled Schrödinger and Poisson equations. Spontaneous and piezoelectric effects across the material interfaces are rigorously taken into account. Conduction band profiles, band edges and corresponding envelope functions are calculated in the AlxGa1-xN/GaN structures and likened to those where no polarization effects are included. The combined effect of the polarization-induced bound charge and conduction band offsets between the hexagonal AlGaN and GaN results in the formation of 2DEGs on one side of the DB and a depletion region on the other side. Using the transfer matrix formalism, the vertical transport (J-V characteristics) in AlGaN/GaN RTDs is calculated with a fully SC calculation in the ballistic regime. Compared to standard calculations where the voltage drop along the structure is supposed to be linear, the SC method leads to strong quantitative changes in the J-V characteristics showing that the applied electric field varies significantly in the active region of the structure. The influences of the aluminum composition and the GaN(AlGaN) thickness layers on the evolution of the current characteristics are also self-consistently investigated and discussed. We show that the electrical characteristics are very sensitive to the potential barrier due to the interplay between the potential symmetry and the barrier height and width. More interestingly, we demonstrate that the figures of merit namely the peak-to-valley ratio (PVR) of GaN/AlGaN RTDs can be optimized by increasing the quantum well width.

Optimum Design of Hypersonic Airbreathing Propulsion

NASA Astrophysics Data System (ADS)

Kobayashi, Hiroaki; Sato, Tetsuya; Tanatsugu, Nobuhiro

The flight of Spaceplane is always under accelarating in the assent way and always under decelarating in the desent way and yet cruising in the return way. Besides, its flight envelope is considerably wider than that of airplane. Thus the integrated design method is required to build the best transportation system optimized taking into account the propulsion system and the airframe under the entire flight conditions. In this paper it is shown an optimization method on TSTO spaceplane system. Genetic algorithm (GA) was applied to optimize design parameters of engine, airframe, and trajectory simultaneously. Several types of engine were quantitatively compared using payload ratio as an evaluating function. It was concluded that precooled turbojets is the most promising engine for TSTO among Turbine Based Combined Cycle (TBCC) engines.

Bell-Curve Based Evolutionary Strategies for Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

2001-01-01

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity. However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold. One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumbersome binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back and Dasgupta and Michalesicz. We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

Genetic algorithms for the application of Activated Sludge Model No. 1.

PubMed

Kim, S; Lee, H; Kim, J; Kim, C; Ko, J; Woo, H; Kim, S

2002-01-01

The genetic algorithm (GA) has been integrated into the IWA ASM No. 1 to calibrate important stoichiometric and kinetic parameters. The evolutionary feature of GA was used to configure the multiple local optima as well as the global optimum. The objective function of optimization was designed to minimize the difference between estimated and measured effluent concentrations at the activated sludge system. Both steady state and dynamic data of the simulation benchmark were used for calibration using denitrification layout. Depending upon the confidence intervals and objective functions, the proposed method provided distributions of parameter space. Field data have been collected and applied to validate calibration capacity of GA. Dynamic calibration was suggested to capture periodic variations of inflow concentrations. Also, in order to verify this proposed method in real wastewater treatment plant, measured data sets for substrate concentrations were obtained from Haeundae wastewater treatment plant and used to estimate parameters in the dynamic system. The simulation results with calibrated parameters matched well with the observed concentrations of effluent COD.

Optimal activation condition of nonpolar a-plane p-type GaN layers grown on r-plane sapphire substrates by MOCVD

NASA Astrophysics Data System (ADS)

Son, Ji-Su; Hyeon Baik, Kwang; Gon Seo, Yong; Song, Hooyoung; Hoon Kim, Ji; Hwang, Sung-Min; Kim, Tae-Geun

2011-07-01

The optimal conditions of p-type activation for nonpolar a-plane (1 1 -2 0) p-type GaN films on r-plane (1 -1 0 2) sapphire substrates with various off-axis orientations have been investigated. Secondary ion mass spectrometry (SIMS) measurements show that Mg doping concentrations of 6.58×10 19 cm -3 were maintained in GaN during epitaxial growth. The samples were activated at various temperatures and periods of time in air, oxygen (O 2) and nitrogen (N 2) gas ambient by conventional furnace annealing (CFA) and rapid thermal annealing (RTA). The activation of nonpolar a-plane p-type GaN was successful in similar annealing times and temperatures when compared with polar c-plane p-type GaN. However, activation ambient of nonpolar a-plane p-type GaN was clearly different, where a-plane p-type GaN was effectively activated in air ambient. Photoluminescence shows that the optical properties of Mg-doped a-plane GaN samples are enhanced when activated in air ambient.

Processing of AlGaAs/GaAs quantum-cascade structures for terahertz laser

NASA Astrophysics Data System (ADS)

Szerling, Anna; Kosiel, Kamil; Szymański, Michał; Wasilewski, Zbig; Gołaszewska, Krystyna; Łaszcz, Adam; Płuska, Mariusz; Trajnerowicz, Artur; Sakowicz, Maciej; Walczakowski, Michał; Pałka, Norbert; Jakieła, Rafał; Piotrowska, Anna

2015-01-01

We report research results with regard to AlGaAs/GaAs structure processing for THz quantum-cascade lasers (QCLs). We focus on the processes of Ti/Au cladding fabrication for metal-metal waveguides and wafer bonding with indium solder. Particular emphasis is placed on optimization of technological parameters for the said processes that result in working devices. A wide range of technological parameters was studied using test structures and the analysis of their electrical, optical, chemical, and mechanical properties performed by electron microscopic techniques, energy dispersive x-ray spectrometry, secondary ion mass spectroscopy, atomic force microscopy, Fourier-transform infrared spectroscopy, and circular transmission line method. On that basis, a set of technological parameters was selected for the fabrication of devices lasing at a maximum temperature of 130 K from AlGaAs/GaAs structures grown by means of molecular beam epitaxy. Their resulting threshold-current densities were on a level of 1.5 kA/cm2. Furthermore, initial stage research regarding fabrication of Cu-based claddings is reported as these are theoretically more promising than the Au-based ones with regard to low-loss waveguide fabrication for THz QCLs.

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

PubMed Central

Long, Yi; Du, Zhi-jiang; Wang, Wei-dong; Dong, Wei

2016-01-01

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems. PMID:27069353

Optimization of lamp arrangement in a closed-conduit UV reactor based on a genetic algorithm.

PubMed

Sultan, Tipu; Ahmad, Zeshan; Cho, Jinsoo

2016-01-01

The choice for the arrangement of the UV lamps in a closed-conduit ultraviolet (CCUV) reactor significantly affects the performance. However, a systematic methodology for the optimal lamp arrangement within the chamber of the CCUV reactor is not well established in the literature. In this research work, we propose a viable systematic methodology for the lamp arrangement based on a genetic algorithm (GA). In addition, we analyze the impacts of the diameter, angle, and symmetry of the lamp arrangement on the reduction equivalent dose (RED). The results are compared based on the simulated RED values and evaluated using the computational fluid dynamics simulations software ANSYS FLUENT. The fluence rate was calculated using commercial software UVCalc3D, and the GA-based lamp arrangement optimization was achieved using MATLAB. The simulation results provide detailed information about the GA-based methodology for the lamp arrangement, the pathogen transport, and the simulated RED values. A significant increase in the RED values was achieved by using the GA-based lamp arrangement methodology. This increase in RED value was highest for the asymmetric lamp arrangement within the chamber of the CCUV reactor. These results demonstrate that the proposed GA-based methodology for symmetric and asymmetric lamp arrangement provides a viable technical solution to the design and optimization of the CCUV reactor.

Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton.

PubMed

Long, Yi; Du, Zhi-Jiang; Wang, Wei-Dong; Dong, Wei

2016-01-01

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user's intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems.

An Analytic Approach for Optimal Geometrical Design of GaAs Nanowires for Maximal Light Harvesting in Photovoltaic Cells

PubMed Central

Wu, Dan; Tang, Xiaohong; Wang, Kai; Li, Xianqiang

2017-01-01

Semiconductor nanowires(NWs) with subwavelength scale diameters have demonstrated superior light trapping features, which unravel a new pathway for low cost and high efficiency future generation solar cells. Unlike other published work, a fully analytic design is for the first time proposed for optimal geometrical parameters of vertically-aligned GaAs NW arrays for maximal energy harvesting. Using photocurrent density as the light absorbing evaluation standard, 2 μm length NW arrays whose multiple diameters and periodicity are quantitatively identified achieving the maximal value of 29.88 mA/cm2 under solar illumination. It also turns out that our method has wide suitability for single, double and four different diameters of NW arrays for highest photon energy harvesting. To validate this analytical method, intensive numerical three-dimensional finite-difference time-domain simulations of the NWs’ light harvesting are also carried out. Compared with the simulation results, the predicted maximal photocurrent densities lie within 1.5% tolerance for all cases. Along with the high accuracy, through directly disclosing the exact geometrical dimensions of NW arrays, this method provides an effective and efficient route for high performance photovoltaic design. PMID:28425488

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crowder, Jeff; Cornish, Neil J.; Reddinger, J. Lucas

This work presents the first application of the method of genetic algorithms (GAs) to data analysis for the Laser Interferometer Space Antenna (LISA). In the low frequency regime of the LISA band there are expected to be tens of thousands of galactic binary systems that will be emitting gravitational waves detectable by LISA. The challenge of parameter extraction of such a large number of sources in the LISA data stream requires a search method that can efficiently explore the large parameter spaces involved. As signals of many of these sources will overlap, a global search method is desired. GAs representmore » such a global search method for parameter extraction of multiple overlapping sources in the LISA data stream. We find that GAs are able to correctly extract source parameters for overlapping sources. Several optimizations of a basic GA are presented with results derived from applications of the GA searches to simulated LISA data.« less

Design of CGMP Production of 18F- and 68Ga-Radiopharmaceuticals

PubMed Central

Chu, Pei-Chun; Chao, Hao-Yu; Shieh, Wei-Chen; Chen, Chuck C.

2014-01-01

Objective. Radiopharmaceutical production process must adhere to current good manufacturing process (CGMP) compliance to ensure the quality of precursor, prodrug (active pharmaceutical ingredient, API), and the final drug product that meet acceptance criteria. We aimed to develop an automated system for production of CGMP grade of PET radiopharmaceuticals. Methods. The hardware and software of the automated synthesizer that fit in the hot cell under cGMP requirement were developed. Examples of production yield and purity for 68Ga-DOTATATE and 18F-FDG at CGMP facility were optimized. Analytical assays and acceptance criteria for cGMP grade of 68Ga-DOTATATE and 18F-FDG were established. Results. CGMP facility for the production of PET radiopharmaceuticals has been established. Radio-TLC and HPLC analyses of 68Ga-DOTATATE and 18F-FDG showed that the radiochemical purity was 92% and 96%, respectively. The products were sterile and pyrogenic-free. Conclusion. CGMP compliance of radiopharmaceuticals has been reviewed. 68Ga-DOTATATE and 18F-FDG were synthesized with high radiochemical yield under CGMP process. PMID:25276810

Growth of InP, InGaAs, and InGaAsP on InP by gas-source molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Asonen, H.; Rakennus, K.; Tappura, K.; Hovinen, M.; Pessa, M.

1990-10-01

Gas-source molecular beam epitaxy (GSMBE), designating the method where the group III beams are derived from the evaporation of solid materials while the group V beams are derived from the high-temperature cracking of AsH 3 and PH 3, is a very promising method. We show in this work that using indium of high purity and optimizing the growth conditions, unintentional impurities in these films prepared by GSMBE can be reduced to a level comparable to that obtained by all-vapor-source chemical beam epitaxy (CBE). The films grown by GSMBE are of very high quality, as deduced from the measurements of electrical, optical, and structural properties. Furthermore, we have found that the alloy composition in InGaAsP for the wavelength λ of 1.1 μm changes significantly in a range of growth temperature from 525 to 530°C, likely due to an abrupt change in the sticking probability of phosphorus. We have also found that the phosphorus-to-gallium flux ratio strongly affects surface morphology of InGaAsP for λ = 1.3 μm.

Arrangement Analysis of Leaves Optimized on Photon Flux Density or Photosynthetic Rate

NASA Astrophysics Data System (ADS)

Obara, Shin'ya; Tanno, Itaru

By clarifying a plant evolutive process, useful information may be obtained on engineering. Consequently, an analysis algorithm that investigates the optimal arrangement of plant leaves was developed. In the developed algorithm, the Monte Carlo method is introduced and sunlight is simulated. Moreover, the arrangement optimization of leaves is analyzed using a Genetic Algorithm (GA). The number of light quanta (photon flux density) that reaches leaves, or the average photosynthetic rate of the same was set as the objective function, and leaf models of a dogwood and a ginkgo tree were analyzed. The number of leaf models was set between two to four, and the position of the leaf was expressed in terms of the angle of direction, elevation angle, rotation angle, and the representative length of the branch of a leaf. The chromosome model introduced into GA consists of information concerning the position of the leaf. Based on the analysis results, the characteristics of the leaf of an actual plant could be simulated by ensuring the algorithm had multiple constrained conditions. The optimal arrangement of leaves differs in maximization of the photon flux density, and that of the average value of a photosynthetic rate. Furthermore, the leaf form affecting the optimal arrangement of leave and also having a significant influence also on a photosynthetic rate was shown.

Gallium-68-labelled NOTA-oligonucleotides: an optimized method for their preparation.

PubMed

Gijs, Marlies; Dammicco, Sylvestre; Warnier, Corentin; Aerts, An; Impens, Nathalie R E N; D'Huyvetter, Matthias; Léonard, Marc; Baatout, Sarah; Luxen, André

2016-02-01

One of the most essential aspects to the success of radiopharmaceuticals is an easy and reliable radiolabelling protocol to obtain pure and stable products. In this study, we optimized the bioconjugation and gallium-68 ((68) Ga) radiolabelling conditions for a single-stranded 40-mer DNA oligonucleotide, in order to obtain highly pure and stable radiolabelled oligonucleotides. Quantitative bioconjugation was obtained for a disulfide-functionalized oligonucleotide conjugated to the macrocylic bifunctional chelator MMA-NOTA (maleimido-mono-amide (1,4,7-triazanonane-1,4,7-triyl)triacetic acid). Next, this NOTA-oligonucleotide bioconjugate was radiolabelled at room temperature with purified and pre-concentrated (68) Ga with quantitative levels of radioactive incorporation and high radiochemical and chemical purity. In addition, high chelate stability was observed in physiological-like conditions (37 °C, PBS and serum), in the presence of a transchelator (EDTA) and transferrin. A specific activity of 51.1 MBq/nmol was reached using a 1470-fold molar excess bioconjugate over (68) Ga. This study presents a fast, straightforward and reliable protocol for the preparation of (68) Ga-radiolabelled DNA oligonucleotides under mild reaction conditions and without the use of organic solvents. The methodology herein developed will be applied to the preparation of oligonucleotidic sequences (aptamers) targeting the human epidermal growth factor receptor 2 (HER2) for cancer imaging. Copyright © 2015 John Wiley & Sons, Ltd.

Decision making for best cogeneration power integration into a grid

NASA Astrophysics Data System (ADS)

Al Asmar, Joseph; Zakhia, Nadim; Kouta, Raed; Wack, Maxime

2016-07-01

Cogeneration systems are known to be efficient power systems for their ability to reduce pollution. Their integration into a grid requires simultaneous consideration of the economic and environmental challenges. Thus, an optimal cogeneration power are adopted to face such challenges. This work presents a novelty in selectinga suitable solution using heuristic optimization method. Its aim is to optimize the cogeneration capacity to be installed according to the economic and environmental concerns. This novelty is based on the sensitivity and data analysis method, namely, Multiple Linear Regression (MLR). This later establishes a compromise between power, economy, and pollution, which leads to find asuitable cogeneration power, and further, to be integrated into a grid. The data exploited were the results of the Genetic Algorithm (GA) multi-objective optimization. Moreover, the impact of the utility's subsidy on the selected power is shown.

Simulation of the Mars Surface Solar Spectra for Optimized Performance of Triple-Junction Solar Cells

NASA Technical Reports Server (NTRS)

Edmondson, Kenneth M.; Joslin, David E.; Fetzer, Chris M.; King, RIchard R.; Karam, Nasser H.; Mardesich, Nick; Stella, Paul M.; Rapp, Donald; Mueller, Robert

2007-01-01

The unparalleled success of the Mars Exploration Rovers (MER) powered by GaInP/GaAs/Ge triple-junction solar cells has demonstrated a lifetime for the rovers that exceeded the baseline mission duration by more than a factor of five. This provides confidence in future longer-term solar powered missions on the surface of Mars. However, the solar cells used on the rovers are not optimized for the Mars surface solar spectrum, which is attenuated at shorter wavelengths due to scattering by the dusty atmosphere. The difference between the Mars surface spectrum and the AM0 spectrum increases with solar zenith angle and optical depth. The recent results of a program between JPL and Spectrolab to optimize GaInP/GaAs/Ge solar cells for Mars are presented. Initial characterization focuses on the solar spectrum at 60-degrees zenith angle at an optical depth of 0.5. The 60-degree spectrum is reduced to 1/6 of the AM0 intensity and is further reduced in the blue portion of the spectrum. JPL has modeled the Mars surface solar spectra, modified an X-25 solar simulator, and completed testing of Mars-optimized solar cells previously developed by Spectrolab with the modified X-25 solar simulator. Spectrolab has focused on the optimization of the higher efficiency Ultra Triple-Junction (UTJ) solar cell for Mars. The attenuated blue portion of the spectrum requires the modification of the top sub-cell in the GaInP/GaAs/Ge solar cell for improved current balancing in the triple-junction cell. Initial characterization confirms the predicted increase in power and current matched operation for the Mars surface 60-degree zenith angle solar spectrum.

Heterojunction solar cell with 6% efficiency based on an n-type aluminum-gallium-oxide thin film and p-type sodium-doped Cu2O sheet

NASA Astrophysics Data System (ADS)

Minami, Tadatsugu; Nishi, Yuki; Miyata, Toshihiro

2015-02-01

In this paper, we describe efforts to enhance the efficiency of Cu2O-based heterojunction solar cells fabricated with an aluminum-gallium-oxide (Al-Ga-O) thin film as the n-type layer and a p-type sodium (Na)-doped Cu2O (Cu2O:Na) sheet prepared by thermally oxidizing copper sheets. The optimal Al content [X; Al/(Ga + Al) atomic ratio] of an AlX-Ga1-X-O thin-film n-type layer was found to be approximately 2.5 at. %. The optimized resistivity was approximately 15 Ω cm for n-type AlX-Ga1-X-O/p-type Cu2O:Na heterojunction solar cells. A MgF2/AZO/Al0.025-Ga0.975-O/Cu2O:Na heterojunction solar cell with 6.1% efficiency was fabricated using a 60-nm-thick n-type oxide thin-film layer and a 0.2-mm-thick Cu2O:Na sheet with the optimized resistivity.

Ultrafast mid-infrared spectroscopy by chirped pulse upconversion in 1800-1000cm(-1) region.

PubMed

Zhu, Jingyi; Mathes, Tilo; Stahl, Andreas D; Kennis, John T M; Groot, Marie Louise

2012-05-07

Broadband femtosecond mid-infrared pulses can be converted into the visible spectral region by chirped pulse upconversion. We report here the upconversion of pump probe transient signals in the frequency region below 1800cm(-1), using the nonlinear optical crystal AgGaGeS4, realizing an important expansion of the application range of this method. Experiments were demonstrated with a slab of GaAs, in which the upconverted signals cover a window of 120cm(-1), with 1.5cm(-1) resolution. In experiments on the BLUF photoreceptor Slr1694, signals below 1 milliOD were well resolved after baseline correction. Possibilities for further optimization of the method are discussed. We conclude that this method is an attractive alternative for the traditional MCT arrays used in most mid-infrared pump probe experiments.

Two-machine flow shop scheduling integrated with preventive maintenance planning

NASA Astrophysics Data System (ADS)

Wang, Shijin; Liu, Ming

2016-02-01

This paper investigates an integrated optimisation problem of production scheduling and preventive maintenance (PM) in a two-machine flow shop with time to failure of each machine subject to a Weibull probability distribution. The objective is to find the optimal job sequence and the optimal PM decisions before each job such that the expected makespan is minimised. To investigate the value of integrated scheduling solution, computational experiments on small-scale problems with different configurations are conducted with total enumeration method, and the results are compared with those of scheduling without maintenance but with machine degradation, and individual job scheduling combined with independent PM planning. Then, for large-scale problems, four genetic algorithm (GA) based heuristics are proposed. The numerical results with several large problem sizes and different configurations indicate the potential benefits of integrated scheduling solution and the results also show that proposed GA-based heuristics are efficient for the integrated problem.

An FBG acoustic emission source locating system based on PHAT and GA

NASA Astrophysics Data System (ADS)

Shen, Jing-shi; Zeng, Xiao-dong; Li, Wei; Jiang, Ming-shun

2017-09-01

Using the acoustic emission locating technology to monitor the health of the structure is important for ensuring the continuous and healthy operation of the complex engineering structures and large mechanical equipment. In this paper, four fiber Bragg grating (FBG) sensors are used to establish the sensor array to locate the acoustic emission source. Firstly, the nonlinear locating equations are established based on the principle of acoustic emission, and the solution of these equations is transformed into an optimization problem. Secondly, time difference extraction algorithm based on the phase transform (PHAT) weighted generalized cross correlation provides the necessary conditions for the accurate localization. Finally, the genetic algorithm (GA) is used to solve the optimization model. In this paper, twenty points are tested in the marble plate surface, and the results show that the absolute locating error is within the range of 10 mm, which proves the accuracy of this locating method.

Development of Salvianolic acid B-Tanshinone II A-Glycyrrhetinic acid compound liposomes: formulation optimization and its effects on proliferation of hepatic stellate cells.

PubMed

Lin, Jiahao; Wang, Xiuli; Wu, Qing; Dai, Jundong; Guan, Huida; Cao, Weiyi; He, Liangying; Wang, Yurong

2014-02-28

The aim of this study was to systematically optimize and characterize the co-encapsulation process of Salvianolic acid B (Sal B), Tanshinone II A (TSN) and Glycyrrhetinic acid (GA) into liposomes. The liposomes (GTS-lip) were prepared using film hydration method combined with probe sonication to encapsulate two hydrophobic components (TSN and GA), and using pH gradient method to load hydrophilic component Sal B. The concentration of encapsulated drugs was measured by a reversed phase high performance liquid chromatography (RP-HPLC) method. Systematic optimization of encapsulation process was performed using single factor test, orthogonal test in combination with Box-Behnken Design. Optimum conditions are as follows: ratio of GA to lipid (w/w)=0.08, ratio of Sal B to lipid (w/w)=0.12 and pH of buffer=3.3. Based on the conditions mentioned above, encapsulation efficiency of Sal B, TSN and GA reached target levels: (96.03 ± 0.28)%, (80.63 ± 0.91)% and (88.56 ± 0.17)%, respectively. The GTS-lip had a unimodal size-distribution and a mean diameter of 191.3 ± 6.31 nm. Morphology determination of the GTS-lip indicated that the liposomes were spherical, and there was no free drug crystal in the visual field of transmission electron microscopy. Also, the ζ potential of GTS-lip was detected to be -11.6 ± 0.35 mV. In vitro release investigation of GTS-lip suggested that the release rate of GTS-lip significantly decreased compared to drug solution. The accumulative release percentage of TSN, GA and Sal B were 10% in 36 h, 4% in 36 h and 77% in 24 h. Meanwhile, GTS-lip exhibited definite activity on proliferative inhibition of hepatic stellate cells (HSC). GTS-lip decreased the viability of the HSC to higher than 75% at two high drug concentration groups in 24h. At the same time, GTS-lip of two low drug concentration groups increased the inhibition rates by 2.3 folds and 1.9 folds separately at 48 h compared to 24h. By contrast, inhibition activity of G-T-S solution group showed less change between 48 h and 24 h. The prolonged and enhanced activity in 48 h which GTS-lip group manifested might contribute to its sustained release effect. Copyright © 2014 Elsevier B.V. All rights reserved.

Effective coupled optoelectrical design method for fully infiltrated semiconductor nanowires based hybrid solar cells.

PubMed

Wu, Dan; Tang, Xiaohong; Wang, Kai; Li, Xianqiang

2016-10-31

We present a novel coupled design method that both optimizes light absorption and predicts electrical performance of fully infiltrated inorganic semiconductor nanowires (NWs) based hybrid solar cells (HSC). This method provides a thorough insight of hybrid photovoltaic process as a function of geometrical parameters of NWs. An active layer consisting of GaAs NWs as acceptor and poly(3-hexylthiophene-2,5-diyl) (P3HT) as donor were used as a design example. Absorption spectra features were studied by the evolution of the leaky modes and Fabry-Perot resonance with wavelength focusing firstly on the GaAs/air layer before extending to GaAs/P3HT hybrid active layer. The highest absorption efficiency reached 39% for the hybrid active layer of 2 μm thickness under AM 1.5G illumination. Combined with the optical absorption analysis, our method further codesigns the energy harvesting to predict electrical performance of HSC considering exciton dissociation efficiencies within both inorganic NWs and a polymeric shell of 20 nm thickness. The validity of the simulation model was also proved by the well agreement of the simulation results with the published experimental work indicating an effective guidance for future high performance HSC design.

Photovoltaic Properties of Selenized CuGa/In Films with Varied Compositions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muzzillo, Christopher P.; Mansfield, Lorelle M.; Ramanathan, Kannan

2016-11-21

Thin CuGa/In films with varied compositions were deposited by co-evaporation and then selenized in situ with evaporated selenium. The selenized Cu(In, Ga)Se2 absorbers were used to fabricate 390 solar cells. Cu/(Ga+In) and Ga/(Ga+In) (Cu/III and Ga/III) were independently varied, and photovoltaic performance was optimal at Cu/III of 77-92% for all Ga/III compositions studied (Ga/III ~ 30, 50, and 70%). The best absorbers at each Ga/III composition were characterized with time-resolved photoluminescence, scanning electron microscopy, and secondary ion mass spectrometry, and devices were studied with temperature-dependent current density-voltage, light and electrical biased quantum efficiency, and capacitance-voltage. The best cells with Ga/IIImore » ~ 30, 50, and 70% had efficiencies of 14.5, 14.4, and 12.2% and maximum power temperature coefficients of -0.496, -0.452, and -0.413%/degrees C, respectively. This resulted in the Ga/III ~ 50% champion having the highest efficiency at temperatures greater than 40 degrees C, making it the optimal composition for practical purposes. This optimum is understood as a result of the absorber's band gap grading- where minimum band gap dominates short-circuit current density, maximum space charge region band gap dominates open-circuit voltage, and average absorber band gap dominates maximum power temperature coefficient.« less

Paroxysmal atrial fibrillation prediction method with shorter HRV sequences.

PubMed

Boon, K H; Khalil-Hani, M; Malarvili, M B; Sia, C W

2016-10-01

This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Emission and material gain spectra of polar compressive strained AlGaN quantum wells grown on virtual AlGaN substrates: Tuning emission wavelength and mixing TE and TM mode of light polarization

NASA Astrophysics Data System (ADS)

Gladysiewicz, Marta; Rudzinski, Mariusz; Hommel, Detlef; Kudrawiec, Robert

2018-07-01

It is shown that compressively strained polar AlxGa1‑xN/AlyGa1‑yN quantum wells (QWs) of various contents grown on virtual AlYGa1‑YN substrates (Y = 20, 40, 60, 80, and 100%) are able to cover the whole UV-A, -B, and -C spectral range but their contents and widths have to be carefully optimized if they are to be used as the active region of light emitting diodes and laser diodes. The emission wavelength from AlGaN multi QWs can be tuned by both the QW width and barrier thickness, but the range of QW width for which an efficient luminescence is expected is very small (2–4 nm) due to a very weak electron-hole overlap for wider QWs. The most effective method for wavelength tuning in this QW system is content engineering, i.e., lowering Al concentration in the QW region. The decrease of Al concentration in the QW shifts the emission peak to red, broadens this peak, weakens its intensity, and changes its polarization from transverse magnetic (TM) to TM mixed with transverse electric (TE). For laser diodes the optimal QW design is more rigorous concerning the QW width since this width should be below 3 nm. Moreover it is shown that the TE and TM mode of materials gain overlap and are strongly blueshifted in comparison to emission spectrum.

Preclinical Study of 68Ga-DOTATOC: Biodistribution Assessment in Syrian Rats and Evaluation of Absorbed Dose in Human Organs.

PubMed

Naderi, Mojdeh; Zolghadri, Samaneh; Yousefnia, Hassan; Ramazani, Ali; Jalilian, Amir Reza

2016-01-01

Gallium-68 DOTA-DPhe 1 -Tyr 3 -Octreotide ( 68 Ga-DOTATOC) has been applied by several European centers for the treatment of a variety of human malignancies. Nevertheless, definitive dosimetric data are yet unavailable. According to the Society of Nuclear Medicine and Molecular Imaging, researchers are investigating the safety and efficacy of this radiotracer to meet Food and Drug Administration requirements. The aim of this study was to introduce the optimized procedure for 68 Ga-DOTATOC preparation, using a novel germanium-68 ( 68 Ge)/ 68 Ga generator in Iran and evaluate the absorbed doses in numerous organs with high accuracy. The optimized conditions for preparing the radiolabeled complex were determined via several experiments by changing the ligand concentration, pH, temperature and incubation time. Radiochemical purity of the complex was assessed, using high-performance liquid chromatography and instant thin-layer chromatography. The absorbed dose of human organs was evaluated, based on biodistribution studies on Syrian rats via Radiation Absorbed Dose Assessment Resource Method. 68 Ga-DOTATOC was prepared with radiochemical purity of >98% and specific activity of 39.6 MBq/nmol. The complex demonstrated great stability at room temperature and in human serum at 37°C at least two hours after preparation. Significant uptake was observed in somatostatin receptor-positive tissues such as pancreatic and adrenal tissues (12.83 %ID/g and 0.91 %ID/g, respectively). Dose estimations in human organs showed that the pancreas, kidneys and adrenal glands received the maximum absorbed doses (0.105, 0.074 and 0.010 mGy/MBq, respectively). Also, the effective absorbed dose was estimated at 0.026 mSv/MBq for 68 Ga-DOTATOC. The obtained results showed that 68 Ga-DOTATOC can be considered as an effective agent for clinical PET imaging in Iran.

One-step Conjugation of Glycyrrhetinic Acid to Cationic Polymers for High-performance Gene Delivery to Cultured Liver Cell.

PubMed

Cong, Yue; Shi, Bingyang; Lu, Yiqing; Wen, Shihui; Chung, Roger; Jin, Dayong

2016-02-23

Gene therapies represent a promising therapeutic route for liver cancers, but major challenges remain in the design of safe and efficient gene-targeting delivery systems. For example, cationic polymers show good transfection efficiency as gene carriers, but are hindered by cytotoxicity and non-specific targeting. Here we report a versatile method of one-step conjugation of glycyrrhetinic acid (GA) to reduce cytotoxicity and improve the cultured liver cell -targeting capability of cationic polymers. We have explored a series of cationic polymer derivatives by coupling different ratios of GA to polypropylenimine (PPI) dendrimer. These new gene carriers (GA-PPI dendrimer) were systematically characterized by UV-vis,(1)H NMR titration, electron microscopy, zeta potential, dynamic light-scattering, gel electrophoresis, confocal microscopy and flow cytometry. We demonstrate that GA-PPI dendrimers can efficiently load and protect pDNA, via formation of nanostructured GA-PPI/pDNA polyplexes. With optimal GA substitution degree (6.31%), GA-PPI dendrimers deliver higher liver cell transfection efficiency (43.5% vs 22.3%) and lower cytotoxicity (94.3% vs 62.5%, cell viability) than the commercial bench-mark DNA carrier bPEI (25 kDa) with cultured liver model cells (HepG2). There results suggest that our new GA-PPI dendrimer are a promising candidate gene carrier for targeted liver cancer therapy.

One-step Conjugation of Glycyrrhetinic Acid to Cationic Polymers for High-performance Gene Delivery to Cultured Liver Cell

PubMed Central

Cong, Yue; Shi, Bingyang; Lu, Yiqing; Wen, Shihui; Chung, Roger; Jin, Dayong

2016-01-01

Gene therapies represent a promising therapeutic route for liver cancers, but major challenges remain in the design of safe and efficient gene-targeting delivery systems. For example, cationic polymers show good transfection efficiency as gene carriers, but are hindered by cytotoxicity and non-specific targeting. Here we report a versatile method of one-step conjugation of glycyrrhetinic acid (GA) to reduce cytotoxicity and improve the cultured liver cell -targeting capability of cationic polymers. We have explored a series of cationic polymer derivatives by coupling different ratios of GA to polypropylenimine (PPI) dendrimer. These new gene carriers (GA-PPI dendrimer) were systematically characterized by UV-vis,1H NMR titration, electron microscopy, zeta potential, dynamic light-scattering, gel electrophoresis, confocal microscopy and flow cytometry. We demonstrate that GA-PPI dendrimers can efficiently load and protect pDNA, via formation of nanostructured GA-PPI/pDNA polyplexes. With optimal GA substitution degree (6.31%), GA-PPI dendrimers deliver higher liver cell transfection efficiency (43.5% vs 22.3%) and lower cytotoxicity (94.3% vs 62.5%, cell viability) than the commercial bench-mark DNA carrier bPEI (25kDa) with cultured liver model cells (HepG2). There results suggest that our new GA-PPI dendrimer are a promising candidate gene carrier for targeted liver cancer therapy. PMID:26902258

Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms

NASA Astrophysics Data System (ADS)

Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming

2008-11-01

An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.

Microbial Production of Glyceric Acid, an Organic Acid That Can Be Mass Produced from Glycerol ▿ †

PubMed Central

Habe, Hiroshi; Shimada, Yuko; Yakushi, Toshiharu; Hattori, Hiromi; Ano, Yoshitaka; Fukuoka, Tokuma; Kitamoto, Dai; Itagaki, Masayuki; Watanabe, Kunihiro; Yanagishita, Hiroshi; Matsushita, Kazunobu; Sakaki, Keiji

2009-01-01

Glyceric acid (GA), an unfamiliar biotechnological product, is currently produced as a small by-product of dihydroxyacetone production from glycerol by Gluconobacter oxydans. We developed a method for the efficient biotechnological production of GA as a target compound for new surplus glycerol applications in the biodiesel and oleochemical industries. We investigated the ability of 162 acetic acid bacterial strains to produce GA from glycerol and found that the patterns of productivity and enantiomeric GA compositions obtained from several strains differed significantly. The growth parameters of two different strain types, Gluconobacter frateurii NBRC103465 and Acetobacter tropicalis NBRC16470, were optimized using a jar fermentor. G. frateurii accumulated 136.5 g/liter of GA with a 72% d-GA enantiomeric excess (ee) in the culture broth, whereas A. tropicalis produced 101.8 g/liter of d-GA with a 99% ee. The 136.5 g/liter of glycerate in the culture broth was concentrated to 236.5 g/liter by desalting electrodialysis during the 140-min operating time, and then, from 50 ml of the concentrated solution, 9.35 g of GA calcium salt was obtained by crystallization. Gene disruption analysis using G. oxydans IFO12528 revealed that the membrane-bound alcohol dehydrogenase (mADH)-encoding gene (adhA) is required for GA production, and purified mADH from G. oxydans IFO12528 catalyzed the oxidation of glycerol. These results strongly suggest that mADH is involved in GA production by acetic acid bacteria. We propose that GA is potentially mass producible from glycerol feedstock by a biotechnological process. PMID:19837846

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

PubMed Central

Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C. K. M.; Mishra, B. N.

2015-01-01

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500. PMID:26368924

Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

PubMed

Pathak, Lakshmi; Singh, Vineeta; Niwas, Ram; Osama, Khwaja; Khan, Saif; Haque, Shafiul; Tripathi, C K M; Mishra, B N

2015-01-01

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

Poly (Methyl Methacrylate) (PMMA) and Polyactic Acid Nanoparticles as Adjuvents for Peroral Vaccines

DTIC Science & Technology

1999-06-01

pH 7.4) 10 2 PMMA DC GA adsorbed Miglyol 10 3 PMMA DC GA adsorbed Paraffine 10 4 PMMA DC GA adsorbed Olive oil 10 5 PMMA DC GA adsorbed...polyethylenglycole, paraffin, miglyol and phosphate buffered saline. - 17 - 6.2. Testing of the Optimized Vaccine Preparations The vaccine preparations

Closing loop base pairs in RNA loop-loop complexes: structural behavior, interaction energy and solvation analysis through molecular dynamics simulations.

PubMed

Golebiowski, Jérôme; Antonczak, Serge; Fernandez-Carmona, Juan; Condom, Roger; Cabrol-Bass, Daniel

2004-12-01

Nanosecond molecular dynamics using the Ewald summation method have been performed to elucidate the structural and energetic role of the closing base pair in loop-loop RNA duplexes neutralized by Mg2+ counterions in aqueous phases. Mismatches GA, CU and Watson-Crick GC base pairs have been considered for closing the loop of an RNA in complementary interaction with HIV-1 TAR. The simulations reveal that the mismatch GA base, mediated by a water molecule, leads to a complex that presents the best compromise between flexibility and energetic contributions. The mismatch CU base pair, in spite of the presence of an inserted water molecule, is too short to achieve a tight interaction at the closing-loop junction and seems to force TAR to reorganize upon binding. An energetic analysis has allowed us to quantify the strength of the interactions of the closing and the loop-loop pairs throughout the simulations. Although the water-mediated GA closing base pair presents an interaction energy similar to that found on fully geometry-optimized structure, the water-mediated CU closing base pair energy interaction reaches less than half the optimal value.

High Performance 50 nm InAlAs/In0.75GaAs Metamorphic High Electron Mobility Transistors with Si3N4 Passivation on Thin InGaAs Layer

NASA Astrophysics Data System (ADS)

Yeon, Seongjin; Seo, Kwangseok

2008-04-01

We fabricated 50 nm InAlAs/InGaAs metamorphic high electron mobility transistors (HEMTs) with a very thin barrier. Through the reduction of the gate-channel distance (dGC) in the epitaxial structure, a channel aspect ratio (ARC) of over three was achieved when Lg was 50 nm. We inserted a thin InGaAs layer as a protective layer, and tested various gate structures to reduce surface problems induced by barrier shrinkage and to optimize the device characteristics. Through the optimization of the gate structure with the thin InGaAs layer, the fabricated 50 nm metamorphic HEMT exhibited high DC and RF characteristics, Gm of 1.5 S/mm, and fT of 490 GHz.

Prediction and Optimization of Key Performance Indicators in the Production of Stator Core Using a GA-NN Approach

NASA Astrophysics Data System (ADS)

Rajora, M.; Zou, P.; Xu, W.; Jin, L.; Chen, W.; Liang, S. Y.

2017-12-01

With the rapidly changing demands of the manufacturing market, intelligent techniques are being used to solve engineering problems due to their ability to handle nonlinear complex problems. For example, in the conventional production of stator cores, it is relied upon experienced engineers to make an initial plan on the number of compensation sheets to be added to achieve uniform pressure distribution throughout the laminations. Additionally, these engineers must use their experience to revise the initial plans based upon the measurements made during the production of stator core. However, this method yields inconsistent results as humans are incapable of storing and analysing large amounts of data. In this article, first, a Neural Network (NN), trained using a hybrid Levenberg-Marquardt (LM) - Genetic Algorithm (GA), is developed to assist the engineers with the decision-making process. Next, the trained NN is used as a fitness function in an optimization algorithm to find the optimal values of the initial compensation sheet plan with the aim of minimizing the required revisions during the production of the stator core.

Mid-infrared emission and Judd-Ofelt analysis of Dy3+-doped infrared Ga-Sb-S and Ga-Sb-S-PbI2 chalcohalide glasses

NASA Astrophysics Data System (ADS)

Guo, Jixiao; Jiao, Qing; He, Xiaolong; Guo, Hansong; Tong, Jianghao; Zhang, Zhihang; Jiang, Fuchao; Wang, Guoxiang

2018-03-01

Dy3+-doped Ga-Sb-S and Ga-Sb-S-PbI2 chalcohalide glasses were prepared by traditional melt quenching method. The effect of halide PbI2 on the physical and optical properties of Dy3+ ions was investigated. The density and ionic concentration of the host sample increased with the introduction of PbI2 halides, whereas the refractive index at 1.55 μm decreased. The Judd-Ofelt parameters showed that Ω2 increased in PbI2-modified glass, whereas the Ω6 value showed the opposite tendency. Infrared emission spectrum also showed that the intensity increased with PbI2 addition, and considerable enhancement at 2.8 μm was observed in the mid-infrared region. The halide PbI2 promoted the reduction of phonon energy of the host and the improvement of the laser pump efficiency, which led to the construction of optimized infrared glass materials for optical applications.

Imaging TiO2 nanoparticles on GaN nanowires with electrostatic force microscopy

NASA Astrophysics Data System (ADS)

Xie, Ting; Wen, Baomei; Liu, Guannan; Guo, Shiqi; Motayed, Abhishek; Murphy, Thomas; Gomez, R. D.

Gallium nitride (GaN) nanowires that are functionalized with metal-oxides nanoparticles have been explored extensively for gas sensing applications in the past few years. These sensors have several advantages over conventional schemes, including miniature size, low-power consumption and fast response and recovery times. The morphology of the oxide functionalization layer is critical to achieve faster response and recovery times, with the optimal size distribution of nanoparticles being in the range of 10 to 30 nm. However, it is challenging to characterize these nanoparticles on GaN nanowires using common techniques such as scanning electron microscopy, transmission electron microscopy, and x-ray diffraction. Here, we demonstrate electrostatic force microscopy in combination with atomic force microscopy as a non-destructive technique for morphological characterization of the dispersed TiO2 nanoparticles on GaN nanowires. We also discuss the applicability of this method to other material systems with a proposed tip-surface capacitor model. This project was sponsored through N5 Sensors and the Maryland Industrial Partnerships (MIPS, #5418).

High-Temperature Growth of GaN and Al x Ga1- x N via Ammonia-Based Metalorganic Molecular-Beam Epitaxy

NASA Astrophysics Data System (ADS)

Billingsley, Daniel; Henderson, Walter; Doolittle, W. Alan

2010-05-01

The effect of high-temperature growth on the crystalline quality and surface morphology of GaN and Al x Ga1- x N grown by ammonia-based metalorganic molecular-beam epitaxy (NH3-MOMBE) has been investigated as a means of producing atomically smooth films suitable for device structures. The effects of V/III ratio on the growth rate and surface morphology are described herein. The crystalline quality of both GaN and AlGaN was found to mimic that of the GaN templates, with (002) x-ray diffraction (XRD) full-widths at half- maximum (FWHMs) of ~350 arcsec. Nitrogen-rich growth conditions have been found to provide optimal surface morphologies with a root-mean-square (RMS) roughness of ~0.8 nm, yet excessive N-rich environments have been found to reduce the growth rate and result in the formation of faceted surface pitting. AlGaN exhibits a decreased growth rate, as compared with GaN, due to increased N recombination as a result of the increased pyrolysis of NH3 in the presence of Al. AlGaN films grown directly on GaN templates exhibited Pendellösung x-ray fringes, indicating an abrupt interface and a planar AlGaN film. AlGaN films grown for this study resulted in an optimal RMS roughness of ~0.85 nm with visible atomic steps.

Optimal design of solidification processes

NASA Technical Reports Server (NTRS)

Dantzig, Jonathan A.; Tortorelli, Daniel A.

1991-01-01

An optimal design algorithm is presented for the analysis of general solidification processes, and is demonstrated for the growth of GaAs crystals in a Bridgman furnace. The system is optimal in the sense that the prespecified temperature distribution in the solidifying materials is obtained to maximize product quality. The optimization uses traditional numerical programming techniques which require the evaluation of cost and constraint functions and their sensitivities. The finite element method is incorporated to analyze the crystal solidification problem, evaluate the cost and constraint functions, and compute the sensitivities. These techniques are demonstrated in the crystal growth application by determining an optimal furnace wall temperature distribution to obtain the desired temperature profile in the crystal, and hence to maximize the crystal's quality. Several numerical optimization algorithms are studied to determine the proper convergence criteria, effective 1-D search strategies, appropriate forms of the cost and constraint functions, etc. In particular, we incorporate the conjugate gradient and quasi-Newton methods for unconstrained problems. The efficiency and effectiveness of each algorithm is presented in the example problem.

High energy proton radiation damage to (AlGa)As-G aAs solar cells

NASA Technical Reports Server (NTRS)

Loo, R.; Goldhammer, L.; Kamath, S.; Knechtli, R. C.

1979-01-01

Twelve 2 + 2 sq cm (AlGa)As-GaAs solar cells were fabricated and were subjected to 15.4 and 40 MeV of proton irradiation. The results showed that the GaAs cells degrade considerably less than do conventional and developmental K7 silicon cells. The detailed characteristics of the GaAs and silicon cells, both before and after irradiation, are described. Further optimization of the GaAs cells seems feasible, and areas for future work are suggested.

Medium optimization for pyrroloquinoline quinone (PQQ) production by Methylobacillus sp. zju323 using response surface methodology and artificial neural network-genetic algorithm.

PubMed

Wei, Peilian; Si, Zhenjun; Lu, Yao; Yu, Qingfei; Huang, Lei; Xu, Zhinan

2017-08-09

Methylobacillus sp. zju323 was adopted to improve the biosynthesis of pyrroloquinoline quinone (PQQ) by systematic optimization of the fermentation medium. The Plackett-Burman design was implemented to screen for the key medium components for the PQQ production. CoCl 2  · 6H 2 O, ρ-amino benzoic acid, and MgSO 4  · 7H 2 O were found capable of enhancing the PQQ production most significantly. A five-level three-factor central composite design was used to investigate the direct and interactive effects of these variables. Both response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) were used to predict the PQQ production and to optimize the medium composition. The results showed that the medium optimized by ANN-GA was better than that by RSM in maximizing PQQ production and the experimental PQQ concentration in the ANN-GA-optimized medium was improved by 44.3% compared with that in the unoptimized medium. Further study showed that this ANN-GA-optimized medium was also effective in improving PQQ production by fed-batch mode, reaching the highest PQQ accumulation of 232.0 mg/L, which was about 47.6% increase relative to that in the original medium. The present work provided an optimized medium and developed a fed-batch strategy which might be potentially applicable in industrial PQQ production.

Response surface modeling of boron adsorption from aqueous solution by vermiculite using different adsorption agents: Box-Behnken experimental design.

PubMed

Demirçivi, Pelin; Saygılı, Gülhayat Nasün

2017-07-01

In this study, a different method was applied for boron removal by using vermiculite as the adsorbent. Vermiculite, which was used in the experiments, was not modified with adsorption agents before boron adsorption using a separate process. Hexadecyltrimethylammonium bromide (HDTMA) and Gallic acid (GA) were used as adsorption agents for vermiculite by maintaining the solid/liquid ratio at 12.5 g/L. HDTMA/GA concentration, contact time, pH, initial boron concentration, inert electrolyte and temperature effects on boron adsorption were analyzed. A three-factor, three-level Box-Behnken design model combined with response surface method (RSM) was employed to examine and optimize process variables for boron adsorption from aqueous solution by vermiculite using HDTMA and GA. Solution pH (2-12), temperature (25-60 °C) and initial boron concentration (50-8,000 mg/L) were chosen as independent variables and coded x 1 , x 2 and x 3 at three levels (-1, 0 and 1). Analysis of variance was used to test the significance of variables and their interactions with 95% confidence limit (α = 0.05). According to the regression coefficients, a second-order empirical equation was evaluated between the adsorption capacity (q i ) and the coded variables tested (x i ). Optimum values of the variables were also evaluated for maximum boron adsorption by vermiculite-HDTMA (HDTMA-Verm) and vermiculite-GA (GA-Verm).

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

Sun, Tao; Xu, Ming-Hai

2017-01-01

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

Focusing of light through turbid media by curve fitting optimization

NASA Astrophysics Data System (ADS)

Gong, Changmei; Wu, Tengfei; Liu, Jietao; Li, Huijuan; Shao, Xiaopeng; Zhang, Jianqi

2016-12-01

The construction of wavefront phase plays a critical role in focusing light through turbid media. We introduce the curve fitting algorithm (CFA) into the feedback control procedure for wavefront optimization. Unlike the existing continuous sequential algorithm (CSA), the CFA locates the optimal phase by fitting a curve to the measured signals. Simulation results show that, similar to the genetic algorithm (GA), the proposed CFA technique is far less susceptible to the experimental noise than the CSA. Furthermore, only three measurements of feedback signals are enough for CFA to fit the optimal phase while obtaining a higher focal intensity than the CSA and the GA, dramatically shortening the optimization time by a factor of 3 compared with the CSA and the GA. The proposed CFA approach can be applied to enhance the focus intensity and boost the focusing speed in the fields of biological imaging, particle trapping, laser therapy, and so on, and might help to focus light through dynamic turbid media.

Built-in-polarization field effect on lattice thermal conductivity of AlxGa1-xN/GaN heterostructure

NASA Astrophysics Data System (ADS)

Pansari, Anju; Gedam, Vikas; Kumar Sahoo, Bijaya

2015-12-01

The built-in-polarization field at the interface of AlxGa1-xN/GaN heterostructure enhances elastic constant, phonon velocity, Debye temperature and their bowing constants of barrier material AlxGa1-xN. The combined phonon relaxation time of acoustics phonons has been computed for with and without built-in-polarization field at room temperature for different aluminum (Al) content (x). Our result shows that the built-in-polarization field suppresses the scattering mechanisms and enhances the combined relaxation time. The thermal conductivity of AlxGa1-xN has been estimated as a function of temperature for x=0, 0.1, 0.5 and 1 for with and without polarization field. Minimum thermal conductivity has been observed for x=0.1 and 0.5. Analysis shows that up to a certain temperature (different for different x) the polarization field acts as negative effect and reduces the thermal conductivity and after this temperature thermal conductivity is significantly contributed by polarization field. This signifies pyroelectric character of AlxGa1-xN. The pyroelectric transition temperature of AlxGa1-xN alloy has been predicted for different x. Our study reports that room temperature thermal conductivity of AlxGa1-xN/GaN heterostructure is enhanced by built-in-polarization field. The temperature dependence of thermal conductivity for x=0.1 and 0.5 are in line with prior experimental studies. The method we have developed can be used for the simulation of heat transport in nitride devices to minimize the self heating processes and in polarization engineering strategies to optimize the thermoelectric performance of AlxGa1-xN/GaN heterostructures.

Analyses of gibberellins in coconut (Cocos nucifera L.) water by partial filling-micellar electrokinetic chromatography-mass spectrometry with reversal of electroosmotic flow.

PubMed

Ge, Liya; Yong, Jean Wan Hong; Tan, Swee Ngin; Hua, Lin; Ong, Eng Shi

2008-05-01

In this paper, we present the results of simultaneous screening of eight gibberellins (GAs) in coconut (Cocos nucifera L.) water by MEKC directly coupled to ESI-MS detection. During the development of MEKC-MS, partial filling (PF) was used to prevent the micelles from reaching the mass spectrometer as this is detrimental to the MS signal, and a cationic surfactant, cetyltrimethylammonium hydroxide, was added to the electrolyte to reverse the EOF. On the basis of the resolution of the neighboring peaks, different parameters (i.e., the pH and concentration of buffer, surfactant concentrations, length of the injected micellar plug, organic modifier, and applied separation voltage) were optimized to achieve a satisfactory PF-MEKC separation of eight GA standards. Under optimum conditions, a baseline separation of GA standards, including GA1, GA3, GA5, GA6, GA7, GA9, GA12, and GA13, was accomplished within 25 min. Satisfactory results were obtained in terms of precision (RSD of migration time below 0.9%), sensitivity (LODs in the range of 0.8-1.9 microM) and linearity (R2 between 0.981 and 0.997). MS/MS with multiple reaction monitoring (MRM) detection was carried out to obtain sufficient selectivity. PF-MEKC-MS/MS allowed the direct identification and confirmation of the GAs presented in coconut water (CW) sample after SPE, while, the quantitative analysis of GAs was performed by PF-MEKC-MS approach. GA1 and GA3 were successfully detected and quantified in CW. It is anticipated that the current PF-MEKC-MS method can be applicable to analyze GAs in a wide range of biological samples.

All-in-one model for designing optimal water distribution pipe networks

NASA Astrophysics Data System (ADS)

Aklog, Dagnachew; Hosoi, Yoshihiko

2017-05-01

This paper discusses the development of an easy-to-use, all-in-one model for designing optimal water distribution networks. The model combines different optimization techniques into a single package in which a user can easily choose what optimizer to use and compare the results of different optimizers to gain confidence in the performances of the models. At present, three optimization techniques are included in the model: linear programming (LP), genetic algorithm (GA) and a heuristic one-by-one reduction method (OBORM) that was previously developed by the authors. The optimizers were tested on a number of benchmark problems and performed very well in terms of finding optimal or near-optimal solutions with a reasonable computation effort. The results indicate that the model effectively addresses the issues of complexity and limited performance trust associated with previous models and can thus be used for practical purposes.

Non-invasive continuous blood pressure measurement based on mean impact value method, BP neural network, and genetic algorithm.

PubMed

Tan, Xia; Ji, Zhong; Zhang, Yadan

2018-04-25

Non-invasive continuous blood pressure monitoring can provide an important reference and guidance for doctors wishing to analyze the physiological and pathological status of patients and to prevent and diagnose cardiovascular diseases in the clinical setting. Therefore, it is very important to explore a more accurate method of non-invasive continuous blood pressure measurement. To address the shortcomings of existing blood pressure measurement models based on pulse wave transit time or pulse wave parameters, a new method of non-invasive continuous blood pressure measurement - the GA-MIV-BP neural network model - is presented. The mean impact value (MIV) method is used to select the factors that greatly influence blood pressure from the extracted pulse wave transit time and pulse wave parameters. These factors are used as inputs, and the actual blood pressure values as outputs, to train the BP neural network model. The individual parameters are then optimized using a genetic algorithm (GA) to establish the GA-MIV-BP neural network model. Bland-Altman consistency analysis indicated that the measured and predicted blood pressure values were consistent and interchangeable. Therefore, this algorithm is of great significance to promote the clinical application of a non-invasive continuous blood pressure monitoring method.

Production of Fungal Glucoamylase for Glucose Production from Food Waste

PubMed Central

Lam, Wan Chi; Pleissner, Daniel; Lin, Carol Sze Ki

2013-01-01

The feasibility of using pastry waste as resource for glucoamylase (GA) production via solid state fermentation (SSF) was studied. The crude GA extract obtained was used for glucose production from mixed food waste. Our results showed that pastry waste could be used as a sole substrate for GA production. A maximal GA activity of 76.1 ± 6.1 U/mL was obtained at Day 10. The optimal pH and reaction temperature for the crude GA extract for hydrolysis were pH 5.5 and 55 °C, respectively. Under this condition, the half-life of the GA extract was 315.0 minutes with a deactivation constant (kd) 2.20 × 10−3 minutes−1. The application of the crude GA extract for mixed food waste hydrolysis and glucose production was successfully demonstrated. Approximately 53 g glucose was recovered from 100 g of mixed food waste in 1 h under the optimal digestion conditions, highlighting the potential of this approach as an alternative strategy for waste management and sustainable production of glucose applicable as carbon source in many biotechnological processes. PMID:24970186

Prediction and optimization of the laccase-mediated synthesis of the antimicrobial compound iodine (I2).

PubMed

Schubert, M; Fey, A; Ihssen, J; Civardi, C; Schwarze, F W M R; Mourad, S

2015-01-10

An artificial neural network (ANN) and genetic algorithm (GA) were applied to improve the laccase-mediated oxidation of iodide (I(-)) to elemental iodine (I2). Biosynthesis of iodine (I2) was studied with a 5-level-4-factor central composite design (CCD). The generated ANN network was mathematically evaluated by several statistical indices and revealed better results than a classical quadratic response surface (RS) model. Determination of the relative significance of model input parameters, ranking the process parameters in order of importance (pH>laccase>mediator>iodide), was performed by sensitivity analysis. ANN-GA methodology was used to optimize the input space of the neural network model to find optimal settings for the laccase-mediated synthesis of iodine. ANN-GA optimized parameters resulted in a 9.9% increase in the conversion rate. Copyright © 2014 Elsevier B.V. All rights reserved.

Molecular beam epitaxy growth of high electron mobility InAs/AlSb deep quantum well structure

NASA Astrophysics Data System (ADS)

Wang, Juan; Wang, Guo-Wei; Xu, Ying-Qiang; Xing, Jun-Liang; Xiang, Wei; Tang, Bao; Zhu, Yan; Ren, Zheng-Wei; He, Zhen-Hong; Niu, Zhi-Chuan

2013-07-01

InAs/AlSb deep quantum well (QW) structures with high electron mobility were grown by molecular beam epitaxy (MBE) on semi-insulating GaAs substrates. AlSb and Al0.75Ga0.25Sb buffer layers were grown to accommodate the lattice mismatch (7%) between the InAs/AlSb QW active region and GaAs substrate. Transmission electron microscopy shows abrupt interface and atomic force microscopy measurements display smooth surface morphology. Growth conditions of AlSb and Al0.75Ga0.25Sb buffer were optimized. Al0.75Ga0.25Sb is better than AlSb as a buffer layer as indicated. The sample with optimal Al0.75Ga0.25Sb buffer layer shows a smooth surface morphology with root-mean-square roughness of 6.67 Å. The electron mobility has reached as high as 27 000 cm2/Vs with a sheet density of 4.54 × 1011/cm2 at room temperature.

Optimum Design of ARC-less InGaP/GaAs DJ Solar Cell with Hetero Tunnel Junction

NASA Astrophysics Data System (ADS)

Abbasian, Sobhan; Sabbaghi-Nadooshan, Reza

2018-07-01

The operation of hetero In0.49Ga0.51P-Al0.7Ga0.3As tunnel diodes has been evaluated, and an approach for optimizing the back surface field (BSF) layer of a InGaP/GaAs dual-junction (DJ) solar cell developed. The results show that the hetero In0.49Ga0.51P-Al0.7Ga0.3As tunnel diode transferred more electrons and holes and showed less recombination between the top and bottom cells with increased efficiency ( η) in the InGaP/GaAs DJ solar cell. To achieve higher open-circuit voltage ( V oc), GaAs semiconductor was investigated to match with Al0.52In0.48P with bandgap of 2.4 eV, and replacement of the bottom cell in the InGaP/GaAs DJ solar cell with such an Al0.52In0.48P-GaAs heterojunction increased the photogeneration in this region. In the next step, addition of a BSF layer to the top cell required two BSF layers in the bottom cell to optimize the short-circuit current ( J sc) and η. The thickness and doping of the BSF layers were increased to obtain the highest η for the cell. The proposed structure was then compared with previous works. The proposed structure yielded V oc = 2.46 V, J sc = 30 mA/cm2, fill factor (FF) = 88.61%, and η = 65.51% under AM1.5 (1 sun) illumination.

Two-level optimization of composite wing structures based on panel genetic optimization

NASA Astrophysics Data System (ADS)

Liu, Boyang

The design of complex composite structures used in aerospace or automotive vehicles presents a major challenge in terms of computational cost. Discrete choices for ply thicknesses and ply angles leads to a combinatorial optimization problem that is too expensive to solve with presently available computational resources. We developed the following methodology for handling this problem for wing structural design: we used a two-level optimization approach with response-surface approximations to optimize panel failure loads for the upper-level wing optimization. We tailored efficient permutation genetic algorithms to the panel stacking sequence design on the lower level. We also developed approach for improving continuity of ply stacking sequences among adjacent panels. The decomposition approach led to a lower-level optimization of stacking sequence with a given number of plies in each orientation. An efficient permutation genetic algorithm (GA) was developed for handling this problem. We demonstrated through examples that the permutation GAs are more efficient for stacking sequence optimization than a standard GA. Repair strategies for standard GA and the permutation GAs for dealing with constraints were also developed. The repair strategies can significantly reduce computation costs for both standard GA and permutation GA. A two-level optimization procedure for composite wing design subject to strength and buckling constraints is presented. At wing-level design, continuous optimization of ply thicknesses with orientations of 0°, 90°, and +/-45° is performed to minimize weight. At the panel level, the number of plies of each orientation (rounded to integers) and inplane loads are specified, and a permutation genetic algorithm is used to optimize the stacking sequence. The process begins with many panel genetic optimizations for a range of loads and numbers of plies of each orientation. Next, a cubic polynomial response surface is fitted to the optimum buckling load. The resulting response surface is used for wing-level optimization. In general, complex composite structures consist of several laminates. A common problem in the design of such structures is that some plies in the adjacent laminates terminate in the boundary between the laminates. These discontinuities may cause stress concentrations and may increase manufacturing difficulty and cost. We developed measures of continuity of two adjacent laminates. We studied tradeoffs between weight and continuity through a simple composite wing design. Finally, we compared the two-level optimization to a single-level optimization based on flexural lamination parameters. The single-level optimization is efficient and feasible for a wing consisting of unstiffened panels.

Bell-Curve Based Evolutionary Strategies for Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

2000-01-01

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity (Reeves 1997). However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold (Glover 1998). One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumber-some binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back (1996) and Dasgupta and Michalesicz (1997). We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Jiang, Xue; Lu, Wenxi; Hou, Zeyu; Zhao, Haiqing; Na, Jin

2015-11-01

The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

Ensemble of Surrogates-based Optimization for Identifying an Optimal Surfactant-enhanced Aquifer Remediation Strategy at Heterogeneous DNAPL-contaminated Sites

NASA Astrophysics Data System (ADS)

Lu, W., Sr.; Xin, X.; Luo, J.; Jiang, X.; Zhang, Y.; Zhao, Y.; Chen, M.; Hou, Z.; Ouyang, Q.

2015-12-01

The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

Further improvements in conducting and transparent properties of ZnO:Ga films with perpetual c-axis orientation: Materials optimization and application in silicon solar cells

NASA Astrophysics Data System (ADS)

Mondal, Praloy; Das, Debajyoti

2017-07-01

Technologically appropriate device friendly ZnO:Ga films have been prepared at a low growth temperature (100 °C) by changing the RF power (P) applied to the magnetron plasma. Structurally preferred c-axis orientation of the ZnO:Ga network has been attained with I〈002〉/I〈103〉 > 5. The c-axis oriented grains of wurtzite ZnO:Ga grows geometrically and settles in tangentially, providing favorable conduction path for stacked layer devices. Nano-sheet like structures produced at the surface are interconnected and provide conducting path across the surface; however, those accommodate a lot of pores in between that help better light trapping and reduce the reflection loss. The optimized ZnO:Ga thin film prepared at RF power of 200 W has 〈002〉 oriented grains of average size ∼10 nm and exhibits a very high conductivity ∼200 S cm-1 and elevated transmission (∼93% at 500 nm) in the visible range. The optimized ZnO:Ga film has been used as the transparent conducting oxide (TCO) window layer of RF-PECVD grown silicon thin film solar cells in glass/TCO/p-i-n-Si/Al configuration. The characteristics of identically prepared p-i-n-Si solar cells are compared by replacing presently developed ZnO:Ga TCO with the best quality U-type SnO2 coated Asahi glass substrates. The ZnO:Ga coated glass substrate offers a higher open circuit voltage (VOC) and the higher fill factor (FF). The ZnO:Ga film being more stable in hydrogen plasma than its SnO2 counterpart, maintains a high transparency to the solar radiation and improves the VOC, while reduced diffusion of Zn across the p-layer creates less defects at the p-i interface in Si:H cells and thereby, increases the FF. Nearly identical conversion efficiency is preserved for both TCO substrates. Excellent c-axis orientation even at low growth temperature promises improved device performance by extended parametric optimization.

Investigating the optimal passive and active vibration controls of adjacent buildings based on performance indices using genetic algorithms

NASA Astrophysics Data System (ADS)

Hadi, Muhammad N. S.; Uz, Mehmet E.

2015-02-01

This study proposes the optimal passive and active damper parameters for achieving the best results in seismic response mitigation of coupled buildings connected to each other by dampers. The optimization to minimize the H2 and H∞ norms in the performance indices is carried out by genetic algorithms (GAs). The final passive and active damper parameters are checked for adjacent buildings connected to each other under El Centro NS 1940 and Kobe NS 1995 excitations. Using real coded GA in H∞ norm, the optimal controller gain is obtained by different combinations of the measurement as the feedback for designing the control force between the buildings. The proposed method is more effective than other metaheuristic methods and more feasible, although the control force increased. The results in the active control system show that the response of adjacent buildings is reduced in an efficient manner.

Optimal design of loudspeaker arrays for robust cross-talk cancellation using the Taguchi method and the genetic algorithm.

PubMed

Bai, Mingsian R; Tung, Chih-Wei; Lee, Chih-Chung

2005-05-01

An optimal design technique of loudspeaker arrays for cross-talk cancellation with application in three-dimensional audio is presented. An array focusing scheme is presented on the basis of the inverse propagation that relates the transducers to a set of chosen control points. Tikhonov regularization is employed in designing the inverse cancellation filters. An extensive analysis is conducted to explore the cancellation performance and robustness issues. To best compromise the performance and robustness of the cross-talk cancellation system, optimal configurations are obtained with the aid of the Taguchi method and the genetic algorithm (GA). The proposed systems are further justified by physical as well as subjective experiments. The results reveal that large number of loudspeakers, closely spaced configuration, and optimal control point design all contribute to the robustness of cross-talk cancellation systems (CCS) against head misalignment.

Optimizing Motion Planning for Hyper Dynamic Manipulator

NASA Astrophysics Data System (ADS)

Aboura, Souhila; Omari, Abdelhafid; Meguenni, Kadda Zemalache

2012-01-01

This paper investigates the optimal motion planning for an hyper dynamic manipulator. As case study, we consider a golf swing robot which is consisting with two actuated joint and a mechanical stoppers. Genetic Algorithm (GA) technique is proposed to solve the optimal golf swing motion which is generated by Fourier series approximation. The objective function for GA approach is to minimizing the intermediate and final state, minimizing the robot's energy consummation and maximizing the robot's speed. Obtained simulation results show the effectiveness of the proposed scheme.

Geometrical optimization of a local ballistic magnetic sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kanda, Yuhsuke; Hara, Masahiro; Nomura, Tatsuya

2014-04-07

We have developed a highly sensitive local magnetic sensor by using a ballistic transport property in a two-dimensional conductor. A semiclassical simulation reveals that the sensitivity increases when the geometry of the sensor and the spatial distribution of the local field are optimized. We have also experimentally demonstrated a clear observation of a magnetization process in a permalloy dot whose size is much smaller than the size of an optimized ballistic magnetic sensor fabricated from a GaAs/AlGaAs two-dimensional electron gas.

Production of gymnemic acid depends on medium, explants, PGRs, color lights, temperature, photoperiod, and sucrose sources in batch culture of Gymnema sylvestre.

PubMed

Ahmed, A Bakrudeen Ali; Rao, A S; Rao, M V; Taha, Rosna Mat

2012-01-01

Gymnema sylvestre (R.Br.) is an important diabetic medicinal plant which yields pharmaceutically active compounds called gymnemic acid (GA). The present study describes callus induction and the subsequent batch culture optimization and GA quantification determined by linearity, precision, accuracy, and recovery. Best callus induction of GA was noticed in MS medium combined with 2,4-D (1.5 mg/L) and KN (0.5 mg/L). Evaluation and isolation of GA from the calluses derived from different plant parts, namely, leaf, stem and petioles have been done in the present case for the first time. Factors such as light, temperature, sucrose, and photoperiod were studied to observe their effect on GA production. Temperature conditions completely inhibited GA production. Out of the different sucrose concentrations tested, the highest yield (35.4 mg/g d.w) was found at 5% sucrose followed by 12 h photoperiod (26.86 mg/g d.w). Maximum GA production (58.28 mg/g d.w) was observed in blue light. The results showed that physical and chemical factors greatly influence the production of GA in callus cultures of G. sylvestre. The factors optimized for in vitro production of GA during the present study can successfully be employed for their large-scale production in bioreactors.

Ultra-wide bandgap beta-Ga2O3 for deep-UV solar blind photodetectors(Conference Presentation)

NASA Astrophysics Data System (ADS)

Rafique, Subrina; Han, Lu; Zhao, Hongping

2017-03-01

Deep-ultraviolet (DUV) photodetectors based on wide bandgap (WB) semiconductor materials have attracted strong interest because of their broad applications in military surveillance, fire detection and ozone hole monitoring. Monoclinic β-Ga2O3 with ultra-wide bandgap of 4.9 eV is a promising candidate for such application because of its high optical transparency in UV and visible wavelength region, and excellent thermal and chemical stability at elevated temperatures. Synthesis of high qualityβ-Ga2O3 thin films is still at its early stage and knowledge on the origins of defects in this material is lacking. The conventional epitaxy methods used to grow β-Ga2O3 thin films such as molecular beam epitaxy (MBE) and metal organic chemical vapor deposition (MOCVD) still face great challenges such as limited growth rate and relatively high defects levels. In this work, we present the growth of β-Ga2O3 thin films on c-plane (0001) sapphire substrate by our recently developed low pressure chemical vapor deposition (LPCVD) method. The β-Ga2O3 thin films synthesized using high purity metallic gallium and oxygen as the source precursors and argon as carrier gas show controllable N-type doping and high carrier mobility. Metal-semiconductor-metal (MSM) photodetectors (PDs) were fabricated on the as-grown β-Ga2O3 thin films. Au/Ti thin films deposited by e-beam evaporation served as the contact metals. Optimization of the thin film growth conditions and the effects of thermal annealing on the performance of the PDs were investigated. The responsivity of devices under 250 nm UV light irradiation as well as dark light will be characterized and compared.

Propeller performance analysis and multidisciplinary optimization using a genetic algorithm

NASA Astrophysics Data System (ADS)

Burger, Christoph

A propeller performance analysis program has been developed and integrated into a Genetic Algorithm for design optimization. The design tool will produce optimal propeller geometries for a given goal, which includes performance and/or acoustic signature. A vortex lattice model is used for the propeller performance analysis and a subsonic compact source model is used for the acoustic signature determination. Compressibility effects are taken into account with the implementation of Prandtl-Glauert domain stretching. Viscous effects are considered with a simple Reynolds number based model to account for the effects of viscosity in the spanwise direction. An empirical flow separation model developed from experimental lift and drag coefficient data of a NACA 0012 airfoil is included. The propeller geometry is generated using a recently introduced Class/Shape function methodology to allow for efficient use of a wide design space. Optimizing the angle of attack, the chord, the sweep and the local airfoil sections, produced blades with favorable tradeoffs between single and multiple point optimizations of propeller performance and acoustic noise signatures. Optimizations using a binary encoded IMPROVE(c) Genetic Algorithm (GA) and a real encoded GA were obtained after optimization runs with some premature convergence. The newly developed real encoded GA was used to obtain the majority of the results which produced generally better convergence characteristics when compared to the binary encoded GA. The optimization trade-offs show that single point optimized propellers have favorable performance, but circulation distributions were less smooth when compared to dual point or multiobjective optimizations. Some of the single point optimizations generated propellers with proplets which show a loading shift to the blade tip region. When noise is included into the objective functions some propellers indicate a circulation shift to the inboard sections of the propeller as well as a reduction in propeller diameter. In addition the propeller number was increased in some optimizations to reduce the acoustic blade signature.

Optimal subset selection of primary sequence features using the genetic algorithm for thermophilic proteins identification.

PubMed

Wang, LiQiang; Li, CuiFeng

2014-10-01

A genetic algorithm (GA) coupled with multiple linear regression (MLR) was used to extract useful features from amino acids and g-gap dipeptides for distinguishing between thermophilic and non-thermophilic proteins. The method was trained by a benchmark dataset of 915 thermophilic and 793 non-thermophilic proteins. The method reached an overall accuracy of 95.4 % in a Jackknife test using nine amino acids, 38 0-gap dipeptides and 29 1-gap dipeptides. The accuracy as a function of protein size ranged between 85.8 and 96.9 %. The overall accuracies of three independent tests were 93, 93.4 and 91.8 %. The observed results of detecting thermophilic proteins suggest that the GA-MLR approach described herein should be a powerful method for selecting features that describe thermostabile machines and be an aid in the design of more stable proteins.

An image segmentation method based on fuzzy C-means clustering and Cuckoo search algorithm

NASA Astrophysics Data System (ADS)

Wang, Mingwei; Wan, Youchuan; Gao, Xianjun; Ye, Zhiwei; Chen, Maolin

2018-04-01

Image segmentation is a significant step in image analysis and machine vision. Many approaches have been presented in this topic; among them, fuzzy C-means (FCM) clustering is one of the most widely used methods for its high efficiency and ambiguity of images. However, the success of FCM could not be guaranteed because it easily traps into local optimal solution. Cuckoo search (CS) is a novel evolutionary algorithm, which has been tested on some optimization problems and proved to be high-efficiency. Therefore, a new segmentation technique using FCM and blending of CS algorithm is put forward in the paper. Further, the proposed method has been measured on several images and compared with other existing FCM techniques such as genetic algorithm (GA) based FCM and particle swarm optimization (PSO) based FCM in terms of fitness value. Experimental results indicate that the proposed method is robust, adaptive and exhibits the better performance than other methods involved in the paper.

Accurate modeling of switched reluctance machine based on hybrid trained WNN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shoujun, E-mail: sunnyway@nwpu.edu.cn; Ge, Lefei; Ma, Shaojie

2014-04-15

According to the strong nonlinear electromagnetic characteristics of switched reluctance machine (SRM), a novel accurate modeling method is proposed based on hybrid trained wavelet neural network (WNN) which combines improved genetic algorithm (GA) with gradient descent (GD) method to train the network. In the novel method, WNN is trained by GD method based on the initial weights obtained per improved GA optimization, and the global parallel searching capability of stochastic algorithm and local convergence speed of deterministic algorithm are combined to enhance the training accuracy, stability and speed. Based on the measured electromagnetic characteristics of a 3-phase 12/8-pole SRM, themore » nonlinear simulation model is built by hybrid trained WNN in Matlab. The phase current and mechanical characteristics from simulation under different working conditions meet well with those from experiments, which indicates the accuracy of the model for dynamic and static performance evaluation of SRM and verifies the effectiveness of the proposed modeling method.« less

Optimizing groundwater development strategies by genetic algorithm: a case study for balancing the needs for agricultural irrigation and environmental protection in northern China

NASA Astrophysics Data System (ADS)

Wu, Jianfeng; Zheng, Li; Liu, Depeng

2007-11-01

Gaoqing Plain is a major agriculture center of Shandong Province in northern China. Over the last 30 years, the diversion of Yellow River water for intensive irrigation in Gaoqing Plain has led to elevation of the water table and increased evaporation, and subsequently, a dramatic increase in salt content in soil and rapid degradation of crop productivity. Optimal strategies have been explored, that will balance the need to extract sufficient groundwater for irrigation (to ease the pressure on diverting Yellow River water) with the need to improve the local environment by appropriately lowering the water table. Two simulation-optimization models have been formulated and a genetic algorithm (GA) is applied to search for the optimal groundwater development strategies in Gaoqing Plain, while keeping the adverse environmental impacts in check. Compared with the trial-and-error approach of previous studies, the optimization results demonstrate that using an optimization model coupled with a GA search is both effective and efficient. The optimal solutions identified by the GA will provide Gaoqing Plain with the blueprints for developing sustainable groundwater abstraction plans to support local economic development and improve its environmental quality.

A comparative study of electrochemical machining process parameters by using GA and Taguchi method

NASA Astrophysics Data System (ADS)

Soni, S. K.; Thomas, B.

2017-11-01

In electrochemical machining quality of machined surface strongly depend on the selection of optimal parameter settings. This work deals with the application of Taguchi method and genetic algorithm using MATLAB to maximize the metal removal rate and minimize the surface roughness and overcut. In this paper a comparative study is presented for drilling of LM6 AL/B4C composites by comparing the significant impact of numerous machining process parameters such as, electrolyte concentration (g/l),machining voltage (v),frequency (hz) on the response parameters (surface roughness, material removal rate and over cut). Taguchi L27 orthogonal array was chosen in Minitab 17 software, for the investigation of experimental results and also multiobjective optimization done by genetic algorithm is employed by using MATLAB. After obtaining optimized results from Taguchi method and genetic algorithm, a comparative results are presented.

Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites

PubMed Central

Fan, Mingyi; Li, Tongjun; Hu, Jiwei; Cao, Rensheng; Wei, Xionghui; Shi, Xuedan; Ruan, Wenqian

2017-01-01

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R2 value than the pseudo-first-order model. PMID:28772901

Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites.

PubMed

Fan, Mingyi; Li, Tongjun; Hu, Jiwei; Cao, Rensheng; Wei, Xionghui; Shi, Xuedan; Ruan, Wenqian

2017-05-17

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R² value than the pseudo-first-order model.

Mg doping of GaN by molecular beam epitaxy

NASA Astrophysics Data System (ADS)

Lieten, R. R.; Motsnyi, V.; Zhang, L.; Cheng, K.; Leys, M.; Degroote, S.; Buchowicz, G.; Dubon, O.; Borghs, G.

2011-04-01

We present a systematic study on the influence of growth conditions on the incorporation and activation of Mg in GaN layers grown by plasma-assisted molecular beam epitaxy. We show that high quality p-type GaN layers can be obtained on GaN-on-silicon templates. The Mg incorporation and the electrical properties have been investigated as a function of growth temperature, Ga : N flux ratio and Mg : Ga flux ratio. It was found that the incorporation of Mg and the electrical properties are highly sensitive to the Ga : N flux ratio. The highest hole mobility and lowest resistivity were achieved for slightly Ga-rich conditions. In addition to an optimal Ga : N ratio, an optimum Mg : Ga flux ratio was also observed at around 1%. We observed a clear Mg flux window for p-type doping of GaN : 0.31% < Mg : Ga < 5.0%. A lowest resistivity of 0.98 Ω cm was obtained for optimized growth conditions. The p-type GaN layer then showed a hole concentration of 4.3 × 1017 cm-3 and a mobility of 15 cm2 V-1 s-1. Temperature-dependent Hall effect measurements indicate an acceptor depth in these samples of 100 meV for a hole concentration of 5.5 × 1017 cm-3. The corresponding Mg concentration is 5 × 1019 cm-3, indicating approximately 1% activation at room temperature. In addition to continuous growth of Mg-doped GaN layers we also investigated different modulated growth procedures. We show that a modulated growth procedure has only limited influence on Mg doping at a growth temperature of 800 °C or higher. This result is thus in contrast to previously reported GaN : Mg doping at much lower growth temperatures of 500 °C.

Genetic algorithms used for the optimization of light-emitting diodes and solar thermal collectors

NASA Astrophysics Data System (ADS)

Mayer, Alexandre; Bay, Annick; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

2014-09-01

We present a genetic algorithm (GA) we developed for the optimization of light-emitting diodes (LED) and solar thermal collectors. The surface of a LED can be covered by periodic structures whose geometrical and material parameters must be adjusted in order to maximize the extraction of light. The optimization of these parameters by the GA enabled us to get a light-extraction efficiency η of 11.0% from a GaN LED (for comparison, the flat material has a light-extraction efficiency η of only 3.7%). The solar thermal collector we considered consists of a waffle-shaped Al substrate with NiCrOx and SnO2 conformal coatings. We must in this case maximize the solar absorption α while minimizing the thermal emissivity ɛ in the infrared. A multi-objective genetic algorithm has to be implemented in this case in order to determine optimal geometrical parameters. The parameters we obtained using the multi-objective GA enable α~97.8% and ɛ~4.8%, which improves results achieved previously when considering a flat substrate. These two applications demonstrate the interest of genetic algorithms for addressing complex problems in physics.

apGA: An adaptive parallel genetic algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liepins, G.E.; Baluja, S.

1991-01-01

We develop apGA, a parallel variant of the standard generational GA, that combines aggressive search with perpetual novelty, yet is able to preserve enough genetic structure to optimally solve variably scaled, non-uniform block deceptive and hierarchical deceptive problems. apGA combines elitism, adaptive mutation, adaptive exponential scaling, and temporal memory. We present empirical results for six classes of problems, including the DeJong test suite. Although we have not investigated hybrids, we note that apGA could be incorporated into other recent GA variants such as GENITOR, CHC, and the recombination stage of mGA. 12 refs., 2 figs., 2 tabs.

Recovery of gallium and vanadium from gasification fly ash.

PubMed

Font, Oriol; Querol, Xavier; Juan, Roberto; Casado, Raquel; Ruiz, Carmen R; López-Soler, Angel; Coca, Pilar; García Peña, Francisco

2007-01-31

The Puertollano Integrated Coal Gasification Combined Cycle (IGCC) Power Plant (Spain) fly ash is characterized by a relatively high content of Ga and V, which occurs mainly as Ga2O3 and as Ga3+ and V3+ substituting for Al3+ in the Al-Si fly ash glass matrix. Investigations focused on evaluating the potential recovery of Ga and V from these fly ashes. Several NaOH based extraction tests were performed on the IGCC fly ash, at different temperatures, NaOH/fly ash (NaOH/FA) ratios, NaOH concentrations and extraction times. The optimal Ga extraction conditions was determined as 25 degrees C, NaOH 0.7-1 M, NaOH/FA ratio of 5 L/kg and 6 h, attaining Ga extraction yields of 60-86%, equivalent to 197-275 mg of Ga/kg of fly ash. Re-circulation of leachates increased initial Ga concentrations (25-38 mg/L) to 188-215 mg/L, while reducing both content of impurities and NaOH consumption. Carbonation of concentrated Ga leachate demonstrated that 99% of the bulk Ga content in the leachate precipitates at pH 7.4. At pH 10.5 significant proportions of impurities, mainly Al (91%), co-precipitate while >98% of the bulk Ga remains in solution. A second carbonation of the remaining solution (at pH 7.5) recovers the 98.8% of the bulk Ga. Re-dissolution (at pH 0) of the precipitate increases Ga purity from 7 to 30%, this being a suitable Ga end product for further purification by electrolysis. This method produces higher recovery efficiency than currently applied for Ga on an industrial scale. In contrast, low V extraction yields (<64%) were obtained even when using extreme alkaline extraction conditions, which given the current marked price of this element, limits considerably the feasibility of V recovery from IGCC fly ash.

Performance analysis of nanodisk and core/shell/shell-nanowire type III-Nitride heterojunction solar cell for efficient energy harvesting

NASA Astrophysics Data System (ADS)

Routray, S. R.; Lenka, T. R.

2017-11-01

Now-a-days III-Nitride nanowires with axial (nanodisk) and radial (core/shell/shell-nanowire) junctions are two unique and potential methods for solar energy harvesting adopted by worldwide researchers. In this paper, polarization behavior of GaN/InGaN/GaN junction and its effect on carrier dynamics of nanodisk and CSS-nanowire type solar cells are intensively studied and compared with its planar counterpart by numerical simulations using commercially available Victory TCAD. It is observed that CSS-NW with hexagonal geometrical shapes are robust to detrimental impact of polarization charges and could be good enough to accelerate carrier collection efficiency as compared to nanodisk and planar solar cells. This numerical study provides an innovative aspect of fundamental device physics with respect to polarization charges in CSS-NW and nanodisk type junction towards photovoltaic applications. The internal quantum efficiencies (IQE) are also discussed to evaluate carrier collection mechanisms and recombination losses in each type of junctions of solar cell. Finally, it is interesting to observe a maximum conversion efficiency of 6.46% with 91.6% fill factor from n-GaN/i-In0.1Ga0.9N/p-GaN CSS-nanowire solar cell with an optimized thickness of 180 nm InGaN layer under one Sun AM1.5 illumination.

X-ray photoelectron spectrum and electronic properties of a noncentrosymmetric chalcopyrite compound HgGa(2)S(4): LDA, GGA, and EV-GGA.

PubMed

Reshak, Ali Hussain; Khenata, R; Kityk, I V; Plucinski, K J; Auluck, S

2009-04-30

An all electron full potential linearized augmented plane wave method has been applied for a theoretical study of the band structure, density of states, and electron charge density of a noncentrosymmetric chalcopyrite compound HgGa(2)S(4) using three different approximations for the exchange correlation potential. Our calculations show that the valence band maximum (VBM) and conduction band minimum (CBM) are located at Gamma resulting in a direct energy gap of about 2.0, 2.2, and 2.8 eV for local density approximation (LDA), generalized gradient approximation (GGA), and Engel-Vosko (EVGGA) compared to the experimental value of 2.84 eV. We notice that EVGGA shows excellent agreement with the experimental data. This agreement is attributed to the fact that the Engel-Vosko GGA formalism optimizes the corresponding potential for band structure calculations. We make a detailed comparison of the density of states deduced from the X-ray photoelectron spectra with our calculations. We find that there is a strong covalent bond between the Hg and S atoms and Ga and S atoms. The Hg-Hg, Ga-Ga, and S-S bonds are found to be weaker than the Hg-S and Ga-S bonds showing that a covalent bond exists between Hg and S atoms and Ga and S atoms.

Revealing the preferred interlayer orientations and stackings of two-dimensional bilayer gallium selenide crystals

DOE PAGES

Li, Xufan; Basile Carrasco, Leonardo A.; Yoon, Mina; ...

2015-01-21

Characterizing and controlling the interlayer orientations and stacking order of bilayer two-dimensional (2D) crystals and van der Waals (vdW) heterostructure is crucial to optimize their electrical and optoelectronic properties. The four polymorphs of layered gallium selenide (GaSe) that result from different layer stacking provide an ideal platform to study the stacking configurations in bilayer 2D crystals. Here, through a controllable vapor-phase deposition method we selectively grow bilayer GaSe crystals and investigate their two preferred 0° or 60° interlayer rotations. The commensurate stacking configurations (AA' and AB-stacking) in as-grown 2D bilayer GaSe crystals are clearly observed at the atomic scale andmore » the Ga-terminated edge structure are identified for the first time by using atomic-resolution scanning transmission electron microscopy (STEM). Theoretical analysis of the interlayer coupling energetics vs. interlayer rotation angle reveals that the experimentally-observed orientations are energetically preferred among the bilayer GaSe crystal polytypes. Here, the combined experimental and theoretical characterization of the GaSe bilayers afforded by these growth studies provide a pathway to reveal the atomistic relationships in interlayer orientations responsible for the electronic and optical properties of bilayer 2D crystals and vdW heterostructures.« less

Basic ammonothermal GaN growth in molybdenum capsules

NASA Astrophysics Data System (ADS)

Pimputkar, S.; Speck, J. S.; Nakamura, S.

2016-12-01

Single crystal, bulk gallium nitride (GaN) crystals were grown using the basic ammonothermal method in a high purity growth environment created using a non-hermetically sealed molybdenum (Mo) capsule and compared to growths performed in a similarly designed silver (Ag) capsule and capsule-free René 41 autoclave. Secondary ion mass spectrometry (SIMS) analysis revealed transition metal free (<1×1017 cm-3) GaN crystals. Anomalously low oxygen concentrations ((2-6)×1018 cm-3) were measured in a {0001} seeded crystal boule grown using a Mo capsule, despite higher source material oxygen concentrations ((1-5)×1019 cm-3) suggesting that molybdenum (or molybdenum nitrides) may act to getter oxygen under certain conditions. Total system pressure profiles from growth runs in a Mo capsule system were comparable to those without a capsule, with pressures peaking within 2 days and slowly decaying due to hydrogen diffusional losses. Measured Mo capsule GaN growth rates were comparable to un-optimized growth rates in capsule-free systems and appreciably slower than in Ag-capsule systems. Crystal quality replicated that of the GaN seed crystals for all capsule conditions, with high quality growth occurring on the (0001) Ga-face. Optical absorption and impurity concentration characterization suggests reduced concentrations of hydrogenated gallium vacancies (VGa-Hx).

Genetic algorithm in the structural design of Cooke triplet lenses

NASA Astrophysics Data System (ADS)

Hazra, Lakshminarayan; Banerjee, Saswatee

1999-08-01

This paper is in tune with our efforts to develop a systematic method for multicomponent lens design. Our aim is to find a suitable starting point in the final configuration space, so that popular local search methods like damped least squares (DLS) may directly lead to a useful solution. For 'ab initio' design problems, a thin lens layout specifying the powers of the individual components and the intercomponent separations are worked out analytically. Requirements of central aberration targets for the individual components in order to satisfy the prespecified primary aberration targets for the overall system are then determined by nonlinear optimization. The next step involves structural design of the individual components by optimization techniques. This general method may be adapted for the design of triplets and their derivatives. However, for the thin lens design of a Cooke triplet composed of three airspaced singlets, the two steps of optimization mentioned above may be combined into a single optimization procedure. The optimum configuration for each of the single set, catering to the required Gaussian specification and primary aberration targets for the Cooke triplet, are determined by an application of genetic algorithm (GA). Our implementation of this algorithm is based on simulations of some complex tools of natural evolution, like selection, crossover and mutation. Our version of GA may or may not converge to a unique optimum, depending on some of the algorithm specific parameter values. With our algorithm, practically useful solutions are always available, although convergence to a global optimum can not be guaranteed. This is perfectly in keeping with our need to allow 'floating' of aberration targets in the subproblem level. Some numerical results dealing with our preliminary investigations on this problem are presented.

Growth and Analysis of Highly Oriented (11n) BCSCO Films for Device Research

NASA Technical Reports Server (NTRS)

Raina, K. K.; Pandey, R. K.

1995-01-01

Films of BCSCO superconductor of the type Bi2CaSr2Cu2O(x), have been grown by liquid phase epitaxy method (LPE), using a partially closed growth chamber. The films were grown on (001) and (110) NdGaO3 substrates by slow cooling process in an optimized temperature range below the peritectic melting point (880 C) of Bi2CaSr2Cu2O8. Optimization of parameters, such as seed rotation, soak of initial growth temperature and growth period results in the formation of 2122 phase BCSCO films. The films grown at rotation rates of less than 30 and more than 70 rpm are observed to be associated with the second phase of Sr-Ca-Cu-O system. Higher growth temperatures (greater than 860 C) also encourage to the formation of this phase. XRD measurements show that the films grown on (110) NdGaO3 have a preferred (11n)-orientation. It is pertinent to mention here that in our earlier results published elsewhere we obtained c-axis oriented Bi2CaSr2Cu2O8 phase films on (001) NdGaO3 substrate. Critical current density is found to be higher for the films grown on (110) than (001) NdGaO3 substrate orientation. The best values, zero resistance (T(sab co)) and critical current density obtained are 87 K and 10(exp 5) A/sq cm respectively.

Growth and analysis of highly oriented (11n) BCSCO films for device research

NASA Technical Reports Server (NTRS)

Raina, K. K.; Pandey, R. K.

1995-01-01

Films of BCSCO superconductor of the type Bi2CaSr2Cu2Ox have been grown by liquid phase epitaxy method (LPE), using a partially closed growth chamber. The films were grown on (001) and (110) NdGaO3 substrates by slow cooling process in an optimized temperature range below the peritectic melting point (880 C) of Bi2CaSr2Cu2O8. Optimization of parameters, such as seed rotation, soak of initial growth temperature and growth period results in the formation of 2122 phase BCSCO films. The films grown at rotation rates of less than 30 and more than 70 rpm are observed to be associated with the second phase of Sr-Ca-Cu-O system. Higher growth temperatures (is greater than 860 C) also encourage to the formation of this phase. X-Ray Diffraction (XRD) measurements show that the films grown on (110) NdGaO3 have a preferred (11 n)-orientation. It is pertinent to mention here that in our earlier results published elsewhere we obtained c-axis oriented Bi2CaSr2Cu2O8 phase films on (001) NdGaO3 substrate. Critical current density is found to be higher for the films grown on (110) than (001) NdGaO3 substrate orientation. The best values of zero resistance (T(sub co)) and critical current density obtained are 87 K and 105 A/sq cm, respectively.

Optimization of the graph model of the water conduit network, based on the approach of search space reducing

NASA Astrophysics Data System (ADS)

Korovin, Iakov S.; Tkachenko, Maxim G.

2018-03-01

In this paper we present a heuristic approach, improving the efficiency of methods, used for creation of efficient architecture of water distribution networks. The essence of the approach is a procedure of search space reduction the by limiting the range of available pipe diameters that can be used for each edge of the network graph. In order to proceed the reduction, two opposite boundary scenarios for the distribution of flows are analysed, after which the resulting range is further narrowed by applying a flow rate limitation for each edge of the network. The first boundary scenario provides the most uniform distribution of the flow in the network, the opposite scenario created the net with the highest possible flow level. The parameters of both distributions are calculated by optimizing systems of quadratic functions in a confined space, which can be effectively performed with small time costs. This approach was used to modify the genetic algorithm (GA). The proposed GA provides a variable number of variants of each gene, according to the number of diameters in list, taking into account flow restrictions. The proposed approach was implemented to the evaluation of a well-known test network - the Hanoi water distribution network [1], the results of research were compared with a classical GA with an unlimited search space. On the test data, the proposed trip significantly reduced the search space and provided faster and more obvious convergence in comparison with the classical version of GA.

Aluminum gallium nitride-cladding-free nonpolar m-plane gallium nitride-based laser diodes

NASA Astrophysics Data System (ADS)

Schmidt, Mathew Corey

The recent demonstration of nonpolar GaN laser diode operation along with rapid device improvements signal a paradigm shift in GaN-based optoelectronic technology. Up until now, GaN optoelectronics have been trapped on the c-plane facet, where built-in polarization fields place limitations on device design and performance. The advent of bulk GaN substrates has allowed for the full exploration of not only the nonpolar m-plane facet, but all crystal orientations of GaN. This dissertation focuses on the development of some of the world's first nonpolar m-plane GaN laser diodes as well as on the AlGaN-cladding-free concept invented at UCSB. The absence of built-in electric fields allows for thicker quantum wells (≥8 nm) than those allowed on c-plane which improves the optical waveguiding characteristics and eliminates the need for AlGaN cladding layers. The benefits of this design include more uniform growth, more reproducible growth, no tensile cracking, lower operating voltages and currents, and higher yields. The first iteration of device design optimization is presented. Design and growth aspects investigated include quantum well number, quantum well thickness, Mg doping of the p-GaN cladding, aluminum composition of the AlGaN cladding layer and the implementation of an InGaN separate confined heterostructure. These optimizations led to threshold current densities as low as 2.4 kA/cm2.

Improving the fermentation production of the individual key triterpene ganoderic acid me by the medicinal fungus Ganoderma lucidum in submerged culture.

PubMed

Liu, Gao-Qiang; Wang, Xiao-Ling; Han, Wen-Jun; Lin, Qin-Lu

2012-10-24

Enhanced ganoderic acid Me (GA-Me, an important anti-tumor triterpene) yield was attained with the medicinal fungus Ganoderma lucidum using response surface methodology (RSM). Interactions were studied with three variables, viz. glucose, peptone and culture time using a Central Composite Design (CCD). The CCD contains a total of 20 experiments with the first 14 experiments organized in a fractional factorial design, with the experimental trails from 15 to 20 involving the replications of the central points. A polynomial model, describing the relationships between the yield of GA-Me and the three factors in a second-order equation, was developed. The model predicted the maximum GA-Me yield of 11.9 mg·L−1 for glucose, peptone, culture time values of 44.4 g·L−1, 5.0 g·L−1, 437.1 h, respectively, and a maximum GA-Me yield of 12.4 mg·L−1 was obtained in the validation experiment, which represented a 129.6% increase in titre compared to that of the non-optimized conditions. In addition, 11.4 mg·L−1 of GA-Me was obtained in a 30-L agitated fermenter under the optimized conditions, suggesting the submerged culture conditions optimized in the present study were also suitable for GA-Me production on a large scale.

Design Optimization of Ge/GaAs-Based Heterojunction Gate-All-Around (GAA) Arch-Shaped Tunneling Field-Effect Transistor (A-TFET).

PubMed

Seo, Jae Hwa; Yoon, Young Jun; Kang, In Man

2018-09-01

The Ge/GaAs-based heterojunction gate-all-around (GAA) arch-shaped tunneling field-effect transistor (A-TFET) have been designed and optimized using technology computer-aided design (TCAD) simulations. In our previous work, the silicon-based A-TFET was designed and demonstrated. However, to progress the electrical characteristics of A-TFET, the III-V compound heterojunction structures which has enhanced electrical properties must be adopted. Thus, the germanium with gallium arsenide (Ge/GaAs) is considered as key materials of A-TFET. The proposed device has a Ge-based p-doped source, GaAs-based i-doped channel and GaAs-based n-doped drain. Due to the critical issues of device performances, the doping concentration of source and channel region (Dsource, Dchannel), height of source region (Hsource) and epitaxially grown thickness of channel (tepi) was selected as design optimization variables of Ge/GaAs-based GAA A-TFET. The DC characteristics such as on-state current (ion), off-state current (ioff), subthreshold-swing (S) were of extracted and analyzed. Finally, the proposed device has a gate length (LG) of 90 nm, Dsource 5 × 1019 cm-3, Dchannel of 1018 cm-3, tepi of 4 nm, Hsource of 90 nm, R of 10 nm and demonstrate an ion of 2 mA/μm, S of 12.9 mV/dec.

Optimization of structural and growth parameters of metamorphic InGaAs photovoltaic converters grown by MOCVD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rybalchenko, D. V.; Mintairov, S. A.; Salii, R. A.

Metamorphic Ga{sub 0.76}In{sub 0.24}As heterostructures for photovoltaic converters are grown by the MOCVD (metal–organic chemical vapor deposition) technique. It is found that, due to the valence-band offset at the p-In{sub 0.24}Al{sub 0.76}As/p-In{sub 0.24}Ga{sub 0.76}As (wide-gap window/emitter) heterointerface, a potential barrier for holes arises as a result of a low carrier concentration in the wide-gap material. The use of an InAlGaAs solid solution with an Al content lower than 40% makes it possible to raise the hole concentration in the widegap window up ~9 × 10{sup 18} cm{sup –3} and completely remove the potential barrier, thereby reducing the series resistance ofmore » the device. The parameters of an GaInAs metamorphic buffer layer with a stepwise In content profile are calculated and its epitaxial growth conditions are optimized, which improves carrier collection from the n-GaInAs base region and provides a quantum efficiency of 83% at a wavelength of 1064 nm. Optimization of the metamorphic heterostructure of the photovoltaic converter results in that its conversion efficiency for laser light with a wavelength of 1064 nm is 38.5%.« less

Design and Optimization of a Composite Canard Control Surface of an Advanced Fighter Aircraft under Static Loading

NASA Astrophysics Data System (ADS)

Shrivastava, Sachin; Mohite, P. M.

2015-01-01

The minimization of weight and maximization of payload is an ever challenging design procedure for air vehicles. The present study has been carried out with an objective to redesign control surface of an advanced all-metallic fighter aircraft. In this study, the structure made up of high strength aluminum, titanium and ferrous alloys has been attempted to replace by carbon fiber composite (CFC) skin, ribs and stiffeners. This study presents an approach towards development of a methodology for optimization of first-ply failure index (FI) in unidirectional fibrous laminates using Genetic-Algorithms (GA) under quasi-static loading. The GAs, by the application of its operators like reproduction, cross-over, mutation and elitist strategy, optimize the ply-orientations in laminates so as to have minimum FI of Tsai-Wu first-ply failure criterion. The GA optimization procedure has been implemented in MATLAB and interfaced with commercial software ABAQUS using python scripting. FI calculations have been carried out in ABAQUS with user material subroutine (UMAT). The GA's application gave reasonably well-optimized ply-orientations combination at a faster convergence rate. However, the final optimized sequence of ply-orientations is obtained by tweaking the sequences given by GA's based on industrial practices and experience, whenever needed. The present study of conversion of an all metallic structure to partial CFC structure has led to 12% of weight reduction. Therefore, the approach proposed here motivates designer to use CFC with a confidence.

Improved Ant Algorithms for Software Testing Cases Generation

PubMed Central

Yang, Shunkun; Xu, Jiaqi

2014-01-01

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations. PMID:24883391

[Application of characteristic NIR variables selection in portable detection of soluble solids content of apple by near infrared spectroscopy].

PubMed

Fan, Shu-Xiang; Huang, Wen-Qian; Li, Jiang-Bo; Guo, Zhi-Ming; Zhaq, Chun-Jiang

2014-10-01

In order to detect the soluble solids content(SSC)of apple conveniently and rapidly, a ring fiber probe and a portable spectrometer were applied to obtain the spectroscopy of apple. Different wavelength variable selection methods, including unin- formative variable elimination (UVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were pro- posed to select effective wavelength variables of the NIR spectroscopy of the SSC in apple based on PLS. The back interval LS- SVM (BiLS-SVM) and GA were used to select effective wavelength variables based on LS-SVM. Selected wavelength variables and full wavelength range were set as input variables of PLS model and LS-SVM model, respectively. The results indicated that PLS model built using GA-CARS on 50 characteristic variables selected from full-spectrum which had 1512 wavelengths achieved the optimal performance. The correlation coefficient (Rp) and root mean square error of prediction (RMSEP) for prediction sets were 0.962, 0.403°Brix respectively for SSC. The proposed method of GA-CARS could effectively simplify the portable detection model of SSC in apple based on near infrared spectroscopy and enhance the predictive precision. The study can provide a reference for the development of portable apple soluble solids content spectrometer.

Enhanced production of medicinal polysaccharide by submerged fermentation of Lingzhi or Reishi medicinal mushroom Ganoderma lucidium (W.Curt.:Fr.) P. Karst. Using statistical and evolutionary optimization methods.

PubMed

Baskar, Gurunathan; Sathya, Shree Rajesh K

2011-01-01

Statistical and evolutionary optimization of media composition was employed for the production of medicinal exopolysaccharide (EPS) by Lingzhi or Reishi medicinal mushroom Ganoderma lucidium MTCC 1039 using soya bean meal flour as low-cost substrate. Soya bean meal flour, ammonium chloride, glucose, and pH were identified as the most important variables for EPS yield using the two-level Plackett-Burman design and further optimized using the central composite design (CCD) and the artificial neural network (ANN)-linked genetic algorithm (GA). The high value of coefficient of determination of ANN (R² = 0.982) indicates that the ANN model was more accurate than the second-order polynomial model of CCD (R² = 0.91) for representing the effect of media composition on EPS yield. The predicted optimum media composition using ANN-linked GA was soybean meal flour 2.98%, glucose 3.26%, ammonium chloride 0.25%, and initial pH 7.5 for the maximum predicted EPS yield of 1005.55 mg/L. The experimental EPS yield obtained using the predicted optimum media composition was 1012.36 mg/L, which validates the high degree of accuracy of evolutionary optimization for enhanced production of EPS by submerged fermentation of G. lucidium.

Process optimization of rolling for zincked sheet technology using response surface methodology and genetic algorithm

NASA Astrophysics Data System (ADS)

Ji, Liang-Bo; Chen, Fang

2017-07-01

Numerical simulation and intelligent optimization technology were adopted for rolling and extrusion of zincked sheet. By response surface methodology (RSM), genetic algorithm (GA) and data processing technology, an efficient optimization of process parameters for rolling of zincked sheet was investigated. The influence trend of roller gap, rolling speed and friction factor effects on reduction rate and plate shortening rate were analyzed firstly. Then a predictive response surface model for comprehensive quality index of part was created using RSM. Simulated and predicted values were compared. Through genetic algorithm method, the optimal process parameters for the forming of rolling were solved. They were verified and the optimum process parameters of rolling were obtained. It is feasible and effective.

Solving TSP problem with improved genetic algorithm

NASA Astrophysics Data System (ADS)

Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying

2018-05-01

The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

NASA Astrophysics Data System (ADS)

Zhan, Liwei; Li, Chengwei

2017-02-01

A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

Growth and Optimization of 2 Micrometers InGaSb/AlGaSb Quantum-Well-Based VECSELs on GaAs/AlGaAs DBRs

DTIC Science & Technology

2013-08-01

overwhelming nonradiative recombination losses in the antimonide active region. Furthermore, if the growth of the antimonide active region is done on a GaAs...This is important as threading dislocations would introduce a strong nonradiative recombination process in the QWs and relaxation that is not 100...These defects can act as nonradiative recombination centers. Thus, the source of the threading dislocations and their density in the active region

Optical analysis of AlGaInP laser diodes with real refractive index guided self-aligned structure

NASA Astrophysics Data System (ADS)

Xu, Yun; Zhu, Xiaopeng; Ye, Xiaojun; Kang, Xiangning; Cao, Qing; Guo, Liang; Chen, Lianghui

2004-05-01

Optical modes of AlGaInP laser diodes with real refractive index guided self-aligned (RISA) structure were analyzed theoretically on the basis of two-dimension semivectorial finite-difference methods (SV-FDMs) and the computed simulation results were presented. The eigenvalue and eigenfunction of this two-dimension waveguide were obtained and the dependence of the confinement factor and beam divergence angles in the direction of parallel and perpendicular to the pn junction on the structure parameters such as the number of quantum wells, the Al composition of the cladding layers, the ridge width, the waveguide thickness and the residual thickness of the upper P-cladding layer were investigated. The results can provide optimized structure parameters and help us design and fabricate high performance AlGaInP laser diodes with a low beam aspect ratio required for optical storage applications.

Multilayer porous structures of HVPE and MOCVD grown GaN for photonic applications

NASA Astrophysics Data System (ADS)

Braniste, T.; Ciers, Joachim; Monaico, Ed.; Martin, D.; Carlin, J.-F.; Ursaki, V. V.; Sergentu, V. V.; Tiginyanu, I. M.; Grandjean, N.

2017-02-01

In this paper we report on a comparative study of electrochemical processes for the preparation of multilayer porous structures in hydride vapor phase epitaxy (HVPE) and metal organic chemical vapor phase deposition (MOCVD) grown GaN. It was found that in HVPE-grown GaN, multilayer porous structures are obtained due to self-organization processes leading to a fine modulation of doping during the crystal growth. However, these processes are not totally under control. Multilayer porous structures with a controlled design have been produced by optimizing the technological process of electrochemical etching in MOCVD-grown samples, consisting of five pairs of thin layers with alternating-doping profiles. The samples have been characterized by SEM imaging, photoluminescence spectroscopy, and micro-reflectivity measurements, accompanied by transfer matrix analysis and simulations by a method developed for the calculation of optical reflection spectra. We demonstrate the applicability of the produced structures for the design of Bragg reflectors.

Luminescence and efficiency optimization of InGaN/GaN core-shell nanowire LEDs by numerical modelling

NASA Astrophysics Data System (ADS)

Römer, Friedhard; Deppner, Marcus; Andreev, Zhelio; Kölper, Christopher; Sabathil, Matthias; Strassburg, Martin; Ledig, Johannes; Li, Shunfeng; Waag, Andreas; Witzigmann, Bernd

2012-02-01

We present a computational study on the anisotropic luminescence and the efficiency of a core-shell type nanowire LED based on GaN with InGaN active quantum wells. The physical simulator used for analyzing this device integrates a multidimensional drift-diffusion transport solver and a k . p Schrödinger problem solver for quantization effects and luminescence. The solution of both problems is coupled to achieve self-consistency. Using this solver we investigate the effect of dimensions, design of quantum wells, and current injection on the efficiency and luminescence of the core-shell nanowire LED. The anisotropy of the luminescence and re-absorption is analyzed with respect to the external efficiency of the LED. From the results we derive strategies for design optimization.

Thermal management in closed incubators: New software for assessing the impact of humidity on the optimal incubator air temperature.

PubMed

Delanaud, Stéphane; Decima, Pauline; Pelletier, Amandine; Libert, Jean-Pierre; Durand, Estelle; Stephan-Blanchard, Erwan; Bach, Véronique; Tourneux, Pierre

2017-08-01

Low-birth-weight (LBW) neonates are nursed in closed incubators to prevent transcutaneous water loss. The RH's impact on the optimal incubator air temperature setting has not been studied. On the basis of a clinical cohort study, we modelled all the ambient parameters influencing body heat losses and gains. The algorithm quantifies the change in RH on the air temperature, to maintain optimal thermal conditions in the incubator. Twenty-three neonates (gestational age (GA): 30.0 [28.9-31.6] weeks) were included. A 20% increase and a 20% decrease in the RH induced a change in air temperature of between -1.51 and +1.85°C for a simulated 650g neonate (GA: 26 weeks), between -1.66 and +1.87°C for a 1000g neonate (GA: 31 weeks), and between -1.77 and +1.97°C for a 2000g neonate (GA: 33 weeks) (p<0.001). According to regression analyses, the optimal incubator air temperature=a+b relative humidity +c age +d weight (p<0.001). We have developed new mathematical equations for calculating the optimal temperature for the incubator air as a function of the latter's relative humidity. The software constitutes a decision support tool for improving patient care in routine clinical practice. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

BLGAN: Bayesian learning and genetic algorithm for supporting negotiation with incomplete information.

PubMed

Sim, Kwang Mong; Guo, Yuanyuan; Shi, Benyun

2009-02-01

Automated negotiation provides a means for resolving differences among interacting agents. For negotiation with complete information, this paper provides mathematical proofs to show that an agent's optimal strategy can be computed using its opponent's reserve price (RP) and deadline. The impetus of this work is using the synergy of Bayesian learning (BL) and genetic algorithm (GA) to determine an agent's optimal strategy in negotiation (N) with incomplete information. BLGAN adopts: 1) BL and a deadline-estimation process for estimating an opponent's RP and deadline and 2) GA for generating a proposal at each negotiation round. Learning the RP and deadline of an opponent enables the GA in BLGAN to reduce the size of its search space (SP) by adaptively focusing its search on a specific region in the space of all possible proposals. SP is dynamically defined as a region around an agent's proposal P at each negotiation round. P is generated using the agent's optimal strategy determined using its estimations of its opponent's RP and deadline. Hence, the GA in BLGAN is more likely to generate proposals that are closer to the proposal generated by the optimal strategy. Using GA to search around a proposal generated by its current strategy, an agent in BLGAN compensates for possible errors in estimating its opponent's RP and deadline. Empirical results show that agents adopting BLGAN reached agreements successfully, and achieved: 1) higher utilities and better combined negotiation outcomes (CNOs) than agents that only adopt GA to generate their proposals, 2) higher utilities than agents that adopt BL to learn only RP, and 3) higher utilities and better CNOs than agents that do not learn their opponents' RPs and deadlines.

Streamflow Prediction based on Chaos Theory

NASA Astrophysics Data System (ADS)

Li, X.; Wang, X.; Babovic, V. M.

2015-12-01

Chaos theory is a popular method in hydrologic time series prediction. Local model (LM) based on this theory utilizes time-delay embedding to reconstruct the phase-space diagram. For this method, its efficacy is dependent on the embedding parameters, i.e. embedding dimension, time lag, and nearest neighbor number. The optimal estimation of these parameters is thus critical to the application of Local model. However, these embedding parameters are conventionally estimated using Average Mutual Information (AMI) and False Nearest Neighbors (FNN) separately. This may leads to local optimization and thus has limitation to its prediction accuracy. Considering about these limitation, this paper applies a local model combined with simulated annealing (SA) to find the global optimization of embedding parameters. It is also compared with another global optimization approach of Genetic Algorithm (GA). These proposed hybrid methods are applied in daily and monthly streamflow time series for examination. The results show that global optimization can contribute to the local model to provide more accurate prediction results compared with local optimization. The LM combined with SA shows more advantages in terms of its computational efficiency. The proposed scheme here can also be applied to other fields such as prediction of hydro-climatic time series, error correction, etc.

LROC assessment of non-linear filtering methods in Ga-67 SPECT imaging

NASA Astrophysics Data System (ADS)

De Clercq, Stijn; Staelens, Steven; De Beenhouwer, Jan; D'Asseler, Yves; Lemahieu, Ignace

2006-03-01

In emission tomography, iterative reconstruction is usually followed by a linear smoothing filter to make such images more appropriate for visual inspection and diagnosis by a physician. This will result in a global blurring of the images, smoothing across edges and possibly discarding valuable image information for detection tasks. The purpose of this study is to investigate which possible advantages a non-linear, edge-preserving postfilter could have on lesion detection in Ga-67 SPECT imaging. Image quality can be defined based on the task that has to be performed on the image. This study used LROC observer studies based on a dataset created by CPU-intensive Gate Monte Carlo simulations of a voxelized digital phantom. The filters considered in this study were a linear Gaussian filter, a bilateral filter, the Perona-Malik anisotropic diffusion filter and the Catte filtering scheme. The 3D MCAT software phantom was used to simulate the distribution of Ga-67 citrate in the abdomen. Tumor-present cases had a 1-cm diameter tumor randomly placed near the edges of the anatomical boundaries of the kidneys, bone, liver and spleen. Our data set was generated out of a single noisy background simulation using the bootstrap method, to significantly reduce the simulation time and to allow for a larger observer data set. Lesions were simulated separately and added to the background afterwards. These were then reconstructed with an iterative approach, using a sufficiently large number of MLEM iterations to establish convergence. The output of a numerical observer was used in a simplex optimization method to estimate an optimal set of parameters for each postfilter. No significant improvement was found for using edge-preserving filtering techniques over standard linear Gaussian filtering.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

NASA Astrophysics Data System (ADS)

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-05-01

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

PubMed Central

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-01-01

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization.

PubMed

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.

Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

PubMed Central

Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah

2017-01-01

Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the ever-changing customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multi-objective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GA-PSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GA-PSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994

Correlation between mobility collapse and carbon impurities in Si-doped GaN grown by low pressure metalorganic chemical vapor deposition

NASA Astrophysics Data System (ADS)

Kaess, Felix; Mita, Seiji; Xie, Jingqiao; Reddy, Pramod; Klump, Andrew; Hernandez-Balderrama, Luis H.; Washiyama, Shun; Franke, Alexander; Kirste, Ronny; Hoffmann, Axel; Collazo, Ramón; Sitar, Zlatko

2016-09-01

In the low doping range below 1 × 1017 cm-3, carbon was identified as the main defect attributing to the sudden reduction of the electron mobility, the electron mobility collapse, in n-type GaN grown by low pressure metalorganic chemical vapor deposition. Secondary ion mass spectroscopy has been performed in conjunction with C concentration and the thermodynamic Ga supersaturation model. By controlling the ammonia flow rate, the input partial pressure of Ga precursor, and the diluent gas within the Ga supersaturation model, the C concentration in Si-doped GaN was controllable from 6 × 1019 cm-3 to values as low as 2 × 1015 cm-3. It was found that the electron mobility collapsed as a function of free carrier concentration, once the Si concentration closely approached the C concentration. Lowering the C concentration to the order of 1015 cm-3 by optimizing Ga supersaturation achieved controllable free carrier concentrations down to 5 × 1015 cm-3 with a peak electron mobility of 820 cm2/V s without observing the mobility collapse. The highest electron mobility of 1170 cm2/V s was obtained even in metalorganic vapor deposition-grown GaN on sapphire substrates by optimizing growth parameters in terms of Ga supersaturation to reduce the C concentration.

Production of Gymnemic Acid Depends on Medium, Explants, PGRs, Color Lights, Temperature, Photoperiod, and Sucrose Sources in Batch Culture of Gymnema sylvestre

PubMed Central

Ahmed, A. Bakrudeen Ali; Rao, A. S.; Rao, M. V.; Taha, Rosna Mat

2012-01-01

Gymnema sylvestre (R.Br.) is an important diabetic medicinal plant which yields pharmaceutically active compounds called gymnemic acid (GA). The present study describes callus induction and the subsequent batch culture optimization and GA quantification determined by linearity, precision, accuracy, and recovery. Best callus induction of GA was noticed in MS medium combined with 2,4-D (1.5 mg/L) and KN (0.5 mg/L). Evaluation and isolation of GA from the calluses derived from different plant parts, namely, leaf, stem and petioles have been done in the present case for the first time. Factors such as light, temperature, sucrose, and photoperiod were studied to observe their effect on GA production. Temperature conditions completely inhibited GA production. Out of the different sucrose concentrations tested, the highest yield (35.4 mg/g d.w) was found at 5% sucrose followed by 12 h photoperiod (26.86 mg/g d.w). Maximum GA production (58.28 mg/g d.w) was observed in blue light. The results showed that physical and chemical factors greatly influence the production of GA in callus cultures of G. sylvestre. The factors optimized for in vitro production of GA during the present study can successfully be employed for their large-scale production in bioreactors. PMID:22629221

Gallic acid loaded PEO-core/zein-shell nanofibers for chemopreventive action on gallbladder cancer cells.

PubMed

Acevedo, Francisca; Hermosilla, Jeyson; Sanhueza, Claudia; Mora-Lagos, Barbara; Fuentes, Irma; Rubilar, Mónica; Concheiro, Angel; Alvarez-Lorenzo, Carmen

2018-07-01

Coaxial electrospinning was used to develop gallic acid (GA) loaded poly(ethylene oxide)/zein nanofibers in order to improve its chemopreventive action on human gallbladder cancer cells. Using a Plackett-Burman design, the effects of poly(ethylene oxide) and zein concentration and applied voltage on the diameter and morphology index of nanofibers were investigated. Coaxial nanofibers were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). GA loading efficiency as high as 77% was obtained under optimal process conditions. The coaxial nanofibers controlled GA release in acid and neutral pH medium. Cytotoxicity and reactive oxygen species (ROS) production on gallbladder cancer cell lines GB-d1 and NOZ in the presence of GA-nanofibers were assessed. GA-nanofibers triggered an increase in the cellular cytotoxicity compared with free GA on GB-d1 and NOZ cells. Statistically significant differences were found in ROS levels of GA-nanofibers compared with free GA on NOZ cells. Differently, ROS production on GB-d1 cell line was similar. Based on these results, the coaxial nanofibers obtained in this study under optimized operational conditions offer an alternative for the development of a GA release system with improved chemopreventive action on gallbladder cancer cells. Copyright © 2018 Elsevier B.V. All rights reserved.

Warpage improvement on wheel caster by optimizing the process parameters using genetic algorithm (GA)

NASA Astrophysics Data System (ADS)

Safuan, N. S.; Fathullah, M.; Shayfull, Z.; Nasir, S. M.; Hazwan, M. H. M.

2017-09-01

In injection moulding process, the defects will always encountered and affected the final product shape and functionality. This study is concerning on minimizing warpage and optimizing the process parameter of injection moulding part. Apart from eliminating product wastes, this project also giving out best recommended parameters setting. This research studied on five parameters. The optimization showed that warpage have been improved 42.64% from 0.6524 mm to 0.30879 mm in Autodesk Moldflow Insight (AMI) simulation result and Genetic Algorithm (GA) respectively.

Semi-automated lab-on-a-chip for dispensing GA-68 radiotracers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weinberg, Irving

We solved a technical problem that is hindering American progress in molecular medicine, and restricting US citizens from receiving optimal diagnostic care. Specifically, the project deals with a mother/daughter generator of positron-emitting radiotracers (Ge-68/Ga-68). These generator systems are approved in Europe but cannot be used in the USA, because of safety issues related to possible breakthrough of long-lived Ge-68 (mother) atoms. Europeans have demonstrated abilities of Ga-68-labeled radiotracers to image cancer foci with high sensitivity and specificity, and to use such methods to effectively plan therapy.The USA Food and Drug Administration (FDA) and Nuclear Regulatory Commission (NRC) have taken themore » position that every patient administration of Ga-68 should be preceded by an assay demonstrated that Ge-68 breakthrough is within acceptable limits. Breakthrough of parent elements is a sensitive subject at the FDA, as evidenced by the recent recall of Rb-82 generators due to inadvertent administrations of Sr-82. Commercially, there is no acceptable rapid method for assaying breakthrough of Ge-68 prior to each human administration. The gamma emissions of daughter Ga-68 have higher energies than the parent Ge-68, so that the shielding assays typically employed for Mo-99/Tc-99m generators cannot be applied to Ga-68 generators. The half-life of Ga-68 is 68 minutes, so that the standard 10-half-life delay (used to assess breakthrough in Sr-82/Rb-82 generators) cannot be applied to Ga-68 generators. As a result of the aforementioned regulatory requirements, Ga-68 generators are sold in the USA for animal use only.The American clinical community’s inability to utilize Ga-68 generators impairs abilities to treat patients domestically, and puts the USA at a disadvantage in developing exportable products. The proposed DOE project aimed to take advantage of recent technological advances developed for lab-on-a-chip (LOC) applications. Based on our experiences constructing such devices, the proposed microfluidics-based approach could provide cost-effective validation of breakthrough compliance in minutes.« less

Optical properties of InAsBi and optimal designs of lattice-matched and strain-balanced III-V semiconductor superlattices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Webster, P. T., E-mail: preston.t.webster@asu.edu; Riordan, N. A.; Gogineni, C.

The optical properties of bulk InAs{sub 0.936}Bi{sub 0.064} grown by molecular beam epitaxy on a (100)-oriented GaSb substrate are measured using spectroscopic ellipsometry. The index of refraction and absorption coefficient are measured over photon energies ranging from 44 meV to 4.4 eV and are used to identify the room temperature bandgap energy of bulk InAs{sub 0.936}Bi{sub 0.064} as 60.6 meV. The bandgap of InAsBi is expressed as a function of Bi mole fraction using the band anticrossing model and a characteristic coupling strength of 1.529 eV between the Bi impurity state and the InAs valence band. These results are programmed into a software toolmore » that calculates the miniband structure of semiconductor superlattices and identifies optimal designs in terms of maximizing the electron-hole wavefunction overlap as a function of transition energy. These functionalities are demonstrated by mapping the design spaces of lattice-matched GaSb/InAs{sub 0.911}Sb{sub 0.089} and GaSb/InAs{sub 0.932}Bi{sub 0.068} and strain-balanced InAs/InAsSb, InAs/GaInSb, and InAs/InAsBi superlattices on GaSb. The absorption properties of each of these material systems are directly compared by relating the wavefunction overlap square to the absorption coefficient of each optimized design. Optimal design criteria are provided for key detector wavelengths for each superlattice system. The optimal design mid-wave infrared InAs/InAsSb superlattice is grown using molecular beam epitaxy, and its optical properties are evaluated using spectroscopic ellipsometry and photoluminescence spectroscopy.« less

Enhancing Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Abbaszadeh, P.; Yan, H.

2016-12-01

Particle Filters (PFs) have received increasing attention by the researchers from different disciplines in hydro-geosciences as an effective method to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation by means of data assimilation in hydrology and geoscience has evolved since 2005 from SIR-PF to PF-MCMC and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC. In this framework, the posterior distribution undergoes an evolutionary process to update an ensemble of prior states that more closely resemble realistic posterior probability distribution. The premise of this approach is that the particles move to optimal position using the GA optimization coupled with MCMC increasing the number of effective particles, hence the particle degeneracy is avoided while the particle diversity is improved. The proposed algorithm is applied on a conceptual and highly nonlinear hydrologic model and the effectiveness, robustness and reliability of the method in jointly estimating the states and parameters and also reducing the uncertainty is demonstrated for few river basins across the United States.

Ternary AlGaN Alloys with High Al Content and Enhanced Compositional Homogeneity Grown by Plasma-Assisted Molecular Beam Epitaxy

NASA Astrophysics Data System (ADS)

Fellmann, Vincent; Jaffrennou, Périne; Sam-Giao, Diane; Gayral, Bruno; Lorenz, Katharina; Alves, Eduardo; Daudin, Bruno

2011-03-01

We have studied the influence of III/N flux ratio and growth temperature on structural and optical properties of high Al-content, around 50-60%, AlGaN alloy layers grown by plasma-assisted molecular beam epitaxy. In a first part, based on structural analysis by Rutherford Backscattering Spectroscopy, we establish that a III/N flux ratio slightly above 1 produces layers with low amount of structural defects. In a second part, we study the effect of growth temperature on structural and optical properties of layers grown with previously determined optimal III/N flux ratio. We find that optimal growth temperatures for Al0.50Ga0.50N layers with compositional homogeneity related with narrow UV photoluminescence properties are in the low temperature range for growing GaN layers, i.e., 650-680 °C. We propose that lowering Ga adatom diffusion on the surface favors random incorporation of both Ga and Al adatoms on wurtzite crystallographic sites leading to the formation of an homogeneous alloy.

Human emotion detector based on genetic algorithm using lip features

NASA Astrophysics Data System (ADS)

Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga

2010-04-01

We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.

Solving lot-sizing problem with quantity discount and transportation cost

NASA Astrophysics Data System (ADS)

Lee, Amy H. I.; Kang, He-Yau; Lai, Chun-Mei

2013-04-01

Owing to today's increasingly competitive market and ever-changing manufacturing environment, the inventory problem is becoming more complicated to solve. The incorporation of heuristics methods has become a new trend to tackle the complex problem in the past decade. This article considers a lot-sizing problem, and the objective is to minimise total costs, where the costs include ordering, holding, purchase and transportation costs, under the requirement that no inventory shortage is allowed in the system. We first formulate the lot-sizing problem as a mixed integer programming (MIP) model. Next, an efficient genetic algorithm (GA) model is constructed for solving large-scale lot-sizing problems. An illustrative example with two cases in a touch panel manufacturer is used to illustrate the practicality of these models, and a sensitivity analysis is applied to understand the impact of the changes in parameters to the outcomes. The results demonstrate that both the MIP model and the GA model are effective and relatively accurate tools for determining the replenishment for touch panel manufacturing for multi-periods with quantity discount and batch transportation. The contributions of this article are to construct an MIP model to obtain an optimal solution when the problem is not too complicated itself and to present a GA model to find a near-optimal solution efficiently when the problem is complicated.

Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm

NASA Astrophysics Data System (ADS)

Mahdavi, Seyed Hossein; Razak, Hashim Abdul

2016-06-01

This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.

Modeling the compliance of polyurethane nanofiber tubes for artificial common bile duct

NASA Astrophysics Data System (ADS)

Moazeni, Najmeh; Vadood, Morteza; Semnani, Dariush; Hasani, Hossein

2018-02-01

The common bile duct is one of the body’s most sensitive organs and a polyurethane nanofiber tube can be used as a prosthetic of the common bile duct. The compliance is one of the most important properties of prosthetic which should be adequately compliant as long as possible to keep the behavioral integrity of prosthetic. In the present paper, the prosthetic compliance was measured and modeled using regression method and artificial neural network (ANN) based on the electrospinning process parameters such as polymer concentration, voltage, tip-to-collector distance and flow rate. Whereas, the ANN model contains different parameters affecting on the prediction accuracy directly, the genetic algorithm (GA) was used to optimize the ANN parameters. Finally, it was observed that the optimized ANN model by GA can predict the compliance with high accuracy (mean absolute percentage error = 8.57%). Moreover, the contribution of variables on the compliance was investigated through relative importance analysis and the optimum values of parameters for ideal compliance were determined.

A Photonic 1 × 4 Power Splitter Based on Multimode Interference in Silicon-Gallium-Nitride Slot Waveguide Structures.

PubMed

Malka, Dror; Danan, Yossef; Ramon, Yehonatan; Zalevsky, Zeev

2016-06-25

In this paper, a design for a 1 × 4 optical power splitter based on the multimode interference (MMI) coupler in a silicon (Si)-gallium nitride (GaN) slot waveguide structure is presented-to our knowledge, for the first time. Si and GaN were found as suitable materials for the slot waveguide structure. Numerical optimizations were carried out on the device parameters using the full vectorial-beam propagation method (FV-BPM). Simulation results show that the proposed device can be useful to divide optical signal energy uniformly in the C-band range (1530-1565 nm) into four output ports with low insertion losses (0.07 dB).

Design of high-efficiency, radiation-hard, GaInP/GaAs solar cells

NASA Technical Reports Server (NTRS)

Kurtz, Sarah R.; Bertness, K. A.; Kibbler, A. E.; Kramer, C.; Olson, J. M.

1994-01-01

In recently years, Ga(0.5)In((0.5)P/GaAs cells have drawn increased attention both because of their high efficiencies and because they are well suited for space applications. They can be grown and processed as two-junction devices with roughly twice the voltage and half the current of GaAs cells. They have low temperature coefficients, and have good potential for radiation hardness. We have previously reported the effects of electron irradiation on test cells which were not optimally designed for space. From those results we estimated that an optimally designed cell could achieve 20 percent after irradiation with 10(exp 15) cm(exp -2) 1 MeV electrons. Modeling studies predicted that slightly higher efficiencies may be achievable. Record efficiencies for EOL performance of other types of cells are significantly lower. Even the best Si and InP cells have BOL efficiencies lower than the EOL efficiency we report here. Good GaAs cells have an EOL efficiency of 16 percent. The InP/Ga(0.5)In(0.5)As two-junction, two-terminal device has a BOL efficiency as high as 22.2 percent, but radiation results for these cells were limited. In this study we use the previous modeling and irradiation results to design a set of Ga(0.5)In(0.5)P/GaAs cells that will demonstrate the importance of the design parameters and result in high-efficiency devices. We report record AMO efficiencies: a BOL efficiency of 25.7 percent for a device optimized for BOL performance and two of different designs with EOL efficiencies of 19.6 percent (at 10(exp 15) cm(exp -2) 1MeV electrons). We vary the bottom-cell base doping and the top-cell thickness to show the effects of these two important design parameters. We get an unexpected result indicating that the dopant added to the bottom-cell base also increases the degradation of the top cell.

The effect of p-doping on multi-state lasing in InAs/InGaAs quantum dot lasers for different cavity lengths

NASA Astrophysics Data System (ADS)

Korenev, V. V.; Savelyev, A. V.; Maximov, M. V.; Zubov, F. I.; Shernyakov, Yu M.; Zhukov, A. E.

2017-11-01

The effect of modulation p-doping on multi-state lasing in InAs/InGaAs quantum dot (QD) lasers is studied for different levels of acceptor concentration. It is shown that in case of the short laser cavities, p-doping results in higher output power of the ground-state optical transitions of InAs/InGaAs QDs whereas in longer samples p-doping may result in the decrease of this power component. On the basis of this observation, the optimal design of laser active region and optimal doping level are discussed in details.

Application of the GA-BP Neural Network in Earthwork Calculation

NASA Astrophysics Data System (ADS)

Fang, Peng; Cai, Zhixiong; Zhang, Ping

2018-01-01

The calculation of earthwork quantity is the key factor to determine the project cost estimate and the optimization of the scheme. It is of great significance and function in the excavation of earth and rock works. We use optimization principle of GA-BP intelligent algorithm running process, and on the basis of earthwork quantity and cost information database, the design of the GA-BP neural network intelligent computing model, through the network training and learning, the accuracy of the results meet the actual engineering construction of gauge fan requirements, it provides a new approach for other projects the calculation, and has good popularization value.

Indium gallium nitride-based ultraviolet, blue, and green light-emitting diodes functionalized with shallow periodic hole patterns

PubMed Central

Jeong, Hyun; Salas-Montiel, Rafael; Lerondel, Gilles; Jeong, Mun Seok

2017-01-01

In this study, we investigated the improvement in the light output power of indium gallium nitride (InGaN)-based ultraviolet (UV), blue, and green light-emitting diodes (LEDs) by fabricating shallow periodic hole patterns (PHPs) on the LED surface through laser interference lithography and inductively coupled plasma etching. Noticeably, different enhancements were observed in the light output powers of the UV, blue, and green LEDs with negligible changes in the electrical properties in the light output power versus current and current versus voltage curves. In addition, confocal scanning electroluminescence microscopy is employed to verify the correlation between the enhancement in the light output power of the LEDs with PHPs and carrier localization of InGaN/GaN multiple quantum wells. Light propagation through the PHPs on the UV, blue, and green LEDs is simulated using a three-dimensional finite-difference time-domain method to confirm the experimental results. Finally, we suggest optimal conditions of PHPs for improving the light output power of InGaN LEDs based on the experimental and theoretical results. PMID:28374856

Indium gallium nitride-based ultraviolet, blue, and green light-emitting diodes functionalized with shallow periodic hole patterns.

PubMed

Jeong, Hyun; Salas-Montiel, Rafael; Lerondel, Gilles; Jeong, Mun Seok

2017-04-04

In this study, we investigated the improvement in the light output power of indium gallium nitride (InGaN)-based ultraviolet (UV), blue, and green light-emitting diodes (LEDs) by fabricating shallow periodic hole patterns (PHPs) on the LED surface through laser interference lithography and inductively coupled plasma etching. Noticeably, different enhancements were observed in the light output powers of the UV, blue, and green LEDs with negligible changes in the electrical properties in the light output power versus current and current versus voltage curves. In addition, confocal scanning electroluminescence microscopy is employed to verify the correlation between the enhancement in the light output power of the LEDs with PHPs and carrier localization of InGaN/GaN multiple quantum wells. Light propagation through the PHPs on the UV, blue, and green LEDs is simulated using a three-dimensional finite-difference time-domain method to confirm the experimental results. Finally, we suggest optimal conditions of PHPs for improving the light output power of InGaN LEDs based on the experimental and theoretical results.

Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

NASA Astrophysics Data System (ADS)

Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

2016-11-01

In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

Promoting Charge Separation and Injection by Optimizing the Interfaces of GaN:ZnO Photoanode for Efficient Solar Water Oxidation.

PubMed

Wang, Zhiliang; Zong, Xu; Gao, Yuying; Han, Jingfeng; Xu, Zhiqiang; Li, Zheng; Ding, Chunmei; Wang, Shengyang; Li, Can

2017-09-13

Photoelectrochemical water splitting provides an attractive way to store solar energy in molecular hydrogen as a kind of sustainable fuel. To achieve high solar conversion efficiency, the most stringent criteria are effective charge separation and injection in electrodes. Herein, efficient photoelectrochemical water oxidation is realized by optimizing charge separation and surface charge transfer of GaN:ZnO photoanode. The charge separation can be greatly improved through modified moisture-assisted nitridation and HCl acid treatment, by which the interfaces in GaN:ZnO solid solution particles are optimized and recombination centers existing at the interfaces are depressed in GaN:ZnO photoanode. Moreover, a multimetal phosphide of NiCoFeP was employed as water oxidation cocatalyst to improve the charge injection at the photoanode/electrolyte interface. Consequently, it significantly decreases the overpotential and brings the photocurrent to a benchmark of 3.9 mA cm -2 at 1.23 V vs RHE and a solar conversion efficiency over 1% was obtained.

Optical and scintillation properties of ce-doped (Gd2Y1)Ga2.7Al2.3O12 single crystal grown by Czochralski method

NASA Astrophysics Data System (ADS)

Wang, Chao; Wu, Yuntao; Ding, Dongzhou; Li, Huanying; Chen, Xiaofeng; Shi, Jian; Ren, Guohao

2016-06-01

Multicomponent garnets, due to their excellent light yield and energy resolution, become one of the most promising scintillators used for homeland security and nuclear non-proliferation applications. This work focuses on the optimization of Ce-doped (Gd,Y)3(Ga,Al)5O12 scintillators using a combination strategy of pre-screening and scale-up. Ce-doped GdxY1-xGayAl5-yO12 (x=1, 2 and y=2, 2.2, 2.5, 2.7, 3) polycrystalline powders were prepared by high-temperature solid state reaction method. The desired garnet phase in all the samples was confirmed using X-ray diffraction measurement. By comparing the radioluminescence intensity, the highest scintillation efficiency was achieved at a component of Gd2Y1Ga2.7Al2.3O12:Ce powders. A (Gd2Y1)Ga2.7Al2.3O12 doped with 1% Ce single crystal with dimensions of Ø35×40 mm was grown by Czochralski method using a <111> oriented seed. Luminescence and scintillation properties were measured. An optical transmittance of 84% was achieved in the concerned wavelength from 500 to 800 nm. Its 5d-4f emission of Ce3+ is at 530 nm. The light yield of a Ce1%: Gd2Y1Ga2.7Al2.3O12 single crystal slab at a size of 5×5×1 mm3 can reach about 65,000±3000 Ph/MeV along with two decay components of 94 and 615 ns under 137Cs source irradiation.

Optimal Design of Miniaturized Reflecting Metasurfaces for Ultra-Wideband and Angularly Stable Polarization Conversion.

PubMed

Borgese, Michele; Costa, Filippo; Genovesi, Simone; Monorchio, Agostino; Manara, Giuliano

2018-05-16

An ultra-wideband linear polarization converter based on a reflecting metasurface is presented. The polarizer is composed by a periodic arrangement of miniaturized metallic elements printed on a grounded dielectric substrate. In order to achieve broadband polarization converting properties, the metasurface is optimized by employing a genetic algorithm (GA) which imposes the minimization of the amplitude of the co-polar reflection coefficient over a wide frequency band. The enhanced angular stability of the polarization converter is due to the miniaturized unit cell which is obtained by imposing the maximum periodicity of the metasurface in the GA optimization process. The pixelated polarization converter obtained by the GA exhibits a relative bandwidth of 102% working from 8.12 GHz to 25.16 GHz. The analysis of the surface current distribution of the metasurface led to a methodology for refining the optimized GA solution based on the sequential removal of pixels of the unit cell on which surface currents are not excited. The relative bandwidth of the refined polarizer is extended up to 117.8% with a unit cell periodicity of 0.46 mm, corresponding to λ/20 at the maximum operating frequency. The performance of the proposed ultra-wideband polarization metasurface has been confirmed through full-wave simulations and measurements.

ONLINE MINIMIZATION OF VERTICAL BEAM SIZES AT APS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yipeng

In this paper, online minimization of vertical beam sizes along the APS (Advanced Photon Source) storage ring is presented. A genetic algorithm (GA) was developed and employed for the online optimization in the APS storage ring. A total of 59 families of skew quadrupole magnets were employed as knobs to adjust the coupling and the vertical dispersion in the APS storage ring. Starting from initially zero current skew quadrupoles, small vertical beam sizes along the APS storage ring were achieved in a short optimization time of one hour. The optimization results from this method are briefly compared with the onemore » from LOCO (Linear Optics from Closed Orbits) response matrix correction.« less

Design and optimization of a high-efficiency array generator in the mid-IR with binary subwavelength grooves.

PubMed

Bloom, Guillaume; Larat, Christian; Lallier, Eric; Lee-Bouhours, Mane-Si Laure; Loiseaux, Brigitte; Huignard, Jean-Pierre

2011-02-10

We have designed a high-efficiency array generator composed of subwavelength grooves etched in a GaAs substrate for operation at 4.5 μm. The method used combines rigorous coupled wave analysis with an optimization algorithm. The optimized beam splitter has both a high efficiency (∼96%) and a good intensity uniformity (∼0.2%). The fabrication error tolerances are numerically calculated, and it is shown that this subwavelength array generator could be fabricated with current electron beam writers and inductively coupled plasma etching. Finally, we studied the effect of a simple and realistic antireflection coating on the performance of the beam splitter.

Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

NASA Astrophysics Data System (ADS)

Haupt, Sue Ellen; Beyer-Lout, Anke; Long, Kerrie J.; Young, George S.

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search.

PubMed

Villagra, Andrea; Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology.

Optimization of microphysics in the Unified Model, using the Micro-genetic algorithm.

NASA Astrophysics Data System (ADS)

Jang, J.; Lee, Y.; Lee, H.; Lee, J.; Joo, S.

2016-12-01

This study focuses on parameter optimization of microphysics in the Unified Model (UM) using the Micro-genetic algorithm (Micro-GA). We need the optimization of microphysics in UM. Because, Microphysics in the Numerical Weather Prediction (NWP) model is important to Quantitative Precipitation Forecasting (QPF). The Micro-GA searches for optimal parameters on the basis of fitness function. The five parameters are chosen. The target parameters include x1, x2 related to raindrop size distribution, Cloud-rain correlation coefficient, Surface droplet number and Droplet taper height. The fitness function is based on the skill score that is BIAS and Critical Successive Index (CSI). An interface between UM and Micro-GA is developed and applied to three precipitation cases in Korea. The cases are (ⅰ) heavy rainfall in the Southern area because of typhoon NAKRI, (ⅱ) heavy rainfall in the Youngdong area, and (ⅲ) heavy rainfall in the Seoul metropolitan area. When the optimized result is compared to the control result (using the UM default value, CNTL), the optimized result leads to improvements in precipitation forecast, especially for heavy rainfall of the late forecast time. Also, we analyze the skill score of precipitation forecasts in terms of various thresholds of CNTL, Optimized result, and experiments on each optimized parameter for five parameters. Generally, the improvement is maximized when the five optimized parameters are used simultaneously. Therefore, this study demonstrates the ability to improve Korean precipitation forecasts by optimizing microphysics in UM.

Safety, Biodistribution, and Radiation Dosimetry of 68Ga-OPS202 in Patients with Gastroenteropancreatic Neuroendocrine Tumors: A Prospective Phase I Imaging Study.

PubMed

Nicolas, Guillaume P; Beykan, Seval; Bouterfa, Hakim; Kaufmann, Jens; Bauman, Andreas; Lassmann, Michael; Reubi, Jean Claude; Rivier, Jean E F; Maecke, Helmut R; Fani, Melpomeni; Wild, Damian

2018-06-01

Preclinical and preliminary clinical evidence indicates that radiolabeled somatostatin (sst) receptor antagonists perform better than agonists in detecting neuroendocrine tumors (NETs). We performed a prospective phase I/II study to evaluate the sst receptor antagonist 68 Ga-OPS202 ( 68 Ga-NODAGA-JR11; NODAGA = 1,4,7-triazacyclononane,1-glutaric acid-4,7-acetic acid and JR11 = Cpa-c(dCys-Aph(Hor)-dAph(Cbm)-Lys-Thr-Cys)-dTyr-NH 2 )) for PET imaging. Here, we report the results of phase I of the study. Methods: Patients received 2 single 150-MBq intravenous injections of 68 Ga-OPS202 3-4 wk apart (15 μg of peptide at visit 1 and 50 μg at visit 2). At visit 1, a dynamic PET/CT scan over the kidney was obtained during the first 30 min after injection, and static whole-body scans were obtained at 0.5, 1, 2, and 4 h after injection; at visit 2, a static whole-body scan was obtained at 1 h. Blood samples and urine were collected at regular intervals to determine 68 Ga-OPS202 pharmacokinetics. Safety, biodistribution, radiation dosimetry, and the most appropriate imaging time point for 68 Ga-OPS202 were assessed. Results: Twelve patients with well-differentiated gastroenteropancreatic (GEP) NETs took part in the study. 68 Ga-OPS202 cleared rapidly from the blood, with a mean residence time of 2.4 ± 1.1 min/L. The organs with the highest mean dose coefficients were the urinary bladder wall, kidneys, and spleen. The calculated effective dose was 2.4E-02 ± 0.2E-02 mSv/MBq, corresponding to 3.6 mSv, for a reference activity of 150 MBq. Based on total numbers of detected malignant lesions, the optimal time window for the scan was between 1 and 2 h. For malignant liver lesions, the time point at which most patients had the highest mean tumor contrast was 1 h. 68 Ga-OPS202 was well tolerated; adverse events were grade 1 or 2, and there were no signals of concern from laboratory blood or urinalysis tests. Conclusion: 68 Ga-OPS202 showed favorable biodistribution and imaging properties, with optimal tumor contrast between 1 and 2 h after injection. Dosimetry analysis revealed that the dose delivered by 68 Ga-OPS202 to organs is similar to that delivered by other 68 Ga-labeled sst analogs. Further evaluation of 68 Ga-OPS202 for PET/CT imaging of NETs is therefore warranted. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Statistical and evolutionary optimization for enhanced production of an antileukemic enzyme, L-asparaginase, in a protease-deficient Bacillus aryabhattai ITBHU02 isolated from the soil contaminated with hospital waste.

PubMed

Singh, Yogendra; Srivastav, S K

2013-04-01

Over the past few decades, L-asparaginase has emerged as an excellent anti-neoplastic agent. In present study, a new strain ITBHU02, isolated from soil site near degrading hospital waste, was investigated for the production of extracellular L-asparaginase. Further, it was renamed as Bacillus aryabhattai ITBHU02 based on its phenotypical features, biochemical characteristics, fatty acid methyl ester (FAME) profile and phylogenetic similarity of 16S rDNA sequences. The strain was found protease-deficient and its optimal growth occurred at 37 degrees C and pH 7.5. The strain was capable of producing enzyme L-asparaginase with maximum specific activity of 3.02 +/- 0.3 Umg(-1) protein, when grown in un-optimized medium composition and physical parameters. In order to improve the production of L-asparaginase by the isolate, response surface methodology (RSM) and genetic algorithm (GA) based techniques were implemented. The data achieved through the statistical design matrix were used for regression analysis and analysis of variance studies. Furthermore, GA was implemented utilizing polynomial regression equation as a fitness function. Maximum average L-asparaginase productivity of 6.35 Umg(-1) was found at GA optimized concentrations of 4.07, 0.82, 4.91, and 5.2 gL(-1) for KH2PO4, MgSO4 x 7H2O, L-asparagine, and glucose respectively. The GA optimized yield of the enzyme was 7.8% higher in comparison to the yield obtained through RSM based optimization.

Neuro-genetic multioptimization of the determination of polychlorinated biphenyl congeners in human milk by headspace solid phase microextraction coupled to gas chromatography with electron capture detection.

PubMed

Kowalski, Cláudia Hoffmann; da Silva, Gilmare Antônia; Poppi, Ronei Jesus; Godoy, Helena Teixeira; Augusto, Fabio

2007-02-28

Polychlorinated biphenyls (PCB) can eventually contaminate breast milk, which is a serious issue to the newborn due to their high vulnerability. Solid phase microextraction (SPME) can be a very convenient technique for their isolation and pre-concentration prior chromatographic analysis. Here, a simultaneous multioptimization strategy based on a neuro-genetic approach was applied to a headspace SPME method for determination of 12 PCB in human milk. Gas chromatography with electron capture detection (ECD) was adopted for the separation and detection of the analytes. Experiments according to a Doehlert design were carried out with varied extraction time and temperature, media ionic strength and concentration of the methanol (co-solvent). To find the best model that simultaneously correlate all PCB peak areas and SPME extraction conditions, a multivariate calibration method based on a Bayesian Neural Network (BNN) was applied. The net output from the neural network was used as input in a genetic algorithm (GA) optimization operation (neuro-genetic approach). The GA pointed out that the best values of the overall SPME operational conditions were the saturation of the media with NaCl, extraction temperature of 95 degrees C, extraction time of 60 min and addition of 5% (v/v) methanol to the media. These optimized parameters resulted in the decrease of the detection limits and increase on the sensitivity for all tested analytes, showing that the use of neuro-genetic approach can be a promising way for optimization of SPME methods.

Development of Agrobacterium-Mediated Virus-Induced Gene Silencing and Performance Evaluation of Four Marker Genes in Gossypium barbadense

PubMed Central

Pang, Jinhuan; Zhu, Yue; Li, Qing; Liu, Jinzhi; Tian, Yingchuan; Liu, Yule; Wu, Jiahe

2013-01-01

Gossypium barbadense is a cultivated cotton species and possesses many desirable traits, including high fiber quality and resistance to pathogens, especially Verticilliumdahliae (a devastating pathogen of Gossypium hirsutum, the main cultivated species). These elite traits are difficult to be introduced into G. hirsutum through classical breeding methods. In addition, genetic transformation of G . barbadense has not been successfully performed. It is therefore important to develop methods for evaluating the function and molecular mechanism of genes in G . barbadense . In this study, we had successfully introduced a virus-induced gene silencing (VIGS) system into three cultivars of G . barbadense by inserting marker genes into the tobacco rattle virus (TRV) vector. After we optimized the VIGS conditions, including light intensity, photoperiod, seedling age and Agrobacterium strain, 100% of plants agroinfiltrated with the GaPDS silencing vector showed white colored leaves. Three other marker genes, GaCLA1, GaANS and GaANR, were employed to further test this VIGS system in G . barbadense . The transcript levels of the endogenous genes in the silenced plants were reduced by more than 99% compared to control plants; these plants presented phenotypic symptoms 2 weeks after inoculation. We introduced a fusing sequence fragment of GaPDS and GaANR gene silencing vectors into a single plant, which resulted in both photobleaching and brownish coloration. The extent of silencing in plants agroinfiltrated with fusing two-gene-silencing vector was consistent with plants harboring a single gene silencing vector. The development of this VIGS system should promote analysis of gene function in G . barbadense , and help to contribute desirable traits for breeding of G . barbadense and G. hirsutum. PMID:24023833

Influence of the AlN nucleation layer on the properties of AlGaN/GaN heterostructure on Si (1 1 1) substrates

NASA Astrophysics Data System (ADS)

Pan, Lei; Dong, Xun; Li, Zhonghui; Luo, Weike; Ni, Jinyu

2018-07-01

AlGaN/GaN heterostructures were grown on Si (1 1 1) substrates with different AlN nucleation layers (NL) by metal-organic chemical vapor deposition (MOCVD). The results indicate that the growth temperature of AlN NL has a noticeable influence on the structural, electronic and optical properties of the AlGaN/GaN heterostructures. Optimizing the growth temperature to 1040 °C led to quasi-2D smooth surface of the AlN NL with providing sufficient compressive stress to suppress cracking of the subsequent GaN layer during the cooling process, resulting in improved crystalline quality of GaN layer and superior two-dimensional electron gas (2DEG) performance of the AlGaN/GaN heterostructure.

Structural and optical studies of nitrogen incorporation into GaSb-based GaInSb quantum wells

NASA Astrophysics Data System (ADS)

Nair, Hari P.; Crook, Adam M.; Yu, Kin M.; Bank, Seth R.

2012-01-01

We investigate the incorporation of nitrogen into (Ga,In)Sb grown on GaSb and report room temperature photoluminescence from GaInSb(N) quantum wells. X-ray diffraction and channeling nuclear reaction analysis, together with Rutherford backscattering, were employed to identify the optimal molecular beam epitaxial growth conditions that minimized the incorporation of non-substitutional nitrogen into GaNSb. Consistent with this hypothesis, GaInSb(N) quantum wells grown under the conditions that minimized non-substitutional nitrogen exhibited room temperature photoluminescence, indicative of significantly improved radiative efficiency. Further development of this material system could enable type-I laser diodes emitting throughout the (3-5 μm) wavelength range.

Global search in photoelectron diffraction structure determination using genetic algorithms

NASA Astrophysics Data System (ADS)

Viana, M. L.; Díez Muiño, R.; Soares, E. A.; Van Hove, M. A.; de Carvalho, V. E.

2007-11-01

Photoelectron diffraction (PED) is an experimental technique widely used to perform structural determinations of solid surfaces. Similarly to low-energy electron diffraction (LEED), structural determination by PED requires a fitting procedure between the experimental intensities and theoretical results obtained through simulations. Multiple scattering has been shown to be an effective approach for making such simulations. The quality of the fit can be quantified through the so-called R-factor. Therefore, the fitting procedure is, indeed, an R-factor minimization problem. However, the topography of the R-factor as a function of the structural and non-structural surface parameters to be determined is complex, and the task of finding the global minimum becomes tough, particularly for complex structures in which many parameters have to be adjusted. In this work we investigate the applicability of the genetic algorithm (GA) global optimization method to this problem. The GA is based on the evolution of species, and makes use of concepts such as crossover, elitism and mutation to perform the search. We show results of its application in the structural determination of three different systems: the Cu(111) surface through the use of energy-scanned experimental curves; the Ag(110)-c(2 × 2)-Sb system, in which a theory-theory fit was performed; and the Ag(111) surface for which angle-scanned experimental curves were used. We conclude that the GA is a highly efficient method to search for global minima in the optimization of the parameters that best fit the experimental photoelectron diffraction intensities to the theoretical ones.

[Use of hypnosis in radiotherapy as an alternative to general anesthesia in pediatric radiation oncology].

PubMed

Claude, Line; Morelle, Magali; Mancini, Sandrine; Duncan, Anita; Sebban, Henri; Carrie, Christian; Marec-Berard, Perrine

2016-11-01

General anesthesia (GA) is often needed for radiotherapy (RT) in young children. This study aimed to evaluate the place of the rituals and/or hypnosis in pediatric in a reference center in pediatric radiation oncology in Rhône-Alpes Auvergne. This observational study retrospectively collected data on AG in children<5 years treated by RT in Leon-Berard regional center, Lyon, France between 2003 and 2014. Two-time periods, before and after 2008 have been compared, the second one introducing accompaniment methods such as hypnosis systematically. Explanatory analyses of AG were performed using logistic regression. One hundred and thirty-two children benefited from RT in that period and were included (70 patients until 2008, 62 after 2008). Fifty-three percent were irradiated under GA. There was significant reduction (P<0.1) in the use of GA after 2008. The use of GA was not significantly associated with the RT techniques. The patients more likely to undergo RT without GA were the oldest and the patients treated for abdominal lesions (P<0.01). The study confirms that rituals and hypnosis can be used instead of GA in about half of patients under 5 years, even also with high-technicity RT requiring optimal immobilization. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

Improvement of the GaSb/Al2O3 interface using a thin InAs surface layer

NASA Astrophysics Data System (ADS)

Greene, Andrew; Madisetti, Shailesh; Nagaiah, Padmaja; Yakimov, Michael; Tokranov, Vadim; Moore, Richard; Oktyabrsky, Serge

2012-12-01

The highly reactive GaSb surface was passivated with a thin InAs layer to limit interface trap state density (Dit) at the III-V/high-k oxide interface. This InAs surface was subjected to various cleaning processes to effectively reduce native oxides before atomic layer deposition (ALD). Ammonium sulfide pre-cleaning and trimethylaluminum/water ALD were used in conjunction to provide a clean interface and annealing in forming gas (FG) at 350 °C resulted in an optimized fabrication for n-GaSb/InAs/high-k gate stacks. Interface trap density, Dit ≈ 2-3 × 1012 cm-2eV-1 resided near the n-GaSb conductance band which was extracted and compared with three different methods. Conductance-voltage-frequency plots showed efficient Fermi level movement and a sub-threshold slope of 200 mV/dec. A composite high-k oxide process was also developed using ALD of Al2O3 and HfO2 resulting in a Dit ≈ 6-7 × 1012 cm-2eV-1. Subjecting these samples to a higher (450 °C) processing temperature results in increased oxidation and a thermally unstable interface. p-GaSb displayed very fast minority carrier generation/recombination likely due to a high density of bulk traps in GaSb.

Optically Based Rapid Screening Method for Proven Optimal Treatment Strategies before Treatment Begins

DTIC Science & Technology

2014-08-01

tumor size, measured by mammography, MRI, or ultrasound. These methods evaluate the regimen that the patient received. Molecular changes induced by...photon counting electronics (SPC-150, Becker and Hickl) and a GaAsP PMT (H7422P-40, Hamamatsu). Photon count rates were maintained above 5x10 5...conditions (33), thus making them an attractive system to evaluate tumor response to drugs. We used OMI to assess the response of primary breast tumor

An algorithmic framework for multiobjective optimization.

PubMed

Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.

An Algorithmic Framework for Multiobjective Optimization

PubMed Central

Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.

2013-01-01

Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795

InGaN/GaN multilayer quantum dots yellow-green light-emitting diode with optimized GaN barriers.

PubMed

Lv, Wenbin; Wang, Lai; Wang, Jiaxing; Hao, Zhibiao; Luo, Yi

2012-11-07

InGaN/GaN multilayer quantum dot (QD) structure is a potential type of active regions for yellow-green light-emitting diodes (LEDs). The surface morphologies and crystalline quality of GaN barriers are critical to the uniformity of InGaN QD layers. While GaN barriers were grown in multi-QD layers, we used improved growth parameters by increasing the growth temperature and switching the carrier gas from N2 to H2 in the metal organic vapor phase epitaxy. As a result, a 10-layer InGaN/GaN QD LED is demonstrated successfully. The transmission electron microscopy image shows the uniform multilayer InGaN QDs clearly. As the injection current increases from 5 to 50 mA, the electroluminescence peak wavelength shifts from 574 to 537 nm.

InGaN/GaN multilayer quantum dots yellow-green light-emitting diode with optimized GaN barriers

PubMed Central

2012-01-01

InGaN/GaN multilayer quantum dot (QD) structure is a potential type of active regions for yellow-green light-emitting diodes (LEDs). The surface morphologies and crystalline quality of GaN barriers are critical to the uniformity of InGaN QD layers. While GaN barriers were grown in multi-QD layers, we used improved growth parameters by increasing the growth temperature and switching the carrier gas from N2 to H2 in the metal organic vapor phase epitaxy. As a result, a 10-layer InGaN/GaN QD LED is demonstrated successfully. The transmission electron microscopy image shows the uniform multilayer InGaN QDs clearly. As the injection current increases from 5 to 50 mA, the electroluminescence peak wavelength shifts from 574 to 537 nm. PMID:23134721

Comparison of the physical, chemical and electrical properties of ALD Al 2 O 3 on c- and m- plane GaN: Comparison of the physical, chemical and electrical properties of ALD Al 2 O 3 on c- and m- plane GaN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wei, D.; Hossain, T.; Nepal, N.

2014-02-01

Our study compares the physical, chemical and electrical properties of Al 2O 3 thin films deposited on gallium polar c- and nonpolar m -plane GaN substrates by atomic layer deposition (ALD). Correlations were sought between the film's structure, composition, and electrical properties. The thickness of the Al 2O 3 films was 19.2 nm as determined from a Si witness sample by spectroscopic ellipsometry. We measured the gate dielectric was slightly aluminum-rich (Al:O=1:1.3) from X-ray photoelectron spectroscopy (XPS) depth profile, and the oxide-semiconductor interface carbon concentration was lower on c -plane GaN. The oxide's surface morphology was similar on both substrates,more » but was smoothest on c -plane GaN as determined by atomic force microscopy (AFM). Circular capacitors (50-300 μm diameter) with Ni/Au (20/100 nm) metal contacts on top of the oxide were created by standard photolithography and e-beam evaporation methods to form metal-oxide-semiconductor capacitors (MOSCAPs). Moreover, the alumina deposited on c -plane GaN showed less hysteresis (0.15 V) than on m -plane GaN (0.24 V) in capacitance-voltage (CV) characteristics, consistent with its better quality of this dielectric as evidenced by negligible carbon contamination and smooth oxide surface. These results demonstrate the promising potential of ALD Al 2O 3 on c -plane GaN, but further optimization of ALD is required to realize the best properties of Al 2O 3 on m -plane GaN.« less

Growth and analysis of highly oriented (11n) BCSCO films for device research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raina, K.K.; Pandey, R.K.

1994-12-31

Films of BCSCO superconductor of the type Bi{sub 2}CaSr{sub 2}Cu{sub 2}O{sub x} have been grown by liquid phase epitaxy method (LPE), using a partially closed growth chamber. The films were grown on (001) and (110) NdGaO{sub 3} substrates by slow cooling process in an optimized temperature range below the peritectic melting point (880{degrees}C) of Bi{sub 2}CaSr{sub 2}Cu{sub 2}O{sub 8}. Optimization of parameters, such as seed rotation, soak of initial growth temperature and growth period results in the formation of 2122 phase BCSCO films. The films grown at rotation rates of less than 30 and more than 70 rpm are observedmore » to be associated with the second phase of Sr-Ca-Cu-O system. Higher growth temperatures (>860{degrees}C) also encourage to the formation of this phase. XRD measurements show that the films grown on (110) NdGaO{sub 3} have a preferred (11n)-orientation. It is pertinent to mention here that in our earlier results published elsewhere we obtained c-axis oriented Bi{sub 2}CaSr{sub 2}Cu{sub 2}O{sub 8} phase films on (001) NdGaO{sub 3} substrate. Critical current density is found to be higher for the films grown on (110) than (001) NdGaO{sub 3} substrate orientation. The best values of zero resistance (T{sub co}) and critical current density obtained are 87 K and 10{sup 5} A/cm{sup 2}, respectively.« less

AlGaAs-GaAs cascade solar cell

NASA Technical Reports Server (NTRS)

Lamorte, M. F.; Abbott, D. H.

1980-01-01

Computer modeling studies are reported for a monolithic, two junction, cascade solar cell using the AlGaAs GaAs materials combination. An optimum design was obtained through a serial optimization procedure by which conversion efficiency is maximized for operation at 300 K, AM 0, and unity solar concentration. Under these conditions the upper limit on efficiency was shown to be in excess of 29 percent, provided surface recombination velocity did not exceed 10,000 cm/sec.

Quadruped Robot Locomotion using a Global Optimization Stochastic Algorithm

NASA Astrophysics Data System (ADS)

Oliveira, Miguel; Santos, Cristina; Costa, Lino; Ferreira, Manuel

2011-09-01

The problem of tuning nonlinear dynamical systems parameters, such that the attained results are considered good ones, is a relevant one. This article describes the development of a gait optimization system that allows a fast but stable robot quadruped crawl gait. We combine bio-inspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). CPGs are modelled as autonomous differential equations, that generate the necessar y limb movement to perform the required walking gait. The GA finds parameterizations of the CPGs parameters which attain good gaits in terms of speed, vibration and stability. Moreover, two constraint handling techniques based on tournament selection and repairing mechanism are embedded in the GA to solve the proposed constrained optimization problem and make the search more efficient. The experimental results, performed on a simulated Aibo robot, demonstrate that our approach allows low vibration with a high velocity and wide stability margin for a quadruped slow crawl gait.

Effects of growth temperature on the properties of InGaN channel heterostructures grown by pulsed metal organic chemical vapor deposition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yachao; Zhou, Xiaowei; Xu, Shengrui

Pulsed metal organic chemical vapor deposition (P-MOCVD) is introduced into the growth of high quality InGaN channel heterostructures. The effects of InGaN channel growth temperature on the structural and transport properties of the heterostructures are investigated in detail. High resolution x-ray diffraction (HRXRD) and Photoluminescence (PL) spectra indicate that the quality of InGaN channel strongly depends on the growth temperature. Meanwhile, the atomic force microscopy (AFM) results show that the interface morphology between the InGaN channel and the barrier layer also relies on the growth temperature. Since the variation of material properties of InGaN channel has a significant influence onmore » the electrical properties of InAlN/InGaN heterostructures, the optimal transport properties can be achieved by adjusting the growth temperature. A very high two dimension electron gas (2DEG) density of 1.92 × 10{sup 13} cm{sup −2} and Hall electron mobility of 1025 cm{sup 2}/(V⋅s) at room temperature are obtained at the optimal growth temperature around 740 °C. The excellent transport properties in our work indicate that the heterostructure with InGaN channel is a promising candidate for the microwave power devices, and the results in this paper will be instructive for further study of the InGaN channel heterostructures.« less

Nonlinear absorption in AlGaAs/GaAs multiple quantum well structures grown by metalorganic chemical vapor deposition

NASA Technical Reports Server (NTRS)

Lee, H. C.; Hariz, A.; Dapkus, P. D.; Kost, A.; Kawase, M.

1987-01-01

This paper reports the study of growth conditions for achieving the sharp exciton resonances and low-intensity saturation of these resonances in AlGaAs-GaAs multiple quantum well structures grown by metalorganic chemical vapor deposition. Low growth temperature is necessary to observe this sharp resonance feature at room temperature. The optimal growth conditions are a tradeoff between the high temperatures required for high quality AlGaAs and low temperatures required for high-purity GaAs. A strong optical saturation of the excitonic absorption has been observed. A saturation density as low as 250 W/sq cm is reported.

Characteristics of Incident Geographic Atrophy in the Complications of Age-related Macular Degeneration Prevention Trial

PubMed Central

Brader, Hilary Smolen; Ying, Gui-shuang; Martin, E. Revell; Maguire, Maureen G.

2013-01-01

Objective To characterize the size, location, conformation, and features of incident geographic atrophy (GA) as detected by annual stereoscopic color photographs and fluorescein angiograms (FAs). Design Retrospective cohort study within a larger clinical trial Participants Patients with bilateral large drusen who developed GA during the course of the Complications of Age-related Macular Degeneration Prevention Trial (CAPT). Methods Annual stereoscopic color photographs and FAs were reviewed from 114 CAPT patients who developed GA in the untreated eye during 5-6 years of follow-up. Geographic atrophy was defined according to the Revised GA Criteria for identifying early GA23. Color-optimized fundus photographs were viewed concurrently with the FAs during grading. Main Outcome Measures Size and distance from the fovea of individual GA lesions, number of areas of atrophy, and change in visual acuity (VA) when GA first developed in an eye. Results At presentation, the median total GA area was 0.26mm2 (0.1 Disc area). GA presented as a single lesion in 89 (78%) of eyes. The median distance from the fovea was 395μm. Twenty percent of incident GA lesions were subfoveal and an additional 18% were within 250μm of the foveal center. Development of GA was associated with a mean decrease of 7 letters from the baseline visual acuity level compared to 1 letter among matched early age-related macular degeneration (AMD) eyes without GA. GA that formed in areas previously occupied by drusenoid pigment epithelial detachments (DPED) were on average larger (0.53 vs. 0.20 mm2; p=0.0001), more central (50 vs. 500 microns from the center of the fovea; p<0.0001), and associated with significantly worse visual outcome (20/50 vs. 20/25; p=0.0003) than GA with other drusen types as precursors. Conclusions Incident geographic atrophy most often appears on color fundus photographs and fluorescein angiograms as a small, singular, parafoveal lesion, though a large minority of lesions are subfoveal or multifocal at initial detection. The characteristics of incident geographic atrophy vary with precursor drusen types. These data can facilitate design of future clinical trials of therapies for GA. PMID:23622873

Modelling and optimization of environmental conditions for kefiran production by Lactobacillus kefiranofaciens.

PubMed

Cheirsilp, B; Shimizu, H; Shioya, S

2001-12-01

A mathematical model for kefiran production by Lactobacillus kefiranofaciens was established, in which the effects of pH, substrate and product on cell growth, exopolysaccharide formation and substrate assimilation were considered. The model gave a good representation both of the formation of exopolysaccharides (which are not only attached to cells but also released into the medium) and of the time courses of the production of galactose and glucose in the medium (which are produced and consumed by the cells). Since pH and both lactose and lactic acid concentrations differently affected production and growth activity, the model included the effects of pH and the concentrations of lactose and lactic acid. Based on the mathematical model, an optimal pH profile for the maximum production of kefiran in batch culture was obtained. In this study, a simplified optimization method was developed, in which the optimal pH profile was determined at a particular final fermentation time. This was based on the principle that, at a certain time, switching from the maximum specific growth rate to the critical one (which yields the maximum specific production rate) results in maximum production. Maximum kefiran production was obtained, which was 20% higher than that obtained in the constant-pH control fermentation. A genetic algorithm (GA) was also applied to obtain the optimal pH profile; and it was found that practically the same solution was obtained using the GA.

A new approach to human microRNA target prediction using ensemble pruning and rotation forest.

PubMed

Mousavi, Reza; Eftekhari, Mahdi; Haghighi, Mehdi Ghezelbash

2015-12-01

MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.

Optimized hyperspectral band selection using hybrid genetic algorithm and gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2015-12-01

The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.

Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China.

PubMed

Hong, Haoyuan; Tsangaratos, Paraskevas; Ilia, Ioanna; Liu, Junzhi; Zhu, A-Xing; Xu, Chong

2018-07-15

The main objective of the present study was to utilize Genetic Algorithms (GA) in order to obtain the optimal combination of forest fire related variables and apply data mining methods for constructing a forest fire susceptibility map. In the proposed approach, a Random Forest (RF) and a Support Vector Machine (SVM) was used to produce a forest fire susceptibility map for the Dayu County which is located in southwest of Jiangxi Province, China. For this purpose, historic forest fires and thirteen forest fire related variables were analyzed, namely: elevation, slope angle, aspect, curvature, land use, soil cover, heat load index, normalized difference vegetation index, mean annual temperature, mean annual wind speed, mean annual rainfall, distance to river network and distance to road network. The Natural Break and the Certainty Factor method were used to classify and weight the thirteen variables, while a multicollinearity analysis was performed to determine the correlation among the variables and decide about their usability. The optimal set of variables, determined by the GA limited the number of variables into eight excluding from the analysis, aspect, land use, heat load index, distance to river network and mean annual rainfall. The performance of the forest fire models was evaluated by using the area under the Receiver Operating Characteristic curve (ROC-AUC) based on the validation dataset. Overall, the RF models gave higher AUC values. Also the results showed that the proposed optimized models outperform the original models. Specifically, the optimized RF model gave the best results (0.8495), followed by the original RF (0.8169), while the optimized SVM gave lower values (0.7456) than the RF, however higher than the original SVM (0.7148) model. The study highlights the significance of feature selection techniques in forest fire susceptibility, whereas data mining methods could be considered as a valid approach for forest fire susceptibility modeling. Copyright © 2018 Elsevier B.V. All rights reserved.

Rapid design and optimization of low-thrust rendezvous/interception trajectory for asteroid deflection missions

NASA Astrophysics Data System (ADS)

Li, Shuang; Zhu, Yongsheng; Wang, Yukai

2014-02-01

Asteroid deflection techniques are essential in order to protect the Earth from catastrophic impacts by hazardous asteroids. Rapid design and optimization of low-thrust rendezvous/interception trajectories is considered as one of the key technologies to successfully deflect potentially hazardous asteroids. In this paper, we address a general framework for the rapid design and optimization of low-thrust rendezvous/interception trajectories for future asteroid deflection missions. The design and optimization process includes three closely associated steps. Firstly, shape-based approaches and genetic algorithm (GA) are adopted to perform preliminary design, which provides a reasonable initial guess for subsequent accurate optimization. Secondly, Radau pseudospectral method is utilized to transcribe the low-thrust trajectory optimization problem into a discrete nonlinear programming (NLP) problem. Finally, sequential quadratic programming (SQP) is used to efficiently solve the nonlinear programming problem and obtain the optimal low-thrust rendezvous/interception trajectories. The rapid design and optimization algorithms developed in this paper are validated by three simulation cases with different performance indexes and boundary constraints.

Lead-germanium ohmic contact on to gallium arsenide formed by the solid phase epitaxy of germanium: A microstructure study

NASA Astrophysics Data System (ADS)

Radulescu, Fabian

2000-12-01

Driven by the remarkable growth in the telecommunication market, the demand for more complex GaAs circuitry continued to increase in the last decade. As a result, the GaAs industry is faced with new challenges in its efforts to fabricate devices with smaller dimensions that would permit higher integration levels. One of the limiting factors is the ohmic contact metallurgy of the metal semiconductor field effect transistor (MESFET), which, during annealing, induces a high degree of lateral diffusion into the substrate. Because of its limited reaction with the substrate, the Pd-Ge contact seems to be the most promising candidate to be used in the next generation of MESFET's. The Pd-Ge system belongs to a new class of ohmic contacts to compound semiconductors, part of an alloying strategy developed only recently, which relies on solid phase epitaxy (SPE) and solid phase regrowth to "un-pin" the Fermi level at the surface of the compound semiconductor. However, implementing this alloy into an integrated process flow proved to be difficult due to our incomplete understanding of the microstructure evolution during annealing and its implications on the electrical properties of the contact. The microstructure evolution and the corresponding solid state reactions that take place during annealing of the Pd-Ge thin films on to GaAs were studied in connection with their effects on the electrical properties of the ohmic contact. The phase transformations sequence, transition temperatures and activation energies were determined by combining differential scanning calorimetry (DSC) for thermal analysis with transmission electron microscopy (TEM) for microstructure identification. In-situ TEM annealing experiments on the Pd/Ge/Pd/GaAs ohmic contact system have permitted real time determination of the evolution of contact microstructure. The kinetics of the solid state reactions, which occur during ohmic contact formation, were determined by measuring the grain growth rates associated with each phase from the videotape recordings. With the exception of the Pd-GaAs interactions, it was found that four phase transformations occur during annealing of the Pd:Ge thin films on top of GaAs. The microstructural information was correlated with specific ohmic contact resistivity measurements performed in accordance with the transmission line method (TLM) and these results demonstrated that the Ge SPE growth on top of GaAs renders the optimal electrical properties for the contact. By using the focused ion beam (FIB) method to produce microcantilever beams, the residual stress present in the thin film system was studied in connection with the microstructure. Although, the PdGe/epi-Ge/GaAs seemed to be the optimal microstructural configuration, the presence of PdGe at the interface with GaAs did not damage the contact resistivity significantly. These results made it difficult to establish a charge transport mechanism across the interface but they explained the wide processing window associated with this contact.

Neural-network-assisted genetic algorithm applied to silicon clusters

NASA Astrophysics Data System (ADS)

Marim, L. R.; Lemes, M. R.; dal Pino, A.

2003-03-01

Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.

Improvement of light extraction for a target wavelength in InGaN/GaN LEDs with an indium tin oxide dual layer by oblique angle deposition

NASA Astrophysics Data System (ADS)

Seo, Dong-Ju; Lee, Dong-Seon

2016-08-01

GaN-based blue LEDs were fabricated and studied with porous, dense, and dual-layer indium tin oxide (ITO) structures as transparent top electrodes to enhance light extraction. The electroluminescence intensity of the LED with a thickness-optimized and refractive-index-tuned ITO dual layer at I = 20 mA was higher by 19.7% than that of the conventional LED with a 200 nm planar ITO. This study confirmed that an ITO dual layer can be made with a single material by optimizing the thickness and tuning the refractive index, which improves the power output without any electrical property degradation.

Growth Optimization of Metal-polar III-Nitride High-electron-mobility Transistor Structures by Molecular Beam Epitaxy

NASA Astrophysics Data System (ADS)

Kaun, Stephen William

GaN-based high-electron-mobility transistors (HEMTs) will play an important role in the next generation of high-frequency amplifiers and power-switching devices. Since parasitic conduction (leakage) through the GaN buffer layer and (Al,Ga,In)N barrier reduces the efficiency of operation, HEMT performance hinges on the epitaxial quality of these layers. Increasing the sheet charge density and mobility of the two-dimensional electron gas (2DEG) is also essential for reducing the channel resistance and improving output. The growth conditions applied in plasma-assisted molecular beam epitaxy (PAMBE) and ammonia-based molecular beam epitaxy (NH3-MBE) that result in high-quality metal-polar HEMT structures are described. The effects of threading dislocations on the gate leakage and channel conductivity of AlGaN/GaN HEMTs were studied in detail. For this purpose, a series of HEMT structures were grown on GaN templates with threading dislocation densities (TDDs) that spanned three orders of magnitude. There was a clear trend of reduced gate leakage with reduced TDD for HEMTs grown by Ga-rich PAMBE; however, a reduction in TDD also entailed an increase in buffer leakage. By reducing the unintentionally doped (UID) GaN buffer thickness and including an AlGaN back barrier, a HEMT regrown by Ga-rich PAMBE on low-TDD free-standing (FS) GaN (~5 x 107 cm-2 TDD) yielded a three-terminal breakdown voltage greater than 50 V and a power output (power-added efficiency) of 6.7 W/mm (50 %) at 4 GHz with a 40 V drain bias. High TDD was then shown to severely degrade the 2DEG mobility of AlxGa1-xN/GaN (x = 0.24, 0.12, 0.06) and AlGaN/AlN/GaN heterostructures grown by Ga-rich PAMBE. By regrowing on low-TDD FS GaN and including a 2.5 nm AlN interlayer, an Al0.24Ga0.76N/AlN/GaN heterostructure achieved a room temperature (RT) 2DEG sheet resistance of 169 Ω/□. As evidenced by atom probe tomography, the AlN interlayer grown by Ga-rich PAMBE was pure with abrupt interfaces. The pure AlN interlayer greatly reduced alloy-related scattering. When AlGaN/AlN/GaN heterostructures were grown by NH3-MBE at 820 °C, the 2DEG sheet density was lower than expected. These AlN interlayers were shown to have a significant concentration of Ga impurities by atom probe tomography. The source of these impurities was most likely the decomposition of the underlying GaN layers, as reduction of the growth temperature below 750 °C yielded a much lower concentration of Ga impurities. Flux optimization and application of an In surfactant was necessary to reduce the interface roughness in AlGaN/AlN/GaN heterostructures grown by NH3-MBE at low temperature, yielding sheet resistances below 300 Ω/□. The growth of InAlN/(GaN)/(AlN)/GaN heterostructures with lattice-matched In0.17Al0.83N barriers by N-rich PAMBE is also described. Through flux optimization, the columnar microstructure previously observed in N-rich PAMBE-grown InAlN layers was eliminated. By including a 3 nm AlN interlayer and 2 nm GaN interlayer, an In0.17Al0.83N/GaN/AlN/GaN heterostructure regrown on low-TDD FS GaN achieved an exceptionally low RT 2DEG sheet resistance of 145 Ω/□.

A multi-product green supply chain under government supervision with price and demand uncertainty

NASA Astrophysics Data System (ADS)

Hafezalkotob, Ashkan; Zamani, Soma

2018-05-01

In this paper, a bi-level game-theoretic model is proposed to investigate the effects of governmental financial intervention on green supply chain. This problem is formulated as a bi-level program for a green supply chain that produces various products with different environmental pollution levels. The problem is also regard uncertainties in market demand and sale price of raw materials and products. The model is further transformed into a single-level nonlinear programming problem by replacing the lower-level optimization problem with its Karush-Kuhn-Tucker optimality conditions. Genetic algorithm is applied as a solution methodology to solve nonlinear programming model. Finally, to investigate the validity of the proposed method, the computational results obtained through genetic algorithm are compared with global optimal solution attained by enumerative method. Analytical results indicate that the proposed GA offers better solutions in large size problems. Also, we conclude that financial intervention by government consists of green taxation and subsidization is an effective method to stabilize green supply chain members' performance.

Inhibition of FUSCA3 degradation at high temperature is dependent on ABA signaling and is regulated by the ABA/GA ratio.

PubMed

Chiu, Rex Shun; Saleh, Yazan; Gazzarrini, Sonia

2016-11-01

During seed imbibition at supra-optimal temperature, an increase in the abscisic acid (ABA)/gibberellin (GA) ratio imposes secondary dormancy to prevent germination (thermoinhibition). FUSCA3 (FUS3), a positive regulator of seed dormancy, accumulates in seeds imbibed at high temperature and increases ABA levels to inhibit germination. Recently, we showed that ABA inhibits FUS3 degradation at high temperature, and that ABA and high temperature also inhibit the ubiquitin-proteasome system, by dampening both proteasome activity and protein polyubiquitination. Here, we investigated the role of ABA signaling components and the ABA antagonizing hormone, GA, in the regulation of FUS3 levels. We show that the ABA receptor mutant, pyl1-1, is less sensitive to ABA and thermoinhibition. In this mutant background, FUS3 degradation in vitro is faster. Similarly, GA alleviates thermoinhibition and also increases FUS3 degradation. These results indicate that inhibition of FUS3 degradation at high temperature is dependent on a high ABA/GA ratio and a functional ABA signaling pathway. Thus, FUS3 constitutes an important node in ABA-GA crosstalk during germination at supra-optimal temperature.

Enhancement of photoluminescence from GaInNAsSb quantum wells upon annealing: improvement of material quality and carrier collection by the quantum well.

PubMed

Baranowski, M; Kudrawiec, R; Latkowska, M; Syperek, M; Misiewicz, J; Sarmiento, T; Harris, J S

2013-02-13

In this study we apply time resolved photoluminescence and contactless electroreflectance to study the carrier collection efficiency of a GaInNAsSb/GaAs quantum well (QW). We show that the enhancement of photoluminescence from GaInNAsSb quantum wells annealed at different temperatures originates not only from (i) the improvement of the optical quality of the GaInNAsSb material (i.e., removal of point defects, which are the source of nonradiative recombination) but it is also affected by (ii) the improvement of carrier collection by the QW region. The total PL efficiency is the product of these two factors, for which the optimal annealing temperatures are found to be ~700 °C and ~760 °C, respectively, whereas the optimal annealing temperature for the integrated PL intensity is found to be between the two temperatures and equals ~720 °C. We connect the variation of the carrier collection efficiency with the modification of the band bending conditions in the investigated structure due to the Fermi level shift in the GaInNAsSb layer after annealing.

Optimization of conditions for thermal smoothing GaAs surfaces

NASA Astrophysics Data System (ADS)

Akhundov, I. O.; Kazantsev, D. M.; Kozhuhov, A. S.; Alperovich, V. L.

2018-03-01

GaAs thermal smoothing by annealing in conditions which are close to equilibrium between the surface and vapors of As and Ga was earlier proved to be effective for the step-terraced surface formation on epi-ready substrates with a small root-mean-square roughness (Rq ≤ 0.15 nm). In the present study, this technique is further developed in order to reduce the annealing duration and to smooth GaAs samples with a larger initial roughness. To this end, we proposed a two-stage anneal with the first high-temperature stage aimed at smoothing "coarse" relief features and the second stage focused on "fine" smoothing at a lower temperature. The optimal temperatures and durations of two-stage annealing are found by Monte Carlo simulations and adjusted after experimentation. It is proved that the temperature and duration of the first high-temperature stage are restricted by the surface roughening, which occurs due to deviations from equilibrium conditions.

Light extraction efficiency of GaN-based LED with pyramid texture by using ray path analysis.

PubMed

Pan, Jui-Wen; Wang, Chia-Shen

2012-09-10

We study three different gallium-nitride (GaN) based light emitting diode (LED) cases based on the different locations of the pyramid textures. In case 1, the pyramid texture is located on the sapphire top surface, in case 2, the pyramid texture is locate on the P-GaN top surface, while in case 3, the pyramid texture is located on both the sapphire and P-GaN top surfaces. We study the relationship between the light extraction efficiency (LEE) and angle of slant of the pyramid texture. The optimization of total LEE was highest for case 3 among the three cases. Moreover, the seven escape paths along which most of the escaped photon flux propagated were selected in a simulation of the LEDs. The seven escape paths were used to estimate the slant angle for the optimization of LEE and to precisely analyze the photon escape path.

Effects of two-step Mg doping in p-GaN on efficiency characteristics of InGaN blue light-emitting diodes without AlGaN electron-blocking layers

NASA Astrophysics Data System (ADS)

Ryu, Han-Youl; Lee, Jong-Moo

2013-05-01

A light-emitting diode (LED) structure containing p-type GaN layers with two-step Mg doping profiles is proposed to achieve high-efficiency performance in InGaN-based blue LEDs without any AlGaN electron-blocking-layer structures. Photoluminescence and electroluminescence (EL) measurement results show that, as the hole concentration in the p-GaN interlayer between active region and the p-GaN layer increases, defect-related nonradiative recombination increases, while the electron current leakage decreases. Under a certain hole-concentration condition in the p-GaN interlayer, the electron leakage and active region degradation are optimized so that high EL efficiency can be achieved. The measured efficiency characteristics are analyzed and interpreted using numerical simulations.

Near-infrared cathodoluminescence imaging of defect distributions in In(0.2)Ga(0.8)As/GaAs multiple quantum wells grown on prepatterned GaAs

NASA Technical Reports Server (NTRS)

Rich, D. H.; Fajkumar, K. C.; Chen, LI; Madhukar, A.; Grunthaner, F. J.

1992-01-01

The defect distribution in a highly strained In(0.2)Ga(0.8)As/GaAs multiple-quantum-well (MQW) structure grown on a patterned GaAs substrate is examined with cathodoluminescence imaging and spectroscopy in the near IR. By spatially correlating the luminescence arising from the MQW exciton recombination (950 nm) with the longer wavelength (1000-1200 nm) luminescence arising from the defect-induced recombination, it is demonstrated that it is possible to determine the regions of highest film quality in both the mesa and valley regions. The present approach enables a judicious determination of the optimal regions to be used for active pixels in InGaAs/GaAs spatial light modulators.

A flow method based on solvent extraction coupled on-line to a reversed micellar mediated chemiluminescence detection for selective determination of gold(III) and gallium(III) in water and industrial samples.

PubMed

Hasanin, Tamer H A; Okamoto, Yasuaki; Fujiwara, Terufumi

2016-02-01

A rapid and sensitive flow method, based on the combination of on-line solvent extraction with reversed micellar mediated chemiluminescence (CL) detection using rhodamine B (RB), was investigated for the selective determination of Au(III) and Ga(III) in aqueous solutions. 2.0 M HCl was the optimum for extracting Au(III) while a 5.0M HCl solution containing 2.5M LiCl was selected as an optimum acidic medium for extraction of Ga(III). The Au(III) and Ga(III) chloro-complex anions were extracted from the above aqueous acidic solutions into toluene as their ion-pair complexes with the protonated RBH(+) ion followed by membrane phase separation in a flow system. In a flow cell of a detector, the extract was mixed with the reversed micellar solution of cetyltrimethylammonium chloride (CTAC) in 1-hexanol-cyclohexane/water (1.0M HCl) containing 0.10 M cerium(IV) and 0.05 M lithium sulfate. Then uptake of the ion-pair by the CTAC reversed micelles and the subsequent CL oxidation of RB with Ce(IV) occurred easily and the CL signals produced were recorded. Using a flow injection system, a detection limit (DL) of 0.4 μM Au(III) and 0.6 μM Ga(III), and linear calibration graphs with dynamic ranges from the respective DLs to 10 μM for Au(III) and Ga(III) were obtained under the optimized experimental conditions. The relative standard deviations (n=6) obtained at 2.0 µM Au(III) and 4.0 µM Ga(III) were 3.0% and 2.4%, respectively. The presented CL methodology has been applied for the determination of Au(III) and Ga(III) in water and industrial samples with satisfactory results. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimizing conjunctive use of surface water and groundwater resources with stochastic dynamic programming

NASA Astrophysics Data System (ADS)

Davidsen, Claus; Liu, Suxia; Mo, Xingguo; Rosbjerg, Dan; Bauer-Gottwein, Peter

2014-05-01

Optimal management of conjunctive use of surface water and groundwater has been attempted with different algorithms in the literature. In this study, a hydro-economic modelling approach to optimize conjunctive use of scarce surface water and groundwater resources under uncertainty is presented. A stochastic dynamic programming (SDP) approach is used to minimize the basin-wide total costs arising from water allocations and water curtailments. Dynamic allocation problems with inclusion of groundwater resources proved to be more complex to solve with SDP than pure surface water allocation problems due to head-dependent pumping costs. These dynamic pumping costs strongly affect the total costs and can lead to non-convexity of the future cost function. The water user groups (agriculture, industry, domestic) are characterized by inelastic demands and fixed water allocation and water supply curtailment costs. As in traditional SDP approaches, one step-ahead sub-problems are solved to find the optimal management at any time knowing the inflow scenario and reservoir/aquifer storage levels. These non-linear sub-problems are solved using a genetic algorithm (GA) that minimizes the sum of the immediate and future costs for given surface water reservoir and groundwater aquifer end storages. The immediate cost is found by solving a simple linear allocation sub-problem, and the future costs are assessed by interpolation in the total cost matrix from the following time step. Total costs for all stages, reservoir states, and inflow scenarios are used as future costs to drive a forward moving simulation under uncertain water availability. The use of a GA to solve the sub-problems is computationally more costly than a traditional SDP approach with linearly interpolated future costs. However, in a two-reservoir system the future cost function would have to be represented by a set of planes, and strict convexity in both the surface water and groundwater dimension cannot be maintained. The optimization framework based on the GA is still computationally feasible and represents a clean and customizable method. The method has been applied to the Ziya River basin, China. The basin is located on the North China Plain and is subject to severe water scarcity, which includes surface water droughts and groundwater over-pumping. The head-dependent groundwater pumping costs will enable assessment of the long-term effects of increased electricity prices on the groundwater pumping. The coupled optimization framework is used to assess realistic alternative development scenarios for the basin. In particular the potential for using electricity pricing policies to reach sustainable groundwater pumping is investigated.

Determination of optimal whole body vibration amplitude and frequency parameters with plyometric exercise and its influence on closed-chain lower extremity acute power output and EMG activity in resistance trained males

NASA Astrophysics Data System (ADS)

Hughes, Nikki J.

The optimal combination of Whole body vibration (WBV) amplitude and frequency has not been established. Purpose. To determine optimal combination of WBV amplitude and frequency that will enhance acute mean and peak power (MP and PP) output EMG activity in the lower extremity muscles. Methods. Resistance trained males (n = 13) completed the following testing sessions: On day 1, power spectrum testing of bilateral leg press (BLP) movement was performed on the OMNI. Days 2 and 3 consisted of WBV testing with either average (5.8 mm) or high (9.8 mm) amplitude combined with either 0 (sham control), 10, 20, 30, 40 and 50 Hz frequency. Bipolar surface electrodes were placed on the rectus femoris (RF), vastus lateralis (VL), bicep femoris (BF) and gastrocnemius (GA) muscles for EMG analysis. MP and PP output and EMG activity of the lower extremity were assessed pre-, post-WBV treatments and after sham-controls on the OMNI while participants performed one set of five repetitions of BLP at the optimal resistance determined on Day 1. Results. No significant differences were found between pre- and sham-control on MP and PP output and on EMG activity in RF, VL, BF and GA. Completely randomized one-way ANOVA with repeated measures demonstrated no significant interaction of WBV amplitude and frequency on MP and PP output and peak and mean EMGrms amplitude and EMG rms area under the curve. RF and VL EMGrms area under the curve significantly decreased (p < 0.05) with high WBV amplitude, whereas low amplitude significantly decreased GA mean and peak EMGrms amplitude and EMGrms area under the curve. VL mean EMGrms amplitude and BF mean and peak EMGrms amplitudes were significantly decreased (p < 0.05) with high WBV amplitude when compared to sham-control. WBV frequency significantly decreased (p < 0.05) VL mean and peak EMGrms amplitude. WBV frequency at 30 and 40 Hz significantly decreased (p < 0.05) GA mean EMGrms amplitude and 20 and 30 Hz significantly decreased GA peak EMGrms amplitude. MP and PP output was not significantly effected by either treatment. Conclusions. It is concluded that WBV combined with plyometric exercise does not induce alterations in subsequent MP and PP output and EMGrms activity of the lower extremity. Future studies need to address the time of WBV exposure and magnitude of external loads that will maximize strength and/or power output.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

NASA Astrophysics Data System (ADS)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Impact of MBE deposition conditions on InAs/GaInSb superlattices for very long wavelength infrared detection

NASA Astrophysics Data System (ADS)

Brown, G. J.; Haugan, H. J.; Mahalingam, K.; Grazulis, L.; Elhamri, S.

2015-01-01

The objective of this work is to establish molecular beam epitaxy (MBE) growth processes that can produce high quality InAs/GaInSb superlattice (SL) materials specifically tailored for very long wavelength infrared (VLWIR) detection. To accomplish this goal, several series of MBE growth optimization studies, using a SL structure of 47.0 Å InAs/21.5 Å Ga0.75In0.25Sb, were performed to refine the MBE growth process and optimize growth parameters. Experimental results demonstrated that our "slow" MBE growth process can consistently produce an energy gap near 50 meV. This is an important factor in narrow band gap SLs. However, there are other growth factors that also impact the electrical and optical properties of the SL materials. The SL layers are particularly sensitive to the anion incorporation condition formed during the surface reconstruction process. Since antisite defects are potentially responsible for the inherent residual carrier concentrations and short carrier lifetimes, the optimization of anion incorporation conditions, by manipulating anion fluxes, anion species, and deposition temperature, was systematically studied. Optimization results are reported in the context of comparative studies on the influence of the growth temperature on the crystal structural quality and surface roughness performed under a designed set of deposition conditions. The optimized SL samples produced an overall strong photoresponse signal with a relatively sharp band edge that is essential for developing VLWIR detectors. A quantitative analysis of the lattice strain, performed at the atomic scale by aberration corrected transmission electron microscopy, provided valuable information about the strain distribution at the GaInSb-on-InAs interface and in the InAs layers, which was important for optimizing the anion conditions.

Monolithic integration of InGaN segments emitting in the blue, green, and red spectral range in single ordered nanocolumns

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, S.; Bengoechea-Encabo, A.; Sanchez-Garcia, M. A.

This work reports on the selective area growth by plasma-assisted molecular beam epitaxy and characterization of InGaN/GaN nanocolumnar heterostructures. The optimization of the In/Ga and total III/V ratios, as well as the growth temperature, provides control on the emission wavelength, either in the blue, green, or red spectral range. An adequate structure tailoring and monolithic integration in a single nanocolumnar heterostructure of three InGaN portions emitting in the red-green-blue colors lead to white light emission.

A Photonic 1 × 4 Power Splitter Based on Multimode Interference in Silicon–Gallium-Nitride Slot Waveguide Structures

PubMed Central

Malka, Dror; Danan, Yossef; Ramon, Yehonatan; Zalevsky, Zeev

2016-01-01

In this paper, a design for a 1 × 4 optical power splitter based on the multimode interference (MMI) coupler in a silicon (Si)–gallium nitride (GaN) slot waveguide structure is presented—to our knowledge, for the first time. Si and GaN were found as suitable materials for the slot waveguide structure. Numerical optimizations were carried out on the device parameters using the full vectorial-beam propagation method (FV-BPM). Simulation results show that the proposed device can be useful to divide optical signal energy uniformly in the C-band range (1530–1565 nm) into four output ports with low insertion losses (0.07 dB). PMID:28773638

Nanostructuring of conduction channels in (In,Ga)As-InP heterostructures: Overcoming carrier generation caused by Ar ion milling

NASA Astrophysics Data System (ADS)

Hortelano, V.; Weidlich, H.; Semtsiv, M. P.; Masselink, W. T.; Ramsteiner, M.; Jahn, U.; Biermann, K.; Takagaki, Y.

2018-04-01

Nanometer-sized channels are fabricated in (In,Ga)As-InP heterostructures using Ar ion milling. The ion milling causes spontaneous creation of nanowires, and moreover, electrical conduction of the surface as carriers is generated by sputtering-induced defects. We demonstrate a method to restore electrical isolation in the etched area that is compatible with the presence of the nanochannels. We remove the heavily damaged surface layer using a diluted HCl solution and subsequently recover the crystalline order in the moderately damaged part by annealing. We optimize the HCl concentration to make the removal stop on its own before reaching the conduction channel part. The lateral depletion in the channels is shown to be almost absent.

Conformational analysis of capsaicin using 13C, 15N MAS NMR, GIAO DFT and GA calculations

NASA Astrophysics Data System (ADS)

Siudem, Paweł; Paradowska, Katarzyna; Bukowicki, Jarosław

2017-10-01

Capsaicin produced by plants from genus Capsicum exerts multiple pharmacological effects and has found applications in food and pharmaceutical industry. The alkaloid was studied by a combined approach: solid-state NMR, GA conformational search and GIAO DFT methods. The 13C CPMAS NMR spectra were recorded using variable contact time and dipolar dephasing experiments. The results of cross-polarization (CP) kinetics, such as TCP values and long T1ρH (100-200 ms), indicated that the capsaicin molecule is fairly mobile, especially at the end of the aliphatic chain. The15N MAS NMR spectrum showed one narrow signal at -255 ppm. Genetic algorithm (GA) search with multi modal optimization was used to find low-energy conformations of capsaicin. Theoretical GIAO DFT calculations were performed using different basis sets to characterize five selected conformations. 13C CPMAS NMR was used as a validation method and the experimental chemical shifts were compared with those calculated for selected stable conformers. Conformational analysis suggests that the side chain can be bent or extended. A comparison of the experimental and the calculated chemical shifts indicates that solid capsaicin does not have the same structure as those established by PWXRD.

Optimal GENCO bidding strategy

NASA Astrophysics Data System (ADS)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Defect phase diagram for doping of Ga2O3

NASA Astrophysics Data System (ADS)

Lany, Stephan

2018-04-01

For the case of n-type doping of β-Ga2O3 by group 14 dopants (C, Si, Ge, Sn), a defect phase diagram is constructed from defect equilibria calculated over a range of temperatures (T), O partial pressures (pO2), and dopant concentrations. The underlying defect levels and formation energies are determined from first-principles supercell calculations with GW bandgap corrections. Only Si is found to be a truly shallow donor, C is a deep DX-like (lattice relaxed donor) center, and Ge and Sn have defect levels close to the conduction band minimum. The thermodynamic modeling includes the effect of association of dopant-defect pairs and complexes, which causes the net doping to decline when exceeding a certain optimal dopant concentration. The optimal doping levels are surprisingly low, between about 0.01% and 1% of cation substitution, depending on the (T, pO2) conditions. Considering further the stability constraints due to sublimation of molecular Ga2O, specific predictions of optimized pO2 and Si dopant concentrations are given. The incomplete passivation of dopant-defect complexes in β-Ga2O3 suggests a design rule for metastable doping above the solubility limit.

Determination of Anthocyanins in Cranberry Fruit and Cranberry Fruit Products by High-Performance Liquid Chromatography with Ultraviolet Detection: Single-Laboratory Validation

PubMed Central

Brown, Paula N.; Shipley, Paul R.

2012-01-01

A single-laboratory validation study was conducted on an HPLC method for the detection and quantification of cyanidin-3-O-galactoside (C3Ga), cyanidin-3-O-glucoside (C3Gl), cyanidin-3-O-arabinoside (C3Ar), peonidin-3-O-galactoside (P3Ga), and peonidin-3-O-arabinoside (P3Ar) in cranberry fruit (Vaccinium macrocarpon Aiton) raw material and finished products. An extraction procedure using a combination of sonication and shaking with acidified methanol was optimized for all five anthocyanins in freeze-dried cranberry fruit and finished products (commercial extract powder, juice, and juice cocktail). Final extract solutions were analyzed by HPLC using a C18 RP column. Calibration curves for all anthocyanin concentrations had correlation coefficients (r2) of ≥99.8%. The method detection limits for C3Ga, C3Gl, C3Ar, P3Ga, and P3Ar were estimated to be 0.018, 0.016, 0.006, 0.013, and 0.011 μg/mL, respectively. Separation was achieved with a chromatographic run time of 35 min using a binary mobile phase with gradient elution. Quantitative determination performed in triplicate on four test materials on each of 3 days (n = 12) resulted in RSDr from 1.77 to 3.31%. Analytical range, as defined by the calibration curves, was 0.57–36.53 μg/mL for C3Ga, 0.15–9.83 μg/mL for C3Gl, 0.28–17.67 μg/mL for C3Ar, 1.01–64.71 μg/mL for P3Ga, and 0.42–27.14 μg/mL for P3Ar. For solid materials prepared by the described method, this translates to 0.06–3.65 mg/g for C3Ga, 0.02–0.98 mg/g for C3Gl, 0.03–1.77 mg/g for C3Ar, 0.10–6.47 mg/g for P3Ga, and 0.04–2.71 mg/g for P3Ar. PMID:21563679

Nuclear Electric Vehicle Optimization Toolset (NEVOT)

NASA Technical Reports Server (NTRS)

Tinker, Michael L.; Steincamp, James W.; Stewart, Eric T.; Patton, Bruce W.; Pannell, William P.; Newby, Ronald L.; Coffman, Mark E.; Kos, Larry D.; Qualls, A. Lou; Greene, Sherrell

2004-01-01

The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major nuclear electric propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a genetic algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be considered through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.

A neural-network-based exponential H∞ synchronisation for chaotic secure communication via improved genetic algorithm

NASA Astrophysics Data System (ADS)

Hsiao, Feng-Hsiag

2016-10-01

In this study, a novel approach via improved genetic algorithm (IGA)-based fuzzy observer is proposed to realise exponential optimal H∞ synchronisation and secure communication in multiple time-delay chaotic (MTDC) systems. First, an original message is inserted into the MTDC system. Then, a neural-network (NN) model is employed to approximate the MTDC system. Next, a linear differential inclusion (LDI) state-space representation is established for the dynamics of the NN model. Based on this LDI state-space representation, this study proposes a delay-dependent exponential stability criterion derived in terms of Lyapunov's direct method, thus ensuring that the trajectories of the slave system approach those of the master system. Subsequently, the stability condition of this criterion is reformulated into a linear matrix inequality (LMI). Due to GA's random global optimisation search capabilities, the lower and upper bounds of the search space can be set so that the GA will seek better fuzzy observer feedback gains, accelerating feedback gain-based synchronisation via the LMI-based approach. IGA, which exhibits better performance than traditional GA, is used to synthesise a fuzzy observer to not only realise the exponential synchronisation, but also achieve optimal H∞ performance by minimizing the disturbance attenuation level and recovering the transmitted message. Finally, a numerical example with simulations is given in order to demonstrate the effectiveness of our approach.

A Methodology for the Hybridization Based in Active Components: The Case of cGA and Scatter Search

PubMed Central

Alba, Enrique; Leguizamón, Guillermo

2016-01-01

This work presents the results of a new methodology for hybridizing metaheuristics. By first locating the active components (parts) of one algorithm and then inserting them into second one, we can build efficient and accurate optimization, search, and learning algorithms. This gives a concrete way of constructing new techniques that contrasts the spread ad hoc way of hybridizing. In this paper, the enhanced algorithm is a Cellular Genetic Algorithm (cGA) which has been successfully used in the past to find solutions to such hard optimization problems. In order to extend and corroborate the use of active components as an emerging hybridization methodology, we propose here the use of active components taken from Scatter Search (SS) to improve cGA. The results obtained over a varied set of benchmarks are highly satisfactory in efficacy and efficiency when compared with a standard cGA. Moreover, the proposed hybrid approach (i.e., cGA+SS) has shown encouraging results with regard to earlier applications of our methodology. PMID:27403153

Numerical analysis of light extraction enhancement of GaN-based thin-film flip-chip light-emitting diodes with high-refractive-index buckling nanostructures

NASA Astrophysics Data System (ADS)

Yue, Qing-Yang; Yang, Yang; Cheng, Zhen-Jia; Guo, Cheng-Shan

2018-06-01

In this work, the light extraction efficiency enhancement of GaN-based thin-film flip-chip (TFFC) light-emitting diodes (LEDs) with high-refractive-index (TiO2) buckling nanostructures was studied using the three-dimensional finite difference time domain method. Compared with 2-D photonic crystals, the buckling structures have the advantages of a random directionality and a broad distribution in periodicity, which can effectively extract the guided light propagating in all azimuthal directions over a wide spectrum. Numerical studies revealed that the light extraction efficiency of buckling-structured LEDs reaches 1.1 times that of triangular lattice photonic crystals. The effects of the buckling structure feature sizes and the thickness of the N-GaN layer on the light extraction efficiency for TFFC LEDs were also investigated systematically. With optimized structural parameters, a significant light extraction enhancement of about 2.6 times was achieved for TiO2 buckling-structured TFFC LEDs compared with planar LEDs.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction

PubMed Central

Zhang, Daqing; Xiao, Jianfeng; Zhou, Nannan; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2015-01-01

Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration. PMID:26504797

Collaboration space division in collaborative product development based on a genetic algorithm

NASA Astrophysics Data System (ADS)

Qian, Xueming; Ma, Yanqiao; Feng, Huan

2018-02-01

The advance in the global environment, rapidly changing markets, and information technology has created a new stage for design. In such an environment, one strategy for success is the Collaborative Product Development (CPD). Organizing people effectively is the goal of Collaborative Product Development, and it solves the problem with certain foreseeability. The development group activities are influenced not only by the methods and decisions available, but also by correlation among personnel. Grouping the personnel according to their correlation intensity is defined as collaboration space division (CSD). Upon establishment of a correlation matrix (CM) of personnel and an analysis of the collaboration space, the genetic algorithm (GA) and minimum description length (MDL) principle may be used as tools in optimizing collaboration space. The MDL principle is used in setting up an object function, and the GA is used as a methodology. The algorithm encodes spatial information as a chromosome in binary. After repetitious crossover, mutation, selection and multiplication, a robust chromosome is found, which can be decoded into an optimal collaboration space. This new method can calculate the members in sub-spaces and individual groupings within the staff. Furthermore, the intersection of sub-spaces and public persons belonging to all sub-spaces can be determined simultaneously.

Ultralow-voltage-drop GaN/InGaN/GaN tunnel junctions with 12% indium content

NASA Astrophysics Data System (ADS)

Akyol, Fatih; Zhang, Yuewei; Krishnamoorthy, Sriram; Rajan, Siddharth

2017-12-01

We report a combination of highly doped layers and polarization engineering that achieves highly efficient blue-transparent GaN/InGaN/GaN tunnel junctions (In content = 12%). NPN diode structures with a low voltage drop of 4.04 V at 5 kA/cm2 and a differential resistance of 6.51 × 10-5 Ω·cm2 at 3 kA/cm2 were obtained. The tunnel junction design with n++-GaN (Si: 5 × 1020 cm-3)/3 nm p++-In0.12Ga0.88N (Mg: 1.5 × 1020 cm-3)/p++-GaN (Mg: 5 × 1020 cm-3) showed the best device performance. Device simulations agree well with the experimentally determined optimal design. The combination of low In composition and high doping can facilitate lower tunneling resistance for blue-transparent light-emitting diodes.

Ectopic expression of specific GA2 oxidase mutants promotes yield and stress tolerance in rice.

PubMed

Lo, Shuen-Fang; Ho, Tuan-Hua David; Liu, Yi-Lun; Jiang, Mirng-Jier; Hsieh, Kun-Ting; Chen, Ku-Ting; Yu, Lin-Chih; Lee, Miin-Huey; Chen, Chi-Yu; Huang, Tzu-Pi; Kojima, Mikiko; Sakakibara, Hitoshi; Chen, Liang-Jwu; Yu, Su-May

2017-07-01

A major challenge of modern agricultural biotechnology is the optimization of plant architecture for enhanced productivity, stress tolerance and water use efficiency (WUE). To optimize plant height and tillering that directly link to grain yield in cereals and are known to be tightly regulated by gibberellins (GAs), we attenuated the endogenous levels of GAs in rice via its degradation. GA 2-oxidase (GA2ox) is a key enzyme that inactivates endogenous GAs and their precursors. We identified three conserved domains in a unique class of C 20 GA2ox, GA2ox6, which is known to regulate the architecture and function of rice plants. We mutated nine specific amino acids in these conserved domains and observed a gradient of effects on plant height. Ectopic expression of some of these GA2ox6 mutants moderately lowered GA levels and reprogrammed transcriptional networks, leading to reduced plant height, more productive tillers, expanded root system, higher WUE and photosynthesis rate, and elevated abiotic and biotic stress tolerance in transgenic rice. Combinations of these beneficial traits conferred not only drought and disease tolerance but also increased grain yield by 10-30% in field trials. Our studies hold the promise of manipulating GA levels to substantially improve plant architecture, stress tolerance and grain yield in rice and possibly in other major crops. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Optimizing coherent anti-Stokes Raman scattering by genetic algorithm controlled pulse shaping

NASA Astrophysics Data System (ADS)

Yang, Wenlong; Sokolov, Alexei

2010-10-01

The hybrid coherent anti-Stokes Raman scattering (CARS) has been successful applied to fast chemical sensitive detections. As the development of femto-second pulse shaping techniques, it is of great interest to find the optimum pulse shapes for CARS. The optimum pulse shapes should minimize the non-resonant four wave mixing (NRFWM) background and maximize the CARS signal. A genetic algorithm (GA) is developed to make a heuristic searching for optimized pulse shapes, which give the best signal the background ratio. The GA is shown to be able to rediscover the hybrid CARS scheme and find optimized pulse shapes for customized applications by itself.

Genetic Algorithm for Optimization: Preprocessing with n Dimensional Bisection and Error Estimation

NASA Technical Reports Server (NTRS)

Sen, S. K.; Shaykhian, Gholam Ali

2006-01-01

A knowledge of the appropriate values of the parameters of a genetic algorithm (GA) such as the population size, the shrunk search space containing the solution, crossover and mutation probabilities is not available a priori for a general optimization problem. Recommended here is a polynomial-time preprocessing scheme that includes an n-dimensional bisection and that determines the foregoing parameters before deciding upon an appropriate GA for all problems of similar nature and type. Such a preprocessing is not only fast but also enables us to get the global optimal solution and its reasonably narrow error bounds with a high degree of confidence.

Phase Helps Find Geometrically Optimal Gaits

NASA Astrophysics Data System (ADS)

Revzen, Shai; Hatton, Ross

Geometric motion planning describes motions of animals and machines governed by g ˙ = gA (q) q ˙ - a connection A (.) relating shape q and shape velocity q ˙ to body frame velocity g-1 g ˙ ∈ se (3) . Measuring the entire connection over a multidimensional q is often unfeasible with current experimental methods. We show how using a phase estimator can make tractable measuring the local structure of the connection surrounding a periodic motion q (φ) driven by a phase φ ∈S1 . This approach reduces the complexity of the estimation problem by a factor of dimq . The results suggest that phase estimation can be combined with geometric optimization into an iterative gait optimization algorithm usable on experimental systems, or alternatively, to allow the geometric optimality of an observed gait to be detected. ARO W911NF-14-1-0573, NSF 1462555.

Environmental optimal control strategies based on plant canopy photosynthesis responses and greenhouse climate model

NASA Astrophysics Data System (ADS)

Deng, Lujuan; Xie, Songhe; Cui, Jiantao; Liu, Tao

2006-11-01

It is the essential goal of intelligent greenhouse environment optimal control to enhance income of cropper and energy save. There were some characteristics such as uncertainty, imprecision, nonlinear, strong coupling, bigger inertia and different time scale in greenhouse environment control system. So greenhouse environment optimal control was not easy and especially model-based optimal control method was more difficult. So the optimal control problem of plant environment in intelligent greenhouse was researched. Hierarchical greenhouse environment control system was constructed. In the first level data measuring was carried out and executive machine was controlled. Optimal setting points of climate controlled variable in greenhouse was calculated and chosen in the second level. Market analysis and planning were completed in third level. The problem of the optimal setting point was discussed in this paper. Firstly the model of plant canopy photosynthesis responses and the model of greenhouse climate model were constructed. Afterwards according to experience of the planting expert, in daytime the optimal goals were decided according to the most maximal photosynthesis rate principle. In nighttime on plant better growth conditions the optimal goals were decided by energy saving principle. Whereafter environment optimal control setting points were computed by GA. Compared the optimal result and recording data in real system, the method is reasonable and can achieve energy saving and the maximal photosynthesis rate in intelligent greenhouse

Influence of the gate position on source-to-drain resistance in AlGaN/AlN/GaN heterostructure field-effect transistors

NASA Astrophysics Data System (ADS)

Liu, Yan; Lin, Zhaojun; Cui, Peng; Zhao, Jingtao; Fu, Chen; Yang, Ming; Lv, Yuanjie

2017-08-01

Using a suitable dual-gate structure, the source-to-drain resistance (RSD) of AlGaN/AlN/GaN heterostructure field-effect transistor (HFET) with varying gate position has been studied at room temperature. The theoretical and experimental results have revealed a dependence of RSD on the gate position. The variation of RSD with the gate position is found to stem from the polarization Coulomb field (PCF) scattering. This finding is of great benefit to the optimization of the performance of AlGaN/AlN/GaN HFET. Especially, when the AlGaN/AlN/GaN HFET works as a microwave device, it is beneficial to achieve the impedance matching by designing the appropriate gate position based on PCF scattering.

Lateral Hydrogen Diffusion at p-GaN Layers in Nitride-Based Light Emitting Diodes with Tunnel Junctions

NASA Astrophysics Data System (ADS)

Kuwano, Yuka; Kaga, Mitsuru; Morita, Takatoshi; Yamashita, Kouji; Yagi, Kouta; Iwaya, Motoaki; Takeuchi, Tetsuya; Kamiyama, Satoshi; Akasaki, Isamu

2013-08-01

We demonstrated lateral Mg activation along p-GaN layers underneath n-GaN surface layers in nitride-based light emitting diodes (LEDs) with GaInN tunnel junctions. A high temperature thermal annealing was effective for the lateral Mg activation when the p-GaN layers were partly exposed to an oxygen ambient as etched sidewalls. The activated regions gradually extended from the etched sidewalls to the centers with an increase of annealing time, observed as emission regions with current injection. These results suggest that hydrogen diffuses not vertically thorough the above n-GaN but laterally through the exposed portions of the p-GaN. The lowest voltage drop at the GaInN tunnel junction was estimated to be 0.9 V at 50 mA with the optimized annealing condition.

Preparation and preclinical evaluation of 68Ga-DOTA-amlodipine for L-type calcium channel imaging

PubMed Central

Firuzyar, Tahereh; Jalilian, Amir Reza; Aboudzadeh, Mohammad Reza; Sadeghpour, Hossein; Shafiee-Ardestani, Mahdi; Khalaj, Ali

2016-01-01

Aim: In order to develop a possible tracer for L-type calcium channel imaging, we here report the development of a Ga-68 amlodipine derivative for possible PET imaging. Materials and Methods: Amlodipine DOTA conjugate was synthesized, characterized and went through calcium channel blockade, toxicity, apoptosis/necrosis tests. [68Ga] DOTA AMLO was prepared at optimized conditions followed by stability tests, partition coefficient determination and biodistribution studies using tissue counting and co incidence imaging up to 2 h. Results: [68Ga] DOTA AMLO was prepared at pH 4–5 in 7–10 min at 95°C in high radiochemical purity (>99%, radio thin layer chromatography; specific activity: 1.9–2.1 GBq/mmol) and was stable up to 4 h with a log P of −0.94. Calcium channel rich tissues including myocardium, and tissues with smooth muscle cells such as colon, intestine, and lungs demonstrated significant uptake. Co incidence images supported the biodistribution data up to 2 h. Conclusions: The complex can be a candidate for further positron emission tomography imaging for L type calcium channels. PMID:27833311

Transfer matrix approach to electron transport in monolayer MoS2/MoO x heterostructures

NASA Astrophysics Data System (ADS)

Li, Gen

2018-05-01

Oxygen plasma treatment can introduce oxidation into monolayer MoS2 to transfer MoS2 into MoO x , causing the formation of MoS2/MoO x heterostructures. We find the MoS2/MoO x heterostructures have the similar geometry compared with GaAs/Ga1‑x Al x As semiconductor superlattice. Thus, We employ the established transfer matrix method to analyse the electron transport in the MoS2/MoO x heterostructures with double-well and step-well geometries. We also considere the coupling between transverse and longitudinal kinetic energy because the electron effective mass changes spatially in the MoS2/MoO x heterostructures. We find the resonant peaks show red shift with the increasing of transverse momentum, which is similar to the previous work studying the transverse-momentum-dependent transmission in GaAs/Ga1‑x Al x As double-barrier structure. We find electric field can enhance the magnitude of peaks and intensify the coupling between longitudinal and transverse momentums. Moreover, higher bias is applied to optimize resonant tunnelling condition to show negative differential effect can be observed in the MoS2/MoO x system.

Electroluminescent refrigeration by ultra-efficient GaAs light-emitting diodes

NASA Astrophysics Data System (ADS)

Patrick Xiao, T.; Chen, Kaifeng; Santhanam, Parthiban; Fan, Shanhui; Yablonovitch, Eli

2018-05-01

Electroluminescence—the conversion of electrons to photons in a light-emitting diode (LED)—can be used as a mechanism for refrigeration, provided that the LED has an exceptionally high quantum efficiency. We investigate the practical limits of present optoelectronic technology for cooling applications by optimizing a GaAs/GaInP double heterostructure LED. We develop a model of the design based on the physics of detailed balance and the methods of statistical ray optics, and predict an external luminescence efficiency of ηext = 97.7% at 263 K. To enhance the cooling coefficient of performance, we pair the refrigerated LED with a photovoltaic cell, which partially recovers the emitted optical energy as electricity. For applications near room temperature and moderate power densities (1.0-10 mW/cm2), we project that an electroluminescent refrigerator can operate with up to 1.7× the coefficient of performance of thermoelectric coolers with ZT = 1, using the material quality in existing GaAs devices. We also predict superior cooling efficiency for cryogenic applications relative to both thermoelectric and laser cooling. Large improvements to these results are possible with optoelectronic devices that asymptotically approach unity luminescence efficiency.

A-site compositional effects in Ga-doped hollandite materials of the form BaxCsyGa2x+yTi8−2x−yO16: implications for Cs immobilization in crystalline ceramic waste forms

PubMed Central

Xu, Yun; Wen, Yi; Grote, Rob; Amoroso, Jake; Shuller Nickles, Lindsay; Brinkman, Kyle S.

2016-01-01

The hollandite structure is a promising crystalline host for Cs immobilization. A series of Ga-doped hollandite BaxCsyGa2x+yTi8−2x−yO16 (x = 0, 0.667, 1.04, 1.33; y = 1.33, 0.667, 0.24, 0) was synthesized through a solid oxide reaction method resulting in a tetragonal hollandite structure (space group I4/m). The lattice parameter associated with the tunnel dimension was found to increases as Cs substitution in the tunnel increased. A direct investigation of cation mobility in tunnels using electrochemical impedance spectroscopy was conducted to evaluate the ability of the hollandite structure to immobilize cations over a wide compositional range. Hollandite with the largest tunnel size and highest aspect ratio grain morphology resulting in rod-like microstructural features exhibited the highest ionic conductivity. The results indicate that grain size and optimized Cs stoichiometry control cation motion and by extension, the propensity for Cs release from hollandite. PMID:27273791

Mg incorporation in GaN grown by plasma-assisted molecular beam epitaxy at high temperatures

NASA Astrophysics Data System (ADS)

Yang, W. C.; Lee, P. Y.; Tseng, H. Y.; Lin, C. W.; Tseng, Y. T.; Cheng, K. Y.

2016-04-01

The influence of growth conditions on the incorporation and activation of Mg in GaN grown by plasma-assisted molecular beam epitaxy at high growth temperature (>700 °C) is presented. It is found that the highest Mg incorporation with optimized electrical properties is highly sensitive both to the Mg/Ga flux ratio and III/V flux ratio. A maximum Mg activation of ~5% can be achieved at a growth temperature of 750 °C. The lowest resistivity achieved is 0.56 Ω-cm which is associated with a high hole mobility of 6.42 cm2/V-s and a moderately high hole concentration of 1.7×1018 cm-3. Although the highest hole concentration achieved in a sample grown under a low III/V flux ratio and a high Mg/Ga flux ratio reaches 7.5×1018 cm-3, the mobility is suffered due to the formation of defects by the excess Mg. In addition, we show that modulated beam growth methods do not enhance Mg incorporation at high growth temperature in contrast to those grown at a low temperature of 500 °C (Appl. Phys. Lett. 93, 172112, Namkoong et al., 2008 [19]).

A ripple-spreading genetic algorithm for the aircraft sequencing problem.

PubMed

Hu, Xiao-Bing; Di Paolo, Ezequiel A

2011-01-01

When genetic algorithms (GAs) are applied to combinatorial problems, permutation representations are usually adopted. As a result, such GAs are often confronted with feasibility and memory-efficiency problems. With the aircraft sequencing problem (ASP) as a study case, this paper reports on a novel binary-representation-based GA scheme for combinatorial problems. Unlike existing GAs for the ASP, which typically use permutation representations based on aircraft landing order, the new GA introduces a novel ripple-spreading model which transforms the original landing-order-based ASP solutions into value-based ones. In the new scheme, arriving aircraft are projected as points into an artificial space. A deterministic method inspired by the natural phenomenon of ripple-spreading on liquid surfaces is developed, which uses a few parameters as input to connect points on this space to form a landing sequence. A traditional GA, free of feasibility and memory-efficiency problems, can then be used to evolve the ripple-spreading related parameters in order to find an optimal sequence. Since the ripple-spreading model is the centerpiece of the new algorithm, it is called the ripple-spreading GA (RSGA). The advantages of the proposed RSGA are illustrated by extensive comparative studies for the case of the ASP.

Enhanced light extraction efficiency of GaN-based light-emittng diodes by nitrogen implanted current blocking layer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Yong Deok; Oh, Seung Kyu; Park, Min Joo

Highlights: • A nitrogen implanted current-blocking layer was successfully demonstrated. • Light-extraction efficiency and radiant intensity was increased by more than 20%. • Ion implantation was successfully implemented in GaN based light-emitting diodes. - Abstract: GaN-based light emitting diodes (LEDs) with a nitrogen implanted current-blocking layer (CBL) were successfully demonstrated for improving the light extraction efficiency (LEE) and radiant intensity. The LEE and radiant intensity of the LEDs with a shallow implanted CBL with nitrogen was greatly increased by more than 20% compared to that of a conventional LED without the CBL due to an increase in the effective currentmore » path, which reduces light absorption at the thick p-pad electrode. Meanwhile, deep implanted CBL with a nitrogen resulted in deterioration of the LEE and radiant intensity because of formation of crystal damage, followed by absorption of the light generated at the multi-quantum well(MQW). These results clearly suggest that ion implantation method, which is widely applied in the fabrication of Si based devices, can be successfully implemented in the fabrication of GaN based LEDs by optimization of implanted depth.« less

Antireflective nanostructures for CPV

NASA Astrophysics Data System (ADS)

Buencuerpo, Jeronimo; Torne, Lorena; Alvaro, Raquel; Llorens, Jose Manuel; Dotor, María Luisa; Ripalda, Jose Maria

2017-09-01

We have optimized a periodic antireflective nanostructure. The optimal design has a theoretical broadband reflectivity of 0.54% on top of GaInP with an AlInP window layer. Preliminary fabrication attempts have been carried out on top of GaAs substrates. Due to the lack of a window layer, and the need to fine tune the fabrication process, the fabricated nanostructures have a reflectivity of 3.1%, but this is already significantly lower than the theoretical broadband reflectance of standard MgF2/ZnS bilayers (4.5%).

Diagnosis, treatment and outcome of glutaric aciduria type I in Zhejiang Province, China

PubMed Central

Yang, Lili; Yin, Huaiming; Yang, Rongwang; Huang, Xinwen

2011-01-01

Summary Background Glutaric aciduria type I (GA I; MIM 231670) is a rare autosomal recessive disorder resulting from glutaryl-CoA dehydrogenase deficiency. This article reports our experience in the diagnosis, treatment and outcome of GA I patients in Zhejiang Province, China. Material/Methods A total of 129,415 newborns (accounting for approximately one-tenth of the annual births in Zhejiang Province) and 9640 high-risk infants were screened for inborn errors of metabolism in the Neonatal Screening Center of Zhejiang Province during a 3-year period. Tandem mass spectrometry and gas chromatography-mass spectrometry were used for diagnosis of the patients. Dietary modification, carnitine supplementation and aggressive treatment of intercurrent illnesses were adapted for GA I patients. Results Three infants were diagnosed with GA I by high-risk screening (detection rate: 1/3,213) and 2 were diagnosed by newborn screening (incidence: 1/64,708). Four patients (3 by high-risk screening and 1 by neonatal screening) undergoing MRI examination showed remarkable changes on T2-weighted image. Four patients accepted timely treatment, and in the patient diagnosed by neonatal screening, treatment was delayed until hypotonia appeared 3 months later. Neuropsychological assessment showed mental and motor retardation in 3 patients after treatment, including the patient diagnosed by neonatal screening. Conclusions Individualized timely treatment and close monitoring of GA I patients needs to be optimized in China. Appropriate communication with parents may help to achieve successful management of GA I patients. PMID:21709643

Methods of improving the efficiency of photovoltaic cells. [including X ray analysis

NASA Technical Reports Server (NTRS)

Loferski, J. J.; Roessler, B.; Crisman, E. E.; Chen, L. Y.; Kaul, R.

1974-01-01

Work on aluminum-alloyed silicon grating cells is continued. Optimization of the geometry (grating line width and spacing) confirms the analysis of such cells. A 1 sq cm grating cell was fabricated and its i-V characteristic was measured under an AMO solar simulator. It is found that the efficiency of this cell would be about 7.9%, if it were covered by the usual antireflection coating. The surface of the cell is not covered by a diffused junction. The response is blue shifted; the current is somewhat higher than that produced by a commercial Si cell. However, the open circuit voltage is low, and attempts to optimize the open circuit voltage of the aluminum-alloy junctions are described. A preliminary X-ray topographic examination of GaAs specimens of the type commonly used to make solar cells is studied. The X-ray study shows that the wafers are filled with regions having strain gradients, possibly caused by precipitates. It is possible that a correlation exists between the presence of low mechanical perfection and minority carrier diffusion lengths of GaAs crystals.

Genetic algorithm based active vibration control for a moving flexible smart beam driven by a pneumatic rod cylinder

NASA Astrophysics Data System (ADS)

Qiu, Zhi-cheng; Shi, Ming-li; Wang, Bin; Xie, Zhuo-wei

2012-05-01

A rod cylinder based pneumatic driving scheme is proposed to suppress the vibration of a flexible smart beam. Pulse code modulation (PCM) method is employed to control the motion of the cylinder's piston rod for simultaneous positioning and vibration suppression. Firstly, the system dynamics model is derived using Hamilton principle. Its standard state-space representation is obtained for characteristic analysis, controller design, and simulation. Secondly, a genetic algorithm (GA) is applied to optimize and tune the control gain parameters adaptively based on the specific performance index. Numerical simulations are performed on the pneumatic driving elastic beam system, using the established model and controller with tuned gains by GA optimization process. Finally, an experimental setup for the flexible beam driven by a pneumatic rod cylinder is constructed. Experiments for suppressing vibrations of the flexible beam are conducted. Theoretical analysis, numerical simulation and experimental results demonstrate that the proposed pneumatic drive scheme and the adopted control algorithms are feasible. The large amplitude vibration of the first bending mode can be suppressed effectively.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

NASA Astrophysics Data System (ADS)

Sun, Xiuqiao; Wang, Jian

2018-07-01

Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

Biosurfactant-biopolymer driven microbial enhanced oil recovery (MEOR) and its optimization by an ANN-GA hybrid technique.

PubMed

Dhanarajan, Gunaseelan; Rangarajan, Vivek; Bandi, Chandrakanth; Dixit, Abhivyakti; Das, Susmita; Ale, Kranthikiran; Sen, Ramkrishna

2017-08-20

A lipopeptide biosurfactant produced by marine Bacillus megaterium and a biopolymer produced by thermophilic Bacillus licheniformis were tested for their application potential in the enhanced oil recovery. The crude biosurfactant obtained after acid precipitation effectively reduced the surface tension of deionized water from 70.5 to 28.25mN/m and the interfacial tension between lube oil and water from 18.6 to 1.5mN/m at a concentration of 250mgL -1 . The biosurfactant exhibited a maximum emulsification activity (E 24 ) of 81.66% against lube oil. The lipopeptide micelles were stabilized by addition of Ca 2+ ions to the biosurfactant solution. The oil recovery efficiency of Ca 2+ conditioned lipopeptide solution from a sand-packed column was optimized by using artificial neural network (ANN) modelling coupled with genetic algorithm (GA) optimization. Three important parameters namely lipopeptide concentration, Ca 2+ concentration and solution pH were considered for optimization studies. In order to further improve the recovery efficiency, a water soluble biopolymer produced by Bacillus licheniformis was used as a flooding agent after biosurfactant incubation. Upon ANN-GA optimization, 45% tertiary oil recovery was achieved, when biopolymer at a concentration of 3gL -1 was used as a flooding agent. Oil recovery was only 29% at optimal conditions predicted by ANN-GA, when only water was used as flooding solution. The important characteristics of biopolymers such as its viscosity, pore plugging capabilities and bio-cementing ability have also been tested. Thus, as a result of biosurfactant incubation and biopolymer flooding under the optimal process conditions, a maximum oil recovery of 45% was achieved. Therefore, this study is novel, timely and interesting for it showed the combined influence of biosurfactant and biopolymer on solubilisation and mobilization of oil from the soil. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantitative effects of composting state variables on C/N ratio through GA-aided multivariate analysis.

PubMed

Sun, Wei; Huang, Guo H; Zeng, Guangming; Qin, Xiaosheng; Yu, Hui

2011-03-01

It is widely known that variation of the C/N ratio is dependent on many state variables during composting processes. This study attempted to develop a genetic algorithm aided stepwise cluster analysis (GASCA) method to describe the nonlinear relationships between the selected state variables and the C/N ratio in food waste composting. The experimental data from six bench-scale composting reactors were used to demonstrate the applicability of GASCA. Within the GASCA framework, GA searched optimal sets of both specified state variables and SCA's internal parameters; SCA established statistical nonlinear relationships between state variables and the C/N ratio; to avoid unnecessary and time-consuming calculation, a proxy table was introduced to save around 70% computational efforts. The obtained GASCA cluster trees had smaller sizes and higher prediction accuracy than the conventional SCA trees. Based on the optimal GASCA tree, the effects of the GA-selected state variables on the C/N ratio were ranged in a descending order as: NH₄+-N concentration>Moisture content>Ash Content>Mean Temperature>Mesophilic bacteria biomass. Such a rank implied that the variation of ammonium nitrogen concentration, the associated temperature and the moisture conditions, the total loss of both organic matters and available mineral constituents, and the mesophilic bacteria activity, were critical factors affecting the C/N ratio during the investigated food waste composting. This first application of GASCA to composting modelling indicated that more direct search algorithms could be coupled with SCA or other multivariate analysis methods to analyze complicated relationships during composting and many other environmental processes. Copyright © 2010 Elsevier B.V. All rights reserved.

Optimized efficiency in InP nanowire solar cells with accurate 1D analysis

NASA Astrophysics Data System (ADS)

Chen, Yang; Kivisaari, Pyry; Pistol, Mats-Erik; Anttu, Nicklas

2018-01-01

Semiconductor nanowire arrays are a promising candidate for next generation solar cells due to enhanced absorption and reduced material consumption. However, to optimize their performance, time consuming three-dimensional (3D) opto-electronics modeling is usually performed. Here, we develop an accurate one-dimensional (1D) modeling method for the analysis. The 1D modeling is about 400 times faster than 3D modeling and allows direct application of concepts from planar pn-junctions on the analysis of nanowire solar cells. We show that the superposition principle can break down in InP nanowires due to strong surface recombination in the depletion region, giving rise to an IV-behavior similar to that with low shunt resistance. Importantly, we find that the open-circuit voltage of nanowire solar cells is typically limited by contact leakage. Therefore, to increase the efficiency, we have investigated the effect of high-bandgap GaP carrier-selective contact segments at the top and bottom of the InP nanowire and we find that GaP contact segments improve the solar cell efficiency. Next, we discuss the merit of p-i-n and p-n junction concepts in nanowire solar cells. With GaP carrier selective top and bottom contact segments in the InP nanowire array, we find that a p-n junction design is superior to a p-i-n junction design. We predict a best efficiency of 25% for a surface recombination velocity of 4500 cm s-1, corresponding to a non-radiative lifetime of 1 ns in p-n junction cells. The developed 1D model can be used for general modeling of axial p-n and p-i-n junctions in semiconductor nanowires. This includes also LED applications and we expect faster progress in device modeling using our method.

Optimized efficiency in InP nanowire solar cells with accurate 1D analysis.

PubMed

Chen, Yang; Kivisaari, Pyry; Pistol, Mats-Erik; Anttu, Nicklas

2018-01-26

Semiconductor nanowire arrays are a promising candidate for next generation solar cells due to enhanced absorption and reduced material consumption. However, to optimize their performance, time consuming three-dimensional (3D) opto-electronics modeling is usually performed. Here, we develop an accurate one-dimensional (1D) modeling method for the analysis. The 1D modeling is about 400 times faster than 3D modeling and allows direct application of concepts from planar pn-junctions on the analysis of nanowire solar cells. We show that the superposition principle can break down in InP nanowires due to strong surface recombination in the depletion region, giving rise to an IV-behavior similar to that with low shunt resistance. Importantly, we find that the open-circuit voltage of nanowire solar cells is typically limited by contact leakage. Therefore, to increase the efficiency, we have investigated the effect of high-bandgap GaP carrier-selective contact segments at the top and bottom of the InP nanowire and we find that GaP contact segments improve the solar cell efficiency. Next, we discuss the merit of p-i-n and p-n junction concepts in nanowire solar cells. With GaP carrier selective top and bottom contact segments in the InP nanowire array, we find that a p-n junction design is superior to a p-i-n junction design. We predict a best efficiency of 25% for a surface recombination velocity of 4500 cm s -1 , corresponding to a non-radiative lifetime of 1 ns in p-n junction cells. The developed 1D model can be used for general modeling of axial p-n and p-i-n junctions in semiconductor nanowires. This includes also LED applications and we expect faster progress in device modeling using our method.

Influence of cost functions and optimization methods on solving the inverse problem in spatially resolved diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Rakotomanga, Prisca; Soussen, Charles; Blondel, Walter C. P. M.

2017-03-01

Diffuse reflectance spectroscopy (DRS) has been acknowledged as a valuable optical biopsy tool for in vivo characterizing pathological modifications in epithelial tissues such as cancer. In spatially resolved DRS, accurate and robust estimation of the optical parameters (OP) of biological tissues is a major challenge due to the complexity of the physical models. Solving this inverse problem requires to consider 3 components: the forward model, the cost function, and the optimization algorithm. This paper presents a comparative numerical study of the performances in estimating OP depending on the choice made for each of the latter components. Mono- and bi-layer tissue models are considered. Monowavelength (scalar) absorption and scattering coefficients are estimated. As a forward model, diffusion approximation analytical solutions with and without noise are implemented. Several cost functions are evaluated possibly including normalized data terms. Two local optimization methods, Levenberg-Marquardt and TrustRegion-Reflective, are considered. Because they may be sensitive to the initial setting, a global optimization approach is proposed to improve the estimation accuracy. This algorithm is based on repeated calls to the above-mentioned local methods, with initial parameters randomly sampled. Two global optimization methods, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are also implemented. Estimation performances are evaluated in terms of relative errors between the ground truth and the estimated values for each set of unknown OP. The combination between the number of variables to be estimated, the nature of the forward model, the cost function to be minimized and the optimization method are discussed.

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

2017-01-01

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model. PMID:28587196

Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA).

PubMed

Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

2017-06-03

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N₂-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model.

Selective area growth and characterization of InGaN nanocolumns for phosphor-free white light emission

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, S.; Bengoechea-Encabo, A.; Sanchez-Garcia, M. A.

This work reports on the morphology and light emission characteristics of ordered InGaN nanocolumns grown by plasma-assisted molecular beam epitaxy. Within the growth temperature range of 750 to 650 Degree-Sign C, the In incorporation can be modified either by the growth temperature, the In/Ga ratio, or the III/V ratio, following different mechanisms. Control of these factors allows the optimization of the InGaN nanocolumns light emission wavelength and line-shape. Furthermore, yellow-white emission is obtained at room temperature from nanostructures with a composition-graded active InGaN region obtained by temperature gradients during growth.

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

2005-01-01

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Modeling and Optimization of Multiple Unmanned Aerial Vehicles System Architecture Alternatives

PubMed Central

Wang, Weiping; He, Lei

2014-01-01

Unmanned aerial vehicle (UAV) systems have already been used in civilian activities, although very limitedly. Confronted different types of tasks, multi UAVs usually need to be coordinated. This can be extracted as a multi UAVs system architecture problem. Based on the general system architecture problem, a specific description of the multi UAVs system architecture problem is presented. Then the corresponding optimization problem and an efficient genetic algorithm with a refined crossover operator (GA-RX) is proposed to accomplish the architecting process iteratively in the rest of this paper. The availability and effectiveness of overall method is validated using 2 simulations based on 2 different scenarios. PMID:25140328

Multicycle rapid thermal annealing optimization of Mg-implanted GaN: Evolution of surface, optical, and structural properties

NASA Astrophysics Data System (ADS)

Greenlee, Jordan D.; Feigelson, Boris N.; Anderson, Travis J.; Tadjer, Marko J.; Hite, Jennifer K.; Mastro, Michael A.; Eddy, Charles R.; Hobart, Karl D.; Kub, Francis J.

2014-08-01

The first step of a multi-cycle rapid thermal annealing process was systematically studied. The surface, structure, and optical properties of Mg implanted GaN thin films annealed at temperatures ranging from 900 to 1200 °C were investigated by Raman spectroscopy, photoluminescence, UV-visible spectroscopy, atomic force microscopy, and Nomarski microscopy. The GaN thin films are capped with two layers of in-situ metal organic chemical vapor deposition -grown AlN and annealed in 24 bar of N2 overpressure to avoid GaN decomposition. The crystal quality of the GaN improves with increasing annealing temperature as confirmed by UV-visible spectroscopy and the full widths at half maximums of the E2 and A1 (LO) Raman modes. The crystal quality of films annealed above 1100 °C exceeds the quality of the as-grown films. At 1200 °C, Mg is optically activated, which is determined by photoluminescence measurements. However, at 1200 °C, the GaN begins to decompose as evidenced by pit formation on the surface of the samples. Therefore, it was determined that the optimal temperature for the first step in a multi-cycle rapid thermal anneal process should be conducted at 1150 °C due to crystal quality and surface morphology considerations.

Optimization of Polygalacturonase Production from a Newly Isolated Thalassospira frigidphilosprofundus to Use in Pectin Hydrolysis: Statistical Approach

PubMed Central

Rekha, V. P. B.; Ghosh, Mrinmoy; Adapa, Vijayanand; Oh, Sung-Jong; Pulicherla, K. K.; Sambasiva Rao, K. R. S.

2013-01-01

The present study deals with the production of cold active polygalacturonase (PGase) by submerged fermentation using Thalassospira frigidphilosprofundus, a novel species isolated from deep waters of Bay of Bengal. Nonlinear models were applied to optimize the medium components for enhanced production of PGase. Taguchi orthogonal array design was adopted to evaluate the factors influencing the yield of PGase, followed by the central composite design (CCD) of response surface methodology (RSM) to identify the optimum concentrations of the key factors responsible for PGase production. Data obtained from the above mentioned statistical experimental design was used for final optimization study by linking the artificial neural network and genetic algorithm (ANN-GA). Using ANN-GA hybrid model, the maximum PGase activity (32.54 U/mL) was achieved at the optimized concentrations of medium components. In a comparison between the optimal output of RSM and ANN-GA hybrid, the latter favored the production of PGase. In addition, the study also focused on the determination of factors responsible for pectin hydrolysis by crude pectinase extracted from T. frigidphilosprofundus through the central composite design. Results indicated 80% degradation of pectin in banana fiber at 20°C in 120 min, suggesting the scope of cold active PGase usage in the treatment of raw banana fibers. PMID:24455722

Optimization of polygalacturonase production from a newly isolated Thalassospira frigidphilosprofundus to use in pectin hydrolysis: statistical approach.

PubMed

Rekha, V P B; Ghosh, Mrinmoy; Adapa, Vijayanand; Oh, Sung-Jong; Pulicherla, K K; Sambasiva Rao, K R S

2013-01-01

The present study deals with the production of cold active polygalacturonase (PGase) by submerged fermentation using Thalassospira frigidphilosprofundus, a novel species isolated from deep waters of Bay of Bengal. Nonlinear models were applied to optimize the medium components for enhanced production of PGase. Taguchi orthogonal array design was adopted to evaluate the factors influencing the yield of PGase, followed by the central composite design (CCD) of response surface methodology (RSM) to identify the optimum concentrations of the key factors responsible for PGase production. Data obtained from the above mentioned statistical experimental design was used for final optimization study by linking the artificial neural network and genetic algorithm (ANN-GA). Using ANN-GA hybrid model, the maximum PGase activity (32.54 U/mL) was achieved at the optimized concentrations of medium components. In a comparison between the optimal output of RSM and ANN-GA hybrid, the latter favored the production of PGase. In addition, the study also focused on the determination of factors responsible for pectin hydrolysis by crude pectinase extracted from T. frigidphilosprofundus through the central composite design. Results indicated 80% degradation of pectin in banana fiber at 20 °C in 120 min, suggesting the scope of cold active PGase usage in the treatment of raw banana fibers.

Integrating GIS, cellular automata, and genetic algorithm in urban spatial optimization: a case study of Lanzhou

NASA Astrophysics Data System (ADS)

Xu, Xibao; Zhang, Jianming; Zhou, Xiaojian

2006-10-01

This paper presents a model integrating GIS, cellular automata (CA) and genetic algorithm (GA) in urban spatial optimization. The model involves three objectives of the maximization of land-use efficiency, the maximization of urban spatial harmony and appropriate proportion of each land-use type. CA submodel is designed with standard Moore neighbor and three transition rules to maximize the land-use efficiency and urban spatial harmony, according to the land-use suitability and spatial harmony index. GA submodel is designed with four constraints and seven steps for the maximization of urban spatial harmony and appropriate proportion of each land-use type, including encoding, initializing, calculating fitness, selection, crossover, mutation and elitism. GIS is used to prepare for the input data sets for the model and perform spatial analysis on the results, while CA and GA are integrated to optimize urban spatial structure, programmed with Matlab 7 and coupled with GIS loosely. Lanzhou, a typical valley-basin city with fast urban development, is chosen as the case study. At the end, a detail analysis and evaluation of the spatial optimization with the model are made, and it proves to be a powerful tool in optimizing urban spatial structure and make supplement for urban planning and policy-makers.

Optical gain spectra of 1.55 μm GaAs/GaN.58yAs1-1.58yBiy/GaAs single quantum well

NASA Astrophysics Data System (ADS)

Guizani, I.; Bilel, C.; Habchi, M. M.; Rebey, A.

2017-02-01

The optical gain spectra of doped lattice-matched GaNAsBi-based single quantum well (SQW) was theoretically investigated using a (16 × 16) band anti-crossing (BAC) model combined with self-consistent calculation. For the sake of comparison, we computed the optical gain of both (i-n-i) and (i-p-i) doped well types in GaAs/GaNAsBi/GaAs quantum structure. The highest obtained material gain Gmax was 1.2 ×104 cm-1 for (i-n-i) type doped with N2Dd = 2.5 ×1012 cm-2 . We proposed investigating the p-i-n type structure to enhance the optical performance of GaAs/GaNAsBi/GaAs SQW. The Bi composition was optimized in order to obtain Te 1 - h 1 = 1.55 μ m . The effect of well width on optical gain spectra was also discussed.

Novel model of a AlGaN/GaN high electron mobility transistor based on an artificial neural network

NASA Astrophysics Data System (ADS)

Cheng, Zhi-Qun; Hu, Sha; Liu, Jun; Zhang, Qi-Jun

2011-03-01

In this paper we present a novel approach to modeling AlGaN/GaN high electron mobility transistor (HEMT) with an artificial neural network (ANN). The AlGaN/GaN HEMT device structure and its fabrication process are described. The circuit-based Neuro-space mapping (neuro-SM) technique is studied in detail. The EEHEMT model is implemented according to the measurement results of the designed device, which serves as a coarse model. An ANN is proposed to model AlGaN/GaN HEMT based on the coarse model. Its optimization is performed. The simulation results from the model are compared with the measurement results. It is shown that the simulation results obtained from the ANN model of AlGaN/GaN HEMT are more accurate than those obtained from the EEHEMT model. Project supported by the National Natural Science Foundation of China (Grant No. 60776052).

AlGaN/GaN HEMTs regrown by MBE on epi-ready semi-insulating GaN-on-sapphire with inhibited interface contamination

NASA Astrophysics Data System (ADS)

Cordier, Y.; Azize, M.; Baron, N.; Chenot, S.; Tottereau, O.; Massies, J.

2007-11-01

In this work, we show that, by carefully designing the subsurface Fe doping profile in SI-GaN templates grown by MOVPE and by optimizing the MBE regrowth conditions, a highly resistive GaN buffer can be achieved on these epi-ready GaN-on-sapphire templates without any addition of acceptors during the regrowth. As a result, high-quality high electron mobility transistors can be fabricated. Furthermore, we report on the excellent properties of two-dimensional electron gas and device performances with electron mobility greater than 2000 cm 2/V s at room temperature and off-state buffer leakage currents as low as 5 μA/mm at 100 V.

Optimal placement of FACTS devices using optimization techniques: A review

NASA Astrophysics Data System (ADS)

Gaur, Dipesh; Mathew, Lini

2018-03-01

Modern power system is dealt with overloading problem especially transmission network which works on their maximum limit. Today’s power system network tends to become unstable and prone to collapse due to disturbances. Flexible AC Transmission system (FACTS) provides solution to problems like line overloading, voltage stability, losses, power flow etc. FACTS can play important role in improving static and dynamic performance of power system. FACTS devices need high initial investment. Therefore, FACTS location, type and their rating are vital and should be optimized to place in the network for maximum benefit. In this paper, different optimization methods like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) etc. are discussed and compared for optimal location, type and rating of devices. FACTS devices such as Thyristor Controlled Series Compensator (TCSC), Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM) are considered here. Mentioned FACTS controllers effects on different IEEE bus network parameters like generation cost, active power loss, voltage stability etc. have been analyzed and compared among the devices.

Kinetic-limited etching of magnesium doping nitrogen polar GaN in potassium hydroxide solution

NASA Astrophysics Data System (ADS)

Jiang, Junyan; Zhang, Yuantao; Chi, Chen; Yang, Fan; Li, Pengchong; Zhao, Degang; Zhang, Baolin; Du, Guotong

2016-01-01

KOH based wet etchings were performed on both undoped and Mg-doped N-polar GaN films grown by metal-organic chemical vapor deposition. It is found that the etching rate for Mg-doped N-polar GaN gets slow obviously compared with undoped N-polar GaN. X-ray photoelectron spectroscopy analysis proved that Mg oxide formed on N-polar GaN surface is insoluble in KOH solution so that kinetic-limited etching occurs as the etching process goes on. The etching process model of Mg-doped N-polar GaN in KOH solution is tentatively purposed using a simplified ideal atomic configuration. Raman spectroscopy analysis reveals that Mg doping can induce tensile strain in N-polar GaN films. Meanwhile, p-type N-polar GaN film with a hole concentration of 2.4 ÿ 1017 cm⿿3 was obtained by optimizing bis-cyclopentadienyl magnesium flow rates.

Electron and proton degradation in /AlGa/As-GaAs solar cells

NASA Technical Reports Server (NTRS)

Loo, R.; Knechtli, R. C.; Kamath, G. S.; Goldhammer, L.; Anspaugh, B.

1978-01-01

Results on radiation damage in (AlGa)As-GaAs solar cells by 1 MeV electron fluences up to 10 to the 16th electrons/sq cm and by 15, 20, 30 and 40 MeV proton fluences up to 5 times 10 to the 11th protons/sq cm are presented. The damage is compared with data on state-of-the-art silicon cells which were irradiated along with the gallium arsenide cells. The theoretical expectation that the junction depth has to be kept relatively shallow, to minimize radiation damage has been verified experimentally. The damage to the GaAs cells as a function of irradiation, is correlated with the change in their spectral response and dark I-V characteristics. The effect of thermal annealing on the (AlGa)As-GaAs solar cells was also investigated. This data is used to predict further avenues of optimization of the GaAs cells.

Spiral bacterial foraging optimization method: Algorithm, evaluation and convergence analysis

NASA Astrophysics Data System (ADS)

Kasaiezadeh, Alireza; Khajepour, Amir; Waslander, Steven L.

2014-04-01

A biologically-inspired algorithm called Spiral Bacterial Foraging Optimization (SBFO) is investigated in this article. SBFO, previously proposed by the same authors, is a multi-agent, gradient-based algorithm that minimizes both the main objective function (local cost) and the distance between each agent and a temporary central point (global cost). A random jump is included normal to the connecting line of each agent to the central point, which produces a vortex around the temporary central point. This random jump is also suitable to cope with premature convergence, which is a feature of swarm-based optimization methods. The most important advantages of this algorithm are as follows: First, this algorithm involves a stochastic type of search with a deterministic convergence. Second, as gradient-based methods are employed, faster convergence is demonstrated over GA, DE, BFO, etc. Third, the algorithm can be implemented in a parallel fashion in order to decentralize large-scale computation. Fourth, the algorithm has a limited number of tunable parameters, and finally SBFO has a strong certainty of convergence which is rare in existing global optimization algorithms. A detailed convergence analysis of SBFO for continuously differentiable objective functions has also been investigated in this article.

Comparison of global optimization approaches for robust calibration of hydrologic model parameters

NASA Astrophysics Data System (ADS)

Jung, I. W.

2015-12-01

Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

Investigating and Optimizing Carrier Transport, Carrier Distribution, and Efficiency Droop in GaN-based Light-emitting Diodes

NASA Astrophysics Data System (ADS)

Zhu, Di

2011-12-01

The recent tremendous boost in the number and diversity of applications for light-emitting diodes (LEDs) indicates the emergence of the next-generation lighting and illumination technology. The rapidly improving LED technology is becoming increasingly viable especially for high-power applications. However, the greatest roadblock before finally breaching the main defensive position of conventional fluorescent and incandescent lamps still remains: GaN-based LEDs encounter a significant decrease in efficiency as the drive current increases, and this phenomenon is known as the efficiency droop. This dissertation focuses on uncovering the physical cause of efficiency droop in GaN-based LEDs and looks for solutions to it. GaN-based multiple-quantum-well (MQW) LEDs usually have abnormally high diode-ideality factors. Investigating the origin of the high diode-ideality factors could help to better understand the carrier transport in the LED MQW active region. We investigate the ideality factors of GaInN LEDs with different numbers of doped quantum barriers (QBs). Consistent with the theory, a decrease of the ideality factor as well as a reduction in forward voltage is found with increasing number of doped QBs. Experimental and simulation results indicate that the band profiles of QBs in the active region have a significant impact on the carrier transport mechanism, and the unipolar heterojunctions inside the active region play an important role in determining the diode-ideality factor. This dissertation will discuss several mechanisms leading to electron leakage which could be responsible for the efficiency droop. We show that the inefficient electron capture, the electron-attracting properties of polarized EBL, the inherent asymmetry in electron and hole transport and the inefficient EBL p-doping at high Al contents severely limit the ability to confine electrons to the MQWs. We demonstrate GaInN LEDs employing tailored Si doping in the QBs with strongly enhanced high-current efficiency and reduced efficiency droop. Compared with 4-QB-doped LEDs, 1-QB-doped LEDs show a 37.5% increase in light-output power at high currents. Consistent with the measurements, simulation shows a shift of radiative recombination among the MQWs and a reduced electron leakage current into the p-type GaN when fewer QBs are doped. The results can be attributed to a more symmetric carrier transport and uniform carrier distribution which help to reduce electron leakage and thus reduce the efficiency droop. In this dissertation, artificial evolution is introduced to the LED optimization process which combines a genetic algorithm (GA) and device-simulation software. We show that this approach is capable of generating novel concepts in designing and optimizing LED devices. Application of the GA to the QB-doping in the MQWs yields optimized structures which is consistent with the tailored QB doping experiments. Application of the GA to the EBL region suggests a novel structure with an inverted sheet charge at the spacer-EBL interface. The resulting repulsion of electrons can significantly reduce electron leakage and enhance the efficiency. Finally, dual-wavelength LEDs, which have two types of quantum wells (QWs) emitting at two different wavelengths, are experimentally characterized and compared with numerical simulations. These dual-wavelength LEDs allow us to determine which QW emits most of the light. An experimental observation and a quantitative analysis of the radiative recombination shift within the MQW active region are obtained. In addition, an injection-current dependence of the radiative recombination shift is predicted by numerical simulations and indeed observed in dual-wavelength LEDs. This injection-current dependence of the radiative recombination distribution can be explained very well by incorporating quantum-mechanical tunneling of carriers into and through the QBs into to the classical drift-diffusion model. In summary, using the LEDs with tailored QB doping and dual-wavelength LEDs, we investigate the origin of the high diode-ideality factor of LEDs and gain insight on the control of carrier transport, carrier distribution, and radiative recombination in the LED MQW active region. Our results provide solid evidence on the effectiveness of the GA in the LED device optimization process. In addition, the innovative EBL structure optimized by the GA sheds light on further paths for the optimization of LED design. Our results are the starting point of applying artificial evolution to practical semiconductor devices, opening new perspectives for complex semiconductor device optimization and enabling breakthroughs in high-performance LED design.

Magnetic spectroscopy and microscopy of functional materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jenkins, Catherine Ann

2011-05-01

Heusler intermetallics Mn 2Y Ga and X 2MnGa (X; Y =Fe, Co, Ni) undergo tetragonal magnetostructural transitions that can result in half metallicity, magnetic shape memory, or the magnetocaloric effect. Understanding the magnetism and magnetic behavior in functional materials is often the most direct route to being able to optimize current materials for todays applications and to design novel ones for tomorrow. Synchrotron soft x-ray magnetic spectromicroscopy techniques are well suited to explore the the competing effects from the magnetization and the lattice parameters in these materials as they provide detailed element-, valence-, and site-specifc information on the coupling ofmore » crystallographic ordering and electronic structure as well as external parameters like temperature and pressure on the bonding and exchange. Fundamental work preparing the model systems of spintronic, multiferroic, and energy-related compositions is presented for context. The methodology of synchrotron spectroscopy is presented and applied to not only magnetic characterization but also of developing a systematic screening method for future examples of materials exhibiting any of the above effects. The chapter progression is as follows: an introduction to the concepts and materials under consideration (Chapter 1); an overview of sample preparation techniques and results, and the kinds of characterization methods employed (Chapter 2); spectro- and microscopic explorations of X 2MnGa/Ge (Chapter 3); spectroscopic investigations of the composition series Mn 2Y Ga to the logical Mn 3Ga endpoint (Chapter 4); and a summary and overview of upcoming work (Chapter 5). Appendices include the results of a Think Tank for the Graduate School of Excellence MAINZ (Appendix A) and details of an imaging project now in progress on magnetic reversal and domain wall observation in the classical Heusler material Co 2FeSi (Appendix B).« less

Highly indistinguishable and strongly entangled photons from symmetric GaAs quantum dots.

PubMed

Huber, Daniel; Reindl, Marcus; Huo, Yongheng; Huang, Huiying; Wildmann, Johannes S; Schmidt, Oliver G; Rastelli, Armando; Trotta, Rinaldo

2017-05-26

The development of scalable sources of non-classical light is fundamental to unlocking the technological potential of quantum photonics. Semiconductor quantum dots are emerging as near-optimal sources of indistinguishable single photons. However, their performance as sources of entangled-photon pairs are still modest compared to parametric down converters. Photons emitted from conventional Stranski-Krastanov InGaAs quantum dots have shown non-optimal levels of entanglement and indistinguishability. For quantum networks, both criteria must be met simultaneously. Here, we show that this is possible with a system that has received limited attention so far: GaAs quantum dots. They can emit triggered polarization-entangled photons with high purity (g (2) (0) = 0.002±0.002), high indistinguishability (0.93±0.07 for 2 ns pulse separation) and high entanglement fidelity (0.94±0.01). Our results show that GaAs might be the material of choice for quantum-dot entanglement sources in future quantum technologies.