Deferred High Level Trigger in LHCb: A Boost to CPU Resource Utilization
NASA Astrophysics Data System (ADS)
Frank, M.; Gaspar, C.; Herwijnen, E. v.; Jost, B.; Neufeld, N.
2014-06-01
The LHCb experiment at the LHC accelerator at CERN collects collisions of particle bunches at 40 MHz. After a first level of hardware trigger with output of 1 MHz, the physically interesting collisions are selected by running dedicated trigger algorithms in the High Level Trigger (HLT) computing farm. This farm consists of up to roughly 25000 CPU cores in roughly 1600 physical nodes each equipped with at least 1 TB of local storage space. This work describes the architecture to treble the available CPU power of the HLT farm given that the LHC collider in previous years delivered stable physics beams about 30% of the time. The gain is achieved by splitting the event selection process in two, a first stage reducing the data taken during stable beams and buffering the preselected particle collisions locally. A second processing stage running constantly at lower priority will then finalize the event filtering process and benefits fully from the time when LHC does not deliver stable beams e.g. while preparing a new physics fill or during periods used for machine development.
Utility of fuzzy cross-impact simulation in environmental assessment
Parashar, A.; Paliwal, R.; Rambabu, P.
1997-11-01
Fuzzy cross-impact simulation is a qualitative technique, where interactions within a system are represented by a cross-impact matrix that includes linguistic elements. It can be used effectively to visualize dynamic evolution of a system. The utility of the fuzzy cross-impact simulation approach is: (1) in dealing with uncertainties in environment-development systems; (2) scoping cumulative effect assessment; and (3) integrating societal response structure in environment impact assessment. Use of the method is illustrated in a case concerning the textile industry in Indore, India. Consequences of policy alternatives for cleanup and pollution abatement are predicted in terms of environmental quality and quality of life, using the simulation model. The consequence analysis is used to arrive at preferred policy options.
Computer-Aided Diagnosis Utilizing Interactive Fuzzy Pattern Recognition Techniques
NASA Astrophysics Data System (ADS)
Ismail, M. A.
1984-08-01
Interactive or display-oriented pattern recognition algorithms can be utilized with advantage in the design of efficient computer-aided diagnostic systems. These visual methods may provide a powerful alternative to the pure numerical approach of data analysis for diagnostic and prognostic purposes. Functional as well as pictorial representation techniques are discussed in conjunction with some newly developed semi-fuzzy classification techniques. The blend between the two methodologies leads to the design of a very flexible, yet powerful diagnostic system. Results obtained when applying the proposed system on a group of patients representing several classes of liver dysfunction are also reported, to demonstrate the effectiveness of the proposed methodology.
NASA Astrophysics Data System (ADS)
Yamamoto, Moriki
The data generated at a very high rate by sensors and RFIDs are required to be handled by continuous queries keeping real time response. Because of its purpose, DSMSs are used in several cases of these large scale systems. On the other hand, sensor terminal systems include light RDBMSs generally in many cases. So if light RDBMSs can handle the high rate data directly, it is convenient for several applications. This paper proposes a speed-up method of stream processing by using a light RDBMS SQLite without any special modifications. If DSMSs are categorized by performance such as large, medium and small scale, this method aims at a small or medium scale performance. The database performance mainly depends on storage access time, so this proposed method adopts a memory database, a bulk store records technique and parallel processing while taking advantage of multi-core CPU configurations of terminal systems.
Utilizing QR decomposition for solving singular fuzzy linear systems
NASA Astrophysics Data System (ADS)
Nikuie, M.; Ahmad, M. Z.
2014-06-01
In this paper, we study the solution of n × n fuzzy linear system Ãx = ˜b where A is a singular crisp matrix, ˜x and ˜b are vectors of fuzzy numbers. We first convert the fuzzy linear system Ãx = ˜b to 2n × 2n crisp linear system SX = Y. where S is a singular matrix. We then apply the Drazin inverse to solve the 2n × 2n crisp linear system SX = Y. To investigate the effect of Drazin inverse, we apply the QR decomposition method. Several numerical examples are discussed.
Determining rules for closing customer service centers: A public utility company's fuzzy decision
NASA Technical Reports Server (NTRS)
Dekorvin, Andre; Shipley, Margaret F.
1992-01-01
In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert.
Stereo viewing 3-component, planar PIV utilizing fuzzy inference
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
1996-01-01
An all electronic 3-D Digital Particle Image Velocimetry (DPIV) system has been developed for use in high velocity (supersonic) flows. Two high resolution CCD cameras mounted in a stereo viewing configuration are used to determine the out-of-plane velocity component from the difference of the in-plane velocity measurements. Double exposure image frames are acquired and Fuzzy inference techniques are used to maximize the validity of the velocity estimates obtained from the auto-correlation analysis. The CCD cameras are tilted relative to their respective lens axes to satisfy Scheimpflug's condition. Tilting the camera film plane ensures that the entire image plane is in focus. Perspective distortion still results, but can be corrected by proper calibration of the optical system. A calibration fixture is used to determine the experimental setup parameters and to assess the accuracy to which the z-plane displacements can be estimated. The details of the calibration fixture and procedure are discussed in the text. A pair of pulsed Nd:YAG lasers operating at 532 nm are used to illuminate the seeded flow from a convergent nozzle operated in an underexpanded condition. The light sheet was oriented perpendicular to the nozzle flow, yielding planar cross-sections of the 3-component velocity field at several axial stations. The key features of the supersonic jet are readily observed in the cross-plane vector plots.
Combustion Power Unit--400: CPU-400.
ERIC Educational Resources Information Center
Combustion Power Co., Palo Alto, CA.
Aerospace technology may have led to a unique basic unit for processing solid wastes and controlling pollution. The Combustion Power Unit--400 (CPU-400) is designed as a turboelectric generator plant that will use municipal solid wastes as fuel. The baseline configuration is a modular unit that is designed to utilize 400 tons of refuse per day…
Autonomous vehicle navigation utilizing fuzzy controls concepts for a next generation wheelchair.
Hansen, J D; Barrett, S F; Wright, C H G; Wilcox, M
2008-01-01
Three different positioning techniques were investigated to create an autonomous vehicle that could accurately navigate towards a goal: Global Positioning System (GPS), compass dead reckoning, and Ackerman steering. Each technique utilized a fuzzy logic controller that maneuvered a four-wheel car towards a target. The reliability and the accuracy of the navigation methods were investigated by modeling the algorithms in software and implementing them in hardware. To implement the techniques in hardware, positioning sensors were interfaced to a remote control car and a microprocessor. The microprocessor utilized the sensor measurements to orient the car with respect to the target. Next, a fuzzy logic control algorithm adjusted the front wheel steering angle to minimize the difference between the heading and bearing. After minimizing the heading error, the car maintained a straight steering angle along its path to the final destination. The results of this research can be used to develop applications that require precise navigation. The design techniques can also be implemented on alternate platforms such as a wheelchair to assist with autonomous navigation. PMID:19141895
Qiao, Yuanhua; Keren, Nir; Mannan, M Sam
2009-08-15
Risk assessment and management of transportation of hazardous materials (HazMat) require the estimation of accident frequency. This paper presents a methodology to estimate hazardous materials transportation accident frequency by utilizing publicly available databases and expert knowledge. The estimation process addresses route-dependent and route-independent variables. Negative binomial regression is applied to an analysis of the Department of Public Safety (DPS) accident database to derive basic accident frequency as a function of route-dependent variables, while the effects of route-independent variables are modeled by fuzzy logic. The integrated methodology provides the basis for an overall transportation risk analysis, which can be used later to develop a decision support system. PMID:19250750
STEM image simulation with hybrid CPU/GPU programming.
Yao, Y; Ge, B H; Shen, X; Wang, Y G; Yu, R C
2016-07-01
STEM image simulation is achieved via hybrid CPU/GPU programming under parallel algorithm architecture to speed up calculation on a personal computer (PC). To utilize the calculation power of a PC fully, the simulation is performed using the GPU core and multi-CPU cores at the same time to significantly improve efficiency. GaSb and an artificial GaSb/InAs interface with atom diffusion have been used to verify the computation. PMID:27093687
Using all of your CPU's in HIPE
NASA Astrophysics Data System (ADS)
Jacobson, J. D.; Fadda, D.
2012-09-01
Modern computer architectures increasingly feature multi-core CPU's. For example, the MacbookPro features the Intel quad-core i7 processors. Through the use of hyper-threading, where each core can execute two threads simultaneously, the quad-core i7 can support eight simultaneous processing threads. All this on your laptop! This CPU power can now be put into service by scientists to perform data reduction tasks, but only if the software has been designed to take advantage of the multiple processor architectures. Up to now, software written for Herschel data reduction (HIPE), written in Jython and JAVA, is single-threaded and can only utilize a single processor. Users of HIPE do not get any advantage from the additional processors. Why not put all of the CPU resources to work reducing your data? We present a multi-threaded software application that corrects long-term transients in the signal from the PACS unchopped spectroscopy line scan mode. In this poster, we present a multi-threaded software framework to achieve performance improvements from parallel execution. We will show how a task to correct transients in the PACS Spectroscopy Pipeline for the un-chopped line scan mode, has been threaded. This computation-intensive task uses either a one-parameter or a three parameter exponential function, to characterize the transient. The task uses a JAVA implementation of Minpack, translated from the C (Moshier) and IDL (Markwardt) by the authors, to optimize the correction parameters. We also explain how to determine if a task can benefit from threading (Amdahl's Law), and if it is safe to thread. The design and implementation, using the JAVA concurrency package completions service is described. Pitfalls, timing bugs, thread safety, resource control, testing and performance improvements are described and plotted.
A survey of CPU-GPU heterogeneous computing techniques
Mittal, Sparsh; Vetter, Jeffrey S.
2015-07-04
As both CPU and GPU become employed in a wide range of applications, it has been acknowledged that both of these processing units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this paper, we survey heterogeneous computing techniques (HCTs) such as workload-partitioning which enable utilizing both CPU and GPU to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler and applicationmore » level. Further, we review both discrete and fused CPU-GPU systems; and discuss benchmark suites designed for evaluating heterogeneous computing systems (HCSs). Furthermore, we believe that this paper will provide insights into working and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.« less
A survey of CPU-GPU heterogeneous computing techniques
Mittal, Sparsh; Vetter, Jeffrey S.
2015-07-04
As both CPU and GPU become employed in a wide range of applications, it has been acknowledged that both of these processing units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this paper, we survey heterogeneous computing techniques (HCTs) such as workload-partitioning which enable utilizing both CPU and GPU to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler and application level. Further, we review both discrete and fused CPU-GPU systems; and discuss benchmark suites designed for evaluating heterogeneous computing systems (HCSs). Furthermore, we believe that this paper will provide insights into working and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.
A multi-core CPU pipeline architecture for virtual environments.
Acosta, Eric; Liu, Alan; Sieck, Jennifer; Muniz, Gilbert; Bowyer, Mark; Armonda, Rocco
2009-01-01
Physically-based virtual environments (VEs) provide realistic interactions and behaviors for computer-based medical simulations. Limited CPU resources have traditionally forced VEs to be simplified for real-time performance. Multi-core processors greatly increase the computational capacity of computers and are quickly becoming standard. However, developing non-application specific methods to fully utilize all available CPU cores for processing VEs is difficult. The paper describes a pipeline VE architecture designed for multi-core CPU systems. The architecture enables development of VEs that leverage the computational resources of all CPU cores for VE simulation. A VE's workload is dynamically distributed across the available CPU cores. A VE can be developed once and scale efficiently with the number of cores. The described pipeline architecture makes it possible to develop complex physically-based VEs for medical simulations. Initial results for a craniotomy simulator being developed have shown super-linear and near-linear speedups when tested with up to four cores. PMID:19377102
Determining rules for closing customer service centers: A public utility company's fuzzy decision
NASA Technical Reports Server (NTRS)
Dekorvin, Andre; Shipley, Margaret F.; Lea, Robert N.
1992-01-01
In the present work, we consider the general problem of knowledge acquisition under uncertainty. Simply stated, the problem reduces to the following: how can we capture the knowledge of an expert when the expert is unable to clearly formulate how he or she arrives at a decision? A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision may have been made. Unique to our work is the fuzzy set representation of the conditions or attributes upon which the expert may possibly base his fuzzy decision. From our examples, we infer certain and possible fuzzy rules for closing a customer service center and illustrate the importance of having the decision closely relate to the conditions under consideration.
The Effect of NUMA Tunings on CPU Performance
NASA Astrophysics Data System (ADS)
Hollowell, Christopher; Caramarcu, Costin; Strecker-Kellogg, William; Wong, Antonio; Zaytsev, Alexandr
2015-12-01
Non-Uniform Memory Access (NUMA) is a memory architecture for symmetric multiprocessing (SMP) systems where each processor is directly connected to separate memory. Indirect access to other CPU's (remote) RAM is still possible, but such requests are slower as they must also pass through that memory's controlling CPU. In concert with a NUMA-aware operating system, the NUMA hardware architecture can help eliminate the memory performance reductions generally seen in SMP systems when multiple processors simultaneously attempt to access memory. The x86 CPU architecture has supported NUMA for a number of years. Modern operating systems such as Linux support NUMA-aware scheduling, where the OS attempts to schedule a process to the CPU directly attached to the majority of its RAM. In Linux, it is possible to further manually tune the NUMA subsystem using the numactl utility. With the release of Red Hat Enterprise Linux (RHEL) 6.3, the numad daemon became available in this distribution. This daemon monitors a system's NUMA topology and utilization, and automatically makes adjustments to optimize locality. As the number of cores in x86 servers continues to grow, efficient NUMA mappings of processes to CPUs/memory will become increasingly important. This paper gives a brief overview of NUMA, and discusses the effects of manual tunings and numad on the performance of the HEPSPEC06 benchmark, and ATLAS software.
Singer, R.M.; Gross, K.C. ); Humenik, K.E. . Dept. of Computer Science)
1991-01-01
An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.
A Fuzzy Query Mechanism for Human Resource Websites
NASA Astrophysics Data System (ADS)
Lai, Lien-Fu; Wu, Chao-Chin; Huang, Liang-Tsung; Kuo, Jung-Chih
Users' preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. User's fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to user's preference.
Optimizing Tensor Contraction Expressions for Hybrid CPU-GPU Execution
Ma, Wenjing; Krishnamoorthy, Sriram; Villa, Oreste; Kowalski, Karol; Agrawal, Gagan
2013-03-01
Tensor contractions are generalized multidimensional matrix multiplication operations that widely occur in quantum chemistry. Efficient execution of tensor contractions on Graphics Processing Units (GPUs) requires several challenges to be addressed, including index permutation and small dimension-sizes reducing thread block utilization. Moreover, to apply the same optimizations to various expressions, we need a code generation tool. In this paper, we present our approach to automatically generate CUDA code to execute tensor contractions on GPUs, including management of data movement between CPU and GPU. To evaluate our tool, GPU-enabled code is generated for the most expensive contractions in CCSD(T), a key coupled cluster method, and incorporated into NWChem, a popular computational chemistry suite. For this method, we demonstrate speedup over a factor of 8.4 using one GPU (instead of one core per node) and over 2.6 when utilizing the entire system using hybrid CPU+GPU solution with 2 GPUs and 5 cores (instead of 7 cores per node). Finally, we analyze the implementation behavior on future GPU systems.
New Multithreaded Hybrid CPU/GPU Approach to Hartree-Fock.
Asadchev, Andrey; Gordon, Mark S
2012-11-13
In this article, a new multithreaded Hartree-Fock CPU/GPU method is presented which utilizes automatically generated code and modern C++ techniques to achieve a significant improvement in memory usage and computer time. In particular, the newly implemented Rys Quadrature and Fock Matrix algorithms, implemented as a stand-alone C++ library, with C and Fortran bindings, provides up to 40% improvement over the traditional Fortran Rys Quadrature. The C++ GPU HF code provides approximately a factor of 17.5 improvement over the corresponding C++ CPU code. PMID:26605582
A competition model for two CPU vendors
NASA Astrophysics Data System (ADS)
Tang, Yinan; Zhang, J. W.
2005-03-01
In a severely competing economic environment, the competing ability of a company must be improved continuously as the reaction to the outer competition pressure. We propose a model developed from Lotka-Volterra competition model with time dependent parameters other than the equilibrium theory so as to describe some characteristics of the technology innovation. The time-dependent parameters comprise carrying capacities and competitive effects. We assume that the technological index is represented, in some degree, by the highest CPU clock frequency. We have quantitatively studied quarterly revenues of AMD and Intel, two chief vendors of the central processing unit (CPU). Moreover, we give the empirical values of the basic parameter set according to theoretical analysis and our simulation results fit the revenue data with reasonable agreement. It demonstrates that the model is capable of describing some important commercial phenomena in certain technology-leading industries. The technology innovation but not the strategy, is the crucial factor of competition, and the first-mover advantage will not be always unbroken. Furthermore, we have found that the unwilling mutualism appeared in the present model cannot be explained by the strategic behavior theory.
Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems
Teodoro, George; Kurc, Tahsin M.; Pan, Tony; Cooper, Lee A.D.; Kong, Jun; Widener, Patrick; Saltz, Joel H.
2014-01-01
The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches. PMID:25419545
Flight software memory sizing and CPU loading estimates
NASA Technical Reports Server (NTRS)
1980-01-01
Estimates of the AP101 memory and central processing unit (CPU) requirements for the space shuttle orbiter are presented. The resource estimates reflect OASCAB approved change requests for Release 18 and Release 19. Memory sizes are presented in 32 bit full words, CPU loading is listed by percentage. Memory and CPU information was obtained from actual AP101 code where available, and from estimates provided by flight software development programmers.
NASA Astrophysics Data System (ADS)
Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad
2012-05-01
Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.
Promise of a Low Power Mobile CPU based Embedded System in Artificial Leg Control
Hernandez, Robert; Zhang, Fan; Zhang, Xiaorong; Huang, He; Yang, Qing
2013-01-01
This paper presents the design and implementation of a low power embedded system using mobile processor technology (Intel Atom™ Z530 Processor) specifically tailored for a neural-machine interface (NMI) for artificial limbs. This embedded system effectively performs our previously developed NMI algorithm based on neuromuscular-mechanical fusion and phase-dependent pattern classification. The analysis shows that NMI embedded system can meet real-time constraints with high accuracies for recognizing the user's locomotion mode. Our implementation utilizes the mobile processor efficiently to allow a power consumption of 2.2 watts and low CPU utilization (less than 4.3%) while executing the complex NMI algorithm. Our experiments have shown that the highly optimized C program implementation on the embedded system has superb advantages over existing PC implementations on MATLAB. The study results suggest that mobile-CPU-based embedded system is promising for implementing advanced control for powered lower limb prostheses. PMID:23367113
VERCE - CPU-intensive Applications in Seismology
NASA Astrophysics Data System (ADS)
Simon, Marek; Leong, Siew Hoon; Zad, Kasra Hosseini; Krischer, Lion; Carpene, Michele; Ferini, Graziella; Trani, Luca; Spinuso, Alessandro; Magnoni, Federika; Casarotti, Emanuele; Gemünd, André; Weissenbach, David; Klampanos, Iraklis; Igel, Heiner
2013-04-01
Recently, advances in computational seismology have culminated in the development of a range of scientific codes enabling the calculation of highly accurate 3D wave and rupture propagation in complex 3D media at unprecedented scales and level of detail. Fortunately, the computational hardware has grown at rates at least as vigorous, to match up to the heavy requirements in CPU and memory imposed by realistic applications. However, as algorithmic and hardware complexity increases, making them work efficiently has become difficult: legacy codes need to be adapted and maintained by the community to meet the requirements of the new computational environments and the handling of large volumes of expensively generated data has become a challenge in itself. Within the VERCE (www.verce.eu) project, several specific use cases have been developed, exemplifying the challenges ahead. Seismic 3D-forward modelling of a large number of recorded earthquakes on a continental scale represents a model use case involving HPC. The simulation will be carried out on an HPC machine (SuperMUC, PLX), the resulting data submitted to a publicly accessible community Data-Center (ORFEUS) with the possibility to interactively mine and process the data using Grid infrastructure (Fraunhofer-SCAI, IPGP). As this basic workflow will need to be repeated for each solver, model, frequency range or processing option over and over again, the elements need to be connected within a workflow environment, allowing easy customization, job monitoring and visualisation of results. In collaboration with our VERCE partners, it was possible to define a basic core architecture for the VERCE platform for the proposed use case. Currently established components include JSAGA for job submission to GRAM, gLite Cream, gLite WMS as well as UNICORE6 instances, GridFTP for file transfer, using VOMS enabled certificate-based authentification. Additionally, a few suggested community applications (Seissol, Specfem3D Sesame
Construction of fuzzy S{sup 4}
Abe, Yasuhiro
2004-12-15
We construct a fuzzy S{sup 4}, utilizing the fact that CP{sup 3} is an S{sup 2} bundle over S{sup 4}. We find that the fuzzy S{sup 4} can be described by a block-diagonal form whose embedding square matrix represents a fuzzy CP{sup 3}. We discuss some pending issues on fuzzy S{sup 4}, i.e., precise matrix-function correspondence, associativity of the algebra, and, etc. Similarly, we also obtain a fuzzy S{sup 8}, using the fact that CP{sup 7} is a CP{sup 3} bundle over S{sup 8}.
a Modified Genetic Algorithm for Finding Fuzzy Shortest Paths in Uncertain Networks
NASA Astrophysics Data System (ADS)
Heidari, A. A.; Delavar, M. R.
2016-06-01
In realistic network analysis, there are several uncertainties in the measurements and computation of the arcs and vertices. These uncertainties should also be considered in realizing the shortest path problem (SPP) due to the inherent fuzziness in the body of expert's knowledge. In this paper, we investigated the SPP under uncertainty to evaluate our modified genetic strategy. We improved the performance of genetic algorithm (GA) to investigate a class of shortest path problems on networks with vague arc weights. The solutions of the uncertain SPP with considering fuzzy path lengths are examined and compared in detail. As a robust metaheuristic, GA algorithm is modified and evaluated to tackle the fuzzy SPP (FSPP) with uncertain arcs. For this purpose, first, a dynamic operation is implemented to enrich the exploration/exploitation patterns of the conventional procedure and mitigate the premature convergence of GA technique. Then, the modified GA (MGA) strategy is used to resolve the FSPP. The attained results of the proposed strategy are compared to those of GA with regard to the cost, quality of paths and CPU times. Numerical instances are provided to demonstrate the success of the proposed MGA-FSPP strategy in comparison with GA. The simulations affirm that not only the proposed technique can outperform GA, but also the qualities of the paths are effectively improved. The results clarify that the competence of the proposed GA is preferred in view of quality quantities. The results also demonstrate that the proposed method can efficiently be utilized to handle FSPP in uncertain networks.
NASA Astrophysics Data System (ADS)
Juels, Ari
The purpose of this chapter is to introduce fuzzy commitment, one of the earliest and simplest constructions geared toward cryptography over noisy data. The chapter also explores applications of fuzzy commitment to two problems in data security: (1) secure management of biometrics, with a focus on iriscodes, and (2) use of knowledge-based authentication (i.e., personal questions) for password recovery.
NASA Technical Reports Server (NTRS)
Zadeh, Lofti A.
1988-01-01
The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.
Fuzzy logic control for camera tracking system
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant
1992-01-01
A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.
Evaluation of Soil Quality: Application of Fuzzy Indicators
Technology Transfer Automated Retrieval System (TEKTRAN)
The problem of assessing soil quality is considered as the fuzzy modeling task. Fuzzy indicator concept (FIC) is used as a general platform for the assessment of soil quality as a "degree or grade of perfection”. The FIC can be realized through the utilization of fuzzy soil quality indicators (FSQI)...
Pipelined CPU Design with FPGA in Teaching Computer Architecture
ERIC Educational Resources Information Center
Lee, Jong Hyuk; Lee, Seung Eun; Yu, Heon Chang; Suh, Taeweon
2012-01-01
This paper presents a pipelined CPU design project with a field programmable gate array (FPGA) system in a computer architecture course. The class project is a five-stage pipelined 32-bit MIPS design with experiments on the Altera DE2 board. For proper scheduling, milestones were set every one or two weeks to help students complete the project on…
Using SimCPU in Cooperative Learning Laboratories.
ERIC Educational Resources Information Center
Lin, Janet Mei-Chuen; Wu, Cheng-Chih; Liu, Hsi-Jen
1999-01-01
Reports research findings of an experimental design in which cooperative-learning strategies were applied to closed-lab instruction of computing concepts. SimCPU, a software package specially designed for closed-lab usage was used by 171 high school students of four classes. Results showed that collaboration enhanced learning and that blending…
NASA Astrophysics Data System (ADS)
Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad
2016-06-01
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.
Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad
2016-06-01
Here, collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet taggingmore » variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less
The Creation of a CPU Timer for High Fidelity Programs
NASA Technical Reports Server (NTRS)
Dick, Aidan A.
2011-01-01
Using C and C++ programming languages, a tool was developed that measures the efficiency of a program by recording the amount of CPU time that various functions consume. By inserting the tool between lines of code in the program, one can receive a detailed report of the absolute and relative time consumption associated with each section. After adapting the generic tool for a high-fidelity launch vehicle simulation program called MAVERIC, the components of a frequently used function called "derivatives ( )" were measured. Out of the 34 sub-functions in "derivatives ( )", it was found that the top 8 sub-functions made up 83.1% of the total time spent. In order to decrease the overall run time of MAVERIC, a launch vehicle simulation program, a change was implemented in the sub-function "Event_Controller ( )". Reformatting "Event_Controller ( )" led to a 36.9% decrease in the total CPU time spent by that sub-function, and a 3.2% decrease in the total CPU time spent by the overarching function "derivatives ( )".
Revisiting Molecular Dynamics on a CPU/GPU system: Water Kernel and SHAKE Parallelization.
Ruymgaart, A Peter; Elber, Ron
2012-11-13
We report Graphics Processing Unit (GPU) and Open-MP parallel implementations of water-specific force calculations and of bond constraints for use in Molecular Dynamics simulations. We focus on a typical laboratory computing-environment in which a CPU with a few cores is attached to a GPU. We discuss in detail the design of the code and we illustrate performance comparable to highly optimized codes such as GROMACS. Beside speed our code shows excellent energy conservation. Utilization of water-specific lists allows the efficient calculations of non-bonded interactions that include water molecules and results in a speed-up factor of more than 40 on the GPU compared to code optimized on a single CPU core for systems larger than 20,000 atoms. This is up four-fold from a factor of 10 reported in our initial GPU implementation that did not include a water-specific code. Another optimization is the implementation of constrained dynamics entirely on the GPU. The routine, which enforces constraints of all bonds, runs in parallel on multiple Open-MP cores or entirely on the GPU. It is based on Conjugate Gradient solution of the Lagrange multipliers (CG SHAKE). The GPU implementation is partially in double precision and requires no communication with the CPU during the execution of the SHAKE algorithm. The (parallel) implementation of SHAKE allows an increase of the time step to 2.0fs while maintaining excellent energy conservation. Interestingly, CG SHAKE is faster than the usual bond relaxation algorithm even on a single core if high accuracy is expected. The significant speedup of the optimized components transfers the computational bottleneck of the MD calculation to the reciprocal part of Particle Mesh Ewald (PME). PMID:23264758
Revisiting Molecular Dynamics on a CPU/GPU system: Water Kernel and SHAKE Parallelization
Ruymgaart, A. Peter; Elber, Ron
2012-01-01
We report Graphics Processing Unit (GPU) and Open-MP parallel implementations of water-specific force calculations and of bond constraints for use in Molecular Dynamics simulations. We focus on a typical laboratory computing-environment in which a CPU with a few cores is attached to a GPU. We discuss in detail the design of the code and we illustrate performance comparable to highly optimized codes such as GROMACS. Beside speed our code shows excellent energy conservation. Utilization of water-specific lists allows the efficient calculations of non-bonded interactions that include water molecules and results in a speed-up factor of more than 40 on the GPU compared to code optimized on a single CPU core for systems larger than 20,000 atoms. This is up four-fold from a factor of 10 reported in our initial GPU implementation that did not include a water-specific code. Another optimization is the implementation of constrained dynamics entirely on the GPU. The routine, which enforces constraints of all bonds, runs in parallel on multiple Open-MP cores or entirely on the GPU. It is based on Conjugate Gradient solution of the Lagrange multipliers (CG SHAKE). The GPU implementation is partially in double precision and requires no communication with the CPU during the execution of the SHAKE algorithm. The (parallel) implementation of SHAKE allows an increase of the time step to 2.0fs while maintaining excellent energy conservation. Interestingly, CG SHAKE is faster than the usual bond relaxation algorithm even on a single core if high accuracy is expected. The significant speedup of the optimized components transfers the computational bottleneck of the MD calculation to the reciprocal part of Particle Mesh Ewald (PME). PMID:23264758
Consistent linguistic fuzzy preference relations method with ranking fuzzy numbers
NASA Astrophysics Data System (ADS)
Ridzuan, Siti Amnah Mohd; Mohamad, Daud; Kamis, Nor Hanimah
2014-12-01
Multi-Criteria Decision Making (MCDM) methods have been developed to help decision makers in selecting the best criteria or alternatives from the options given. One of the well known methods in MCDM is the Consistent Fuzzy Preference Relation (CFPR) method, essentially utilizes a pairwise comparison approach. This method was later improved to cater subjectivity in the data by using fuzzy set, known as the Consistent Linguistic Fuzzy Preference Relations (CLFPR). The CLFPR method uses the additive transitivity property in the evaluation of pairwise comparison matrices. However, the calculation involved is lengthy and cumbersome. To overcome this problem, a method of defuzzification was introduced by researchers. Nevertheless, the defuzzification process has a major setback where some information may lose due to the simplification process. In this paper, we propose a method of CLFPR that preserves the fuzzy numbers form throughout the process. In obtaining the desired ordering result, a method of ranking fuzzy numbers is utilized in the procedure. This improved procedure for CLFPR is implemented to a case study to verify its effectiveness. This method is useful for solving decision making problems and can be applied to many areas of applications.
47 CFR 15.32 - Test procedures for CPU boards and computer power supplies.
Code of Federal Regulations, 2014 CFR
2014-10-01
... 47 Telecommunication 1 2014-10-01 2014-10-01 false Test procedures for CPU boards and computer... FREQUENCY DEVICES General § 15.32 Test procedures for CPU boards and computer power supplies. Power supplies and CPU boards used with personal computers and for which separate authorizations are required to...
47 CFR 15.32 - Test procedures for CPU boards and computer power supplies.
Code of Federal Regulations, 2010 CFR
2010-10-01
... 47 Telecommunication 1 2010-10-01 2010-10-01 false Test procedures for CPU boards and computer... FREQUENCY DEVICES General § 15.32 Test procedures for CPU boards and computer power supplies. Power supplies and CPU boards used with personal computers and for which separate authorizations are required to...
47 CFR 15.32 - Test procedures for CPU boards and computer power supplies.
Code of Federal Regulations, 2011 CFR
2011-10-01
... 47 Telecommunication 1 2011-10-01 2011-10-01 false Test procedures for CPU boards and computer... FREQUENCY DEVICES General § 15.32 Test procedures for CPU boards and computer power supplies. Power supplies and CPU boards used with personal computers and for which separate authorizations are required to...
47 CFR 15.32 - Test procedures for CPU boards and computer power supplies.
Code of Federal Regulations, 2013 CFR
2013-10-01
... 47 Telecommunication 1 2013-10-01 2013-10-01 false Test procedures for CPU boards and computer... FREQUENCY DEVICES General § 15.32 Test procedures for CPU boards and computer power supplies. Power supplies and CPU boards used with personal computers and for which separate authorizations are required to...
47 CFR 15.32 - Test procedures for CPU boards and computer power supplies.
Code of Federal Regulations, 2012 CFR
2012-10-01
... 47 Telecommunication 1 2012-10-01 2012-10-01 false Test procedures for CPU boards and computer... FREQUENCY DEVICES General § 15.32 Test procedures for CPU boards and computer power supplies. Power supplies and CPU boards used with personal computers and for which separate authorizations are required to...
Hybrid fuzzy regression with trapezoidal fuzzy data
NASA Astrophysics Data System (ADS)
Razzaghnia, T.; Danesh, S.; Maleki, A.
2011-12-01
In this regard, this research deals with a method for hybrid fuzzy least-squares regression. The extension of symmetric triangular fuzzy coefficients to asymmetric trapezoidal fuzzy coefficients is considered as an effective measure for removing unnecessary fuzziness of the linear fuzzy model. First, trapezoidal fuzzy variable is applied to derive a bivariate regression model. In the following, normal equations are formulated to solve the four parts of hybrid regression coefficients. Also the model is extended to multiple regression analysis. Eventually, method is compared with Y-H.O. chang's model.
Fuzzy coordinator in control problems
NASA Technical Reports Server (NTRS)
Rueda, A.; Pedrycz, W.
1992-01-01
In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant.
An efficient tensor transpose algorithm for multicore CPU, Intel Xeon Phi, and NVidia Tesla GPU
NASA Astrophysics Data System (ADS)
Lyakh, Dmitry I.
2015-04-01
An efficient parallel tensor transpose algorithm is suggested for shared-memory computing units, namely, multicore CPU, Intel Xeon Phi, and NVidia GPU. The algorithm operates on dense tensors (multidimensional arrays) and is based on the optimization of cache utilization on x86 CPU and the use of shared memory on NVidia GPU. From the applied side, the ultimate goal is to minimize the overhead encountered in the transformation of tensor contractions into matrix multiplications in computer implementations of advanced methods of quantum many-body theory (e.g., in electronic structure theory and nuclear physics). A particular accent is made on higher-dimensional tensors that typically appear in the so-called multireference correlated methods of electronic structure theory. Depending on tensor dimensionality, the presented optimized algorithms can achieve an order of magnitude speedup on x86 CPUs and 2-3 times speedup on NVidia Tesla K20X GPU with respect to the naïve scattering algorithm (no memory access optimization). The tensor transpose routines developed in this work have been incorporated into a general-purpose tensor algebra library (TAL-SH).
An efficient tensor transpose algorithm for multicore CPU, Intel Xeon Phi, and NVidia Tesla GPU
Lyakh, Dmitry I.
2015-01-05
An efficient parallel tensor transpose algorithm is suggested for shared-memory computing units, namely, multicore CPU, Intel Xeon Phi, and NVidia GPU. The algorithm operates on dense tensors (multidimensional arrays) and is based on the optimization of cache utilization on x86 CPU and the use of shared memory on NVidia GPU. From the applied side, the ultimate goal is to minimize the overhead encountered in the transformation of tensor contractions into matrix multiplications in computer implementations of advanced methods of quantum many-body theory (e.g., in electronic structure theory and nuclear physics). A particular accent is made on higher-dimensional tensors that typically appear in the so-called multireference correlated methods of electronic structure theory. Depending on tensor dimensionality, the presented optimized algorithms can achieve an order of magnitude speedup on x86 CPUs and 2-3 times speedup on NVidia Tesla K20X GPU with respect to the na ve scattering algorithm (no memory access optimization). Furthermore, the tensor transpose routines developed in this work have been incorporated into a general-purpose tensor algebra library (TAL-SH).
An efficient tensor transpose algorithm for multicore CPU, Intel Xeon Phi, and NVidia Tesla GPU
Lyakh, Dmitry I.
2015-01-05
An efficient parallel tensor transpose algorithm is suggested for shared-memory computing units, namely, multicore CPU, Intel Xeon Phi, and NVidia GPU. The algorithm operates on dense tensors (multidimensional arrays) and is based on the optimization of cache utilization on x86 CPU and the use of shared memory on NVidia GPU. From the applied side, the ultimate goal is to minimize the overhead encountered in the transformation of tensor contractions into matrix multiplications in computer implementations of advanced methods of quantum many-body theory (e.g., in electronic structure theory and nuclear physics). A particular accent is made on higher-dimensional tensors that typicallymore » appear in the so-called multireference correlated methods of electronic structure theory. Depending on tensor dimensionality, the presented optimized algorithms can achieve an order of magnitude speedup on x86 CPUs and 2-3 times speedup on NVidia Tesla K20X GPU with respect to the na ve scattering algorithm (no memory access optimization). Furthermore, the tensor transpose routines developed in this work have been incorporated into a general-purpose tensor algebra library (TAL-SH).« less
Dragojlovic, Z.; Kaminski, D.A.; Ryoo, J.
1999-07-01
Under-relaxation in an iterative CFD solver is guided by fuzzy logic in order to achieve automatic convergence with minimum CPU time. The fuzzy logic set of rules determines the near-optimal relaxation factor during the execution of the code, based on information from a Fourier transform of a set of characteristic values. The control algorithm was tested on four benchmark problems: buoyancy driven flow in a square cavity, lid driven flow in a square enclosure, mixed convection over a backward facing step and Dean flow. The incompressible Newtonian conservation equations are solved by the SIMPLER algorithm with simple substitution. The relaxation factors for u and v velocities and temperatures area adjusted on each iteration using the fuzzy logic algorithm. Close to optimal convergence is achieved in each of the benchmark cases with nearly minimal number of iterations and CPU time.
Fuzzy Model-based Pitch Stabilization and Wing Vibration Suppression of Flexible Wing Aircraft.
NASA Technical Reports Server (NTRS)
Ayoubi, Mohammad A.; Swei, Sean Shan-Min; Nguyen, Nhan T.
2014-01-01
This paper presents a fuzzy nonlinear controller to regulate the longitudinal dynamics of an aircraft and suppress the bending and torsional vibrations of its flexible wings. The fuzzy controller utilizes full-state feedback with input constraint. First, the Takagi-Sugeno fuzzy linear model is developed which approximates the coupled aeroelastic aircraft model. Then, based on the fuzzy linear model, a fuzzy controller is developed to utilize a full-state feedback and stabilize the system while it satisfies the control input constraint. Linear matrix inequality (LMI) techniques are employed to solve the fuzzy control problem. Finally, the performance of the proposed controller is demonstrated on the NASA Generic Transport Model (GTM).
Expert systems and fuzzy systems
Negoita, C.
1985-01-01
This book examines the design of the expert computer system and how fuzzy systems can be used to deal with imprecise information. As the author explores the effects of semantic systems on decision support systems, he asserts that the utilization of fuzzy set theory can help an expert system draw from its knowledge base more efficiently and therefore make more accurate and reliable decisions. The book includes realistic status reports in approximate reasoning and knowledge representation that are supported by a ''theory of categories'' mathematical approach. The differences between symbolic and semantic manipulation are outline, and detailed information is given on the actual theory of knowledge-based systems.
A combined PLC and CPU approach to multiprocessor control
Harris, J.J.; Broesch, J.D.; Coon, R.M.
1995-10-01
A sophisticated multiprocessor control system has been developed for use in the E-Power Supply System Integrated Control (EPSSIC) on the DIII-D tokamak. EPSSIC provides control and interlocks for the ohmic heating coil power supply and its associated systems. Of particular interest is the architecture of this system: both a Programmable Logic Controller (PLC) and a Central Processor Unit (CPU) have been combined on a standard VME bus. The PLC and CPU input and output signals are routed through signal conditioning modules, which provide the necessary voltage and ground isolation. Additionally these modules adapt the signal levels to that of the VME I/O boards. One set of I/O signals is shared between the two processors. The resulting multiprocessor system provides a number of advantages: redundant operation for mission critical situations, flexible communications using conventional TCP/IP protocols, the simplicity of ladder logic programming for the majority of the control code, and an easily maintained and expandable non-proprietary system.
Imprecise (fuzzy) information in geostatistics
Bardossy, A.; Bogardi, I.; Kelly, W.E.
1988-05-01
A methodology based on fuzzy set theory for the utilization of imprecise data in geostatistics is presented. A common problem preventing a broader use of geostatistics has been the insufficient amount of accurate measurement data. In certain cases, additional but uncertain (soft) information is available and can be encoded as subjective probabilities, and then the soft kriging method can be applied (Journal, 1986). In other cases, a fuzzy encoding of soft information may be more realistic and simplify the numerical calculations. Imprecise (fuzzy) spatial information on the possible variogram is integrated into a single variogram which is used in a fuzzy kriging procedure. The overall uncertainty of prediction is represented by the estimation variance and the calculated membership function for each kriged point. The methodology is applied to the permeability prediction of a soil liner for hazardous waste containment. The available number of hard measurement data (20) was not enough for a classical geostatistical analysis. An additional 20 soft data made it possible to prepare kriged contour maps using the fuzzy geostatistical procedure.
Hernandez, Robert; Yang, Qing; Huang, He; Zhang, Fan; Zhang, Xiaorong
2013-01-01
This paper presents the design and implementation of a new neural-machine-interface (NMI) for control of artificial legs. The requirements of high accuracy, real-time processing, low power consumption, and mobility of the NMI place great challenges on the computation engine of the system. By utilizing the architectural features of a mobile embedded CPU, we are able to implement our decision-making algorithm, based on neuromuscular phase-dependant support vector machines (SVM), with exceptional accuracy and processing speed. To demonstrate the superiority of our NMI, real-time experiments were performed on an able bodied subject with a 20 ms window increment. The 20 ms testing yielded accuracies of 99.94% while executing our algorithm efficiently with less than 11% processor loads. PMID:24111049
NASA Technical Reports Server (NTRS)
Kosko, Bart
1991-01-01
Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.
Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers
NASA Technical Reports Server (NTRS)
Yuan, Bo; Klir, George J.
1997-01-01
In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.
The SOPC design based on Nios CPU in EPON system
NASA Astrophysics Data System (ADS)
Zhu, Lili; Fan, Xiliang
2005-02-01
With the need of more and more high quality services, EPON system is widely favored by most people with its advanced technology of Gigabit and PON, which will replace the traditional techniques of copper and MC gradually. We can realize the MPCP protocol defined in IEEE802.3ah by the hardware scheme, such as FPGA or ASIC. Using SNMP protocol to achieve network management is the popular way. SNMP network manager can perform the long-distance configuration of the parameters in EPON system by sending out SET message; on the other hand, it can research the information by sending out GET message. Consequently, the Nios embedded processor acts as a transmission channel or a bridge between SNMP agent and hardware system. Now SOPC is a popular design method, which processes flexible design mode, reducible, expansible, upgradeable, and have the programmable function between hardware and software synchronously in a single chip. Integrated with the advantages of SOC, PLD, and FPGA, SOPC is provided with the following basic characteristics: an embedded processor core; on-chip high speed RAM resources with small capability; processor debug interface and FPGA programmable interface, etc. The Nios embedded processor is a soft core CPU optimized for programmable logic and SOPC (System-on-a-programmable-chip) designs, which accomplishes the data collection and configuration between SNMP agent and hardware system, the report of registration and alarm information, also the fulfillment of DBA which can be operated with all kind of algorithms. SOPC builder is a tool employed as turning out a system based on bus, thereby many components are included in this design, for instance, CPU, memory interface, peripherals interface etc. Developing applications using the Nios embedded processor is slightly different from the traditional processors, since the designer can configure the processor architecture and specify the peripheral content. That is, a designer can build a microcontroller
NASA Astrophysics Data System (ADS)
McClure, J. E.; Prins, J. F.; Miller, C. T.
2014-07-01
Multiphase flow implementations of the lattice Boltzmann method (LBM) are widely applied to the study of porous medium systems. In this work, we construct a new variant of the popular “color” LBM for two-phase flow in which a three-dimensional, 19-velocity (D3Q19) lattice is used to compute the momentum transport solution while a three-dimensional, seven velocity (D3Q7) lattice is used to compute the mass transport solution. Based on this formulation, we implement a novel heterogeneous GPU-accelerated algorithm in which the mass transport solution is computed by multiple shared memory CPU cores programmed using OpenMP while a concurrent solution of the momentum transport is performed using a GPU. The heterogeneous solution is demonstrated to provide speedup of 2.6× as compared to multi-core CPU solution and 1.8× compared to GPU solution due to concurrent utilization of both CPU and GPU bandwidths. Furthermore, we verify that the proposed formulation provides an accurate physical representation of multiphase flow processes and demonstrate that the approach can be applied to perform heterogeneous simulations of two-phase flow in porous media using a typical GPU-accelerated workstation.
Impulsive synchronization of fractional Takagi-Sugeno fuzzy complex networks.
Ma, Weiyuan; Li, Changpin; Wu, Yujiang
2016-08-01
This paper focuses on impulsive synchronization of fractional Takagi-Sugeno (T-S) fuzzy complex networks. A novel comparison principle is built for the fractional impulsive system. Then a synchronization criterion is established for the fractional T-S fuzzy complex networks by utilizing the comparison principle. The method is also illustrated by applying the fractional T-S fuzzy Rössler's complex networks. PMID:27586628
Advanced PID type fuzzy logic power system stabilizer
Hiyama, Takashi; Kugimiya, Masahiko; Satoh, Hironori . Dept. of Electrical Engineering and Computer Science)
1994-09-01
An advanced fuzzy logic control scheme has been proposed for a micro-computer based power system stabilizer to enhance the overall stability of power systems. The proposed control scheme utilizes the PID information of the generator speed. The input signal to the stabilizer is the real power output of a study unit. Simulations show the effectiveness of the advanced fuzzy logic control scheme.
Fuzzy logic controller optimization
Sepe, Jr., Raymond B; Miller, John Michael
2004-03-23
A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.
Microturbine control based on fuzzy neural network
NASA Astrophysics Data System (ADS)
Yan, Shijie; Bian, Chunyuan; Wang, Zhiqiang
2006-11-01
As microturbine generator (MTG) is a clean, efficient, low cost and reliable energy supply system. From outside characteristics of MTG, it is multi-variable, time-varying and coupling system, so it is difficult to be identified on-line and conventional control law adopted before cannot achieve desirable result. A novel fuzzy-neural networks (FNN) control algorithm was proposed in combining with the conventional PID control. In the paper, IF-THEN rules for tuning were applied by a first-order Sugeno fuzzy model with seven fuzzy rules and the membership function was given as the continuous GAUSSIAN function. Some sample data were utilized to train FNN. Through adjusting shape of membership function and weight continually, objective of auto-tuning fuzzy-rules can be achieved. The FNN algorithm had been applied to "100kW Microturbine control and power converter system". The results of simulation and experiment are shown that the algorithm can work very well.
CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications
2012-01-01
Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626
Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.
Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin
2016-01-01
With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. PMID:27070606
FINAL REPORT FOR LOW PRESSURE TESTS OF THE CPU-400 PILOT PLANT
This report presents the progress made during the component design phase of a program to develop an economical and environmentally safe waste-energy system known as the CPU-400. It discusses the hardware development and low pressure testing performed to evaluate CPU-400 operation...
Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing
Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin
2016-01-01
With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate. PMID:27070606
47 CFR 15.102 - CPU boards and power supplies used in personal computers.
Code of Federal Regulations, 2011 CFR
2011-10-01
... computers. 15.102 Section 15.102 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL RADIO FREQUENCY DEVICES Unintentional Radiators § 15.102 CPU boards and power supplies used in personal computers. (a... modifications that must be made to a personal computer, peripheral device, CPU board or power supply...
47 CFR 15.102 - CPU boards and power supplies used in personal computers.
Code of Federal Regulations, 2012 CFR
2012-10-01
... computers. 15.102 Section 15.102 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL RADIO FREQUENCY DEVICES Unintentional Radiators § 15.102 CPU boards and power supplies used in personal computers. (a... modifications that must be made to a personal computer, peripheral device, CPU board or power supply...
47 CFR 15.102 - CPU boards and power supplies used in personal computers.
Code of Federal Regulations, 2013 CFR
2013-10-01
... computers. 15.102 Section 15.102 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL RADIO FREQUENCY DEVICES Unintentional Radiators § 15.102 CPU boards and power supplies used in personal computers. (a... modifications that must be made to a personal computer, peripheral device, CPU board or power supply...
47 CFR 15.102 - CPU boards and power supplies used in personal computers.
Code of Federal Regulations, 2010 CFR
2010-10-01
... computers. 15.102 Section 15.102 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL RADIO FREQUENCY DEVICES Unintentional Radiators § 15.102 CPU boards and power supplies used in personal computers. (a... modifications that must be made to a personal computer, peripheral device, CPU board or power supply...
47 CFR 15.102 - CPU boards and power supplies used in personal computers.
Code of Federal Regulations, 2014 CFR
2014-10-01
... computers. 15.102 Section 15.102 Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL RADIO FREQUENCY DEVICES Unintentional Radiators § 15.102 CPU boards and power supplies used in personal computers. (a... modifications that must be made to a personal computer, peripheral device, CPU board or power supply...
Fuzzy understanding of neighborhoods with nearest unlike neighbor sets
NASA Astrophysics Data System (ADS)
Dasarathy, Belur V.
1995-06-01
A new fuzzy learning and classification scheme based on developing a fuzzy understanding of neighborhoods with nearest unlike neighbor sets (NUNS) is reported in this study. NUNS, by definition, the set of samples identified as the nearest from the other class(es) for each given sample, represent in essence the boundaries between pattern classes known in the problem environment. Accordingly, samples close to the NUNS are likely to have more ambiguity or uncertainty in their labels than those farther away from these NUNS. This information about the uncertainty or imprecision in the labels of the given training set can be extracted and represented in terms of fuzzy memberships. These fuzzy membership values, which may be determined in the learning phase using appropriate fuzzy membership models, can then be utilized in the classification phase to derive the identity of an unknown sample. This classification can be accomplished using any one of the established fuzzy classification techniques.
Kacewicz, M.
1987-11-01
An approach for the description of uncertainty in geology using fuzzy-set theory and an example of slope stability problem is presented. Soil parameters may be described by fuzzy sets. The fuzzy method of slope stability estimation is considered and verified in the case of one of Warsaw's (Poland) slopes.
NASA Technical Reports Server (NTRS)
Howard, Ayanna
2005-01-01
The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.
A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling
Hoang, Roger V.; Tanna, Devyani; Jayet Bray, Laurence C.; Dascalu, Sergiu M.; Harris, Frederick C.
2013-01-01
Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards. PMID:24106475
NASA Astrophysics Data System (ADS)
Wang, Paul P.; Tyan, Ching-Yu
1993-12-01
This paper presents the classification of fuzzy dynamic systems and fuzzy linguistic controllers (FLC) into standard types (TYPE 1 through TYPE 7). The need, utility value, and the logic behind this classification are given. The proposed classification is the result of studying many known examples of FLC applications. The impact of this classification to new designs and to the improved performance of classical and modern control systems is an important consideration.
Recurrent fuzzy ranking methods
NASA Astrophysics Data System (ADS)
Hajjari, Tayebeh
2012-11-01
With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.
Zargham, M.R.
1995-06-01
Recently, fuzzy logic has been applied to many areas, such as process control, image understanding, robots, expert systems, and decision support systems. This paper will explain the basic concepts of fuzzy logic and its application in different fields. The steps to design a control system will be explained in detail. Fuzzy control is the first successful industrial application of fuzzy logic. A fuzzy controller is able to control systems which previously could only be controlled by skilled operators. In recent years Japan has achieved significant progress in this area and has applied it to variety of products such as cruise control for cars, video cameras, rice cookers, washing machines, etc.
Fuzzy Sets and Mathematical Education.
ERIC Educational Resources Information Center
Alsina, C.; Trillas, E.
1991-01-01
Presents the concept of "Fuzzy Sets" and gives some ideas for its potential interest in mathematics education. Defines what a Fuzzy Set is, describes why we need to teach fuzziness, gives some examples of fuzzy questions, and offers some examples of activities related to fuzzy sets. (MDH)
NASA Astrophysics Data System (ADS)
Posey, Stan; Messmer, Peter; Appleyard, Jeremy
2015-04-01
Current trends in high performance computing (HPC) are moving towards the use of graphics processing units (GPUs) to achieve speedups through the extraction of fine-grain parallelism of application software. GPUs have been developed exclusively for computational tasks as massively-parallel co-processors to the CPU, and during 2014 the latest NVIDIA GPU architecture can operate with as many as three CPU platforms. In addition to the conventional use of the x86 CPU architecture with GPUs starting from the mid-2000's, the POWER and ARM-64 architectures have recently become available as x86 alternatives. Today computational efficiency and increased performance per energy-cost are key drivers behind HPC decisions to implement GPU-based servers for atmospheric modeling. The choice of a server CPU platform will influence performance and overall power consumption of a system, and also the available configurations of CPU-to-GPU ratio. It follows that such system design configurations continue to be a critical factor behind scientific decisions to implement models at higher resolutions and possibly with an increased use of ensembles. This presentation will examine the current state of GPU developments for atmospheric modeling with examples from the COSMO dycore and from various WRF physics, and for different CPU platforms. The examples provided will be relevant to science-scale HPC practice of CPU-GPU system configurations based on model resolution requirements of a particular simulation. Performance results will compare use of the latest available CPUs from the three available CPU architectures, both with and without GPU acceleration. Finally a GPU outlook is provided on GPU hardware, software, tools, and programmability for each of the available CPU platforms.
Sharma, Diksha; Badal, Andreu; Badano, Aldo
2012-04-21
The computational modeling of medical imaging systems often requires obtaining a large number of simulated images with low statistical uncertainty which translates into prohibitive computing times. We describe a novel hybrid approach for Monte Carlo simulations that maximizes utilization of CPUs and GPUs in modern workstations. We apply the method to the modeling of indirect x-ray detectors using a new and improved version of the code MANTIS, an open source software tool used for the Monte Carlo simulations of indirect x-ray imagers. We first describe a GPU implementation of the physics and geometry models in fastDETECT2 (the optical transport model) and a serial CPU version of the same code. We discuss its new features like on-the-fly column geometry and columnar crosstalk in relation to the MANTIS code, and point out areas where our model provides more flexibility for the modeling of realistic columnar structures in large area detectors. Second, we modify PENELOPE (the open source software package that handles the x-ray and electron transport in MANTIS) to allow direct output of location and energy deposited during x-ray and electron interactions occurring within the scintillator. This information is then handled by optical transport routines in fastDETECT2. A load balancer dynamically allocates optical transport showers to the GPU and CPU computing cores. Our hybridMANTIS approach achieves a significant speed-up factor of 627 when compared to MANTIS and of 35 when compared to the same code running only in a CPU instead of a GPU. Using hybridMANTIS, we successfully hide hours of optical transport time by running it in parallel with the x-ray and electron transport, thus shifting the computational bottleneck from optical tox-ray transport. The new code requires much less memory than MANTIS and, asa result, allows us to efficiently simulate large area detectors. PMID:22469917
WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell
NASA Technical Reports Server (NTRS)
Pagni, A.; Poluzzi, R.; Rizzotto, G. G.
1992-01-01
During the last five years Fuzzy Logic has gained enormous popularity in the academic and industrial worlds. The success of this new methodology has led the microelectronics industry to create a new class of machines, called Fuzzy Machines, to overcome the limitations of traditional computing systems when utilized as Fuzzy Systems. This paper gives an overview of the methods by which Fuzzy Logic data structures are represented in the machines (each with its own advantages and inefficiencies). Next, the paper introduces WARP (Weight Associative Rule Processor) which is a dedicated VLSI megacell allowing the realization of a fuzzy controller suitable for a wide range of applications. WARP represents an innovative approach to VLSI Fuzzy controllers by utilizing different types of data structures for characterizing the membership functions during the various stages of the Fuzzy processing. WARP dedicated architecture has been designed in order to achieve high performance by exploiting the computational advantages offered by the different data representations.
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines.
Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel
2013-04-01
We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562
Tempest: GPU-CPU computing for high-throughput database spectral matching
Milloy, Jeffrey A.; Faherty, Brendan K.; Gerber, Scott A.
2012-01-01
Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically-labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new “Accelerated Score” for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer. PMID:22640374
Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines
Teodoro, George; Pan, Tony; Kurc, Tahsin; Kong, Jun; Cooper, Lee; Saltz, Joel
2013-01-01
We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively. PMID:23908562
Tempest: GPU-CPU computing for high-throughput database spectral matching.
Milloy, Jeffrey A; Faherty, Brendan K; Gerber, Scott A
2012-07-01
Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer. PMID:22640374
Introduction to Fuzzy Set Theory
NASA Technical Reports Server (NTRS)
Kosko, Bart
1990-01-01
An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.
Fuzzy mathematical techniques with applications
Kandel, A.
1986-01-01
This text presents the basic concepts of fuzzy set theory within a context of real-world applications. The book is self-contained and can be used as a starting point for people interested in this fast growing field as well as by researchers looking for new application techniques. The section on applications includes: Manipulation of knowledge in expert systems; relational database structures; pattern clustering; analysis of transient behavior in digital systems; modeling of uncertainty and search trees. Contents: Fuzzy sets; Possibility theory and fuzzy quantification; Fuzzy functions; Fuzzy events and fuzzy statistics; Fuzzy relations; Fuzzy logics; Some applications; Bibliography.
Efficient simulation of diffusion-based choice RT models on CPU and GPU.
Verdonck, Stijn; Meers, Kristof; Tuerlinckx, Francis
2016-03-01
In this paper, we present software for the efficient simulation of a broad class of linear and nonlinear diffusion models for choice RT, using either CPU or graphical processing unit (GPU) technology. The software is readily accessible from the popular scripting languages MATLAB and R (both 64-bit). The speed obtained on a single high-end GPU is comparable to that of a small CPU cluster, bringing standard statistical inference of complex diffusion models to the desktop platform. PMID:25761391
GPU-based iterative relative fuzzy connectedness image segmentation
NASA Astrophysics Data System (ADS)
Zhuge, Ying; Udupa, Jayaram K.; Ciesielski, Krzysztof C.; Falcão, Alexandre X.; Miranda, Paulo A. V.; Miller, Robert W.
2012-02-01
This paper presents a parallel algorithm for the top of the line among the fuzzy connectedness algorithm family, namely the iterative relative fuzzy connectedness (IRFC) segmentation method. The algorithm of IRFC, realized via image foresting transform (IFT), is implemented by using NVIDIA's compute unified device architecture (CUDA) platform for segmenting large medical image data sets. In the IRFC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations, and (ii) computing the fuzzy connectedness relations and tracking labels for objects of interest. Both tasks are implemented as CUDA kernels, and a substantial improvement in speed for both tasks is achieved. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 2.4x, 17.0x, and 42.7x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm in CPU.
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1992-01-01
Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.
Some Properties of Fuzzy Soft Proximity Spaces
Demir, İzzettin; Özbakır, Oya Bedre
2015-01-01
We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities. PMID:25793224
Engineering application based on fuzzy approach
NASA Astrophysics Data System (ADS)
Pislaru, Marius; Avasilcai, Silvia; Trandabat, Alexandru
2011-12-01
The article focus on an application of chemical engineering. A fuzzy modeling methodology designed to determinate two relevant characteristics of a chemical compound (ferrocenylsiloxane polyamide) for self-assembling - surface tension and maximum UV absorbance measured as temperature and concentration functions. One of the most important parts of a fuzzy rule-based inference system for the polyamide solution characteristics determinations is that it allows to interpret the knowledge contained in the model and also to improve it with a-priori knowledge. The results obtained through proposed method are highly accurate and its can be optimized by utilizing the available information during the modeling process. The results showed that it is feasible in theory and reliable on calculation applying Mamdani fuzzy inference system to the estimation of optical and surface properties of a polyamide solution.
Engineering application based on fuzzy approach
NASA Astrophysics Data System (ADS)
Pislaru, Marius; Avasilcai, Silvia; Trandabat, Alexandru
2012-01-01
The article focus on an application of chemical engineering. A fuzzy modeling methodology designed to determinate two relevant characteristics of a chemical compound (ferrocenylsiloxane polyamide) for self-assembling - surface tension and maximum UV absorbance measured as temperature and concentration functions. One of the most important parts of a fuzzy rule-based inference system for the polyamide solution characteristics determinations is that it allows to interpret the knowledge contained in the model and also to improve it with a-priori knowledge. The results obtained through proposed method are highly accurate and its can be optimized by utilizing the available information during the modeling process. The results showed that it is feasible in theory and reliable on calculation applying Mamdani fuzzy inference system to the estimation of optical and surface properties of a polyamide solution.
Fuzzy logic in autonomous orbital operations
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
Fuzzy logic can be used advantageously in autonomous orbital operations that require the capability of handling imprecise measurements from sensors. Several applications are underway to investigate fuzzy logic approaches and develop guidance and control algorithms for autonomous orbital operations. Translational as well as rotational control of a spacecraft have been demonstrated using space shuttle simulations. An approach to a camera tracking system has been developed to support proximity operations and traffic management around the Space Station Freedom. Pattern recognition and object identification algorithms currently under development will become part of this camera system at an appropriate level in the future. A concept to control environment and life support systems for large Lunar based crew quarters is also under development. Investigations in the area of reinforcement learning, utilizing neural networks, combined with a fuzzy logic controller, are planned as a joint project with the Ames Research Center.
Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters
Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas
2013-01-01
We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization. PMID:24396342
Fuzzy Sarsa with Focussed Replacing Eligibility Traces for Robust and Accurate Control
NASA Astrophysics Data System (ADS)
Kamdem, Sylvain; Ohki, Hidehiro; Sueda, Naomichi
Several methods of reinforcement learning in continuous state and action spaces that utilize fuzzy logic have been proposed in recent years. This paper introduces Fuzzy Sarsa(λ), an on-policy algorithm for fuzzy learning that relies on a novel way of computing replacing eligibility traces to accelerate the policy evaluation. It is tested against several temporal difference learning algorithms: Sarsa(λ), Fuzzy Q(λ), an earlier fuzzy version of Sarsa and an actor-critic algorithm. We perform detailed evaluations on two benchmark problems : a maze domain and the cart pole. Results of various tests highlight the strengths and weaknesses of these algorithms and show that Fuzzy Sarsa(λ) outperforms all other algorithms tested for a larger granularity of design and under noisy conditions. It is a highly competitive method of learning in realistic noisy domains where a denser fuzzy design over the state space is needed for a more precise control.
Simplify fuzzy control implementation
Stoll, K.E.; Ralston, P.A.S.; Ramaganesan, S. )
1993-07-01
A controller that uses fuzzy rules provides better response than a conventional linear controller in some applications. The rules are best implemented as a breakpoint function. A level control example illustrates the technique and advantages over proportional-integral (PI) control. In numerous papers on fuzzy controller development, emphasis has been primarily on formal inferencing, membership functions, and steps in building a fuzzy relation, as described by Zadeh. The rationale used in formulating the required set of rules is usually neglected, and the interpretation of the final controller as an input-output algorithm is overlooked. Also, the details of fuzzy mathematics are unfamiliar to many engineers and the implementation appears cumbersome to most. Process description and control instrumentation. This article compares a fuzzy controller designed by specifying a breakpoint function with a traditional PI controller for a level control system on a laboratory scale. In this discussion, only setpoint changes are considered.
2011-01-01
Background Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Findings Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. Conclusions GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/. PMID:21615923
Integrating GPGPU computations with CPU coroutines in C++
NASA Astrophysics Data System (ADS)
Lebedev, Pavel A.
2016-02-01
We present results on integration of two major GPGPU APIs with reactor-based event processing model in C++ that utilizes coroutines. With current lack of universally usable GPGPU programming interface that gives optimal performance and debates about the style of implementing asynchronous computing in C++, we present a working implementation that allows a uniform and seamless approach to writing C++ code with continuations that allow processing on CPUs or CUDA/OpenCL accelerators. Performance results are provided that show, if corner cases are avoided, this approach has negligible performance cost on latency.
High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms
Teodoro, George; Pan, Tony; Kurc, Tahsin M.; Kong, Jun; Cooper, Lee A. D.; Podhorszki, Norbert; Klasky, Scott; Saltz, Joel H.
2014-01-01
Analysis of large pathology image datasets offers significant opportunities for the investigation of disease morphology, but the resource requirements of analysis pipelines limit the scale of such studies. Motivated by a brain cancer study, we propose and evaluate a parallel image analysis application pipeline for high throughput computation of large datasets of high resolution pathology tissue images on distributed CPU-GPU platforms. To achieve efficient execution on these hybrid systems, we have built runtime support that allows us to express the cancer image analysis application as a hierarchical data processing pipeline. The application is implemented as a coarse-grain pipeline of stages, where each stage may be further partitioned into another pipeline of fine-grain operations. The fine-grain operations are efficiently managed and scheduled for computation on CPUs and GPUs using performance aware scheduling techniques along with several optimizations, including architecture aware process placement, data locality conscious task assignment, data prefetching, and asynchronous data copy. These optimizations are employed to maximize the utilization of the aggregate computing power of CPUs and GPUs and minimize data copy overheads. Our experimental evaluation shows that the cooperative use of CPUs and GPUs achieves significant improvements on top of GPU-only versions (up to 1.6×) and that the execution of the application as a set of fine-grain operations provides more opportunities for runtime optimizations and attains better performance than coarser-grain, monolithic implementations used in other works. An implementation of the cancer image analysis pipeline using the runtime support was able to process an image dataset consisting of 36,848 4Kx4K-pixel image tiles (about 1.8TB uncompressed) in less than 4 minutes (150 tiles/second) on 100 nodes of a state-of-the-art hybrid cluster system. PMID:25419546
High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms.
Teodoro, George; Pan, Tony; Kurc, Tahsin M; Kong, Jun; Cooper, Lee A D; Podhorszki, Norbert; Klasky, Scott; Saltz, Joel H
2013-05-01
Analysis of large pathology image datasets offers significant opportunities for the investigation of disease morphology, but the resource requirements of analysis pipelines limit the scale of such studies. Motivated by a brain cancer study, we propose and evaluate a parallel image analysis application pipeline for high throughput computation of large datasets of high resolution pathology tissue images on distributed CPU-GPU platforms. To achieve efficient execution on these hybrid systems, we have built runtime support that allows us to express the cancer image analysis application as a hierarchical data processing pipeline. The application is implemented as a coarse-grain pipeline of stages, where each stage may be further partitioned into another pipeline of fine-grain operations. The fine-grain operations are efficiently managed and scheduled for computation on CPUs and GPUs using performance aware scheduling techniques along with several optimizations, including architecture aware process placement, data locality conscious task assignment, data prefetching, and asynchronous data copy. These optimizations are employed to maximize the utilization of the aggregate computing power of CPUs and GPUs and minimize data copy overheads. Our experimental evaluation shows that the cooperative use of CPUs and GPUs achieves significant improvements on top of GPU-only versions (up to 1.6×) and that the execution of the application as a set of fine-grain operations provides more opportunities for runtime optimizations and attains better performance than coarser-grain, monolithic implementations used in other works. An implementation of the cancer image analysis pipeline using the runtime support was able to process an image dataset consisting of 36,848 4Kx4K-pixel image tiles (about 1.8TB uncompressed) in less than 4 minutes (150 tiles/second) on 100 nodes of a state-of-the-art hybrid cluster system. PMID:25419546
Visualization of large medical data sets using memory-optimized CPU and GPU algorithms
NASA Astrophysics Data System (ADS)
Kiefer, Gundolf; Lehmann, Helko; Weese, Juergen
2005-04-01
With the evolution of medical scanners towards higher spatial resolutions, the sizes of image data sets are increasing rapidly. To profit from the higher resolution in medical applications such as 3D-angiography for a more efficient and precise diagnosis, high-performance visualization is essential. However, to make sure that the performance of a volume rendering algorithm scales with the performance of future computer architectures, technology trends need to be considered. The design of such scalable volume rendering algorithms remains challenging. One of the major trends in the development of computer architectures is the wider use of cache memory hierarchies to bridge the growing gap between the faster evolving processing power and the slower evolving memory access speed. In this paper we propose ways to exploit the standard PC"s cache memories supporting the main processors (CPU"s) and the graphics hardware (graphics processing unit, GPU), respectively, for computing Maximum Intensity Projections (MIPs). To this end, we describe a generic and flexible way to improve the cache efficiency of software ray casting algorithms and show by means of cache simulations, that it enables cache miss rates close to the theoretical optimum. For GPU-based rendering we propose a similar, brick-based technique to optimize the utilization of onboard caches and the transfer of data to the GPU on-board memory. All algorithms produce images of identical quality, which enables us to compare the performance of their implementations in a fair way without eventually trading quality for speed. Our comparison indicates that the proposed methods perform superior, in particular for large data sets.
A Novel Numerical Method for Fuzzy Boundary Value Problems
NASA Astrophysics Data System (ADS)
Can, E.; Bayrak, M. A.; Hicdurmaz
2016-05-01
In the present paper, a new numerical method is proposed for solving fuzzy differential equations which are utilized for the modeling problems in science and engineering. Fuzzy approach is selected due to its important applications on processing uncertainty or subjective information for mathematical models of physical problems. A second-order fuzzy linear boundary value problem is considered in particular due to its important applications in physics. Moreover, numerical experiments are presented to show the effectiveness of the proposed numerical method on specific physical problems such as heat conduction in an infinite plate and a fin.
Mamdani Fuzzy System for Indoor Autonomous Mobile Robot
NASA Astrophysics Data System (ADS)
Khan, M. K. A. Ahamed; Rashid, Razif; Elamvazuthi, I.
2011-06-01
Several control algorithms for autonomous mobile robot navigation have been proposed in the literature. Recently, the employment of non-analytical methods of computing such as fuzzy logic, evolutionary computation, and neural networks has demonstrated the utility and potential of these paradigms for intelligent control of mobile robot navigation. In this paper, Mamdani fuzzy system for an autonomous mobile robot is developed. The paper begins with the discussion on the conventional controller and then followed by the description of fuzzy logic controller in detail.
Fuzzy logic applications to expert systems and control
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1991-01-01
A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.
Liu, Yu; Hong, Yang; Lin, Chun-Yuan; Hung, Che-Lun
2015-01-01
The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively. PMID:26568953
Liu, Yu; Hong, Yang; Lin, Chun-Yuan; Hung, Che-Lun
2015-01-01
The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively. PMID:26568953
A Mechanism That Bounds Execution Performance for Process Group for Mitigating CPU Abuse
NASA Astrophysics Data System (ADS)
Yamauchi, Toshihiro; Hara, Takayuki; Taniguchi, Hideo
Secure OS has been the focus of several studies. However, CPU resources, which are important resources for executing a program, are not the object of access control. For preventing the abuse of CPU resources, we had earlier proposed a new type of execution resource that controls the maximum CPU usage [5,6] The previously proposed mechanism can control only one process at a time. Because most services involve multiple processes, the mechanism should control all the processes in each service. In this paper, we propose an improved mechanism that helps to achieve a bound on the execution performance of a process group, in order to limit unnecessary processor usage. We report the results of an evaluation of our proposed mechanism.
Hardware/Software Expansion of Display Terminal and CPU
NASA Technical Reports Server (NTRS)
Adams, B. R.
1986-01-01
IBM PC coupling used to expand capabilities of expensive specialpurpose system. IBM PC was interfaced to Tektronix CP1151 computer through teletype port of Tektronix 4010-1 computer display terminal. Electronic interface built to provide isolation, level shifting, and signal inversion between IBM PC RS-232 port and 4010-1 terminal teletype port. Modifications to 4010-1 terminal made to increase teletype rate from 110 to 9,600 baud. Software for both computers developed to give control of DPO system to IBM PC and provide data/program file exchange between two computers. Coupling demonstrates utilization of low-cost microcomputer hardware and software to expand capabilities of expensive special-purpose computer systems.
FUZZY SUPERNOVA TEMPLATES. I. CLASSIFICATION
Rodney, Steven A.; Tonry, John L. E-mail: jt@ifa.hawaii.ed
2009-12-20
Modern supernova (SN) surveys are now uncovering stellar explosions at rates that far surpass what the world's spectroscopic resources can handle. In order to make full use of these SN data sets, it is necessary to use analysis methods that depend only on the survey photometry. This paper presents two methods for utilizing a set of SN light-curve templates to classify SN objects. In the first case, we present an updated version of the Bayesian Adaptive Template Matching program (BATM). To address some shortcomings of that strictly Bayesian approach, we introduce a method for Supernova Ontology with Fuzzy Templates (SOFT), which utilizes fuzzy set theory for the definition and combination of SN light-curve models. For well-sampled light curves with a modest signal-to-noise ratio (S/N >10), the SOFT method can correctly separate thermonuclear (Type Ia) SNe from core collapse SNe with >=98% accuracy. In addition, the SOFT method has the potential to classify SNe into sub-types, providing photometric identification of very rare or peculiar explosions. The accuracy and precision of the SOFT method are verified using Monte Carlo simulations as well as real SN light curves from the Sloan Digital Sky Survey and the SuperNova Legacy Survey. In a subsequent paper, the SOFT method is extended to address the problem of parameter estimation, providing estimates of redshift, distance, and host galaxy extinction without any spectroscopy.
SU-E-J-60: Efficient Monte Carlo Dose Calculation On CPU-GPU Heterogeneous Systems
Xiao, K; Chen, D. Z; Hu, X. S; Zhou, B
2014-06-01
Purpose: It is well-known that the performance of GPU-based Monte Carlo dose calculation implementations is bounded by memory bandwidth. One major cause of this bottleneck is the random memory writing patterns in dose deposition, which leads to several memory efficiency issues on GPU such as un-coalesced writing and atomic operations. We propose a new method to alleviate such issues on CPU-GPU heterogeneous systems, which achieves overall performance improvement for Monte Carlo dose calculation. Methods: Dose deposition is to accumulate dose into the voxels of a dose volume along the trajectories of radiation rays. Our idea is to partition this procedure into the following three steps, which are fine-tuned for CPU or GPU: (1) each GPU thread writes dose results with location information to a buffer on GPU memory, which achieves fully-coalesced and atomic-free memory transactions; (2) the dose results in the buffer are transferred to CPU memory; (3) the dose volume is constructed from the dose buffer on CPU. We organize the processing of all radiation rays into streams. Since the steps within a stream use different hardware resources (i.e., GPU, DMA, CPU), we can overlap the execution of these steps for different streams by pipelining. Results: We evaluated our method using a Monte Carlo Convolution Superposition (MCCS) program and tested our implementation for various clinical cases on a heterogeneous system containing an Intel i7 quad-core CPU and an NVIDIA TITAN GPU. Comparing with a straightforward MCCS implementation on the same system (using both CPU and GPU for radiation ray tracing), our method gained 2-5X speedup without losing dose calculation accuracy. Conclusion: The results show that our new method improves the effective memory bandwidth and overall performance for MCCS on the CPU-GPU systems. Our proposed method can also be applied to accelerate other Monte Carlo dose calculation approaches. This research was supported in part by NSF under Grants CCF
Complex intuitionistic fuzzy sets
NASA Astrophysics Data System (ADS)
Alkouri, Abdulazeez (Moh'd. Jumah) S.; Salleh, Abdul Razak
2012-09-01
This paper presents a new concept of complex intuitionistic fuzzy set (CIFS) which is generalized from the innovative concept of a complex fuzzy set (CFS) by adding the non-membership term to the definition of CFS. The novelty of CIFS lies in its ability for membership and non-membership functions to achieve more range of values. The ranges of values are extended to the unit circle in complex plane for both membership and non-membership functions instead of [0, 1] as in the conventional intuitionistic fuzzy functions. We define basic operations namely complement, union, and intersection on CIFSs. Properties of these operations are derived.
Fuzzy blood pressure measurement
NASA Astrophysics Data System (ADS)
Cuce, Antonino; Di Guardo, Mario; Sicurella, Gaetano
1998-10-01
In this paper, an intelligent system for blood pressure measurement is posed together with a possible implementation using an eight bit fuzzy processor. The system can automatically determine the ideal cuff inflation level eliminating the discomfort and misreading caused by incorrect cuff inflation. Using statistics distribution of the systolic and diastolic blood pressure, in the inflation phase, a fuzzy rule system determine the pressure levels at which checking the presence of heart beat in order to exceed the systolic pressure with the minimum gap. The heart beats, characterized through pressure variations, are recognized by a fuzzy classifier.
NASA Astrophysics Data System (ADS)
Malhas, Othman Qasim
1993-10-01
The concept of “abacus logic” has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain “regularity” condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a “pre-Zadeh orthocomplementation”. It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.
Energy Science and Technology Software Center (ESTSC)
1994-03-04
FRA is a general purpose code for risk analysis using fuzzy, not numeric, attributes. It allows the user to evaluate the risk associated with a composite system on the basis of the risk estimates of the individual components.
Mining fuzzy association rules in spatio-temporal databases
NASA Astrophysics Data System (ADS)
Shu, Hong; Dong, Lin; Zhu, Xinyan
2008-12-01
A huge amount of geospatial and temporal data have been collected through various networks of environment monitoring stations. For instance, daily precipitation and temperature are observed at hundreds of meteorological stations in Northeastern China. However, these massive raw data from the stations are not fully utilized for meeting the requirements of human decision-making. In nature, the discovery of geographical data mining is the computation of multivariate spatio-temporal correlations through the stages of data mining. In this paper, a procedure of mining association rules in regional climate-changing databases is introduced. The methods of Kriging interpolation, fuzzy cmeans clustering, and Apriori-based logical rules extraction are employed subsequently. Formally, we define geographical spatio-temporal transactions and fuzzy association rules. Innovatively, we make fuzzy data conceptualization by means of fuzzy c-means clustering, and transform fuzzy data items with membership grades into Boolean data items with weights by means ofλ-cut sets. When the algorithm Apriori is executed on Boolean transactions with weights, fuzzy association rules are derived. Fuzzy association rules are more nature than crisp association rules for human cognition about the reality.
Fuzzy indicator approach: development of impact factor of soil amendments
Technology Transfer Automated Retrieval System (TEKTRAN)
Soil amendments have been shown to be useful for improving soil condition, but it is often difficult to make management decisions as to their usefulness. Utilization of Fuzzy Set Theory is a promising method for decision support associated with utilization of soil amendments. In this article a tool ...
A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection
Chen, Yaw-Chung
2015-01-01
The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. PMID:26437335
A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection.
Lee, Chun-Liang; Lin, Yi-Shan; Chen, Yaw-Chung
2015-01-01
The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs) are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs) have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA) that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms. PMID:26437335
CPU SIM: A Computer Simulator for Use in an Introductory Computer Organization-Architecture Class.
ERIC Educational Resources Information Center
Skrein, Dale
1994-01-01
CPU SIM, an interactive low-level computer simulation package that runs on the Macintosh computer, is described. The program is designed for instructional use in the first or second year of undergraduate computer science, to teach various features of typical computer organization through hands-on exercises. (MSE)
Technology Transfer Automated Retrieval System (TEKTRAN)
Zoning of agricultural fields is an important task for utilization of precision farming technology. One method for the definition of zones with different levels of productivity is based on fuzzy indicator model. Fuzzy indicator model for identification of zones with different levels of productivit...
FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY
The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...
Application of fuzzy logic in computer-aided design of digital systems
NASA Astrophysics Data System (ADS)
Shragowitz, Eugene B.; Lee, Jun-Yong; Kang, Eric Q.
1996-06-01
Application of fuzzy logic structures in computer-aided design (CAD) of electronic systems substantially improves quality of design solutions by providing designers with flexibility in formulating goals and selecting trade-offs. In addition, the following aspects of a design process are positively impacted by application of fuzzy logic: utilization of domain knowledge, interpretation of uncertainties in design data, and adaptation of design algorithms. We successfully applied fuzzy logic structures in conjunction with constructive and iterative algorithms for selecting of design solutions for different stages of the design process. We also introduced a fuzzy logic software development tool to be used in CAD applications.
Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition
NASA Astrophysics Data System (ADS)
Chen, Tao; Li, Zhi; Mo, Wei
2013-04-01
An approach for automatic identification of terahertz (THz) spectra of biomolecules is proposed based on principal component analysis (PCA) and fuzzy pattern recognition in this paper, and THz transmittance spectra of some typical amino acid and saccharide biomolecular samples are investigated to prove its feasibility. Firstly, PCA is applied to reduce the dimensionality of the original spectrum data and extract features of the data. Secondly, instead of the original spectrum variables, the selected principal component scores matrix is fed into the model of fuzzy pattern recognition, where a principle of fuzzy closeness based optimization is employed to identify those samples. Results demonstrate that THz spectroscopy combined with PCA and fuzzy pattern recognition can be efficiently utilized for automatic identification of biomolecules. The proposed approach provides a new effective method in the detection and identification of biomolecules using THz spectroscopy.
Identification of biomolecules by terahertz spectroscopy and fuzzy pattern recognition.
Chen, Tao; Li, Zhi; Mo, Wei
2013-04-01
An approach for automatic identification of terahertz (THz) spectra of biomolecules is proposed based on principal component analysis (PCA) and fuzzy pattern recognition in this paper, and THz transmittance spectra of some typical amino acid and saccharide biomolecular samples are investigated to prove its feasibility. Firstly, PCA is applied to reduce the dimensionality of the original spectrum data and extract features of the data. Secondly, instead of the original spectrum variables, the selected principal component scores matrix is fed into the model of fuzzy pattern recognition, where a principle of fuzzy closeness based optimization is employed to identify those samples. Results demonstrate that THz spectroscopy combined with PCA and fuzzy pattern recognition can be efficiently utilized for automatic identification of biomolecules. The proposed approach provides a new effective method in the detection and identification of biomolecules using THz spectroscopy. PMID:23357678
Fuzzy Logic Connectivity in Semiconductor Defect Clustering
Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.
1999-01-24
In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.
Fuzzy multiple-criteria decision-making approach for industrial green engineering.
Chiou, Hua-kai; Tzeng, Gwo-hshiung
2002-12-01
This paper describes a fuzzy hierarchical analytic approach to determine the weighting of subjective judgments. In addition, it presents a nonadditive fuzzy integral technique to evaluate a green engineering industry case as a fuzzy multicriteria decision-making (FMCDM) problem. When the investment strategies are evaluated from various aspects, such as economic effectiveness, technical feasibility, and environmental regulation, it can be regarded as an FMCDM problem. Since stakeholders cannot clearly estimate each considered criterion in terms of numerical values for the anticipated alternatives/strategies, fuzziness is considered to be applicable. Consequently, this paper uses triangular fuzzy numbers to establish weights and anticipated achievement values. By ranking fuzzy weights and fuzzy synthetic utility values, we can determine the relative importance of criteria and decide the best strategies. This paper applies what is called a lambda fuzzy measure and nonadditive fuzzy integral technique to evaluate the synthetic performance of green engineering strategies for aquatic products processors in Taiwan. In addition, we demonstrate that the nonadditive fuzzy integral is an effective evaluation and appears to be appropriate, especially when the criteria are not independent. PMID:12402097
NASA Astrophysics Data System (ADS)
Masudin, I.; Saputro, T. E.
2016-02-01
In today's technology, electronic trading transaction via internet has been utilized properly with rapid growth. This paper intends to evaluate related to B2C e-commerce website in order to find out the one which meets the usability factors better than another. The influential factors to B2C e-commerce website are determined for two big retailer websites. The factors are investigated based on the consideration of several studies and conformed to the website characteristics. The evaluation is conducted by using different methods namely fuzzy AHP and hierarchical fuzzy TOPSIS so that the final evaluation can be compared. Fuzzy triangular number is adopted to deal with imprecise judgment under fuzzy environment.
Hamdy, M; Hamdan, I
2015-07-01
In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. PMID:25765955
Fuzzy control of magnetic bearings
NASA Technical Reports Server (NTRS)
Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.
1991-01-01
The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.
Component Models for Fuzzy Data
ERIC Educational Resources Information Center
Coppi, Renato; Giordani, Paolo; D'Urso, Pierpaolo
2006-01-01
The fuzzy perspective in statistical analysis is first illustrated with reference to the "Informational Paradigm" allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to "J" LR fuzzy variables as observed on "I"…
Tao, C W; Taur, Jinshiuh; Chuang, Chen-Chia; Chang, Chia-Wen; Chang, Yeong-Hwa
2011-06-01
In this paper, the interval type-2 fuzzy controllers (FC(IT2)s) are approximated using the fuzzy ratio switching type-1 FCs to avoid the complex type-reduction process required for the interval type-2 FCs. The fuzzy ratio switching type-1 FCs (FC(FRST1)s) are designed to be a fuzzy combination of the possible-leftmost and possible-rightmost type-1 FCs. The fuzzy ratio switching type-1 fuzzy control technique is applied with the sliding control technique to realize the hybrid fuzzy ratio switching type-1 fuzzy sliding controllers (HFSC(FRST1)s) for the double-pendulum-and-cart system. The simulation results and comparisons with other approaches are provided to demonstrate the effectiveness of the proposed HFSC(FRST1)s. PMID:21189244
Multi-GPU and multi-CPU accelerated FDTD scheme for vibroacoustic applications
NASA Astrophysics Data System (ADS)
Francés, J.; Otero, B.; Bleda, S.; Gallego, S.; Neipp, C.; Márquez, A.; Beléndez, A.
2015-06-01
The Finite-Difference Time-Domain (FDTD) method is applied to the analysis of vibroacoustic problems and to study the propagation of longitudinal and transversal waves in a stratified media. The potential of the scheme and the relevance of each acceleration strategy for massively computations in FDTD are demonstrated in this work. In this paper, we propose two new specific implementations of the bi-dimensional scheme of the FDTD method using multi-CPU and multi-GPU, respectively. In the first implementation, an open source message passing interface (OMPI) has been included in order to massively exploit the resources of a biprocessor station with two Intel Xeon processors. Moreover, regarding CPU code version, the streaming SIMD extensions (SSE) and also the advanced vectorial extensions (AVX) have been included with shared memory approaches that take advantage of the multi-core platforms. On the other hand, the second implementation called the multi-GPU code version is based on Peer-to-Peer communications available in CUDA on two GPUs (NVIDIA GTX 670). Subsequently, this paper presents an accurate analysis of the influence of the different code versions including shared memory approaches, vector instructions and multi-processors (both CPU and GPU) and compares them in order to delimit the degree of improvement of using distributed solutions based on multi-CPU and multi-GPU. The performance of both approaches was analysed and it has been demonstrated that the addition of shared memory schemes to CPU computing improves substantially the performance of vector instructions enlarging the simulation sizes that use efficiently the cache memory of CPUs. In this case GPU computing is slightly twice times faster than the fine tuned CPU version in both cases one and two nodes. However, for massively computations explicit vector instructions do not worth it since the memory bandwidth is the limiting factor and the performance tends to be the same than the sequential version
Solving fuzzy polynomial equation and the dual fuzzy polynomial equation using the ranking method
NASA Astrophysics Data System (ADS)
Rahman, Nurhakimah Ab.; Abdullah, Lazim
2014-06-01
Fuzzy polynomials with trapezoidal and triangular fuzzy numbers have attracted interest among some researchers. Many studies have been done by researchers to obtain real roots of fuzzy polynomials. As a result, there are many numerical methods involved in obtaining the real roots of fuzzy polynomials. In this study, we will present the solution to the fuzzy polynomial equation and dual fuzzy polynomial equation using the ranking method of fuzzy numbers and subsequently transforming fuzzy polynomials to crisp polynomials. This transformation is performed using the ranking method based on three parameters, namely Value, Ambiguity and Fuzziness. Finally, we illustrate our approach with two numerical examples for fuzzy polynomial equation and dual fuzzy polynomial equation.
A neural fuzzy controller learning by fuzzy error propagation
NASA Technical Reports Server (NTRS)
Nauck, Detlef; Kruse, Rudolf
1992-01-01
In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.
NASA Technical Reports Server (NTRS)
Salazar, George A. (Inventor)
1993-01-01
This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.
NASA Astrophysics Data System (ADS)
Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.
2011-03-01
To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.
Fuzzy Neuron: Method and Hardware Realization
NASA Technical Reports Server (NTRS)
Krasowski, Michael J.; Prokop, Norman F.
2014-01-01
This innovation represents a method by which single-to-multi-input, single-to-many-output system transfer functions can be estimated from input/output data sets. This innovation can be run in the background while a system is operating under other means (e.g., through human operator effort), or may be utilized offline using data sets created from observations of the estimated system. It utilizes a set of fuzzy membership functions spanning the input space for each input variable. Linear combiners associated with combinations of input membership functions are used to create the output(s) of the estimator. Coefficients are adjusted online through the use of learning algorithms.
GPU techniques applied to Euler flow simulations and comparison to CPU performance
NASA Astrophysics Data System (ADS)
Koop, Blake
With the decrease in cost of computing, and the increasingly friendly programming environments, the demand for computer generated models of real world problems has surged. Each generation of computer hardware becomes marginally faster than its predecessor, allowing for decreases in required computation time. However, the progression is slowing and will soon reach a barrier as lithography reaches its natural limits. General Purpose Graphics Processing Unit (GPGPU) programming, rather than traditional programming written for Central Processing Unit (CPU) architectures may be a viable way for computational scientists to continue to realize wall clock time reductions at a Moore's Law pace. If a code can be modified to take advantage of the Single-Input-Multiple-Data (SIMD) architecture of Graphics Processing Units (GPUs), it may be possible to gain the functionality of hundreds or thousands of cores available on a GPU card. This paper details the investigation of a specific compressible flow simulation and its functionality in both CPU and GPU programming schemes. The flow is governed by the unsteady Euler flow equations and it is checked for validity against the known solution in all three directions. It is then run over varying grid sizes using both the CPU and GPU programming schemes to evaluate wall clock time reductions.
Accelerating DynEarthSol3D on tightly coupled CPU-GPU heterogeneous processors
NASA Astrophysics Data System (ADS)
Ta, Tuan; Choo, Kyoshin; Tan, Eh; Jang, Byunghyun; Choi, Eunseo
2015-06-01
DynEarthSol3D (Dynamic Earth Solver in Three Dimensions) is a flexible, open-source finite element solver that models the momentum balance and the heat transfer of elasto-visco-plastic material in the Lagrangian form using unstructured meshes. It provides a platform for the study of the long-term deformation of earth's lithosphere and various problems in civil and geotechnical engineering. However, the continuous computation and update of a very large mesh poses an intolerably high computational burden to developers and users in practice. For example, simulating a small input mesh containing around 3000 elements in 20 million time steps would take more than 10 days on a high-end desktop CPU. In this paper, we explore tightly coupled CPU-GPU heterogeneous processors to address the computing concern by leveraging their new features and developing hardware-architecture-aware optimizations. Our proposed key optimization techniques are three-fold: memory access pattern improvement, data transfer elimination and kernel launch overhead minimization. Experimental results show that our proposed implementation on a tightly coupled heterogeneous processor outperforms all other alternatives including traditional discrete GPU, quad-core CPU using OpenMP, and serial implementations by 67%, 50%, and 154% respectively even though the embedded GPU in the heterogeneous processor has significantly less number of cores than high-end discrete GPU.
Multi-core CPU or GPU-accelerated Multiscale Modeling for Biomolecular Complexes
Liao, Tao; Zhang, Yongjie; Kekenes-Huskey, Peter M.; Cheng, Yuhui; Michailova, Anushka; McCulloch, Andrew D.; Holst, Michael; McCammon, J. Andrew
2013-01-01
Multi-scale modeling plays an important role in understanding the structure and biological functionalities of large biomolecular complexes. In this paper, we present an efficient computational framework to construct multi-scale models from atomic resolution data in the Protein Data Bank (PDB), which is accelerated by multi-core CPU and programmable Graphics Processing Units (GPU). A multi-level summation of Gaus-sian kernel functions is employed to generate implicit models for biomolecules. The coefficients in the summation are designed as functions of the structure indices, which specify the structures at a certain level and enable a local resolution control on the biomolecular surface. A method called neighboring search is adopted to locate the grid points close to the expected biomolecular surface, and reduce the number of grids to be analyzed. For a specific grid point, a KD-tree or bounding volume hierarchy is applied to search for the atoms contributing to its density computation, and faraway atoms are ignored due to the decay of Gaussian kernel functions. In addition to density map construction, three modes are also employed and compared during mesh generation and quality improvement to generate high quality tetrahedral meshes: CPU sequential, multi-core CPU parallel and GPU parallel. We have applied our algorithm to several large proteins and obtained good results. PMID:24352481
Classification of air quality using fuzzy synthetic multiplication.
Abdullah, Lazim; Khalid, Noor Dalina
2012-11-01
Proper identification of environment's air quality based on limited observations is an essential task to meet the goals of environmental management. Various classification methods have been used to estimate the change of air quality status and health. However, discrepancies frequently arise from the lack of clear distinction between each air quality, the uncertainty in the quality criteria employed and the vagueness or fuzziness embedded in the decision-making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies when describing integrated air quality conditions with respect to various pollutants. Therefore, this paper presents two fuzzy multiplication synthetic techniques to establish classification of air quality. The fuzzy multiplication technique empowers the max-min operations in "or" and "and" in executing the fuzzy arithmetic operations. Based on a set of air pollutants data carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, and particulate matter (PM(10)) collected from a network of 51 stations in Klang Valley, East Malaysia, Sabah, and Sarawak were utilized in this evaluation. The two fuzzy multiplication techniques consistently classified Malaysia's air quality as "good." The findings indicated that the techniques may have successfully harmonized inherent discrepancies and interpret complex conditions. It was demonstrated that fuzzy synthetic multiplication techniques are quite appropriate techniques for air quality management. PMID:22160435
Relativistic Landau models and generation of fuzzy spheres
NASA Astrophysics Data System (ADS)
Hasebe, Kazuki
2016-07-01
Noncommutative geometry naturally emerges in low energy physics of Landau models as a consequence of level projection. In this work, we proactively utilize the level projection as an effective tool to generate fuzzy geometry. The level projection is specifically applied to the relativistic Landau models. In the first half of the paper, a detail analysis of the relativistic Landau problems on a sphere is presented, where a concise expression of the Dirac-Landau operator eigenstates is obtained based on algebraic methods. We establish SU(2) “gauge” transformation between the relativistic Landau model and the Pauli-Schrödinger nonrelativistic quantum mechanics. After the SU(2) transformation, the Dirac operator and the angular momentum operators are found to satisfy the SO(3, 1) algebra. In the second half, the fuzzy geometries generated from the relativistic Landau levels are elucidated, where unique properties of the relativistic fuzzy geometries are clarified. We consider mass deformation of the relativistic Landau models and demonstrate its geometrical effects to fuzzy geometry. Super fuzzy geometry is also constructed from a supersymmetric quantum mechanics as the square of the Dirac-Landau operator. Finally, we apply the level projection method to real graphene system to generate valley fuzzy spheres.
Huang, Zhi-Jiang; Dai, De-Zai; Li, Na; Na, Tao; Ji, Min; Dai, Yin
2007-04-01
1. Torsades de pointes (TDP) is a severe adverse effect during the clinical use of dofetilide, a selective blocker of the rapid component of the delayed rectifier potassium channel (I(Kr)). The present study was designed to test whether CPU228, a derivative of dofetilide with calcium (Ca(2+)) antagonist properties, could reduce TDP without reducing the blockade of I(Kr). 2. The incidence of TDP in a rabbit model and the effective refractory period (ERP) were measured and compared for dofetilide and CPU228. Suppression of I(Kr) and the L-type Ca(2+) current (I(Ca,L)) and the Ca(2+) transients of isolated cardiomyocytes were investigated by whole-cell patch-clamp and Fluo-3 dye spectrophotometry. 3. The incidence of TDP was greatly reduced by CPU228 relative to dofetilide, occurring in only one of six rabbits compared with five of six rabbits following dofetilide (P < 0.05). In isolated atria, prolongation of ERP by CPU228 was less than that of dofetilide and no reverse frequency dependence was observed. Negative inotropism by CPU228 was significant against positive inotropism by dofetilide. CPU228 inhibited both I(Kr) and I(Ca,L) currents and the IC(50) for I(Ca,L) inhibition was 0.909 micromol/L. At 3 micromol/L, CPU228 significantly suppressed the Ca(2+) transients. 4. CPU228 is able to block I(Ca,L), contributing to decreased TDP, while also blocking I(Kr) activity. By combined blockade of I(Kr) and I(Ca,L), CPU228 shares the property of complex Class III anti-arrhythmic agents. PMID:17324143
Huang, G.H.; Cohen, S.J.; Yin, Y.Y.; Bass, B. |
1996-09-01
A climatic change impact assessment was performed for agricultural and timbering activities. An inexact dynamic optimization model was utilized that can reflect complex system features and a related fuzzy system relation analysis method for comprehensive impact patterns assessment.
Tatari, Farzaneh; Akbarzadeh-T, Mohammad-R; Sabahi, Ahmad
2012-12-01
In this paper, we present an agent-based system for distributed risk assessment of breast cancer development employing fuzzy and probabilistic computing. The proposed fuzzy multi agent system consists of multiple fuzzy agents that benefit from fuzzy set theory to demonstrate their soft information (linguistic information). Fuzzy risk assessment is quantified by two linguistic variables of high and low. Through fuzzy computations, the multi agent system computes the fuzzy probabilities of breast cancer development based on various risk factors. By such ranking of high risk and low risk fuzzy probabilities, the multi agent system (MAS) decides whether the risk of breast cancer development is high or low. This information is then fed into an insurance premium adjuster in order to provide preventive decision making as well as to make appropriate adjustment of insurance premium and risk. This final step of insurance analysis also provides a numeric measure to demonstrate the utility of the approach. Furthermore, actual data are gathered from two hospitals in Mashhad during 1 year. The results are then compared with a fuzzy distributed approach. PMID:22692028
Assessment of Flood Vulnerability to Climate Change Using Fuzzy Operators in Seoul
NASA Astrophysics Data System (ADS)
Lee, M. J.
2014-12-01
The goal of this study is to apply the IPCC(Intergovernmental Panel on Climate Change) concept of vulnerability to climate change and verify the use of a combination of vulnerability index and fuzzy operators to flood vulnerability analysis and mapping in Seoul using GIS. In order to achieve this goal, this study identified indicators influencing floods based on literature review. We include indicators of exposure to climate(daily max rainfall, days of 80㎜ over), sensitivity(slope, geological, average DEM, Impermeability layer, topography and drainage), and adaptive capacity(retarding basin and green-infra). Also, this research used fuzzy operator model for aggregating indicators, and utilized frequency ratio to decide fuzzy membership values. Results show that number of days of precipitation above 80㎜, the distance from river and impervious surface have comparatively strong influence on flood damage. Furthermore, when precipitation is over 269㎜, areas with scare flood mitigation capacities, industrial land use, elevation of 16˜20m, within 50m distance from rivers are quite vulnerable to floods. Yeongdeungpo-gu, Yongsan-gu, Mapo-gu include comparatively large vulnerable areas. The relative weight of each factor was then converted into a fuzzy membership value and integrated as a flood vulnerability index using fuzzy operators (fuzzy AND, fuzzy OR, fuzzy algebraic sum, and fuzzy algebraic product). Comparing the results of the highest for the fuzzy AND operator, fuzzy gamma operator (γ = 0.2) is higher with improved computational. This study improved previous flood vulnerability assessment methodology by adopting fuzzy operator model. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing flood mitigation policies. Acknowledgements: The authors appreciate the support that this study has received from "Development of Time Series Disaster Mapping Technologies through Natural Disaster Factor Spatial
Fast Fuzzy Arithmetic Operations
NASA Technical Reports Server (NTRS)
Hampton, Michael; Kosheleva, Olga
1997-01-01
In engineering applications of fuzzy logic, the main goal is not to simulate the way the experts really think, but to come up with a good engineering solution that would (ideally) be better than the expert's control, In such applications, it makes perfect sense to restrict ourselves to simplified approximate expressions for membership functions. If we need to perform arithmetic operations with the resulting fuzzy numbers, then we can use simple and fast algorithms that are known for operations with simple membership functions. In other applications, especially the ones that are related to humanities, simulating experts is one of the main goals. In such applications, we must use membership functions that capture every nuance of the expert's opinion; these functions are therefore complicated, and fuzzy arithmetic operations with the corresponding fuzzy numbers become a computational problem. In this paper, we design a new algorithm for performing such operations. This algorithm is applicable in the case when negative logarithms - log(u(x)) of membership functions u(x) are convex, and reduces computation time from O(n(exp 2))to O(n log(n)) (where n is the number of points x at which we know the membership functions u(x)).
NASA Astrophysics Data System (ADS)
Batic, Davide; Nicolini, Piero
2010-08-01
We study the stability of the noncommutative Schwarzschild black hole interior by analysing the propagation of a massless scalar field between the two horizons. We show that the spacetime fuzziness triggered by the field higher momenta can cure the classical exponential blue-shift divergence, suppressing the emergence of infinite energy density in a region nearby the Cauchy horizon.
Fuzzy logic in control systems: Fuzzy logic controller. I, II
NASA Technical Reports Server (NTRS)
Lee, Chuen Chien
1990-01-01
Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.
Interval-valued fuzzy hypergraph and fuzzy partition.
Chen, S M
1997-01-01
This paper extends the work of H. Lee-Kwang and L.M. Lee (1995) to present the concept of the interval-valued fuzzy hypergraph. In the interval-valued fuzzy hypergraph, the concepts of the dual interval-valued fuzzy hypergraph, the crisp-valued alpha-cut hypergraph, and the interval-valued [alpha(1),alpha(2 )]-cut at beta level hypergraph are developed, where alphain [0, 1], 0fuzzy partition of a system. PMID:18255914
Robust Fuzzy Controllers Using FPGAs
NASA Technical Reports Server (NTRS)
Monroe, Author Gene S., Jr.
2007-01-01
Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.
Fuzzy logic particle tracking velocimetry
NASA Technical Reports Server (NTRS)
Wernet, Mark P.
1993-01-01
Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.
Zoning of agricultural field using a fuzzy indicators model
Technology Transfer Automated Retrieval System (TEKTRAN)
Zoning of agricultural fields is an important task for utilization of precision farming technology. One method for deciding how to subdivide a field into a few relatively homogenous zones is using applications of fuzzy sets theory. Data collected from a precision agriculture study in central Texas...
Li, Yongming; Tong, Shaocheng; Li, Tieshan
2015-10-01
In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial-parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach. PMID:25438335
Commodity CPU-GPU System for Low-Cost , High-Performance Computing
NASA Astrophysics Data System (ADS)
Wang, S.; Zhang, S.; Weiss, R. M.; Barnett, G. A.; Yuen, D. A.
2009-12-01
We have put together a desktop computer system for under 2.5 K dollars from commodity components that consist of one quad-core CPU (Intel Core 2 Quad Q6600 Kentsfield 2.4GHz) and two high end GPUs (nVidia's GeForce GTX 295 and Tesla C1060). A 1200 watt power supply is required. On this commodity system, we have constructed an easy-to-use hybrid computing environment, in which Message Passing Interface (MPI) is used for managing the working loads, for transferring the data among different GPU devices, and for minimizing the need of CPU’s memory. The test runs using the MAGMA (Matrix Algebra on GPU and Multicore Architectures) library show that the speed ups for double precision calculations can be greater than 10 (GPU vs. CPU) and they are bigger (> 20) for single precision calculations. In addition we have enabled the combination of Matlab with CUDA for interactive visualization through MPI, i.e., two GPU devices are used for simulation and one GPU device is used for visualizing the computing results as the simulation goes. Our experience with this commodity system has shown that running multiple applications on one GPU device or running one application across multiple GPU devices can be done as conveniently as on CPUs. With NVIDIA CEO Jen-Hsun Huang's claim that over the next 6 years GPU processing power will increase by 570x compared to the 3x for CPUs, future low-cost commodity computers such as ours may be a remedy for the long wait queues of the world's supercomputers, especially for small- and mid-scale computation. Our goal here is to explore the limits and capabilities of this emerging technology and to get ourselves ready to run large-scale simulations on the next generation of computing environment, which we believe will hybridize CPU and GPU architectures.
A 3D front tracking method on a CPU/GPU system
Bo, Wurigen; Grove, John
2011-01-21
We describe the method to port a sequential 3D interface tracking code to a GPU with CUDA. The interface is represented as a triangular mesh. Interface geometry properties and point propagation are performed on a GPU. Interface mesh adaptation is performed on a CPU. The convergence of the method is assessed from the test problems with given velocity fields. Performance results show overall speedups from 11 to 14 for the test problems under mesh refinement. We also briefly describe our ongoing work to couple the interface tracking method with a hydro solver.
Fuzzy logic based intelligent control of a variable speed cage machine wind generation system
Simoes, M.G.; Bose, B.K.; Spiegel, R.J.
1997-01-01
The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, and validated by simulation study. Performances have then been evaluated in detail.
NASA Technical Reports Server (NTRS)
Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan
1997-01-01
This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.
GPU-based relative fuzzy connectedness image segmentation
Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.
2013-01-15
Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.
GPU-based relative fuzzy connectedness image segmentation
Zhuge, Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.
2013-01-01
Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ∞-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology. PMID:23298094
Fuzzy learning under and about an unfamiliar fuzzy teacher
NASA Technical Reports Server (NTRS)
Dasarathy, Belur V.
1992-01-01
This study addresses the problem of optimal parametric learning in unfamiliar fuzzy environments. Prior studies in the domain of unfamiliar environments, which employed either crisp or fuzzy approaches to model the uncertainty or imperfectness of the learning environment, assumed that the training sample labels provided by the unfamiliar teacher were crisp, even if not perfect. Here, the more realistic problem of fuzzy learning under an unfamiliar teacher who provides only fuzzy (instead of crisp) labels, is tackled by expanding the previously defined fuzzy membership concepts to include an additional component representative of the fuzziness of the teacher. The previously studied scenarios, namely, crisp and fuzzy learning under (crisp) unfamiliar teacher, can be looked upon as special cases of this new methodology. As under the earlier studies, the estimated membership functions can then be deployed during the ensuing classification decision phase to judiciously take into account the imperfectness of the learning environment. The study also offers some insight into the properties of several of these fuzzy membership function estimators by examining their behavior under certain specific scenarios.
Using fuzzy sets for data interpretation in natural analogue studies
De Lemos, F.L.; Sullivan, T.; Hellmuth, K.H.
2008-07-01
Natural analogue studies can play a key role in deep geological radioactive disposal systems safety assessment. These studies can help develop a better understanding of complex natural processes and, therefore, provide valuable means of confidence building in the safety assessment. In evaluation of natural analogues, there are, however, several sources of uncertainties that stem from factors such as complexity; lack of data; and ignorance. Often, analysts have to simplify the mathematical models in order to cope with the various sources of complexity and this ads uncertainty to the model results. The uncertainties reflected in model predictions must be addressed to understand their impact on safety assessment and therefore, the utility of natural analogues. Fuzzy sets can be used to represent the information regarding the natural processes and their mutual connections. With this methodology we are able to quantify and propagate the epistemic uncertainties in both processes and, thereby, assign degrees of truth to the similarities between them. An example calculation with literature data is provided. In conclusion: Fuzzy sets are an effective way of quantifying semi-quantitative information such as natural analogues data. Epistemic uncertainty that stems from complexity and lack of knowledge regarding natural processes are represented by the degrees of membership. It also facilitates the propagation of this uncertainty throughout the performance assessment by the extension principle. This principle allows calculation with fuzzy numbers, where fuzzy input results in fuzzy output. This may be one of the main applications of fuzzy sets theory to radioactive waste disposal facility performance assessment. Through the translation of natural data into fuzzy numbers, the effect of parameters in important processes in one site can be quantified and compared to processes in other sites with different conditions. The approach presented in this paper can be extended to
A fuzzy neural network approach for power system evaluations
NASA Astrophysics Data System (ADS)
Moghaddas, Javad
Every real-world dynamical system is nonlinear. Existing methods for solving a nonlinear problem entail linearizing the nonlinear problem and then using the different tools available for solving the linear system. These tools have been well understood for many decades. This research presents the application of Fuzzy Neural Network in reducing the large computational requirements associated with solving nonlinear systems. This approach utilizes fewer and faster steps for solving nonlinear problems. A practical use of this technique is power flow calculation where a large number of nonlinear equations are involved. The power flow problem is formulated as a nonlinear constrained optimization problem with the bus voltages, bus angles, real power injected into the buses, and reactive power injected into the buses as the problem variables. The equality and inequality constraints are appended to this objective function. Fuzzy rules based control is used to assist in choosing suitable penalty functions to form an augmented cost function. The linearized power flow equations at each iteration are translated to a scalar objective function of quadratic form. A neural network structure is given which implements the steepest descent method for minimizing the objective function. This research also presents the application of Fuzzy Clustering to power systems. The technique of Fuzzy Clustering reduces large system states into a few representative clusters, which are sufficient for reliability analysis. The method is then shown for optimal network decomposition based on Fuzzy Clustering. Fuzzy Clustering presents a powerful, globally oriented optimization method, which exploits the mechanism of natural response to reach optima or near optima. The results for an IEEE 14-Bus test system are given and the Fuzzy Clustering algorithm approach is found to produce significantly better solution.
Comparison of CPU and GPU based coding on low-complexity algorithms for display signals
NASA Astrophysics Data System (ADS)
Richter, Thomas; Simon, Sven
2013-09-01
Graphics Processing Units (GPUs) are freely programmable massively parallel general purpose processing units and thus offer the opportunity to off-load heavy computations from the CPU to the GPU. One application for GPU programming is image compression, where the massively parallel nature of GPUs promises high speed benefits. This article analyzes the predicaments of data-parallel image coding on the example of two high-throughput coding algorithms. The codecs discussed here were designed to answer a call from the Video Electronics Standards Association (VESA), and require only minimal buffering at encoder and decoder side while avoiding any pixel-based feedback loops limiting the operating frequency of hardware implementations. Comparing CPU and GPU implementations of the codes show that GPU based codes are usually not considerably faster, or perform only with less than ideal rate-distortion performance. Analyzing the details of this result provides theoretical evidence that for any coding engine either parts of the entropy coding and bit-stream build-up must remain serial, or rate-distortion penalties must be paid when offloading all computations on the GPU.
Fast CPU-based Monte Carlo simulation for radiotherapy dose calculation
NASA Astrophysics Data System (ADS)
Ziegenhein, Peter; Pirner, Sven; Kamerling, Cornelis Ph; Oelfke, Uwe
2015-08-01
Monte-Carlo (MC) simulations are considered to be the most accurate method for calculating dose distributions in radiotherapy. Its clinical application, however, still is limited by the long runtimes conventional implementations of MC algorithms require to deliver sufficiently accurate results on high resolution imaging data. In order to overcome this obstacle we developed the software-package PhiMC, which is capable of computing precise dose distributions in a sub-minute time-frame by leveraging the potential of modern many- and multi-core CPU-based computers. PhiMC is based on the well verified dose planning method (DPM). We could demonstrate that PhiMC delivers dose distributions which are in excellent agreement to DPM. The multi-core implementation of PhiMC scales well between different computer architectures and achieves a speed-up of up to 37× compared to the original DPM code executed on a modern system. Furthermore, we could show that our CPU-based implementation on a modern workstation is between 1.25× and 1.95× faster than a well-known GPU implementation of the same simulation method on a NVIDIA Tesla C2050. Since CPUs work on several hundreds of GB RAM the typical GPU memory limitation does not apply for our implementation and high resolution clinical plans can be calculated.
Autonomous vehicle motion control, approximate maps, and fuzzy logic
NASA Technical Reports Server (NTRS)
Ruspini, Enrique H.
1993-01-01
Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.
Design and performance comparison of fuzzy logic based tracking controllers
NASA Technical Reports Server (NTRS)
Lea, Robert N.; Jani, Yashvant
1992-01-01
Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.
Fuzzy logic and neural networks in artificial intelligence and pattern recognition
NASA Astrophysics Data System (ADS)
Sanchez, Elie
1991-10-01
With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.
A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology
Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali
2015-01-01
The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology. PMID:26380357
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.
On fuzzy ideals of BL-algebras.
Meng, Biao Long; Xin, Xiao Long
2014-01-01
In this paper we investigate further properties of fuzzy ideals of a BL-algebra. The notions of fuzzy prime ideals, fuzzy irreducible ideals, and fuzzy Gödel ideals of a BL-algebra are introduced and their several properties are investigated. We give a procedure to generate a fuzzy ideal by a fuzzy set. We prove that every fuzzy irreducible ideal is a fuzzy prime ideal but a fuzzy prime ideal may not be a fuzzy irreducible ideal and prove that a fuzzy prime ideal ω is a fuzzy irreducible ideal if and only if ω(0) = 1 and |Im(ω)| = 2. We give the Krull-Stone representation theorem of fuzzy ideals in BL-algebras. Furthermore, we prove that the lattice of all fuzzy ideals of a BL-algebra is a complete distributive lattice. Finally, it is proved that every fuzzy Boolean ideal is a fuzzy Gödel ideal, but the converse implication is not true. PMID:24892085
Fuzzy Logic in Medicine and Bioinformatics
Torres, Angela; Nieto, Juan J.
2006-01-01
The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). PMID:16883057
Fuzzy models for pattern recognition
Bezdek, James C.; Pal, Sankar K.
1994-01-01
FUZZY sets were introduced in 1965 by Lotfi Zadeh as a new way to represent vagueness in everyday life. They are a generalization of conventional set theory, one of the basic structures underlying computational mathematics and models. Computational pattern recognition has played a central role in the development of fuzzy models because fuzzy interpretations of data structures are a very natural and intuitively plausible way to formulate and solve various problems. Fuzzy control theory has also provided a wide variety of real, fielded system applications of fuzzy technology. We shall have little more to say about the growth of fuzzy models in control, except to the extent that pattern recognition algorithms and methods described in this book impact control systems. Collected here are many of the seminal papers in the field. There will be, of course, omissions that are neither by intent nor ignorance; we cannot reproduce all of the important papers that have helped in the evolution of fuzzy pattern recognition (there may be as many as five hundred) even in this narrow application domain. We will attempt, in each chapter introduction, to comment on some of the important papers that not been included and we ask both readers and authors to understand that a book such as this simply cannot {open_quotes}contain everything.{close_quotes} Our objective in Chapter 1 is to describe the basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system.
Teaching Machines to Think Fuzzy
ERIC Educational Resources Information Center
Technology Teacher, 2004
2004-01-01
Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…
NASA Astrophysics Data System (ADS)
Qing Hu, Bao
2015-11-01
The fuzzy rough set model and interval-valued fuzzy rough set model have been introduced to handle databases with real values and interval values, respectively. Variable precision rough set was advanced by Ziarko to overcome the shortcomings of misclassification and/or perturbation in Pawlak rough sets. By combining fuzzy rough set and variable precision rough set, a variety of fuzzy variable precision rough sets were studied, which cannot only handle numerical data, but are also less sensitive to misclassification. However, fuzzy variable precision rough sets cannot effectively handle interval-valued data-sets. Research into interval-valued fuzzy rough sets for interval-valued fuzzy data-sets has commenced; however, variable precision problems have not been considered in interval-valued fuzzy rough sets and generalized interval-valued fuzzy rough sets based on fuzzy logical operators nor have interval-valued fuzzy sets been considered in variable precision rough sets and fuzzy variable precision rough sets. These current models are incapable of wide application, especially on misclassification and/or perturbation and on interval-valued fuzzy data-sets. In this paper, these models are generalized to a more integrative approach that not only considers interval-valued fuzzy sets, but also variable precision. First, we review generalized interval-valued fuzzy rough sets based on two fuzzy logical operators: interval-valued fuzzy triangular norms and interval-valued fuzzy residual implicators. Second, we propose generalized interval-valued fuzzy variable precision rough sets based on the above two fuzzy logical operators. Finally, we confirm that some existing models, including rough sets, fuzzy variable precision rough sets, interval-valued fuzzy rough sets, generalized fuzzy rough sets and generalized interval-valued fuzzy variable precision rough sets based on fuzzy logical operators, are special cases of the proposed models.
Application of fuzzy set and Dempster-Shafer theory to organic geochemistry interpretation
NASA Technical Reports Server (NTRS)
Kim, C. S.; Isaksen, G. H.
1993-01-01
An application of fuzzy sets and Dempster Shafter Theory (DST) in modeling the interpretational process of organic geochemistry data for predicting the level of maturities of oil and source rock samples is presented. This was accomplished by (1) representing linguistic imprecision and imprecision associated with experience by a fuzzy set theory, (2) capturing the probabilistic nature of imperfect evidences by a DST, and (3) combining multiple evidences by utilizing John Yen's generalized Dempster-Shafter Theory (GDST), which allows DST to deal with fuzzy information. The current prototype provides collective beliefs on the predicted levels of maturity by combining multiple evidences through GDST's rule of combination.
Source parameter inversion of compound earthquakes on GPU/CPU hybrid platform
NASA Astrophysics Data System (ADS)
Wang, Y.; Ni, S.; Chen, W.
2012-12-01
Source parameter of earthquakes is essential problem in seismology. Accurate and timely determination of the earthquake parameters (such as moment, depth, strike, dip and rake of fault planes) is significant for both the rupture dynamics and ground motion prediction or simulation. And the rupture process study, especially for the moderate and large earthquakes, is essential as the more detailed kinematic study has became the routine work of seismologists. However, among these events, some events behave very specially and intrigue seismologists. These earthquakes usually consist of two similar size sub-events which occurred with very little time interval, such as mb4.5 Dec.9, 2003 in Virginia. The studying of these special events including the source parameter determination of each sub-events will be helpful to the understanding of earthquake dynamics. However, seismic signals of two distinctive sources are mixed up bringing in the difficulty of inversion. As to common events, the method(Cut and Paste) has been proven effective for resolving source parameters, which jointly use body wave and surface wave with independent time shift and weights. CAP could resolve fault orientation and focal depth using a grid search algorithm. Based on this method, we developed an algorithm(MUL_CAP) to simultaneously acquire parameters of two distinctive events. However, the simultaneous inversion of both sub-events make the computation very time consuming, so we develop a hybrid GPU and CPU version of CAP(HYBRID_CAP) to improve the computation efficiency. Thanks to advantages on multiple dimension storage and processing in GPU, we obtain excellent performance of the revised code on GPU-CPU combined architecture and the speedup factors can be as high as 40x-90x compared to classical cap on traditional CPU architecture.As the benchmark, we take the synthetics as observation and inverse the source parameters of two given sub-events and the inversion results are very consistent with the
Fuzzy θ-generalized semi-continuous and fuzzy θ-generalized semi-irresolute mappings
NASA Astrophysics Data System (ADS)
Wahab, Nurul Adilla Farhana Abdul; Salleh, Zabidin
2015-10-01
In this paper, some fuzzy generalized continuity are introduced and studied using the concept of fuzzy θ-generalized semi-closed sets namely fuzzy θ-generalized semi-continuity, fuzzy θ-generalized semi-irresolute and fuzzy θ-generalized semi-closed maps. Fuzzy θ-generalized semi-T1/2 spaces are also introduced and their characterizations are studied. Several interesting results are obtained.
Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions
NASA Astrophysics Data System (ADS)
Tsaur, Ruey-Chyn
2015-02-01
In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.
NASA Astrophysics Data System (ADS)
Alam Khan, Najeeb; Razzaq, Oyoon Abdul
2016-03-01
In the present work a wavelets approximation method is employed to solve fuzzy boundary value differential equations (FBVDEs). Essentially, a truncated Legendre wavelets series together with the Legendre wavelets operational matrix of derivative are utilized to convert FB- VDE into a simple computational problem by reducing it into a system of fuzzy algebraic linear equations. The capability of scheme is investigated on second order FB- VDE considered under generalized H-differentiability. Solutions are represented graphically showing competency and accuracy of this method.
Ultra-fast hybrid CPU-GPU multiple scatter simulation for 3-D PET.
Kim, Kyung Sang; Son, Young Don; Cho, Zang Hee; Ra, Jong Beom; Ye, Jong Chul
2014-01-01
Scatter correction is very important in 3-D PET reconstruction due to a large scatter contribution in measurements. Currently, one of the most popular methods is the so-called single scatter simulation (SSS), which considers single Compton scattering contributions from many randomly distributed scatter points. The SSS enables a fast calculation of scattering with a relatively high accuracy; however, the accuracy of SSS is dependent on the accuracy of tail fitting to find a correct scaling factor, which is often difficult in low photon count measurements. To overcome this drawback as well as to improve accuracy of scatter estimation by incorporating multiple scattering contribution, we propose a multiple scatter simulation (MSS) based on a simplified Monte Carlo (MC) simulation that considers photon migration and interactions due to photoelectric absorption and Compton scattering. Unlike the SSS, the MSS calculates a scaling factor by comparing simulated prompt data with the measured data in the whole volume, which enables a more robust estimation of a scaling factor. Even though the proposed MSS is based on MC, a significant acceleration of the computational time is possible by using a virtual detector array with a larger pitch by exploiting that the scatter distribution varies slowly in spatial domain. Furthermore, our MSS implementation is nicely fit to a parallel implementation using graphic processor unit (GPU). In particular, we exploit a hybrid CPU-GPU technique using the open multiprocessing and the compute unified device architecture, which results in 128.3 times faster than using a single CPU. Overall, the computational time of MSS is 9.4 s for a high-resolution research tomograph (HRRT) system. The performance of the proposed MSS is validated through actual experiments using an HRRT. PMID:24403412
Fuzzy conceptual rainfall runoff models
NASA Astrophysics Data System (ADS)
Özelkan, Ertunga C.; Duckstein, Lucien
2001-11-01
A fuzzy conceptual rainfall-runoff (CRR) framework is proposed herein to deal with those parameter uncertainties of conceptual rainfall-runoff models, that are related to data and/or model structure: with every element of the rainfall-runoff model assumed to be possibly uncertain, taken here as being fuzzy. First, the conceptual rainfall-runoff system is fuzzified and then different operational modes are formulated using fuzzy rules; second, the parameter identification aspect is examined using fuzzy regression techniques. In particular, bi-objective and tri-objective fuzzy regression models are applied in the case of linear conceptual rainfall-runoff models so that the decision maker may be able to trade off prediction vagueness (uncertainty) and the embedding outliers. For the non-linear models, a fuzzy least squares regression framework is applied to derive the model parameters. The methodology is illustrated using: (1) a linear conceptual rainfall-runoff model; (2) an experimental two-parameter model; and (3) a simplified version of the Sacramento soil moisture accounting model of the US National Weather Services river forecast system (SAC-SMA) known as the six-parameter model. It is shown that the fuzzy logic framework enables the decision maker to gain insight about the model sensitivity and the uncertainty stemming from the elements of the CRR model.
Fuzzy expert systems using CLIPS
NASA Technical Reports Server (NTRS)
Le, Thach C.
1994-01-01
This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.
Entanglement entropy on fuzzy spaces
Dou, Djamel; Ydri, Badis
2006-08-15
We study the entanglement entropy of a scalar field in 2+1 spacetime where space is modeled by a fuzzy sphere and a fuzzy disc. In both models we evaluate numerically the resulting entropies and find that they are proportional to the number of boundary degrees of freedom. In the Moyal plane limit of the fuzzy disc the entanglement entropy per unite area (length) diverges if the ignored region is of infinite size. The divergence is (interpreted) of IR-UV mixing origin. In general we expect the entanglement entropy per unite area to be finite on a noncommutative space if the ignored region is of finite size.
Fuzzy resource optimization for safeguards
Zardecki, A.; Markin, J.T.
1991-01-01
Authorization, enforcement, and verification -- three key functions of safeguards systems -- form the basis of a hierarchical description of the system risk. When formulated in terms of linguistic rather than numeric attributes, the risk can be computed through an algorithm based on the notion of fuzzy sets. Similarly, this formulation allows one to analyze the optimal resource allocation by maximizing the overall detection probability, regarded as a linguistic variable. After summarizing the necessary elements of the fuzzy sets theory, we outline the basic algorithm. This is followed by a sample computation of the fuzzy optimization. 10 refs., 1 tab.
NASA Astrophysics Data System (ADS)
Sharma, Diksha; Badal, Andreu; Badano, Aldo
2012-04-01
The computational modeling of medical imaging systems often requires obtaining a large number of simulated images with low statistical uncertainty which translates into prohibitive computing times. We describe a novel hybrid approach for Monte Carlo simulations that maximizes utilization of CPUs and GPUs in modern workstations. We apply the method to the modeling of indirect x-ray detectors using a new and improved version of the code \\scriptsize{{MANTIS}}, an open source software tool used for the Monte Carlo simulations of indirect x-ray imagers. We first describe a GPU implementation of the physics and geometry models in fast\\scriptsize{{DETECT}}2 (the optical transport model) and a serial CPU version of the same code. We discuss its new features like on-the-fly column geometry and columnar crosstalk in relation to the \\scriptsize{{MANTIS}} code, and point out areas where our model provides more flexibility for the modeling of realistic columnar structures in large area detectors. Second, we modify \\scriptsize{{PENELOPE}} (the open source software package that handles the x-ray and electron transport in \\scriptsize{{MANTIS}}) to allow direct output of location and energy deposited during x-ray and electron interactions occurring within the scintillator. This information is then handled by optical transport routines in fast\\scriptsize{{DETECT}}2. A load balancer dynamically allocates optical transport showers to the GPU and CPU computing cores. Our hybrid\\scriptsize{{MANTIS}} approach achieves a significant speed-up factor of 627 when compared to \\scriptsize{{MANTIS}} and of 35 when compared to the same code running only in a CPU instead of a GPU. Using hybrid\\scriptsize{{MANTIS}}, we successfully hide hours of optical transport time by running it in parallel with the x-ray and electron transport, thus shifting the computational bottleneck from optical to x-ray transport. The new code requires much less memory than \\scriptsize{{MANTIS}} and, as a result
Evaluation of fuzzy inference systems using fuzzy least squares
NASA Technical Reports Server (NTRS)
Barone, Joseph M.
1992-01-01
Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.
Normal forms of fuzzy middle and fuzzy contradictions.
Turksen, I B; Kandel, A; Zhang, Y Q
1999-01-01
The expressions of "excluded middle" and "crisp contradiction" are reexamined starting with their original linguistic expressions which are first restated in propositional and then predicate forms. It is shown that, in order to generalize the truth tables and hence the normal forms, the membership assignments in predicate expressions must be separated from their truth qualification. In two-valued logic, there is no need to separate them from each other due to reductionist Aristotalean dichotomy. Whereas, in infinite (fuzzy) valued set and logic, the separation of membership assignments from their truth qualification forms the bases of a new reconstruction of the truth tables. The results obtained from these extended truth tables are reducible to their Boolean equivalents under the axioms of Boolean theory. Whereas, in fuzzy set and logic theory, we obtain a richer and more complex interpretations of the "fuzzy middle" and "fuzzy contradiction." PMID:18252295
Fuzzy Thinking in Non-Fuzzy Situations: Understanding Students' Perspective.
ERIC Educational Resources Information Center
Zazkis, Rina
1995-01-01
In mathematics a true statement is always true, but some false statements are more false than others. Fuzzy logic provides a way of handling degrees of set membership and has implications for helping students appreciate logical thinking. (MKR)
Fuzzy Content-Based Retrieval in Image Databases.
ERIC Educational Resources Information Center
Wu, Jian Kang; Narasimhalu, A. Desai
1998-01-01
Proposes a fuzzy-image database model and a concept of fuzzy space; describes fuzzy-query processing in fuzzy space and fuzzy indexing on complete fuzzy vectors; and uses an example image database, the computer-aided facial-image inference and retrieval system (CAFIIR), for explanation throughout. (Author/LRW)
Performance of the OVERFLOW-MLP and LAURA-MLP CFD Codes on the NASA Ames 512 CPU Origin System
NASA Technical Reports Server (NTRS)
Taft, James R.
2000-01-01
The shared memory Multi-Level Parallelism (MLP) technique, developed last year at NASA Ames has been very successful in dramatically improving the performance of important NASA CFD codes. This new and very simple parallel programming technique was first inserted into the OVERFLOW production CFD code in FY 1998. The OVERFLOW-MLP code's parallel performance scaled linearly to 256 CPUs on the NASA Ames 256 CPU Origin 2000 system (steger). Overall performance exceeded 20.1 GFLOP/s, or about 4.5x the performance of a dedicated 16 CPU C90 system. All of this was achieved without any major modification to the original vector based code. The OVERFLOW-MLP code is now in production on the inhouse Origin systems as well as being used offsite at commercial aerospace companies. Partially as a result of this work, NASA Ames has purchased a new 512 CPU Origin 2000 system to further test the limits of parallel performance for NASA codes of interest. This paper presents the performance obtained from the latest optimization efforts on this machine for the LAURA-MLP and OVERFLOW-MLP codes. The Langley Aerothermodynamics Upwind Relaxation Algorithm (LAURA) code is a key simulation tool in the development of the next generation shuttle, interplanetary reentry vehicles, and nearly all "X" plane development. This code sustains about 4-5 GFLOP/s on a dedicated 16 CPU C90. At this rate, expected workloads would require over 100 C90 CPU years of computing over the next few calendar years. It is not feasible to expect that this would be affordable or available to the user community. Dramatic performance gains on cheaper systems are needed. This code is expected to be perhaps the largest consumer of NASA Ames compute cycles per run in the coming year.The OVERFLOW CFD code is extensively used in the government and commercial aerospace communities to evaluate new aircraft designs. It is one of the largest consumers of NASA supercomputing cycles and large simulations of highly resolved full
Fuzzy logic components for iterative deconvolution systems
NASA Astrophysics Data System (ADS)
Northan, Brian M.
2013-02-01
Deconvolution systems rely heavily on expert knowledge and would benefit from approaches that capture this expert knowledge. Fuzzy logic is an approach that is used to capture expert knowledge rules and produce outputs that range in degree. This paper describes a fuzzy-deconvolution-system that integrates traditional Richardson-Lucy deconvolution with fuzzy components. The system is intended for restoration of 3D widefield images taken under conditions of refractive index mismatch. The system uses a fuzzy rule set for calculating sample refractive index, a fuzzy median filter for inter-iteration noise reduction, and a fuzzy rule set for stopping criteria.
Dynamical tachyons on fuzzy spheres
NASA Astrophysics Data System (ADS)
Berenstein, David; Trancanelli, Diego
2011-05-01
We study the spectrum of off-diagonal fluctuations between displaced fuzzy spheres in the Berenstein-Maldacena-Nastase plane wave matrix model. The displacement is along the plane of the fuzzy spheres. We find that when two fuzzy spheres intersect at angles, classical tachyons develop and that the spectrum of these modes can be computed analytically. These tachyons can be related to the familiar Nielsen-Olesen instabilities in Yang-Mills theory on a constant magnetic background. Many features of the problem become more apparent when we compare with maximally supersymmetric Yang-Mills theory on a sphere, of which this system is a truncation. We also set up a simple oscillatory trajectory on the displacement between the fuzzy spheres and study the dynamics of the modes as they become tachyonic for part of the oscillations. We speculate on their role regarding the possible thermalization of the system.
Knowledge representation in fuzzy logic
NASA Technical Reports Server (NTRS)
Zadeh, Lotfi A.
1989-01-01
The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.
Dynamical tachyons on fuzzy spheres
Berenstein, David; Trancanelli, Diego
2011-05-15
We study the spectrum of off-diagonal fluctuations between displaced fuzzy spheres in the Berenstein-Maldacena-Nastase plane wave matrix model. The displacement is along the plane of the fuzzy spheres. We find that when two fuzzy spheres intersect at angles, classical tachyons develop and that the spectrum of these modes can be computed analytically. These tachyons can be related to the familiar Nielsen-Olesen instabilities in Yang-Mills theory on a constant magnetic background. Many features of the problem become more apparent when we compare with maximally supersymmetric Yang-Mills theory on a sphere, of which this system is a truncation. We also set up a simple oscillatory trajectory on the displacement between the fuzzy spheres and study the dynamics of the modes as they become tachyonic for part of the oscillations. We speculate on their role regarding the possible thermalization of the system.
Current projects in Fuzzy Control
NASA Technical Reports Server (NTRS)
Sugeno, Michio
1990-01-01
Viewgraphs on current projects in fuzzy control are presented. Three projects on helicopter flight control are discussed. The projects are (1) radio control by oral instructions; (2) automatic autorotation entry in engine failure; and (3) unmanned helicopter for sea rescue.
A genetic algorithms approach for altering the membership functions in fuzzy logic controllers
NASA Technical Reports Server (NTRS)
Shehadeh, Hana; Lea, Robert N.
1992-01-01
Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.
NASA Technical Reports Server (NTRS)
2005-01-01
A new all-electronic Particle Image Velocimetry technique that can efficiently map high speed gas flows has been developed in-house at the NASA Lewis Research Center. Particle Image Velocimetry is an optical technique for measuring the instantaneous two component velocity field across a planar region of a seeded flow field. A pulsed laser light sheet is used to illuminate the seed particles entrained in the flow field at two instances in time. One or more charged coupled device (CCD) cameras can be used to record the instantaneous positions of particles. Using the time between light sheet pulses and determining either the individual particle displacements or the average displacement of particles over a small subregion of the recorded image enables the calculation of the fluid velocity. Fuzzy logic minimizes the required operator intervention in identifying particles and computing velocity. Using two cameras that have the same view of the illumination plane yields two single exposure image frames. Two competing techniques that yield unambiguous velocity vector direction information have been widely used for reducing the single-exposure, multiple image frame data: (1) cross-correlation and (2) particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. For the correlation technique, the correlation peak corresponding to the average displacement of particles across the subregion must be identified. Noise on the images and particle dropout result in misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work, fuzzy logic techniques are used to determine the true
A Novel Method for Discovering Fuzzy Sequential Patterns Using the Simple Fuzzy Partition Method.
ERIC Educational Resources Information Center
Chen, Ruey-Shun; Hu, Yi-Chung
2003-01-01
Discusses sequential patterns, data mining, knowledge acquisition, and fuzzy sequential patterns described by natural language. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method which allows the linguistic interpretation of each fuzzy set to be easily obtained. (Author/LRW)
NASA Astrophysics Data System (ADS)
Chaney, A.; Lu, Lei; Stern, A.
2015-09-01
We show that fuzzy spheres are solutions of Lorentzian Ishibashi-Kawai-Kitazawa-Tsuchiya-type matrix models. The solutions serve as toy models of closed noncommutative cosmologies where big bang/crunch singularities appear only after taking the commutative limit. The commutative limit of these solutions corresponds to a sphere embedded in Minkowski space. This "sphere" has several novel features. The induced metric does not agree with the standard metric on the sphere, and, moreover, it does not have a fixed signature. The curvature computed from the induced metric is not constant, has singularities at fixed latitudes (not corresponding to the poles) and is negative. Perturbations are made about the solutions, and are shown to yield a scalar field theory on the sphere in the commutative limit. The scalar field can become tachyonic for a range of the parameters of the theory.
NASA Astrophysics Data System (ADS)
Francés, J.; Bleda, S.; Neipp, C.; Márquez, A.; Pascual, I.; Beléndez, A.
2013-03-01
The finite-difference time-domain method (FDTD) allows electromagnetic field distribution analysis as a function of time and space. The method is applied to analyze holographic volume gratings (HVGs) for the near-field distribution at optical wavelengths. Usually, this application requires the simulation of wide areas, which implies more memory and time processing. In this work, we propose a specific implementation of the FDTD method including several add-ons for a precise simulation of optical diffractive elements. Values in the near-field region are computed considering the illumination of the grating by means of a plane wave for different angles of incidence and including absorbing boundaries as well. We compare the results obtained by FDTD with those obtained using a matrix method (MM) applied to diffraction gratings. In addition, we have developed two optimized versions of the algorithm, for both CPU and GPU, in order to analyze the improvement of using the new NVIDIA Fermi GPU architecture versus highly tuned multi-core CPU as a function of the size simulation. In particular, the optimized CPU implementation takes advantage of the arithmetic and data transfer streaming SIMD (single instruction multiple data) extensions (SSE) included explicitly in the code and also of multi-threading by means of OpenMP directives. A good agreement between the results obtained using both FDTD and MM methods is obtained, thus validating our methodology. Moreover, the performance of the GPU is compared to the SSE+OpenMP CPU implementation, and it is quantitatively determined that a highly optimized CPU program can be competitive for a wider range of simulation sizes, whereas GPU computing becomes more powerful for large-scale simulations.
A fuzzy logic based approach to direct load control
Bhattacharyya, K.; Crow, M.L.
1996-05-01
Demand side management programs are strategies designed to alter the shape of the load curve. In order to successfully implement such a strategy, customer acceptance of the program is vital. It is thus desirable to design a model for direct load control which may accommodate customer preferences. This paper presents a methodology for optimizing both customer satisfaction and utility unit commitment savings, based on a fuzzy load model for the direct load control of appliances.
NASA Astrophysics Data System (ADS)
Fatah, Abd.; Rozaimi
2015-12-01
In this paper, we will discuss about the construction of fuzzy tuning B-spline curve based on fuzzy set theory. The concept of fuzzy tuning in designing this B-spline curve is based on the uncertain knots values which has to be defined first and then the result will be blended together with B-spline function which exists in users presumption in deciding the best knots value of tuning. Therefore, fuzzy set theory especially fuzzy number concepts are used to define the uncertain knots values and then it will be become fuzzy knots values. The Result by using different values of fuzzy knots for constructing a fuzzy tuning of B-spline curves will be illustrated.
Fuzzy logic controller for the electric motor driving the astronomical telescope
NASA Astrophysics Data System (ADS)
Soliman, Hussein F.; Attia, Abdel-Fattah A.; Badr, Mohammed A.; Osman, Anas M.; Gamaleldin, Abdul A.
1998-05-01
The paper presents an application of fuzzy logic controller to regulate the DC motor driver system of astronomical telescope. The mathematical model of such a telescope is highly nonlinear coupled equations. However, the accuracy requirement in telescope system exceed those of other industrial plants. Fuzzy logic controller provides means to deal with nonlinear functions. A fuzzy logic controller (FLC) was designed to enhance the performance of a two-link model of astronomical telescope. The proposed FLC utilizes the position deviation for the desired value, and its rate of change to regulate the armature voltage of the DC motor drive of each link. The final action of FLC is equivalent to PD controller with a variable gain by using an expert look- up table. This work presents the derivation of the mathematical model of 14 inch Celestron telescope and computer simulation of its motion. The FLC contains two groups of fuzzy sets.
Observed-Based Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems With Dead-Zone.
Tong, Shaocheng; Sui, Shuai; Li, Yongming
2015-12-01
In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach. PMID:25594991
Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System
Hosseini, Monireh Sheikh; Zekri, Maryam
2012-01-01
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054
Regular black holes and noncommutative geometry inspired fuzzy sources
NASA Astrophysics Data System (ADS)
Kobayashi, Shinpei
2016-05-01
We investigated regular black holes with fuzzy sources in three and four dimensions. The density distributions of such fuzzy sources are inspired by noncommutative geometry and given by Gaussian or generalized Gaussian functions. We utilized mass functions to give a physical interpretation of the horizon formation condition for the black holes. In particular, we investigated three-dimensional BTZ-like black holes and four-dimensional Schwarzschild-like black holes in detail, and found that the number of horizons is related to the space-time dimensions, and the existence of a void in the vicinity of the center of the space-time is significant, rather than noncommutativity. As an application, we considered a three-dimensional black hole with the fuzzy disc which is a disc-shaped region known in the context of noncommutative geometry as a source. We also analyzed a four-dimensional black hole with a source whose density distribution is an extension of the fuzzy disc, and investigated the horizon formation condition for it.
NASA Astrophysics Data System (ADS)
Ibrahim, Wael Refaat Anis
The present research involves the development of several fuzzy expert systems for power quality analysis and diagnosis. Intelligent systems for the prediction of abnormal system operation were also developed. The performance of all intelligent modules developed was either enhanced or completely produced through adaptive fuzzy learning techniques. Neuro-fuzzy learning is the main adaptive technique utilized. The work presents a novel approach to the interpretation of power quality from the perspective of the continuous operation of a single system. The research includes an extensive literature review pertaining to the applications of intelligent systems to power quality analysis. Basic definitions and signature events related to power quality are introduced. In addition, detailed discussions of various artificial intelligence paradigms as well as wavelet theory are included. A fuzzy-based intelligent system capable of identifying normal from abnormal operation for a given system was developed. Adaptive neuro-fuzzy learning was applied to enhance its performance. A group of fuzzy expert systems that could perform full operational diagnosis were also developed successfully. The developed systems were applied to the operational diagnosis of 3-phase induction motors and rectifier bridges. A novel approach for learning power quality waveforms and trends was developed. The technique, which is adaptive neuro fuzzy-based, learned, compressed, and stored the waveform data. The new technique was successfully tested using a wide variety of power quality signature waveforms, and using real site data. The trend-learning technique was incorporated into a fuzzy expert system that was designed to predict abnormal operation of a monitored system. The intelligent system learns and stores, in compressed format, trends leading to abnormal operation. The system then compares incoming data to the retained trends continuously. If the incoming data matches any of the learned trends, an
Universal Keyword Classifier on Public Key Based Encrypted Multikeyword Fuzzy Search in Public Cloud
Munisamy, Shyamala Devi; Chokkalingam, Arun
2015-01-01
Cloud computing has pioneered the emerging world by manifesting itself as a service through internet and facilitates third party infrastructure and applications. While customers have no visibility on how their data is stored on service provider's premises, it offers greater benefits in lowering infrastructure costs and delivering more flexibility and simplicity in managing private data. The opportunity to use cloud services on pay-per-use basis provides comfort for private data owners in managing costs and data. With the pervasive usage of internet, the focus has now shifted towards effective data utilization on the cloud without compromising security concerns. In the pursuit of increasing data utilization on public cloud storage, the key is to make effective data access through several fuzzy searching techniques. In this paper, we have discussed the existing fuzzy searching techniques and focused on reducing the searching time on the cloud storage server for effective data utilization. Our proposed Asymmetric Classifier Multikeyword Fuzzy Search method provides classifier search server that creates universal keyword classifier for the multiple keyword request which greatly reduces the searching time by learning the search path pattern for all the keywords in the fuzzy keyword set. The objective of using BTree fuzzy searchable index is to resolve typos and representation inconsistencies and also to facilitate effective data utilization. PMID:26380364
Munisamy, Shyamala Devi; Chokkalingam, Arun
2015-01-01
Cloud computing has pioneered the emerging world by manifesting itself as a service through internet and facilitates third party infrastructure and applications. While customers have no visibility on how their data is stored on service provider's premises, it offers greater benefits in lowering infrastructure costs and delivering more flexibility and simplicity in managing private data. The opportunity to use cloud services on pay-per-use basis provides comfort for private data owners in managing costs and data. With the pervasive usage of internet, the focus has now shifted towards effective data utilization on the cloud without compromising security concerns. In the pursuit of increasing data utilization on public cloud storage, the key is to make effective data access through several fuzzy searching techniques. In this paper, we have discussed the existing fuzzy searching techniques and focused on reducing the searching time on the cloud storage server for effective data utilization. Our proposed Asymmetric Classifier Multikeyword Fuzzy Search method provides classifier search server that creates universal keyword classifier for the multiple keyword request which greatly reduces the searching time by learning the search path pattern for all the keywords in the fuzzy keyword set. The objective of using BTree fuzzy searchable index is to resolve typos and representation inconsistencies and also to facilitate effective data utilization. PMID:26380364
A Programming Framework for Scientific Applications on CPU-GPU Systems
Owens, John
2013-03-24
At a high level, my research interests center around designing, programming, and evaluating computer systems that use new approaches to solve interesting problems. The rapid change of technology allows a variety of different architectural approaches to computationally difficult problems, and a constantly shifting set of constraints and trends makes the solutions to these problems both challenging and interesting. One of the most important recent trends in computing has been a move to commodity parallel architectures. This sea change is motivated by the industry’s inability to continue to profitably increase performance on a single processor and instead to move to multiple parallel processors. In the period of review, my most significant work has been leading a research group looking at the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver superior performance on a broad range of problems than their CPU counterparts, but effectively mapping complex applications to a parallel programming model with an emerging programming environment is a significant and important research problem.
Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions
NASA Astrophysics Data System (ADS)
Sutter, P. M.; Wandelt, B. D.; Elsner, F.
2015-06-01
We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 10-5 in the power spectrum of the output map.
The Lattices of Group Fuzzy Congruences and Normal Fuzzy Subsemigroups on E-Inversive Semigroups
Wang, Shoufeng
2014-01-01
The aim of this paper is to investigate the lattices of group fuzzy congruences and normal fuzzy subsemigroups on E-inversive semigroups. We prove that group fuzzy congruences and normal fuzzy subsemigroups determined each other in E-inversive semigroups. Moreover, we show that the set of group t-fuzzy congruences and the set of normal subsemigroups with tip t in a given E-inversive semigroup form two mutually isomorphic modular lattices for every t∈ [0,1]. PMID:24892045
Fuzzy logic and neural networks
Loos, J.R.
1994-11-01
Combine fuzzy logic`s fuzzy sets, fuzzy operators, fuzzy inference, and fuzzy rules - like defuzzification - with neural networks and you can arrive at very unfuzzy real-time control. Fuzzy logic, cursed with a very whimsical title, simply means multivalued logic, which includes not only the conventional two-valued (true/false) crisp logic, but also the logic of three or more values. This means one can assign logic values of true, false, and somewhere in between. This is where fuzziness comes in. Multi-valued logic avoids the black-and-white, all-or-nothing assignment of true or false to an assertion. Instead, it permits the assignment of shades of gray. When assigning a value of true or false to an assertion, the numbers typically used are {open_quotes}1{close_quotes} or {open_quotes}0{close_quotes}. This is the case for programmed systems. If {open_quotes}0{close_quotes} means {open_quotes}false{close_quotes} and {open_quotes}1{close_quotes} means {open_quotes}true,{close_quotes} then {open_quotes}shades of gray{close_quotes} are any numbers between 0 and 1. Therefore, {open_quotes}nearly true{close_quotes} may be represented by 0.8 or 0.9, {open_quotes}nearly false{close_quotes} may be represented by 0.1 or 0.2, and {close_quotes}your guess is as good as mine{close_quotes} may be represented by 0.5. The flexibility available to one is limitless. One can associate any meaning, such as {open_quotes}nearly true{close_quotes}, to any value of any granularity, such as 0.9999. 2 figs.
Fuzzy image processing in sun sensor
NASA Technical Reports Server (NTRS)
Mobasser, S.; Liebe, C. C.; Howard, A.
2003-01-01
This paper will describe how the fuzzy image processing is implemented in the instrument. Comparison of the Fuzzy image processing and a more conventional image processing algorithm is provided and shows that the Fuzzy image processing yields better accuracy then conventional image processing.
Forecasting Enrollments with Fuzzy Time Series.
ERIC Educational Resources Information Center
Song, Qiang; Chissom, Brad S.
The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…
Wu, Xiao; Shen, Jiong; Li, Yiguo; Lee, Kwang Y
2014-05-01
This paper develops a novel data-driven fuzzy modeling strategy and predictive controller for boiler-turbine unit using fuzzy clustering and subspace identification (SID) methods. To deal with the nonlinear behavior of boiler-turbine unit, fuzzy clustering is used to provide an appropriate division of the operation region and develop the structure of the fuzzy model. Then by combining the input data with the corresponding fuzzy membership functions, the SID method is extended to extract the local state-space model parameters. Owing to the advantages of the both methods, the resulting fuzzy model can represent the boiler-turbine unit very closely, and a fuzzy model predictive controller is designed based on this model. As an alternative approach, a direct data-driven fuzzy predictive control is also developed following the same clustering and subspace methods, where intermediate subspace matrices developed during the identification procedure are utilized directly as the predictor. Simulation results show the advantages and effectiveness of the proposed approach. PMID:24559835
Fuzzy-algebra uncertainty assessment
Cooper, J.A.; Cooper, D.K.
1994-12-01
A significant number of analytical problems (for example, abnormal-environment safety analysis) depend on data that are partly or mostly subjective. Since fuzzy algebra depends on subjective operands, we have been investigating its applicability to these forms of assessment, particularly for portraying uncertainty in the results of PRA (probabilistic risk analysis) and in risk-analysis-aided decision-making. Since analysis results can be a major contributor to a safety-measure decision process, risk management depends on relating uncertainty to only known (not assumed) information. The uncertainties due to abnormal environments are even more challenging than those in normal-environment safety assessments; and therefore require an even more judicious approach. Fuzzy algebra matches these requirements well. One of the most useful aspects of this work is that we have shown the potential for significant differences (especially in perceived margin relative to a decision threshold) between fuzzy assessment and probabilistic assessment based on subtle factors inherent in the choice of probability distribution models. We have also shown the relation of fuzzy algebra assessment to ``bounds`` analysis, as well as a description of how analyses can migrate from bounds analysis to fuzzy-algebra analysis, and to probabilistic analysis as information about the process to be analyzed is obtained. Instructive examples are used to illustrate the points.
Fuzzy logic of Aristotelian forms
Perlovsky, L.I.
1996-12-31
Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.
Decentralized Grid Scheduling with Evolutionary Fuzzy Systems
NASA Astrophysics Data System (ADS)
Fölling, Alexander; Grimme, Christian; Lepping, Joachim; Papaspyrou, Alexander
In this paper, we address the problem of finding workload exchange policies for decentralized Computational Grids using an Evolutionary Fuzzy System. To this end, we establish a non-invasive collaboration model on the Grid layer which requires minimal information about the participating High Performance and High Throughput Computing (HPC/HTC) centers and which leaves the local resource managers completely untouched. In this environment of fully autonomous sites, independent users are assumed to submit their jobs to the Grid middleware layer of their local site, which in turn decides on the delegation and execution either on the local system or on remote sites in a situation-dependent, adaptive way. We find for different scenarios that the exchange policies show good performance characteristics not only with respect to traditional metrics such as average weighted response time and utilization, but also in terms of robustness and stability in changing environments.
Interactive Dose Shaping - efficient strategies for CPU-based real-time treatment planning
NASA Astrophysics Data System (ADS)
Ziegenhein, P.; Kamerling, C. P.; Oelfke, U.
2014-03-01
Conventional intensity modulated radiation therapy (IMRT) treatment planning is based on the traditional concept of iterative optimization using an objective function specified by dose volume histogram constraints for pre-segmented VOIs. This indirect approach suffers from unavoidable shortcomings: i) The control of local dose features is limited to segmented VOIs. ii) Any objective function is a mathematical measure of the plan quality, i.e., is not able to define the clinically optimal treatment plan. iii) Adapting an existing plan to changed patient anatomy as detected by IGRT procedures is difficult. To overcome these shortcomings, we introduce the method of Interactive Dose Shaping (IDS) as a new paradigm for IMRT treatment planning. IDS allows for a direct and interactive manipulation of local dose features in real-time. The key element driving the IDS process is a two-step Dose Modification and Recovery (DMR) strategy: A local dose modification is initiated by the user which translates into modified fluence patterns. This also affects existing desired dose features elsewhere which is compensated by a heuristic recovery process. The IDS paradigm was implemented together with a CPU-based ultra-fast dose calculation and a 3D GUI for dose manipulation and visualization. A local dose feature can be implemented via the DMR strategy within 1-2 seconds. By imposing a series of local dose features, equal plan qualities could be achieved compared to conventional planning for prostate and head and neck cases within 1-2 minutes. The idea of Interactive Dose Shaping for treatment planning has been introduced and first applications of this concept have been realized.
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease
Shamonin, Denis P.; Bron, Esther E.; Lelieveldt, Boudewijn P. F.; Smits, Marion; Klein, Stefan; Staring, Marius
2013-01-01
Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4–5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15–60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license
Shamonin, Denis P; Bron, Esther E; Lelieveldt, Boudewijn P F; Smits, Marion; Klein, Stefan; Staring, Marius
2013-01-01
Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license. PMID
Accelerating COBAYA3 on multi-core CPU and GPU systems using PARALUTION
NASA Astrophysics Data System (ADS)
Trost, Nico; Jiménez, Javier; Lukarski, Dimitar; Sanchez, Victor
2014-06-01
COBAYA3 is a multi-physics system of codes which includes two 3D multi-group neutron diffusion codes, ANDES and COBAYA3-PBP, coupled with COBRA-TF, COBRA-IIIc and SUBCHANFLOW sub-channel thermal-hydraulic codes, for the simulation of LWR core transients. The 3D multi-group neutron diffusion equations are expressed in terms of a sparse linear system which can be solved using different iterative Krylov subspace solvers. The mathematical SPARSKIT library has been used for these purposes as it implements among others, external GMRES, PGMRES and BiCGStab solvers. Multi-core CPUs and graphical processing units (GPUs) provide high performance capabilities which are able to accelerate many scientific computations. To take advantage of these new hardware features in daily use computer codes, the integration of the PARALUTION library to solve sparse systems of linear equations is a good choice. It features several types of iterative solvers and preconditioners which can run on both multi-core CPUs and GPU devices without any modification from the interface point of view. This feature is due to the great portability obtained by the modular and flexible design of the library. By exploring this technology, namely the implementation of the PARALUTION library in COBAYA3, we are able to decrease the solution time of the sparse linear systems by a factor 5.15x on GPU and 2.56x on multi-core CPU using standard hardware. These obtained speedup factors in addition to the implementation details are discussed in this paper.
MOIL-opt: Energy-Conserving Molecular Dynamics on a GPU/CPU system.
Ruymgaart, A Peter; Cardenas, Alfredo E; Elber, Ron
2011-08-26
We report an optimized version of the molecular dynamics program MOIL that runs on a shared memory system with OpenMP and exploits the power of a Graphics Processing Unit (GPU). The model is of heterogeneous computing system on a single node with several cores sharing the same memory and a GPU. This is a typical laboratory tool, which provides excellent performance at minimal cost. Besides performance, emphasis is made on accuracy and stability of the algorithm probed by energy conservation for explicit-solvent atomically-detailed-models. Especially for long simulations energy conservation is critical due to the phenomenon known as "energy drift" in which energy errors accumulate linearly as a function of simulation time. To achieve long time dynamics with acceptable accuracy the drift must be particularly small. We identify several means of controlling long-time numerical accuracy while maintaining excellent speedup. To maintain a high level of energy conservation SHAKE and the Ewald reciprocal summation are run in double precision. Double precision summation of real-space non-bonded interactions improves energy conservation. In our best option, the energy drift using 1fs for a time step while constraining the distances of all bonds, is undetectable in 10ns simulation of solvated DHFR (Dihydrofolate reductase). Faster options, shaking only bonds with hydrogen atoms, are also very well behaved and have drifts of less than 1kcal/mol per nanosecond of the same system. CPU/GPU implementations require changes in programming models. We consider the use of a list of neighbors and quadratic versus linear interpolation in lookup tables of different sizes. Quadratic interpolation with a smaller number of grid points is faster than linear lookup tables (with finer representation) without loss of accuracy. Atomic neighbor lists were found most efficient. Typical speedups are about a factor of 10 compared to a single-core single-precision code. PMID:22328867
Effect of noise on chaotic fuzzy mappings
Zardecki, A.
1996-03-01
Chaotic mappings in the space of fuzzy sets induced by mappings of the underlying reference set are investigated. Different fuzzification schemes are considered and their impact on the resultant iterated fuzzy set, under a quadratic mapping, is studied numerically. The fuzzy set mapping is described in terms of the mapping of level cuts, resulting from the resolution theorem for fuzzy sets. In the two-dimensional case, a generalized notion, given as a fuzzy set, of the Hausdorff dimension is formulated. An example, based on the Henon Mapping, is provided.
Parallel Fuzzy Segmentation of Multiple Objects.
Garduño, Edgar; Herman, Gabor T
2008-01-01
The usefulness of fuzzy segmentation algorithms based on fuzzy connectedness principles has been established in numerous publications. New technologies are capable of producing larger-and-larger datasets and this causes the sequential implementations of fuzzy segmentation algorithms to be time-consuming. We have adapted a sequential fuzzy segmentation algorithm to multi-processor machines. We demonstrate the efficacy of such a distributed fuzzy segmentation algorithm by testing it with large datasets (of the order of 50 million points/voxels/items): a speed-up factor of approximately five over the sequential implementation seems to be the norm. PMID:19444333
Fuzzy-rule-based image reconstruction for positron emission tomography
NASA Astrophysics Data System (ADS)
Mondal, Partha P.; Rajan, K.
2005-09-01
Positron emission tomography (PET) and single-photon emission computed tomography have revolutionized the field of medicine and biology. Penalized iterative algorithms based on maximum a posteriori (MAP) estimation eliminate noisy artifacts by utilizing available prior information in the reconstruction process but often result in a blurring effect. MAP-based algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class. Reconstruction with better edge information is often difficult because prior knowledge is not taken into account. The recently introduced median-root-prior (MRP)-based algorithm preserves the edges, but a steplike streaking effect is observed in the reconstructed image, which is undesirable. A fuzzy approach is proposed for modeling the nature of interpixel interaction in order to build an artifact-free edge-preserving reconstruction. The proposed algorithm consists of two elementary steps: (1) edge detection, in which fuzzy-rule-based derivatives are used for the detection of edges in the nearest neighborhood window (which is equivalent to recognizing nearby density classes), and (2) fuzzy smoothing, in which penalization is performed only for those pixels for which no edge is detected in the nearest neighborhood. Both of these operations are carried out iteratively until the image converges. Analysis shows that the proposed fuzzy-rule-based reconstruction algorithm is capable of producing qualitatively better reconstructed images than those reconstructed by MAP and MRP algorithms. The reconstructed images are sharper, with small features being better resolved owing to the nature of the fuzzy potential function.
Automatic control of pressure support mechanical ventilation using fuzzy logic.
Nemoto, T; Hatzakis, G E; Thorpe, C W; Olivenstein, R; Dial, S; Bates, J H
1999-08-01
There is currently no universally accepted approach to weaning patients from mechanical ventilation, but there is clearly a feeling within the medical community that it may be possible to formulate the weaning process algorithmically in some manner. Fuzzy logic seems suited this task because of the way it so naturally represents the subjective human notions employed in much of medical decision-making. The purpose of the present study was to develop a fuzzy logic algorithm for controlling pressure support ventilation in patients in the intensive care unit, utilizing measurements of heart rate, tidal volume, breathing frequency, and arterial oxygen saturation. In this report we describe the fuzzy logic algorithm, and demonstrate its use retrospectively in 13 patients with severe chronic obstructive pulmonary disease, by comparing the decisions made by the algorithm with what actually transpired. The fuzzy logic recommendations agreed with the status quo to within 2 cm H(2)O an average of 76% of the time, and to within 4 cm H(2)O an average of 88% of the time (although in most of these instances no medical decisions were taken as to whether or not to change the level of ventilatory support). We also compared the predictions of our algorithm with those cases in which changes in pressure support level were actually made by an attending physician, and found that the physicians tended to reduce the support level somewhat more aggressively than the algorithm did. We conclude that our fuzzy algorithm has the potential to control the level of pressure support ventilation from ongoing measurements of a patient's vital signs. PMID:10430727
Fuzzy self-learning control for magnetic servo system
NASA Technical Reports Server (NTRS)
Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.
1994-01-01
It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.
Fuzzy reliability analysis of structures by using the method of fuzzy optimization
Hu, Y.; Chen, B.
1996-12-31
There are two kinds of uncertainties in safety assessment of engineering structures. One is of the nature of randomness, and the other fuzziness. Fuzzy uncertainties exist in defining certain structural performances, conditions, parameters, and their interrelationships. The theory of fuzzy sets should be employed to cope with the fuzzy uncertainties. In this paper, a general definition for structural failure considering the fuzzy uncertainties is introduced firstly. Failure of the structure is modelled by a fuzzy event, and described by the membership function. The limit state surface is then replaced by a fuzzy limit state zone, in which every point represents a state belonging to the failure with a certain degree of membership. Then a fuzzy optimization problem for solving the reliability index is formulated. In classical structural reliability theory, the reliability index is defined by the minimum distance from the limit state surface to the origin of the standard normal variable space. It can be evaluated by solving an optimization problem. When the fuzzy uncertainties are taken into consideration, the crisp limit state surface becomes a fuzzy limit state zone. In this case, a corresponding fuzzy optimization problem can be formulated. Fuzzy reliability index can be obtained by solving the fuzzy optimization problem by an iterative procedure with some criteria base on the fuzzy decision-making. Numerical examples are given in the paper.
Fuzzy sensitivity analysis for reliability assessment of building structures
NASA Astrophysics Data System (ADS)
Kala, Zdeněk
2016-06-01
The mathematical concept of fuzzy sensitivity analysis, which studies the effects of the fuzziness of input fuzzy numbers on the fuzziness of the output fuzzy number, is described in the article. The output fuzzy number is evaluated using Zadeh's general extension principle. The contribution of stochastic and fuzzy uncertainty in reliability analysis tasks of building structures is discussed. The algorithm of fuzzy sensitivity analysis is an alternative to stochastic sensitivity analysis in tasks in which input and output variables are considered as fuzzy numbers.
Discovering fuzzy spatial association rules
NASA Astrophysics Data System (ADS)
Kacar, Esen; Cicekli, Nihan K.
2002-03-01
Discovering interesting, implicit knowledge and general relationships in geographic information databases is very important to understand and use these spatial data. One of the methods for discovering this implicit knowledge is mining spatial association rules. A spatial association rule is a rule indicating certain association relationships among a set of spatial and possibly non-spatial predicates. In the mining process, data is organized in a hierarchical manner. However, in real-world applications it may not be possible to construct a crisp structure for this data, instead some fuzzy structures should be used. Fuzziness, i.e. partial belonging of an item to more than one sub-item in the hierarchy, could be applied to the data itself, and also to the hierarchy of spatial relations. This paper shows that, strong association rules can be mined from large spatial databases using fuzzy concept and spatial relation hierarchies.
An experimental methodology for a fuzzy set preference model
NASA Technical Reports Server (NTRS)
Turksen, I. B.; Willson, Ian A.
1992-01-01
A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate
NASA Astrophysics Data System (ADS)
Zheng, Zhixue; Péra, Marie-Cécile; Hissel, Daniel; Becherif, Mohamed; Agbli, Kréhi-Serge; Li, Yongdong
2014-12-01
To improve the performance and lifetime of the low temperature polymer electrolyte membrane fuel cell (PEMFC) stack, water management is an important issue. This paper aims at developing an online diagnostic methodology with the capability of discriminating different degrees of flooding/drying inside the fuel cell stack. Electrochemical impedance spectroscopy (EIS) is utilized as a basis tool and a double-fuzzy method consisting of fuzzy clustering and fuzzy logic is developed to mine diagnostic rules from the experimental data automatically. Through online experimental verification, a high interpretability and computational efficiency of the proposed methodology can be achieved.
Fuzzy lattice neurocomputing (FLN) models.
Kaburlasos, V G; Petridis, V
2000-12-01
In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, 'missing' and 'don't care' data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely 'sigma-FLN' for competitive clustering, and 'FLN with tightest fits (FLNtf)' for supervised clustering. Learning by the sigma-FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf, is incremental, data order independent, polynomial theta(n3), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The sigma-FLN is presented here as fuzzy-ART's extension in the FL-framework such that sigma-FLN widens fuzzy-ART's domain of application to (mathematical) lattices by augmenting the scope of both of fuzzy-ART's choice (Weber) and match functions, and by enhancing fuzzy-ART's complement coding technique. The FLNtf neural model is applied to four benchmark data sets of various sizes for pattern recognition and rule extraction. The benchmark data sets in question involve jointly numeric and nominal data with 'missing' and/or 'don't care' attribute values, whereas the lattices involved include the unit-hypercube, a probability space, and a Boolean algebra. The potential of the FL-framework in computing is also delineated. PMID:11156192
Learning and Tuning of Fuzzy Rules
NASA Technical Reports Server (NTRS)
Berenji, Hamid R.
1997-01-01
In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.
An application of fuzzy logic to power generation control
Tarabishy, M.N.; Grudzinski, J.J.
1996-10-01
The high demand for more energy at lower prices, coupled with tighter safety and environmental regulations made it necessary for utility companies to provide reliable power more efficiently, and for that purpose new control methods are being utilized to meet those challenges. Fuzzy Logic Control (FLC) technology produces controllers that are more robust at lower development cost and time. These qualities give FLC advantage over conventional control technologies particularly in dealing with increasingly complex nonlinear systems. In this paper the authors examine some of the main applications of FLC in power systems and demonstrate it`s usefulness in the control of a gas turbine.
Applications of fuzzy ranking methods to risk-management decisions
NASA Astrophysics Data System (ADS)
Mitchell, Harold A.; Carter, James C., III
1993-12-01
The Department of Energy is making significant improvements to its nuclear facilities as a result of more stringent regulation, internal audits, and recommendations from external review groups. A large backlog of upgrades has resulted. Currently, a prioritization method is being utilized which relies on a matrix of potential consequence and probability of occurrence. The attributes of the potential consequences considered include likelihood, exposure, public health and safety, environmental impact, site personnel safety, public relations, legal liability, and business loss. This paper describes an improved method which utilizes fuzzy multiple attribute decision methods to rank proposed improvement projects.
NASA Astrophysics Data System (ADS)
Deng, Liang; Bai, Hanli; Wang, Fang; Xu, Qingxin
2016-06-01
CPU/GPU computing allows scientists to tremendously accelerate their numerical codes. In this paper, we port and optimize a double precision alternating direction implicit (ADI) solver for three-dimensional compressible Navier-Stokes equations from our in-house Computational Fluid Dynamics (CFD) software on heterogeneous platform. First, we implement a full GPU version of the ADI solver to remove a lot of redundant data transfers between CPU and GPU, and then design two fine-grain schemes, namely “one-thread-one-point” and “one-thread-one-line”, to maximize the performance. Second, we present a dual-level parallelization scheme using the CPU/GPU collaborative model to exploit the computational resources of both multi-core CPUs and many-core GPUs within the heterogeneous platform. Finally, considering the fact that memory on a single node becomes inadequate when the simulation size grows, we present a tri-level hybrid programming pattern MPI-OpenMP-CUDA that merges fine-grain parallelism using OpenMP and CUDA threads with coarse-grain parallelism using MPI for inter-node communication. We also propose a strategy to overlap the computation with communication using the advanced features of CUDA and MPI programming. We obtain speedups of 6.0 for the ADI solver on one Tesla M2050 GPU in contrast to two Xeon X5670 CPUs. Scalability tests show that our implementation can offer significant performance improvement on heterogeneous platform.
NASA Technical Reports Server (NTRS)
Ruspini, Enrique H.
1991-01-01
Summarized here are the results of recent research on the conceptual foundations of fuzzy logic. The focus is primarily on the principle characteristics of a model that quantifies resemblance between possible worlds by means of a similarity function that assigns a number between 0 and 1 to every pair of possible worlds. Introduction of such a function permits one to interpret the major constructs and methods of fuzzy logic: conditional and unconditional possibility and necessity distributions and the generalized modus ponens of Zadeh on the basis of related metric relationships between subsets of possible worlds.
Fuzzy simulation in concurrent engineering
NASA Technical Reports Server (NTRS)
Kraslawski, A.; Nystrom, L.
1992-01-01
Concurrent engineering is becoming a very important practice in manufacturing. A problem in concurrent engineering is the uncertainty associated with the values of the input variables and operating conditions. The problem discussed in this paper concerns the simulation of processes where the raw materials and the operational parameters possess fuzzy characteristics. The processing of fuzzy input information is performed by the vertex method and the commercial simulation packages POLYMATH and GEMS. The examples are presented to illustrate the usefulness of the method in the simulation of chemical engineering processes.
Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.
Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu
2015-05-01
This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems. PMID:25137736
Kumarasabapathy, N.; Manoharan, P. S.
2015-01-01
This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895
Kumarasabapathy, N; Manoharan, P S
2015-01-01
This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895