Book Selection, Collection Development, and Bounded Rationality.

ERIC Educational Resources Information Center

Schwartz, Charles A.

1989-01-01

Reviews previously proposed schemes of classical rationality in book selection, describes new approaches to rational choice behavior, and presents a model of book selection based on bounded rationality in a garbage can decision process. The role of tacit knowledge and symbolic content in the selection process are also discussed. (102 references)…

The EUVE Proposal Database

NASA Astrophysics Data System (ADS)

Christian, C. A.; Olson, E. C.

1993-01-01

The proposal database and scheduling system for the Extreme Ultraviolet Explorer is described. The proposal database has been implemented to take input for approved observations selected by the EUVE Peer Review Panel and output target information suitable for the scheduling system to digest. The scheduling system is a hybrid of the SPIKE program and EUVE software which checks spacecraft constraints, produces a proposed schedule and selects spacecraft orientations with optimal configurations for acquiring star trackers, etc. This system is used to schedule the In Orbit Calibration activities that took place this summer, following the EUVE launch in early June 1992. The strategy we have implemented has implications for the selection of approved targets, which have impacted the Peer Review process. In addition, we will discuss how the proposal database, founded on Sybase, controls the processing of EUVE Guest Observer data.

Fantasy-Testing-Assessment: A Proposed Model for the Investigation of Mate Selection.

ERIC Educational Resources Information Center

Nofz, Michael P.

1984-01-01

Proposes a model for mate selection which outlines three modes of interpersonal relating--fantasy, testing, and assessment (FTA). The model is viewed as a more accurate representation of mate selection processes than suggested by earlier theories, and can be used to clarify couples' understandings of their own relationships. (JAC)

48 CFR 2015.305 - Proposal evaluation.

Code of Federal Regulations, 2013 CFR

2013-10-01

... METHODS AND CONTRACT TYPES CONTRACTING BY NEGOTIATION Source Selection Processes and Techniques 2015.305 Proposal evaluation. The contracting officer may provide offerors' cost proposals and supporting financial...

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

Szoka de Valladares, M.R.; Mack, S.

The DOE Hydrogen Program needs to develop criteria as part of a systematic evaluation process for proposal identification, evaluation and selection. The H Scan component of this process provides a framework in which a project proposer can fully describe their candidate technology system and its components. The H Scan complements traditional methods of capturing cost and technical information. It consists of a special set of survey forms designed to elicit information so expert reviewers can assess the proposal relative to DOE specified selection criteria. The Analytic Hierarchy Process (AHP) component of the decision process assembles the management defined evaluation andmore » selection criteria into a coherent multi-level decision construct by which projects can be evaluated in pair-wise comparisons. The AHP model will reflect management`s objectives and it will assist in the ranking of individual projects based on the extent to which each contributes to management`s objectives. This paper contains a detailed description of the products and activities associated with the planning and evaluation process: The objectives or criteria; the H Scan; and The Analytic Hierarchy Process (AHP).« less

An Ensemble Framework Coping with Instability in the Gene Selection Process.

PubMed

Castellanos-Garzón, José A; Ramos, Juan; López-Sánchez, Daniel; de Paz, Juan F; Corchado, Juan M

2018-03-01

This paper proposes an ensemble framework for gene selection, which is aimed at addressing instability problems presented in the gene filtering task. The complex process of gene selection from gene expression data faces different instability problems from the informative gene subsets found by different filter methods. This makes the identification of significant genes by the experts difficult. The instability of results can come from filter methods, gene classifier methods, different datasets of the same disease and multiple valid groups of biomarkers. Even though there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This work proposes a framework involving five stages of gene filtering to discover biomarkers for diagnosis and classification tasks. This framework performs a process of stable feature selection, facing the problems above and, thus, providing a more suitable and reliable solution for clinical and research purposes. Our proposal involves a process of multistage gene filtering, in which several ensemble strategies for gene selection were added in such a way that different classifiers simultaneously assess gene subsets to face instability. Firstly, we apply an ensemble of recent gene selection methods to obtain diversity in the genes found (stability according to filter methods). Next, we apply an ensemble of known classifiers to filter genes relevant to all classifiers at a time (stability according to classification methods). The achieved results were evaluated in two different datasets of the same disease (pancreatic ductal adenocarcinoma), in search of stability according to the disease, for which promising results were achieved.

Beyond perceptual load and dilution: a review of the role of working memory in selective attention

PubMed Central

de Fockert, Jan W.

2013-01-01

The perceptual load and dilution models differ fundamentally in terms of the proposed mechanism underlying variation in distractibility during different perceptual conditions. However, both models predict that distracting information can be processed beyond perceptual processing under certain conditions, a prediction that is well-supported by the literature. Load theory proposes that in such cases, where perceptual task aspects do not allow for sufficient attentional selectivity, the maintenance of task-relevant processing depends on cognitive control mechanisms, including working memory. The key prediction is that working memory plays a role in keeping clear processing priorities in the face of potential distraction, and the evidence reviewed and evaluated in a meta-analysis here supports this claim, by showing that the processing of distracting information tends to be enhanced when load on a concurrent task of working memory is high. Low working memory capacity is similarly associated with greater distractor processing in selective attention, again suggesting that the unavailability of working memory during selective attention leads to an increase in distractibility. Together, these findings suggest that selective attention against distractors that are processed beyond perception depends on the availability of working memory. Possible mechanisms for the effects of working memory on selective attention are discussed. PMID:23734139

Beyond perceptual load and dilution: a review of the role of working memory in selective attention.

PubMed

de Fockert, Jan W

2013-01-01

The perceptual load and dilution models differ fundamentally in terms of the proposed mechanism underlying variation in distractibility during different perceptual conditions. However, both models predict that distracting information can be processed beyond perceptual processing under certain conditions, a prediction that is well-supported by the literature. Load theory proposes that in such cases, where perceptual task aspects do not allow for sufficient attentional selectivity, the maintenance of task-relevant processing depends on cognitive control mechanisms, including working memory. The key prediction is that working memory plays a role in keeping clear processing priorities in the face of potential distraction, and the evidence reviewed and evaluated in a meta-analysis here supports this claim, by showing that the processing of distracting information tends to be enhanced when load on a concurrent task of working memory is high. Low working memory capacity is similarly associated with greater distractor processing in selective attention, again suggesting that the unavailability of working memory during selective attention leads to an increase in distractibility. Together, these findings suggest that selective attention against distractors that are processed beyond perception depends on the availability of working memory. Possible mechanisms for the effects of working memory on selective attention are discussed.

Selective emitter solar cell formation by NH3 plasma nitridation and single diffusion

NASA Astrophysics Data System (ADS)

Wu, Yung-Hsien; Chen, Lun-Lun; Wu, Jia-Rong; Wu, Min-Lin

2010-01-01

A new and simple process for fabricating a selective emitter solar cell has been proposed. Lightly and heavily doped emitters could be concurrently formed after a single POCl3 diffusion step through the selective formation of SiNx, which serves as the diffusion barrier and can be grown by NH3 plasma nitridation of the Si surface. The desired phosphorus depth profile for the lightly and heavily doped region verifies the eligibility of this process. From the electrical characterization, the selective emitter solar cell fabricated by this process manifests a higher absolute conversion efficiency than a conventional one by 0.5%. It is the enhanced response to the short wavelength light and the reduced surface recombination that causes the considerable improvement in conversion efficiency which is beneficial to further hold the competitive advantage for solar cell manufacturers. Most importantly, the proposed process can be fully integrated into the conventional solar cell process in a mass-production laboratory.

Optimal PMU placement using topology transformation method in power systems.

PubMed

Rahman, Nadia H A; Zobaa, Ahmed F

2016-09-01

Optimal phasor measurement units (PMUs) placement involves the process of minimizing the number of PMUs needed while ensuring the entire power system completely observable. A power system is identified observable when the voltages of all buses in the power system are known. This paper proposes selection rules for topology transformation method that involves a merging process of zero-injection bus with one of its neighbors. The result from the merging process is influenced by the selection of bus selected to merge with the zero-injection bus. The proposed method will determine the best candidate bus to merge with zero-injection bus according to the three rules created in order to determine the minimum number of PMUs required for full observability of the power system. In addition, this paper also considered the case of power flow measurements. The problem is formulated as integer linear programming (ILP). The simulation for the proposed method is tested by using MATLAB for different IEEE bus systems. The explanation of the proposed method is demonstrated by using IEEE 14-bus system. The results obtained in this paper proved the effectiveness of the proposed method since the number of PMUs obtained is comparable with other available techniques.

Green material selection for sustainability: A hybrid MCDM approach.

PubMed

Zhang, Honghao; Peng, Yong; Tian, Guangdong; Wang, Danqi; Xie, Pengpeng

2017-01-01

Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection.

Green material selection for sustainability: A hybrid MCDM approach

PubMed Central

Zhang, Honghao; Peng, Yong; Tian, Guangdong; Wang, Danqi; Xie, Pengpeng

2017-01-01

Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection. PMID:28498864

A Textbook Selection Process.

ERIC Educational Resources Information Center

Prosser, Daniel R.; Bondavalli, Bonnie J.

In response to the problem facing college faculties of choosing textbooks that are both "readable" by students and adequate in content coverage, a text selection process has been developed that can be used with or without the aid of a reading specialist. The first step in the process, a preliminary check, examines each proposed text's publication…

7 CFR 3560.56 - Processing section 515 housing proposals.

Code of Federal Regulations, 2010 CFR

2010-01-01

... their non-selection, and the process that may be used to seek a review of the non-selection decision. (d... development costs do not exceed what they would be to purchase and develop an alternative site; (D) The... initial loan applications. The Agency will process initial loan applications in rank order, taking into...

Selecting automation for the clinical chemistry laboratory.

PubMed

Melanson, Stacy E F; Lindeman, Neal I; Jarolim, Petr

2007-07-01

Laboratory automation proposes to improve the quality and efficiency of laboratory operations, and may provide a solution to the quality demands and staff shortages faced by today's clinical laboratories. Several vendors offer automation systems in the United States, with both subtle and obvious differences. Arriving at a decision to automate, and the ensuing evaluation of available products, can be time-consuming and challenging. Although considerable discussion concerning the decision to automate has been published, relatively little attention has been paid to the process of evaluating and selecting automation systems. To outline a process for evaluating and selecting automation systems as a reference for laboratories contemplating laboratory automation. Our Clinical Chemistry Laboratory staff recently evaluated all major laboratory automation systems in the United States, with their respective chemistry and immunochemistry analyzers. Our experience is described and organized according to the selection process, the important considerations in clinical chemistry automation, decisions and implementation, and we give conclusions pertaining to this experience. Including the formation of a committee, workflow analysis, submitting a request for proposal, site visits, and making a final decision, the process of selecting chemistry automation took approximately 14 months. We outline important considerations in automation design, preanalytical processing, analyzer selection, postanalytical storage, and data management. Selecting clinical chemistry laboratory automation is a complex, time-consuming process. Laboratories considering laboratory automation may benefit from the concise overview and narrative and tabular suggestions provided.

Category-selective attention modulates unconscious processes in the middle occipital gyrus.

PubMed

Tu, Shen; Qiu, Jiang; Martens, Ulla; Zhang, Qinglin

2013-06-01

Many studies have revealed the top-down modulation (spatial attention, attentional load, etc.) on unconscious processing. However, there is little research about how category-selective attention could modulate the unconscious processing. In the present study, using functional magnetic resonance imaging (fMRI), the results showed that category-selective attention modulated unconscious face/tool processing in the middle occipital gyrus (MOG). Interestingly, MOG effects were of opposed direction for face and tool processes. During unconscious face processing, activation in MOG decreased under the face-selective attention compared with tool-selective attention. This result was in line with the predictive coding theory. During unconscious tool processing, however, activation in MOG increased under the tool-selective attention compared with face-selective attention. The different effects might be ascribed to an interaction between top-down category-selective processes and bottom-up processes in the partial awareness level as proposed by Kouider, De Gardelle, Sackur, and Dupoux (2010). Specifically, we suppose an "excessive activation" hypothesis. Copyright © 2013 Elsevier Inc. All rights reserved.

Selection of Construction Methods: A Knowledge-Based Approach

PubMed Central

Skibniewski, Miroslaw

2013-01-01

The appropriate selection of construction methods to be used during the execution of a construction project is a major determinant of high productivity, but sometimes this selection process is performed without the care and the systematic approach that it deserves, bringing negative consequences. This paper proposes a knowledge management approach that will enable the intelligent use of corporate experience and information and help to improve the selection of construction methods for a project. Then a knowledge-based system to support this decision-making process is proposed and described. To define and design the system, semistructured interviews were conducted within three construction companies with the purpose of studying the way that the method' selection process is carried out in practice and the knowledge associated with it. A prototype of a Construction Methods Knowledge System (CMKS) was developed and then validated with construction industry professionals. As a conclusion, the CMKS was perceived as a valuable tool for construction methods' selection, by helping companies to generate a corporate memory on this issue, reducing the reliance on individual knowledge and also the subjectivity of the decision-making process. The described benefits as provided by the system favor a better performance of construction projects. PMID:24453925

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Evaluation criteria for commercially oriented materials processing in space proposals

NASA Technical Reports Server (NTRS)

Moore, W. F.; Mcdowell, J. R.

1979-01-01

An approach and criteria for evaluating NASA funded experiments and demonstrations which have commercial potential were developed. Methods for insuring quick initial screening of commercial proposals are presented. Recommendations are given for modifying the current evaluation approach. New criteria for evaluating commercially orientated materials processing in space (MPS) proposals are introduced. The process for selection of qualified individuals to evaluate the phases of this approach and criteria is considered and guidelines are set for its implementation.

15 CFR 290.7 - Proposal selection process.

Code of Federal Regulations, 2013 CFR

2013-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS REGIONAL CENTERS FOR... proposals will be reviewed by NIST to assure compliance with § 290.5 of these procedures. Proposals which... Director of NIST will appoint an evaluation panel to review and evaluate all qualified proposals in...

15 CFR 290.7 - Proposal selection process.

Code of Federal Regulations, 2011 CFR

2011-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS REGIONAL CENTERS FOR... proposals will be reviewed by NIST to assure compliance with § 290.5 of these procedures. Proposals which... Director of NIST will appoint an evaluation panel to review and evaluate all qualified proposals in...

15 CFR 290.7 - Proposal selection process.

Code of Federal Regulations, 2012 CFR

2012-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS REGIONAL CENTERS FOR... proposals will be reviewed by NIST to assure compliance with § 290.5 of these procedures. Proposals which... Director of NIST will appoint an evaluation panel to review and evaluate all qualified proposals in...

15 CFR 290.7 - Proposal selection process.

Code of Federal Regulations, 2014 CFR

2014-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS REGIONAL CENTERS FOR... proposals will be reviewed by NIST to assure compliance with § 290.5 of these procedures. Proposals which... Director of NIST will appoint an evaluation panel to review and evaluate all qualified proposals in...

15 CFR 290.7 - Proposal selection process.

Code of Federal Regulations, 2010 CFR

2010-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS REGIONAL CENTERS FOR... proposals will be reviewed by NIST to assure compliance with § 290.5 of these procedures. Proposals which... Director of NIST will appoint an evaluation panel to review and evaluate all qualified proposals in...

75 FR 51502 - Proposed Collection Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-20

...-0006). This process is conducted in accordance with 5 CFR 1320.10. DATES: Comments must be submitted on... description of collection: The form is an integral part of the screening and selection process conducted by the Office of Volunteer Recruitment and Selection. The purpose of this information collection is to...

34 CFR 79.11 - What are the Secretary's obligations in interstate situations?

Code of Federal Regulations, 2010 CFR

2010-07-01

... interstate situations? (a) The Secretary is responsible for: (1) Identifying proposed federal financial... which have adopted a process and which select the Department's program or activity. (3) Making efforts... those states that have not adopted a process under the Order or do not select the Department's program...

Adaptive memory: young children show enhanced retention of fitness-related information.

PubMed

Aslan, Alp; Bäuml, Karl-Heinz T

2012-01-01

Evolutionary psychologists propose that human cognition evolved through natural selection to solve adaptive problems related to survival and reproduction, with its ultimate function being the enhancement of reproductive fitness. Following this proposal and the evolutionary-developmental view that ancestral selection pressures operated not only on reproductive adults, but also on pre-reproductive children, the present study examined whether young children show superior memory for information that is processed in terms of its survival value. In two experiments, we found such survival processing to enhance retention in 4- to 10-year-old children, relative to various control conditions that also required deep, meaningful processing but were not related to survival. These results suggest that, already in very young children, survival processing is a special and extraordinarily effective form of memory encoding. The results support the functional-evolutionary proposal that young children's memory is "tuned" to process and retain fitness-related information. Copyright © 2011 Elsevier B.V. All rights reserved.

76 FR 14737 - Bureau of Educational and Cultural Affairs (ECA) Request for Grant Proposals: One Beat

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-17

... selected and provide contact information at posts to award recipient; Advise selected countries for... participants to ECA for review and approval; Inform posts of final selections. Program Development and... programs, and then advise posts on the application, recruitment and participant selection process. Outlines...

Long-term care information systems: an overview of the selection process.

PubMed

Nahm, Eun-Shim; Mills, Mary Etta; Feege, Barbara

2006-06-01

Under the current Medicare Prospective Payment System method and the ever-changing managed care environment, the long-term care information system is vital to providing quality care and to surviving in business. system selection process should be an interdisciplinary effort involving all necessary stakeholders for the proposed system. The system selection process can be modeled following the Systems Developmental Life Cycle: identifying problems, opportunities, and objectives; determining information requirements; analyzing system needs; designing the recommended system; and developing and documenting software.

CLAss-Specific Subspace Kernel Representations and Adaptive Margin Slack Minimization for Large Scale Classification.

PubMed

Yu, Yinan; Diamantaras, Konstantinos I; McKelvey, Tomas; Kung, Sun-Yuan

2018-02-01

In kernel-based classification models, given limited computational power and storage capacity, operations over the full kernel matrix becomes prohibitive. In this paper, we propose a new supervised learning framework using kernel models for sequential data processing. The framework is based on two components that both aim at enhancing the classification capability with a subset selection scheme. The first part is a subspace projection technique in the reproducing kernel Hilbert space using a CLAss-specific Subspace Kernel representation for kernel approximation. In the second part, we propose a novel structural risk minimization algorithm called the adaptive margin slack minimization to iteratively improve the classification accuracy by an adaptive data selection. We motivate each part separately, and then integrate them into learning frameworks for large scale data. We propose two such frameworks: the memory efficient sequential processing for sequential data processing and the parallelized sequential processing for distributed computing with sequential data acquisition. We test our methods on several benchmark data sets and compared with the state-of-the-art techniques to verify the validity of the proposed techniques.

A decision support system using analytical hierarchy process (AHP) for the optimal environmental reclamation of an open-pit mine

NASA Astrophysics Data System (ADS)

Bascetin, A.

2007-04-01

The selection of an optimal reclamation method is one of the most important factors in open-pit design and production planning. It also affects economic considerations in open-pit design as a function of plan location and depth. Furthermore, the selection is a complex multi-person, multi-criteria decision problem. The group decision-making process can be improved by applying a systematic and logical approach to assess the priorities based on the inputs of several specialists from different functional areas within the mine company. The analytical hierarchy process (AHP) can be very useful in involving several decision makers with different conflicting objectives to arrive at a consensus decision. In this paper, the selection of an optimal reclamation method using an AHP-based model was evaluated for coal production in an open-pit coal mine located at Seyitomer region in Turkey. The use of the proposed model indicates that it can be applied to improve the group decision making in selecting a reclamation method that satisfies optimal specifications. Also, it is found that the decision process is systematic and using the proposed model can reduce the time taken to select a optimal method.

44 CFR 4.11 - What are the Administrator's obligations in interstate situations?

Code of Federal Regulations, 2012 CFR

2012-10-01

... interstate situations? (a) The Administrator is responsible for: (1) Identifying proposed Federal financial... officials and entities in states which have adopted a process and which select FEMA's program or activity... and entities in those States that have not adopted a process under the Order or do not select FEMA's...

Analytical network process based optimum cluster head selection in wireless sensor network.

PubMed

Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process.

Analytical network process based optimum cluster head selection in wireless sensor network

PubMed Central

Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

2017-01-01

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process. PMID:28719616

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

A combined approach of self-referencing and Principle Component Thermography for transient, steady, and selective heating scenarios

NASA Astrophysics Data System (ADS)

Omar, M. A.; Parvataneni, R.; Zhou, Y.

2010-09-01

Proposed manuscript describes the implementation of a two step processing procedure, composed of the self-referencing and the Principle Component Thermography (PCT). The combined approach enables the processing of thermograms from transient (flash), steady (halogen) and selective (induction) thermal perturbations. Firstly, the research discusses the three basic processing schemes typically applied for thermography; namely mathematical transformation based processing, curve-fitting processing, and direct contrast based calculations. Proposed algorithm utilizes the self-referencing scheme to create a sub-sequence that contains the maximum contrast information and also compute the anomalies' depth values. While, the Principle Component Thermography operates on the sub-sequence frames by re-arranging its data content (pixel values) spatially and temporally then it highlights the data variance. The PCT is mainly used as a mathematical mean to enhance the defects' contrast thus enabling its shape and size retrieval. The results show that the proposed combined scheme is effective in processing multiple size defects in sandwich steel structure in real-time (<30 Hz) and with full spatial coverage, without the need for a priori defect-free area.

Quantum-enhanced feature selection with forward selection and backward elimination

NASA Astrophysics Data System (ADS)

He, Zhimin; Li, Lvzhou; Huang, Zhiming; Situ, Haozhen

2018-07-01

Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

Adaptive marginal median filter for colour images.

PubMed

Morillas, Samuel; Gregori, Valentín; Sapena, Almanzor

2011-01-01

This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.

An automated process for building reliable and optimal in vitro/in vivo correlation models based on Monte Carlo simulations.

PubMed

Sutton, Steven C; Hu, Mingxiu

2006-05-05

Many mathematical models have been proposed for establishing an in vitro/in vivo correlation (IVIVC). The traditional IVIVC model building process consists of 5 steps: deconvolution, model fitting, convolution, prediction error evaluation, and cross-validation. This is a time-consuming process and typically a few models at most are tested for any given data set. The objectives of this work were to (1) propose a statistical tool to screen models for further development of an IVIVC, (2) evaluate the performance of each model under different circumstances, and (3) investigate the effectiveness of common statistical model selection criteria for choosing IVIVC models. A computer program was developed to explore which model(s) would be most likely to work well with a random variation from the original formulation. The process used Monte Carlo simulation techniques to build IVIVC models. Data-based model selection criteria (Akaike Information Criteria [AIC], R2) and the probability of passing the Food and Drug Administration "prediction error" requirement was calculated. To illustrate this approach, several real data sets representing a broad range of release profiles are used to illustrate the process and to demonstrate the advantages of this automated process over the traditional approach. The Hixson-Crowell and Weibull models were often preferred over the linear. When evaluating whether a Level A IVIVC model was possible, the model selection criteria AIC generally selected the best model. We believe that the approach we proposed may be a rapid tool to determine which IVIVC model (if any) is the most applicable.

SSL: A Theory of How People Learn to Select Strategies

ERIC Educational Resources Information Center

Rieskamp, Jorg; Otto, Philipp E.

2006-01-01

The assumption that people possess a repertoire of strategies to solve the inference problems they face has been raised repeatedly. However, a computational model specifying how people select strategies from their repertoire is still lacking. The proposed strategy selection learning (SSL) theory predicts a strategy selection process on the basis…

Fusiform gyrus face selectivity relates to individual differences in facial recognition ability.

PubMed

Furl, Nicholas; Garrido, Lúcia; Dolan, Raymond J; Driver, Jon; Duchaine, Bradley

2011-07-01

Regions of the occipital and temporal lobes, including a region in the fusiform gyrus (FG), have been proposed to constitute a "core" visual representation system for faces, in part because they show face selectivity and face repetition suppression. But recent fMRI studies of developmental prosopagnosics (DPs) raise questions about whether these measures relate to face processing skills. Although DPs manifest deficient face processing, most studies to date have not shown unequivocal reductions of functional responses in the proposed core regions. We scanned 15 DPs and 15 non-DP control participants with fMRI while employing factor analysis to derive behavioral components related to face identification or other processes. Repetition suppression specific to facial identities in FG or to expression in FG and STS did not show compelling relationships with face identification ability. However, we identified robust relationships between face selectivity and face identification ability in FG across our sample for several convergent measures, including voxel-wise statistical parametric mapping, peak face selectivity in individually defined "fusiform face areas" (FFAs), and anatomical extents (cluster sizes) of those FFAs. None of these measures showed associations with behavioral expression or object recognition ability. As a group, DPs had reduced face-selective responses in bilateral FFA when compared with non-DPs. Individual DPs were also more likely than non-DPs to lack expected face-selective activity in core regions. These findings associate individual differences in face processing ability with selectivity in core face processing regions. This confirms that face selectivity can provide a valid marker for neural mechanisms that contribute to face identification ability.

Fast cat-eye effect target recognition based on saliency extraction

NASA Astrophysics Data System (ADS)

Li, Li; Ren, Jianlin; Wang, Xingbin

2015-09-01

Background complexity is a main reason that results in false detection in cat-eye target recognition. Human vision has selective attention property which can help search the salient target from complex unknown scenes quickly and precisely. In the paper, we propose a novel cat-eye effect target recognition method named Multi-channel Saliency Processing before Fusion (MSPF). This method combines traditional cat-eye target recognition with the selective characters of visual attention. Furthermore, parallel processing enables it to achieve fast recognition. Experimental results show that the proposed method performs better in accuracy, robustness and speed compared to other methods.

Service Delivery to the Handicapped: The Role of the Federal Procurement Process. Part II. Selecting Contractors by Competitive Award.

ERIC Educational Resources Information Center

Donaldosn, William S.; Stephens, Thomas M.

1979-01-01

Sections address the RFP/IFB (Request for Proposals/Invitation for Bids) process and procedures for selecting the "best" contract. In reviewing the federal procurement code, and the recent decision of the Government Accounting Office, particularly regarding the NIMIS (National Instructional Materials Information System contract), inconsistencies…

77 FR 38701 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing of Proposed Rule Change Amending...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-28

... process for the nomination and selection of fair representation directors for the NYSE Arca Board of Directors (``NYSE Arca Board''), and NYSE Arca Equities Rule 3.2 sets forth a similar process for the nomination and selection of fair representation directors for the NYSE Arca Equities Board of Directors...

Competitive Parallel Processing For Compression Of Data

NASA Technical Reports Server (NTRS)

Diner, Daniel B.; Fender, Antony R. H.

1990-01-01

Momentarily-best compression algorithm selected. Proposed competitive-parallel-processing system compresses data for transmission in channel of limited band-width. Likely application for compression lies in high-resolution, stereoscopic color-television broadcasting. Data from information-rich source like color-television camera compressed by several processors, each operating with different algorithm. Referee processor selects momentarily-best compressed output.

Toward a Theory of Information Processing in Teaching.

ERIC Educational Resources Information Center

Joyce, Bruce

1978-01-01

Major concepts of information processing in teaching were reviewed, and a proposed framework was designed. The author contends that teachers' information processing primarily affects long term decisions, flow of activities, and the selection of materials. (Author/JKS)

Improving the Bandwidth Selection in Kernel Equating

ERIC Educational Resources Information Center

Andersson, Björn; von Davier, Alina A.

2014-01-01

We investigate the current bandwidth selection methods in kernel equating and propose a method based on Silverman's rule of thumb for selecting the bandwidth parameters. In kernel equating, the bandwidth parameters have previously been obtained by minimizing a penalty function. This minimization process has been criticized by practitioners…

Selective Attention in the Learning Disabled Child.

ERIC Educational Resources Information Center

Wooten, Ann M.

The paper reviews literature relating to selective attention in the learning disabled child. Three processes related to the concept of selective attention (as proposed by D. Berlyne) are discussed: attention in learning, attention in remembering, and attention in performance. It is pointed out that verbal mediation, the use of verbal labels to…

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Automatic Tool Selection in V-bending Processes by Using an Intelligent Collision Detection Algorithm

NASA Astrophysics Data System (ADS)

Salem, A. A.

2017-09-01

V-bending is widely used to produce the sheet metal components. There are global Changes in the shape of the sheet metal component during progressive bending processes. Accordingly, collisions may be occurred between part and tool during bending. Collision-free is considered one of the feasibility conditions of V-bending process planning which the tool selection is verified by the absence of the collisions. This paper proposes an intelligent collision detection algorithm which has the ability to distinguish between 2D bent parts and the other bent parts. Due to this ability, 2D and 3D collision detection subroutines have been developed in the proposed algorithm. This division of algorithm’s subroutines could reduce the computational operations during collisions detecting.

Selecting practice management information systems.

PubMed

Worley, R; Ciotti, V

1997-01-01

Despite enormous advances in information systems, the process by which most medical practices select them has remained virtually unchanged for decades: the request for proposal (RFP). Unfortunately, vendors have learned ways to minimize the value of RFP checklists to where purchasers now learn little about the system functionality. The authors describe a selection methodology that replaces the RFP with scored demos, reviews of vendor user manuals and mathematically structured reference checking. In a recent selection process at a major medical center, these techniques yielded greater user buy-in and favorable contract terms as well.

An integrated fuzzy approach for strategic alliance partner selection in third-party logistics.

PubMed

Erkayman, Burak; Gundogar, Emin; Yilmaz, Aysegul

2012-01-01

Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model.

An Integrated Fuzzy Approach for Strategic Alliance Partner Selection in Third-Party Logistics

PubMed Central

Gundogar, Emin; Yılmaz, Aysegul

2012-01-01

Outsourcing some of the logistic activities is a useful strategy for companies in recent years. This makes it possible for firms to concentrate on their main issues and processes and presents facility to improve logistics performance, to reduce costs, and to improve quality. Therefore provider selection and evaluation in third-party logistics become important activities for companies. Making a strategic decision like this is significantly hard and crucial. In this study we proposed a fuzzy multicriteria decision making (MCDM) approach to effectively select the most appropriate provider. First we identify the provider selection criteria and build the hierarchical structure of decision model. After building the hierarchical structure we determined the selection criteria weights by using fuzzy analytical hierarchy process (AHP) technique. Then we applied fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to obtain final rankings for providers. And finally an illustrative example is also given to demonstrate the effectiveness of the proposed model. PMID:23365520

An Alternative to Adaptation by Sexual Selection: Habitat Choice.

PubMed

Porter, Cody K; Akcali, Christopher K

2018-06-09

Adaptation in mating signals and preferences has generally been explained by sexual selection. We propose that adaptation in such mating traits might also arise via a non-mutually exclusive process wherein individuals preferentially disperse to habitats where they experience high mating performance. Here we explore the evolutionary implications of this process. Copyright © 2018 Elsevier Ltd. All rights reserved.

78 FR 2295 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Notice of Filing of Proposed Rule...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-10

... Emerging Markets Corporate Bond Fund). Additionally, the Commission has previously approved the listing and... fundamental credit selection process with top down relative value analysis when selecting investment... [[Page 2297

HST Peer Review, Where We've Been, Where We Are Now and Possibly Where the Future Lies

NASA Astrophysics Data System (ADS)

Blacker, Brett S.; Macchetto, Duccio; Meylan, Georges; Stanghellini, Letizia; van der Marel, Roeland P.

2002-12-01

In some eyes, the Phase I proposal selection process is the most important activity handled by the Space Telescope Science Institute (STScI). Proposing for HST and other missions consists of requesting observing time and/or archival research funding. This step is called Phase I, where the scientific merit of a proposal is considered by a community based peer-review process. Accepted proposals then proceed thru Phase II, where the observations are specified in sufficient detail to enable scheduling on the telescope. Each cycle the Hubble Space Telescope (HST) Telescope Allocation Committee (TAC) reviews proposals and awards observing time that is valued at $0.5B, when the total expenditures for HST over its lifetime are figured on an annual basis. This is in fact a very important endeavor that we continue to fine-tune and tweak. This process is open to the science community and we constantly receive comments and praise for this process. Several cycles ago we instituted several significant changes to the process to address concerns such as: Fewer, broader panels, with redundancy to avoid conflicts of interest; Redefinition of the TAC role, to focus on Larger programs; and incentives for the panels to award time to medium sized proposals. In the last cycle, we offered new initiatives to try to enhance the scientific output of the telescope. Some of these initiatives were: Hubble Treasury Program; AR Legacy Program; and the AR Theory Program. This paper will outline the current HST Peer review process. We will discuss why we made changes and how we made changes from our original system. We will also discuss some ideas as to where we may go in the future to generate a stronger science program for HST and to reduce the burden on the science community. This paper is an update of the status of the HST Peer Review Process that was described in the published paper "Evolution of the HST Proposal Selection Process".

The proactive brain and the fate of dead hypotheses

PubMed Central

Tal, Amir; Bar, Moshe

2014-01-01

A substantial portion of information flow in the brain is directed top-down, from high processing areas downwards. Signals of this sort are regarded as conveying prior expectations, biasing the processing and eventual perception of incoming stimuli. In this perspective we describe a framework of top-down processing in the visual system in which predictions on the identity of objects in sight aid in their recognition. Focus is placed, in particular, on a relatively uncharted ramification of this framework, that of the fate of initial predictions that are eventually rejected during the process of selection. We propose that such predictions are rapidly inhibited in the brain after a competing option has been selected. Empirical support, along with behavioral, neuronal and computational aspects of this proposal are discussed, and future directions for related research are offered. PMID:25408645

The proactive brain and the fate of dead hypotheses.

PubMed

Tal, Amir; Bar, Moshe

2014-01-01

A substantial portion of information flow in the brain is directed top-down, from high processing areas downwards. Signals of this sort are regarded as conveying prior expectations, biasing the processing and eventual perception of incoming stimuli. In this perspective we describe a framework of top-down processing in the visual system in which predictions on the identity of objects in sight aid in their recognition. Focus is placed, in particular, on a relatively uncharted ramification of this framework, that of the fate of initial predictions that are eventually rejected during the process of selection. We propose that such predictions are rapidly inhibited in the brain after a competing option has been selected. Empirical support, along with behavioral, neuronal and computational aspects of this proposal are discussed, and future directions for related research are offered.

The Abstract Selection Task: New Data and an Almost Comprehensive Model

ERIC Educational Resources Information Center

Klauer, Karl Christoph; Stahl, Christoph; Erdfelder, Edgar

2007-01-01

A complete quantitative account of P. Wason's (1966) abstract selection task is proposed. The account takes the form of a mathematical model. It is assumed that some response patterns are caused by inferential reasoning, whereas other responses reflect cognitive processes that affect each card selection separately and independently of other card…

A simultaneous multi-slice selective J-resolved experiment for fully resolved scalar coupling information

NASA Astrophysics Data System (ADS)

Zeng, Qing; Lin, Liangjie; Chen, Jinyong; Lin, Yanqin; Barker, Peter B.; Chen, Zhong

2017-09-01

Proton-proton scalar coupling plays an important role in molecular structure elucidation. Many methods have been proposed for revealing scalar coupling networks involving chosen protons. However, determining all JHH values within a fully coupled network remains as a tedious process. Here, we propose a method termed as simultaneous multi-slice selective J-resolved spectroscopy (SMS-SEJRES) for simultaneously measuring JHH values out of all coupling networks in a sample within one experiment. In this work, gradient-encoded selective refocusing, PSYCHE decoupling and echo planar spectroscopic imaging (EPSI) detection module are adopted, resulting in different selective J-edited spectra extracted from different spatial positions. The proposed pulse sequence can facilitate the analysis of molecular structures. Therefore, it will interest scientists who would like to efficiently address the structural analysis of molecules.

Site Selection for the Disposal of LLW in Taiwan

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

Chuang, W.S.; Chi, L.M.; Tien, N.C.

2006-07-01

This paper presents the implementation status of the low-level radioactive waste (LLW) disposal program in Taiwan, including the disposal facility regulations, status of waste management, final disposal program, licensing procedures, waste acceptance criteria, site selection criteria and processes and preliminary disposal concepts. The first phase of site selection for low-level radioactive waste final disposal in Taiwan was implemented between 1992 and 2002. The site selection process adopted a Geographic Information System (GIS), Hierarchical Analysis System, Expert Evaluation System, and site reconnaissance. An incentive program for voluntary sites was also initiated. After a series of evaluations and discussion of 30 potentialmore » candidate sites, including 8 recommended sites, 5 qualified voluntary townships, and several remote uninhabited small islets, Hsiao-chiou islet was selected as the first priority candidate site in February 1998. The geological investigation work in Hsiao-chiou was conducted from March 1999 through October 2000. An Environmental Impact Statement Report (EIS) and the Investment Feasibility Study Report (IFS) were submitted to the Environmental Protection Agency (EPA) in November 2000 and to the Ministry of Economic Affairs (MOEA) in June 2001, respectively. Unfortunately, the site investigation was discontinued in 2002 due to political and public acceptance consideration. After years of planning, the second phase of the site selection process was launched in August 2004 and will be conducted through 2008. It is planned that a repository will be constructed in early 2009 and start to operate in 2014. The site selection process for the second phase is based on the earlier work and four potential candidate sites were selected for evaluation until 2005. A near surface disposal concept is proposed for a site located in the Taiwan strait, and cavern disposal concepts are proposed for three other sites located on the main island. This paper presents the implementation status of the LLW disposal program in Taiwan, including the disposal facility regulations, status of waste management, final disposal program, licensing procedures, waste acceptance criteria, site selection criteria and processes, and preliminary disposal concepts 'NIMBY' (Not in my backyard) is a critical problem for implementation of the final disposal project. Resistance from local communities has been continuously received during site characterization. To overcome this, an incentive program to encourage community acceptance has been approved by the Government. Programs for community promotion are being proposed and negotiations are also underway. (authors)« less

Toward a formalization of the process to select IMIA Yearbook best papers.

PubMed

Lamy, J-B; Séroussi, B; Griffon, N; Kerdelhué, G; Jaulent, M-C; Bouaud, J

2015-01-01

Each year, the International Medical Informatics Association Yearbook recognizes significant scientific papers, labelled as "best papers", published the previous year in the subfields of biomedical informatics that correspond to the different section topics of the journal. For each section, about fifteen pre-selected "candidate" best papers are externally peer-reviewed to select the actual best papers. Although based on the available literature, little is known about the pre-selection process. To move toward an explicit formalization of the candidate best papers selection process to reduce variability in the literature search across sections and over years. A methodological framework is proposed to build for each section topic specific queries tailored to PubMed and Web of Science citation databases. The two sets of returned papers are merged and reviewed by two independent section editors and citations are tagged as "discarded", "pending", and "kept". A protocolized consolidation step is then jointly conducted to resolve conflicts. A bibliographic software tool, BibReview, was developed to support the whole process. The proposed search strategy was fully applied to the Decision Support section of the 2013 edition of the Yearbook. For this section, 1124 references were returned (689 PubMed-specific, 254 WoS-specific, 181 common to both databases) among which the 15 candidate best papers were selected. The search strategy for determining candidate best papers for an IMIA Yearbook's section is now explicitly specified and allows for reproducibility. However, some aspects of the whole process remain reviewer-dependent, mostly because there is no characterization of a "best paper".

A thermodynamic review of cryogenic refrigeration cycles for liquefaction of natural gas

NASA Astrophysics Data System (ADS)

Chang, Ho-Myung

2015-12-01

A thermodynamic review is presented on cryogenic refrigeration cycles for the liquefaction process of natural gas. The main purpose of this review is to examine the thermodynamic structure of various cycles and provide a theoretical basis for selecting a cycle in accordance with different needs and design criteria. Based on existing or proposed liquefaction processes, sixteen ideal cycles are selected and the optimal conditions to achieve their best thermodynamic performance are investigated. The selected cycles include standard and modified versions of Joule-Thomson (JT) cycle, Brayton cycle, and their combined cycle with pure refrigerants (PR) or mixed refrigerants (MR). Full details of the cycles are presented and discussed in terms of FOM (figure of merit) and thermodynamic irreversibility. In addition, a new method of nomenclature is proposed to clearly identify the structure of cycles by abbreviation.

Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

PubMed

Addeh, Abdoljalil; Khormali, Aminollah; Golilarz, Noorbakhsh Amiri

2018-05-04

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Integrated manufacturing flow for selective-etching SADP/SAQP

NASA Astrophysics Data System (ADS)

Ali, Rehab Kotb; Fatehy, Ahmed Hamed; Word, James

2018-03-01

Printing cut mask in SAMP (Self Aligned Multi Patterning) is very challenging at advanced nodes. One of the proposed solutions is to print the cut shapes selectively. Which means the design is decomposed into mandrel tracks, Mandrel cuts and non-Mandrel cuts. The mandrel and non-Mandrel cuts are mutually independent which results in relaxing spacing constrains and as a consequence more dense metal lines. In this paper, we proposed the manufacturing flow of selective etching process. The results are quantified in terms of measuring PVBand, EPE and the number of hard bridging and pinching across the layout.

77 FR 61307 - New Postal Product

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-09

...: Transfer Mail Processing Cost Model for Machinable and Irregular Standard Mail Parcels to the Mail Processing Cost Model for Parcel Select/Parcel Return Service. The Postal Service proposes to move the machinable and irregular cost worksheets contained in the Standard Mail parcel mail processing cost model to...

On-Site Additive Manufacturing by Selective Laser Melting of Composite Objects

NASA Astrophysics Data System (ADS)

Fateri, M.; Khosravi, M.

2012-06-01

This paper proposes a method for cost reduction of future space missions by manufacturing parts on foreign planets. The suitability of Selective Laser Melting process for on-site production of metallic, ceramic and glass products on mars is examined.

Attentional selection of relative SF mediates global versus local processing: evidence from EEG.

PubMed

Flevaris, Anastasia V; Bentin, Shlomo; Robertson, Lynn C

2011-06-13

Previous research on functional hemispheric differences in visual processing has associated global perception with low spatial frequency (LSF) processing biases of the right hemisphere (RH) and local perception with high spatial frequency (HSF) processing biases of the left hemisphere (LH). The Double Filtering by Frequency (DFF) theory expanded this hypothesis by proposing that visual attention selects and is directed to relatively LSFs by the RH and relatively HSFs by the LH, suggesting a direct causal relationship between SF selection and global versus local perception. We tested this idea in the current experiment by comparing activity in the EEG recorded at posterior right and posterior left hemisphere sites while participants' attention was directed to global or local levels of processing after selection of relatively LSFs versus HSFs in a previous stimulus. Hemispheric asymmetry in the alpha band (8-12 Hz) during preparation for global versus local processing was modulated by the selected SF. In contrast, preparatory activity associated with selection of SF was not modulated by the previously attended level (global/local). These results support the DFF theory that top-down attentional selection of SF mediates global and local processing.

Abnormal externally guided movement preparation in recent-onset schizophrenia is associated with impaired selective attention to external input.

PubMed

Smid, Henderikus G O M; Westenbroek, Joanna M; Bruggeman, Richard; Knegtering, Henderikus; Van den Bosch, Robert J

2009-11-30

Several theories propose that the primary cognitive impairment in schizophrenia concerns a deficit in the processing of external input information. There is also evidence, however, for impaired motor preparation in schizophrenia. This provokes the question whether the impaired motor preparation in schizophrenia is a secondary consequence of disturbed (selective) processing of the input needed for that preparation, or an independent primary deficit. The aim of the present study was to discriminate between these hypotheses, by investigating externally guided movement preparation in relation to selective stimulus processing. The sample comprised 16 recent-onset schizophrenia patients and 16 controls who performed a movement-precuing task. In this task, a precue delivered information about one, two or no parameters of a movement summoned by a subsequent stimulus. Performance measures and measures derived from the electroencephalogram showed that patients yielded smaller benefits from the precues and showed less cue-based preparatory activity in advance of the imperative stimulus than the controls, suggesting a response preparation deficit. However, patients also showed less activity reflecting selective attention to the precue. We therefore conclude that the existing evidence for an impairment of externally guided motor preparation in schizophrenia is most likely due to a deficit in selective attention to the external input, which lends support to theories proposing that the primary cognitive deficit in schizophrenia concerns the processing of input information.

Three Tier Unified Process Model for Requirement Negotiations and Stakeholder Collaborations

NASA Astrophysics Data System (ADS)

Niazi, Muhammad Ashraf Khan; Abbas, Muhammad; Shahzad, Muhammad

2012-11-01

This research paper is focused towards carrying out a pragmatic qualitative analysis of various models and approaches of requirements negotiations (a sub process of requirements management plan which is an output of scope managementís collect requirements process) and studies stakeholder collaborations methodologies (i.e. from within communication management knowledge area). Experiential analysis encompass two tiers; first tier refers to the weighted scoring model while second tier focuses on development of SWOT matrices on the basis of findings of weighted scoring model for selecting an appropriate requirements negotiation model. Finally the results are simulated with the help of statistical pie charts. On the basis of simulated results of prevalent models and approaches of negotiations, a unified approach for requirements negotiations and stakeholder collaborations is proposed where the collaboration methodologies are embeded into selected requirements negotiation model as internal parameters of the proposed process alongside some external required parameters like MBTI, opportunity analysis etc.

15 CFR 291.5 - Proposal selection process.

Code of Federal Regulations, 2010 CFR

2010-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS MANUFACTURING... reviewed by NIST to assure compliance with the proposal content and other basic provisions of this notice... finalists. NIST will appoint an evaluation panel composed of NIST and in some cases other federal employees...

15 CFR 291.5 - Proposal selection process.

Code of Federal Regulations, 2011 CFR

2011-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS MANUFACTURING... reviewed by NIST to assure compliance with the proposal content and other basic provisions of this notice... finalists. NIST will appoint an evaluation panel composed of NIST and in some cases other federal employees...

15 CFR 295.4 - The selection process.

Code of Federal Regulations, 2014 CFR

2014-01-01

... OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS ADVANCED TECHNOLOGY.... NIST will also examine proposals that have been submitted to a previous competition to determine... review of their proposals with NIST, and in some cases site visits may be required. Subject to the...

15 CFR 291.5 - Proposal selection process.

Code of Federal Regulations, 2012 CFR

2012-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS MANUFACTURING... reviewed by NIST to assure compliance with the proposal content and other basic provisions of this notice... finalists. NIST will appoint an evaluation panel composed of NIST and in some cases other federal employees...

15 CFR 295.4 - The selection process.

Code of Federal Regulations, 2012 CFR

2012-01-01

... OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS ADVANCED TECHNOLOGY.... NIST will also examine proposals that have been submitted to a previous competition to determine... review of their proposals with NIST, and in some cases site visits may be required. Subject to the...

15 CFR 295.4 - The selection process.

Code of Federal Regulations, 2013 CFR

2013-01-01

... OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS ADVANCED TECHNOLOGY.... NIST will also examine proposals that have been submitted to a previous competition to determine... review of their proposals with NIST, and in some cases site visits may be required. Subject to the...

15 CFR 291.5 - Proposal selection process.

Code of Federal Regulations, 2013 CFR

2013-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS MANUFACTURING... reviewed by NIST to assure compliance with the proposal content and other basic provisions of this notice... finalists. NIST will appoint an evaluation panel composed of NIST and in some cases other federal employees...

15 CFR 291.5 - Proposal selection process.

Code of Federal Regulations, 2014 CFR

2014-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS MANUFACTURING... reviewed by NIST to assure compliance with the proposal content and other basic provisions of this notice... finalists. NIST will appoint an evaluation panel composed of NIST and in some cases other federal employees...

15 CFR 295.4 - The selection process.

Code of Federal Regulations, 2010 CFR

2010-01-01

... OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS ADVANCED TECHNOLOGY.... NIST will also examine proposals that have been submitted to a previous competition to determine... review of their proposals with NIST, and in some cases site visits may be required. Subject to the...

15 CFR 295.4 - The selection process.

Code of Federal Regulations, 2011 CFR

2011-01-01

... OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS ADVANCED TECHNOLOGY.... NIST will also examine proposals that have been submitted to a previous competition to determine... review of their proposals with NIST, and in some cases site visits may be required. Subject to the...

Sensory evaluation based fuzzy AHP approach for material selection in customized garment design and development process

NASA Astrophysics Data System (ADS)

Hong, Y.; Curteza, A.; Zeng, X.; Bruniaux, P.; Chen, Y.

2016-06-01

Material selection is the most difficult section in the customized garment product design and development process. This study aims to create a hierarchical framework for material selection. The analytic hierarchy process and fuzzy sets theories have been applied to mindshare the diverse requirements from the customer and inherent interaction/interdependencies among these requirements. Sensory evaluation ensures a quick and effective selection without complex laboratory test such as KES and FAST, using the professional knowledge of the designers. A real empirical application for the physically disabled people is carried out to demonstrate the proposed method. Both the theoretical and practical background of this paper have indicated the fuzzy analytical network process can capture expert's knowledge existing in the form of incomplete, ambiguous and vague information for the mutual influence on attribute and criteria of the material selection.

Batch-processed semiconductor gas sensor array for the selective detection of NOx in automotive exhaust gas

NASA Astrophysics Data System (ADS)

Jang, Hani; Kim, Minki; Kim, Yongjun

2016-12-01

This paper reports on a semiconductor gas sensor array to detect nitrogen oxides (NOx) in automotive exhaust gas. The proposed semiconductor gas sensor array consisted of one common electrode and three individual electrodes to minimize the size of the sensor array, and three sensing layers [TiO2 + SnO2 (15 wt%), SnO2, and Ga2O3] were deposited using screen printing. In addition, sensing materials were sintered under the same conditions in order to take advantage of batch processing. The sensing properties of the proposed sensor array were verified by experimental measurements, and the selectivity improved by using pattern recognition.

A natural language query system for Hubble Space Telescope proposal selection

NASA Technical Reports Server (NTRS)

Hornick, Thomas; Cohen, William; Miller, Glenn

1987-01-01

The proposal selection process for the Hubble Space Telescope is assisted by a robust and easy to use query program (TACOS). The system parses an English subset language sentence regardless of the order of the keyword phases, allowing the user a greater flexibility than a standard command query language. Capabilities for macro and procedure definition are also integrated. The system was designed for flexibility in both use and maintenance. In addition, TACOS can be applied to any knowledge domain that can be expressed in terms of a single reaction. The system was implemented mostly in Common LISP. The TACOS design is described in detail, with particular attention given to the implementation methods of sentence processing.

An opinion formation based binary optimization approach for feature selection

NASA Astrophysics Data System (ADS)

Hamedmoghadam, Homayoun; Jalili, Mahdi; Yu, Xinghuo

2018-02-01

This paper proposed a novel optimization method based on opinion formation in complex network systems. The proposed optimization technique mimics human-human interaction mechanism based on a mathematical model derived from social sciences. Our method encodes a subset of selected features to the opinion of an artificial agent and simulates the opinion formation process among a population of agents to solve the feature selection problem. The agents interact using an underlying interaction network structure and get into consensus in their opinions, while finding better solutions to the problem. A number of mechanisms are employed to avoid getting trapped in local minima. We compare the performance of the proposed method with a number of classical population-based optimization methods and a state-of-the-art opinion formation based method. Our experiments on a number of high dimensional datasets reveal outperformance of the proposed algorithm over others.

Comprehensive Mass Analysis for Chemical Processes, a Case Study on L-Dopa Manufacture

EPA Science Inventory

To evaluate the “greenness” of chemical processes in route selection and process development, we propose a comprehensive mass analysis to inform the stakeholders from different fields. This is carried out by characterizing the mass intensity for each contributing chemical or wast...

Early melanoma diagnosis with mobile imaging.

PubMed

Do, Thanh-Toan; Zhou, Yiren; Zheng, Haitian; Cheung, Ngai-Man; Koh, Dawn

2014-01-01

We research a mobile imaging system for early diagnosis of melanoma. Different from previous work, we focus on smartphone-captured images, and propose a detection system that runs entirely on the smartphone. Smartphone-captured images taken under loosely-controlled conditions introduce new challenges for melanoma detection, while processing performed on the smartphone is subject to computation and memory constraints. To address these challenges, we propose to localize the skin lesion by combining fast skin detection and fusion of two fast segmentation results. We propose new features to capture color variation and border irregularity which are useful for smartphone-captured images. We also propose a new feature selection criterion to select a small set of good features used in the final lightweight system. Our evaluation confirms the effectiveness of proposed algorithms and features. In addition, we present our system prototype which computes selected visual features from a user-captured skin lesion image, and analyzes them to estimate the likelihood of malignance, all on an off-the-shelf smartphone.

A novel heterogeneous training sample selection method on space-time adaptive processing

NASA Astrophysics Data System (ADS)

Wang, Qiang; Zhang, Yongshun; Guo, Yiduo

2018-04-01

The performance of ground target detection about space-time adaptive processing (STAP) decreases when non-homogeneity of clutter power is caused because of training samples contaminated by target-like signals. In order to solve this problem, a novel nonhomogeneous training sample selection method based on sample similarity is proposed, which converts the training sample selection into a convex optimization problem. Firstly, the existing deficiencies on the sample selection using generalized inner product (GIP) are analyzed. Secondly, the similarities of different training samples are obtained by calculating mean-hausdorff distance so as to reject the contaminated training samples. Thirdly, cell under test (CUT) and the residual training samples are projected into the orthogonal subspace of the target in the CUT, and mean-hausdorff distances between the projected CUT and training samples are calculated. Fourthly, the distances are sorted in order of value and the training samples which have the bigger value are selective preference to realize the reduced-dimension. Finally, simulation results with Mountain-Top data verify the effectiveness of the proposed method.

Modified ADALINE algorithm for harmonic estimation and selective harmonic elimination in inverters

NASA Astrophysics Data System (ADS)

Vasumathi, B.; Moorthi, S.

2011-11-01

In digital signal processing, algorithms are very well developed for the estimation of harmonic components. In power electronic applications, an objective like fast response of a system is of primary importance. An effective method for the estimation of instantaneous harmonic components, along with conventional harmonic elimination technique, is presented in this article. The primary function is to eliminate undesirable higher harmonic components from the selected signal (current or voltage) and it requires only the knowledge of the frequency of the component to be eliminated. A signal processing technique using modified ADALINE algorithm has been proposed for harmonic estimation. The proposed method stays effective as it converges to a minimum error and brings out a finer estimation. A conventional control based on pulse width modulation for selective harmonic elimination is used to eliminate harmonic components after its estimation. This method can be applied to a wide range of equipment. The validity of the proposed method to estimate and eliminate voltage harmonics is proved with a dc/ac inverter as a simulation example. Then, the results are compared with existing ADALINE algorithm for illustrating its effectiveness.

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

PubMed

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

The source of dual-task limitations: Serial or parallel processing of multiple response selections?

PubMed Central

Marois, René

2014-01-01

Although it is generally recognized that the concurrent performance of two tasks incurs costs, the sources of these dual-task costs remain controversial. The serial bottleneck model suggests that serial postponement of task performance in dual-task conditions results from a central stage of response selection that can only process one task at a time. Cognitive-control models, by contrast, propose that multiple response selections can proceed in parallel, but that serial processing of task performance is predominantly adopted because its processing efficiency is higher than that of parallel processing. In the present study, we empirically tested this proposition by examining whether parallel processing would occur when it was more efficient and financially rewarded. The results indicated that even when parallel processing was more efficient and was incentivized by financial reward, participants still failed to process tasks in parallel. We conclude that central information processing is limited by a serial bottleneck. PMID:23864266

Low-Temperature Oxidation-Free Selective Laser Sintering of Cu Nanoparticle Paste on a Polymer Substrate for the Flexible Touch Panel Applications.

PubMed

Kwon, Jinhyeong; Cho, Hyunmin; Eom, Hyeonjin; Lee, Habeom; Suh, Young Duk; Moon, Hyunjin; Shin, Jaeho; Hong, Sukjoon; Ko, Seung Hwan

2016-05-11

Copper nanomaterials suffer from severe oxidation problem despite the huge cost effectiveness. The effect of two different processes for conventional tube furnace heating and selective laser sintering on copper nanoparticle paste is compared in the aspects of chemical, electrical and surface morphology. The thermal behavior of the copper thin films by furnace and laser is compared by SEM, XRD, FT-IR, and XPS analysis. The selective laser sintering process ensures low annealing temperature, fast processing speed with remarkable oxidation suppression even in air environment while conventional tube furnace heating experiences moderate oxidation even in Ar environment. Moreover, the laser-sintered copper nanoparticle thin film shows good electrical property and reduced oxidation than conventional thermal heating process. Consequently, the proposed selective laser sintering process can be compatible with plastic substrate for copper based flexible electronics applications.

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

PubMed

Attallah, Omneya; Karthikesalingam, Alan; Holt, Peter Je; Thompson, Matthew M; Sayers, Rob; Bown, Matthew J; Choke, Eddie C; Ma, Xianghong

2017-11-01

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.

A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

PubMed Central

Li, Ke; Liu, Yi; Wang, Quanxin; Wu, Yalei; Song, Shimin; Sun, Yi; Liu, Tengchong; Wang, Jun; Li, Yang; Du, Shaoyi

2015-01-01

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively. PMID:26544549

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

NASA Astrophysics Data System (ADS)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information

NASA Astrophysics Data System (ADS)

Peng, Hong-Gang; Wang, Jian-Qiang

2017-11-01

In recent years, sustainable energy crop has become an important energy development strategy topic in many countries. Selecting the most sustainable energy crop is a significant problem that must be addressed during any biofuel production process. The focus of this study is the development of an innovative multi-criteria decision-making (MCDM) method to handle sustainable energy crop selection problems. Given that various uncertain data are encountered in the evaluation of sustainable energy crops, linguistic intuitionistic fuzzy numbers (LIFNs) are introduced to present the information necessary to the evaluation process. Processing qualitative concepts requires the effective support of reliable tools; then, a cloud model can be used to deal with linguistic intuitionistic information. First, LIFNs are converted and a novel concept of linguistic intuitionistic cloud (LIC) is proposed. The operations, score function and similarity measurement of the LICs are defined. Subsequently, the linguistic intuitionistic cloud density-prioritised weighted Heronian mean operator is developed, which served as the basis for the construction of an applicable MCDM model for sustainable energy crop selection. Finally, an illustrative example is provided to demonstrate the proposed method, and its feasibility and validity are further verified by comparing it with other existing methods.

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

Evaluation of Roadway Reallocation Projects: Analysis of Before-and-After Travel Speeds and Congestion Utilizing High-Resolution Bus Transit Data

DOT National Transportation Integrated Search

2017-11-01

The traditional process of identifying corridors for road diet improvements involves selecting potential corridors (mostly based on identifying fourlane roads) and conducting a traffic impact analysis of proposed changes on a selected roadway before ...

Lexical Selection and Verbal Self-Monitoring: Effects of Lexicality, Context, and Time Pressure in Picture-Word Interference

ERIC Educational Resources Information Center

Dhooge, Elisah; Hartsuiker, Robert J.

2012-01-01

Current views of lexical selection in language production differ in whether they assume lexical selection by competition or not. To account for recent data with the picture-word interference (PWI) task, both views need to be supplemented with assumptions about the control processes that block distractor naming. In this paper, we propose that such…

Deinking selectivity (Z-factor) : a new parameter to evaluate the performance of flotation deinking process

Treesearch

J.Y.: Tan Zhu; K.L. Scallon; Y.L. Zhao; Y. Deng

2005-01-01

This study proposes a deinking selectivity concept that considers both ink removal and fiber yield in determining the performance of deinking operations. The defined deinking selectivity. or Z -factor, is expressed by the ratio of ink removal expressed by the International Standards Organization (ISO) brightness gain or the reduction in relative effective residual ink...

Deinking selectivity (Z-factor) : a new parameter to evaluate the performance of flotation deinking process

Treesearch

J.Y. Zhu; F. Tan; K.L. Scallon; Y. Zhao; Y. Deng

2004-01-01

Reducing fiber loss is also important to conserve resources and reduce the cost of secondary fibers. This study proposes a deinking selectivity concept that considers both ink removal and fiber yield in determining the performance of deinking operations. The defined deinking selectivity, or Z-factor, is expressed by the ratio of ink removal expressed by the...

The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

NASA Astrophysics Data System (ADS)

Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti

2014-06-01

In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

Concurrent working memory load can facilitate selective attention: evidence for specialized load.

PubMed

Park, Soojin; Kim, Min-Shik; Chun, Marvin M

2007-10-01

Load theory predicts that concurrent working memory load impairs selective attention and increases distractor interference (N. Lavie, A. Hirst, J. W. de Fockert, & E. Viding). Here, the authors present new evidence that the type of concurrent working memory load determines whether load impairs selective attention or not. Working memory load was paired with a same/different matching task that required focusing on targets while ignoring distractors. When working memory items shared the same limited-capacity processing mechanisms with targets in the matching task, distractor interference increased. However, when working memory items shared processing with distractors in the matching task, distractor interference decreased, facilitating target selection. A specialized load account is proposed to describe the dissociable effects of working memory load on selective processing depending on whether the load overlaps with targets or with distractors. (c) 2007 APA

An Ensemble Successive Project Algorithm for Liquor Detection Using Near Infrared Sensor.

PubMed

Qu, Fangfang; Ren, Dong; Wang, Jihua; Zhang, Zhong; Lu, Na; Meng, Lei

2016-01-11

Spectral analysis technique based on near infrared (NIR) sensor is a powerful tool for complex information processing and high precision recognition, and it has been widely applied to quality analysis and online inspection of agricultural products. This paper proposes a new method to address the instability of small sample sizes in the successive projections algorithm (SPA) as well as the lack of association between selected variables and the analyte. The proposed method is an evaluated bootstrap ensemble SPA method (EBSPA) based on a variable evaluation index (EI) for variable selection, and is applied to the quantitative prediction of alcohol concentrations in liquor using NIR sensor. In the experiment, the proposed EBSPA with three kinds of modeling methods are established to test their performance. In addition, the proposed EBSPA combined with partial least square is compared with other state-of-the-art variable selection methods. The results show that the proposed method can solve the defects of SPA and it has the best generalization performance and stability. Furthermore, the physical meaning of the selected variables from the near infrared sensor data is clear, which can effectively reduce the variables and improve their prediction accuracy.

Interim Action Proposed Plan for the Chemicals, Metals, and Pesticides (CMP) Pits Operable Unit

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

Bradley, J.

2002-06-18

The purpose of this Interim Action Proposed Plan (IAPP) is to describe the preferred interim remedial action for addressing the Chemicals, Metals, and Pesticides (CMP) Pits Operable Unit and to provide an opportunity for public input into the remedial action selection process.

15 CFR 296.20 - The selection process.

Code of Federal Regulations, 2013 CFR

2013-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS TECHNOLOGY INNOVATION... award criteria listed in § 296.22. In some cases NIST may conduct oral reviews and/or site visits. The.... (e) NIST reserves the right to negotiate the cost and scope of the proposed work with the proposers...

15 CFR 296.20 - The selection process.

Code of Federal Regulations, 2011 CFR

2011-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS TECHNOLOGY INNOVATION... award criteria listed in § 296.22. In some cases NIST may conduct oral reviews and/or site visits. The.... (e) NIST reserves the right to negotiate the cost and scope of the proposed work with the proposers...

15 CFR 296.20 - The selection process.

Code of Federal Regulations, 2012 CFR

2012-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS TECHNOLOGY INNOVATION... award criteria listed in § 296.22. In some cases NIST may conduct oral reviews and/or site visits. The.... (e) NIST reserves the right to negotiate the cost and scope of the proposed work with the proposers...

15 CFR 296.20 - The selection process.

Code of Federal Regulations, 2010 CFR

2010-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS TECHNOLOGY INNOVATION... award criteria listed in § 296.22. In some cases NIST may conduct oral reviews and/or site visits. The.... (e) NIST reserves the right to negotiate the cost and scope of the proposed work with the proposers...

15 CFR 296.20 - The selection process.

Code of Federal Regulations, 2014 CFR

2014-01-01

... INSTITUTE OF STANDARDS AND TECHNOLOGY, DEPARTMENT OF COMMERCE NIST EXTRAMURAL PROGRAMS TECHNOLOGY INNOVATION... award criteria listed in § 296.22. In some cases NIST may conduct oral reviews and/or site visits. The.... (e) NIST reserves the right to negotiate the cost and scope of the proposed work with the proposers...

Robust frequency diversity based algorithm for clutter noise reduction of ultrasonic signals using multiple sub-spectrum phase coherence

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

Gongzhang, R.; Xiao, B.; Lardner, T.

2014-02-18

This paper presents a robust frequency diversity based algorithm for clutter reduction in ultrasonic A-scan waveforms. The performance of conventional spectral-temporal techniques like Split Spectrum Processing (SSP) is highly dependent on the parameter selection, especially when the signal to noise ratio (SNR) is low. Although spatial beamforming offers noise reduction with less sensitivity to parameter variation, phased array techniques are not always available. The proposed algorithm first selects an ascending series of frequency bands. A signal is reconstructed for each selected band in which a defect is present when all frequency components are in uniform sign. Combining all reconstructed signalsmore » through averaging gives a probability profile of potential defect position. To facilitate data collection and validate the proposed algorithm, Full Matrix Capture is applied on the austenitic steel and high nickel alloy (HNA) samples with 5MHz transducer arrays. When processing A-scan signals with unrefined parameters, the proposed algorithm enhances SNR by 20dB for both samples and consequently, defects are more visible in B-scan images created from the large amount of A-scan traces. Importantly, the proposed algorithm is considered robust, while SSP is shown to fail on the austenitic steel data and achieves less SNR enhancement on the HNA data.« less

A Framework for the Selection of Electronic Marketplaces: A Content Analysis Approach.

ERIC Educational Resources Information Center

Stockdale, Rosemary; Standing, Craig

2002-01-01

Discussion of electronic marketplaces focuses on a content analysis of research and practitioner articles that evaluated issues that prospective participants, seeking to purchase goods and services online, need to address in their selection process. Proposes a framework to support electronic marketplace decision making that includes internal…

A Fast Algorithm of Convex Hull Vertices Selection for Online Classification.

PubMed

Ding, Shuguang; Nie, Xiangli; Qiao, Hong; Zhang, Bo

2018-04-01

Reducing samples through convex hull vertices selection (CHVS) within each class is an important and effective method for online classification problems, since the classifier can be trained rapidly with the selected samples. However, the process of CHVS is NP-hard. In this paper, we propose a fast algorithm to select the convex hull vertices, based on the convex hull decomposition and the property of projection. In the proposed algorithm, the quadratic minimization problem of computing the distance between a point and a convex hull is converted into a linear equation problem with a low computational complexity. When the data dimension is high, an approximate, instead of exact, convex hull is allowed to be selected by setting an appropriate termination condition in order to delete more nonimportant samples. In addition, the impact of outliers is also considered, and the proposed algorithm is improved by deleting the outliers in the initial procedure. Furthermore, a dimension convention technique via the kernel trick is used to deal with nonlinearly separable problems. An upper bound is theoretically proved for the difference between the support vector machines based on the approximate convex hull vertices selected and all the training samples. Experimental results on both synthetic and real data sets show the effectiveness and validity of the proposed algorithm.

Improved targeted immunization strategies based on two rounds of selection

NASA Astrophysics Data System (ADS)

Xia, Ling-Ling; Song, Yu-Rong; Li, Chan-Chan; Jiang, Guo-Ping

2018-04-01

In the case of high degree targeted immunization where the number of vaccine is limited, when more than one node associated with the same degree meets the requirement of high degree centrality, how can we choose a certain number of nodes from those nodes, so that the number of immunized nodes will not exceed the limit? In this paper, we introduce a new idea derived from the selection process of second-round exam to solve this problem and then propose three improved targeted immunization strategies. In these proposed strategies, the immunized nodes are selected through two rounds of selection, where we increase the quotas of first-round selection according the evaluation criterion of degree centrality and then consider another characteristic parameter of node, such as node's clustering coefficient, betweenness and closeness, to help choose targeted nodes in the second-round selection. To validate the effectiveness of the proposed strategies, we compare them with the degree immunizations including the high degree targeted and the high degree adaptive immunizations using two metrics: the size of the largest connected component of immunized network and the number of infected nodes. Simulation results demonstrate that the proposed strategies based on two rounds of sorting are effective for heterogeneous networks and their immunization effects are better than that of the degree immunizations.

The National Research Service Award: strategies for developing a successful proposal.

PubMed

Parker, Barbara; Steeves, Richard

2005-01-01

An important experience for doctoral students is developing and submitting an application for a National Research Service Award (NRSA) from the National Institutes of Health (NIH). This article provides an overview of the process of developing and submitting an NRSA proposal from the perspective of a sponsor of successful proposals as well as a member of the Scientific Review Section. Topics included are suggestions for writing and rewriting the proposal, developing a training plan specific to the proposal, selection of sponsors consultants and references, the review process, and revising and resubmitting a proposal. Tables give examples of (a) applicants identifying strengths and areas for growth, (b) activities to address areas for growth (c), and responses to a previous review. The intended audience is beginning doctoral students and novice sponsors.

Pervaporation and Vapor Permeation Tutorial: Membrane Processes for the Selective Separation of Liquid and Vapor Mixtures

EPA Science Inventory

Pervaporation and vapor permeation are membrane-based processes proposed as alternatives to conventional separation technologies. Applications range from organic solvent removal from water, ethanol or butanol recovery from fermentation broths, solvent/biofuel dehydration to meet ...

Pervaporation & Vapor Permeation Membrane Processes for the Selective Separation of Liquid and Vapor Mixtures

EPA Science Inventory

Pervaporation and vapor permeation are membrane-based processes which have been proposed as alternatives to conventional separation technologies. Applications range from organic solvent removal from water, ethanol or butanol recovery from dilute fermentation broths, solvent/biofu...

Attention to Color Sharpens Neural Population Tuning via Feedback Processing in the Human Visual Cortex Hierarchy.

PubMed

Bartsch, Mandy V; Loewe, Kristian; Merkel, Christian; Heinze, Hans-Jochen; Schoenfeld, Mircea A; Tsotsos, John K; Hopf, Jens-Max

2017-10-25

Attention can facilitate the selection of elementary object features such as color, orientation, or motion. This is referred to as feature-based attention and it is commonly attributed to a modulation of the gain and tuning of feature-selective units in visual cortex. Although gain mechanisms are well characterized, little is known about the cortical processes underlying the sharpening of feature selectivity. Here, we show with high-resolution magnetoencephalography in human observers (men and women) that sharpened selectivity for a particular color arises from feedback processing in the human visual cortex hierarchy. To assess color selectivity, we analyze the response to a color probe that varies in color distance from an attended color target. We find that attention causes an initial gain enhancement in anterior ventral extrastriate cortex that is coarsely selective for the target color and transitions within ∼100 ms into a sharper tuned profile in more posterior ventral occipital cortex. We conclude that attention sharpens selectivity over time by attenuating the response at lower levels of the cortical hierarchy to color values neighboring the target in color space. These observations support computational models proposing that attention tunes feature selectivity in visual cortex through backward-propagating attenuation of units less tuned to the target. SIGNIFICANCE STATEMENT Whether searching for your car, a particular item of clothing, or just obeying traffic lights, in everyday life, we must select items based on color. But how does attention allow us to select a specific color? Here, we use high spatiotemporal resolution neuromagnetic recordings to examine how color selectivity emerges in the human brain. We find that color selectivity evolves as a coarse to fine process from higher to lower levels within the visual cortex hierarchy. Our observations support computational models proposing that feature selectivity increases over time by attenuating the responses of less-selective cells in lower-level brain areas. These data emphasize that color perception involves multiple areas across a hierarchy of regions, interacting with each other in a complex, recursive manner. Copyright © 2017 the authors 0270-6474/17/3710346-12$15.00/0.

Removal and recovery of acetic acid and two furans during sugar purification of simulated phenols-free biomass hydrolysates.

PubMed

Lee, Sang Cheol

2017-12-01

A cost-effective five-step sugar purification process involving simultaneous removal and recovery of fermentation inhibitors from biomass hydrolysates was first proposed here. Only the three separation steps (PB, PC and PD) in the process were investigated here. Furfural was selectively removed up to 98.4% from a simulated five-component hydrolysate in a cross-current three-stage extraction system with n-hexane. Most of acetic acid in a simulated four-component hydrolysate was selectively removed by emulsion liquid membrane, and it could be concentrated in the stripping solution up to 4.5 times its initial concentration in the feed solution. 5-Hydroxymethylfurfural was selectively removed from a simulated three-component hydrolysate in batch and continuous fixed-bed column adsorption systems with L-493 adsorbent. Also, 5-hydroxymethylfurfural could be concentrated to about 9 times its feed concentration in the continuous adsorption system through a fixed-bed column desorption experiment with aqueous ethanol solution. These results have shown that the proposed purification process was valid. Copyright © 2017 Elsevier Ltd. All rights reserved.

RADIOACTIVE WASTE PROCESSING AND DISPOSAL: A BIBLIOGRAPHY OF SELECTED REPORT LITERATURE

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

Voress, H.E.; Davis, T.F.; Hubbard, T.N. Jr.

1958-06-01

An annotated bibliography is presented containing 698 references to unclassifled reports on currert and proposed ranioactive waste processing and disposal practices for solutions from radiochemical processing plants and laboratories, decontamination of surfaces, air cleaning, and other related subjects. Author, corporate author, subject, and report nuunber indexes are included. (auth)

A proposal for a drug product Manufacturing Classification System (MCS) for oral solid dosage forms.

PubMed

Leane, Michael; Pitt, Kendal; Reynolds, Gavin

2015-01-01

This paper proposes the development of a drug product Manufacturing Classification System (MCS) based on processing route. It summarizes conclusions from a dedicated APS conference and subsequent discussion within APS focus groups and the MCS working party. The MCS is intended as a tool for pharmaceutical scientists to rank the feasibility of different processing routes for the manufacture of oral solid dosage forms, based on selected properties of the API and the needs of the formulation. It has many applications in pharmaceutical development, in particular, it will provide a common understanding of risk by defining what the "right particles" are, enable the selection of the best process, and aid subsequent transfer to manufacturing. The ultimate aim is one of prediction of product developability and processability based upon previous experience. This paper is intended to stimulate contribution from a broad range of stakeholders to develop the MCS concept further and apply it to practice. In particular, opinions are sought on what API properties are important when selecting or modifying materials to enable an efficient and robust pharmaceutical manufacturing process. Feedback can be given by replying to our dedicated e-mail address (mcs@apsgb.org); completing the survey on our LinkedIn site; or by attending one of our planned conference roundtable sessions.

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.

PubMed

Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Comparison of fuzzy AHP and fuzzy TODIM methods for landfill location selection.

PubMed

Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

2016-01-01

Landfill location selection is a multi-criteria decision problem and has a strategic importance for many regions. The conventional methods for landfill location selection are insufficient in dealing with the vague or imprecise nature of linguistic assessment. To resolve this problem, fuzzy multi-criteria decision-making methods are proposed. The aim of this paper is to use fuzzy TODIM (the acronym for Interactive and Multi-criteria Decision Making in Portuguese) and the fuzzy analytic hierarchy process (AHP) methods for the selection of landfill location. The proposed methods have been applied to a landfill location selection problem in the region of Casablanca, Morocco. After determining the criteria affecting the landfill location decisions, fuzzy TODIM and fuzzy AHP methods are applied to the problem and results are presented. The comparisons of these two methods are also discussed.

In situ process monitoring in selective laser sintering using optical coherence tomography

NASA Astrophysics Data System (ADS)

Gardner, Michael R.; Lewis, Adam; Park, Jongwan; McElroy, Austin B.; Estrada, Arnold D.; Fish, Scott; Beaman, Joseph J.; Milner, Thomas E.

2018-04-01

Selective laser sintering (SLS) is an efficient process in additive manufacturing that enables rapid part production from computer-based designs. However, SLS is limited by its notable lack of in situ process monitoring when compared with other manufacturing processes. We report the incorporation of optical coherence tomography (OCT) into an SLS system in detail and demonstrate access to surface and subsurface features. Video frame rate cross-sectional imaging reveals areas of sintering uniformity and areas of excessive heat error with high temporal resolution. We propose a set of image processing techniques for SLS process monitoring with OCT and report the limitations and obstacles for further OCT integration with SLS systems.

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology.

PubMed

Cárdenas, V; Cordobés, M; Blanco, M; Alcalà, M

2015-10-10

The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required. Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate calibration set to construct models affording accurate predictions. In this work, we developed calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the calibration set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated. Also, we established a "model space" defined by Hotelling's T(2) and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in calibration set construction. The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry. Copyright © 2015 Elsevier B.V. All rights reserved.

Playing quantum games by a scheme with pre- and post-selection

NASA Astrophysics Data System (ADS)

Weng, Guo-Fu; Yu, Yang

2016-01-01

We propose a scheme to play quantum games by assuming that the two players interact with each other. Thus, by pre-selection, two players can choose their initial states, and some dilemma in classical game may be removed by post-selection, which is particularly useful for the cooperative games. We apply the proposal to both of BoS and Prisoners' dilemma games in cooperative situations. The examples show that the proposal would guarantee a remarkably binding agreement between two parties. Any deviation during the game will be detected, and the game may be abnegated. By illuminating the examples, we find that the initial state in the cooperative game does not destroy process to get preferable payoffs by pre- and post-selections, which is not true in other schemes for implementing the quantum game. We point out that one player can use the scheme to detect his opponent's choices if he is advantageous in information theory and technology.

Multiple-Diode-Laser Gas-Detection Spectrometer

NASA Technical Reports Server (NTRS)

Webster, Christopher R.; Beer, Reinhard; Sander, Stanley P.

1988-01-01

Small concentrations of selected gases measured automatically. Proposed multiple-laser-diode spectrometer part of system for measuring automatically concentrations of selected gases at part-per-billion level. Array of laser/photodetector pairs measure infrared absorption spectrum of atmosphere along probing laser beams. Adaptable to terrestrial uses as monitoring pollution or control of industrial processes.

The Selective Cue Integration Framework: A Theory of Postidentification Witness Confidence Assessment

ERIC Educational Resources Information Center

Charman, Steve D.; Carlucci, Marianna; Vallano, Jon; Gregory, Amy Hyman

2010-01-01

The current manuscript proposes a theory of how witnesses assess their confidence following a lineup identification, called the selective cue integration framework (SCIF). Drawing from past research on the postidentification feedback effect, the SCIF details a three-stage process of confidence assessment that is based largely on a…

Statistical analysis for validating ACO-KNN algorithm as feature selection in sentiment analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Rohaidah; Yusop, Nurhafizah Moziyana Mohd; Bakar, Azuraliza Abu; Yaakub, Mohd Ridzwan

2017-10-01

This research paper aims to propose a hybrid of ant colony optimization (ACO) and k-nearest neighbor (KNN) algorithms as feature selections for selecting and choosing relevant features from customer review datasets. Information gain (IG), genetic algorithm (GA), and rough set attribute reduction (RSAR) were used as baseline algorithms in a performance comparison with the proposed algorithm. This paper will also discuss the significance test, which was used to evaluate the performance differences between the ACO-KNN, IG-GA, and IG-RSAR algorithms. This study evaluated the performance of the ACO-KNN algorithm using precision, recall, and F-score, which were validated using the parametric statistical significance tests. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. The evaluation process has statistically proven that this ACO-KNN algorithm has been significantly improved compared to the baseline algorithms. In addition, the experimental results have proven that the ACO-KNN can be used as a feature selection technique in sentiment analysis to obtain quality, optimal feature subset that can represent the actual data in customer review data.

Multicriteria framework for selecting a process modelling language

NASA Astrophysics Data System (ADS)

Scanavachi Moreira Campos, Ana Carolina; Teixeira de Almeida, Adiel

2016-01-01

The choice of process modelling language can affect business process management (BPM) since each modelling language shows different features of a given process and may limit the ways in which a process can be described and analysed. However, choosing the appropriate modelling language for process modelling has become a difficult task because of the availability of a large number modelling languages and also due to the lack of guidelines on evaluating, and comparing languages so as to assist in selecting the most appropriate one. This paper proposes a framework for selecting a modelling language in accordance with the purposes of modelling. This framework is based on the semiotic quality framework (SEQUAL) for evaluating process modelling languages and a multicriteria decision aid (MCDA) approach in order to select the most appropriate language for BPM. This study does not attempt to set out new forms of assessment and evaluation criteria, but does attempt to demonstrate how two existing approaches can be combined so as to solve the problem of selection of modelling language. The framework is described in this paper and then demonstrated by means of an example. Finally, the advantages and disadvantages of using SEQUAL and MCDA in an integrated manner are discussed.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Chemically Patterned Inverse Opal Created by a Selective Photolysis Modification Process.

PubMed

Tian, Tian; Gao, Ning; Gu, Chen; Li, Jian; Wang, Hui; Lan, Yue; Yin, Xianpeng; Li, Guangtao

2015-09-02

Anisotropic photonic crystal materials have long been pursued for their broad applications. A novel method for creating chemically patterned inverse opals is proposed here. The patterning technique is based on selective photolysis of a photolabile polymer together with postmodification on released amine groups. The patterning method allows regioselective modification within an inverse opal structure, taking advantage of selective chemical reaction. Moreover, combined with the unique signal self-reporting feature of the photonic crystal, the fabricated structure is capable of various applications, including gradient photonic bandgap and dynamic chemical patterns. The proposed method provides the ability to extend the structural and chemical complexity of the photonic crystal, as well as its potential applications.

Content-based quality evaluation of color images: overview and proposals

NASA Astrophysics Data System (ADS)

Tremeau, Alain; Richard, Noel; Colantoni, Philippe; Fernandez-Maloigne, Christine

2003-12-01

The automatic prediction of perceived quality from image data in general, and the assessment of particular image characteristics or attributes that may need improvement in particular, becomes an increasingly important part of intelligent imaging systems. The purpose of this paper is to propose to the color imaging community in general to develop a software package available on internet to help the user to select among all these approaches which is better appropriated to a given application. The ultimate goal of this project is to propose, next to implement, an open and unified color imaging system to set up a favourable context for the evaluation and analysis of color imaging processes. Many different methods for measuring the performance of a process have been proposed by different researchers. In this paper, we will discuss the advantages and shortcomings of most of main analysis criteria and performance measures currently used. The aim is not to establish a harsh competition between algorithms or processes, but rather to test and compare the efficiency of methodologies firstly to highlight strengths and weaknesses of a given algorithm or methodology on a given image type and secondly to have these results publicly available. This paper is focused on two important unsolved problems. Why it is so difficult to select a color space which gives better results than another one? Why it is so difficult to select an image quality metric which gives better results than another one, with respect to the judgment of the Human Visual System? Several methods used either in color imaging or in image quality will be thus discussed. Proposals for content-based image measures and means of developing a standard test suite for will be then presented. The above reference advocates for an evaluation protocol based on an automated procedure. This is the ultimate goal of our proposal.

Mass Transit: Implementation of FTA’s New Starts Evaluation Process and FY 2001 Funding Proposals

DTIC Science & Technology

2000-04-01

formalize the process. FTA issued a proposed rule on April 7, 1999, and plans to issue final regulations by the summer of 2000. In selecting projects for...commit funds to any more New Starts projects during the last 2 years of TEA-21—through fiscal year 2003. Because there are plans for many more...regional review of alternatives, develop preliminary engineering plans , and meet FTA’s approval for the final design. TEA-21 requires that FTA evaluate

Manufacturing process and material selection in concurrent collaborative design of MEMS devices

NASA Astrophysics Data System (ADS)

Zha, Xuan F.; Du, H.

2003-09-01

In this paper we present knowledge of an intensive approach and system for selecting suitable manufacturing processes and materials for microelectromechanical systems (MEMS) devices in concurrent collaborative design environment. In the paper, fundamental issues on MEMS manufacturing process and material selection such as concurrent design framework, manufacturing process and material hierarchies, and selection strategy are first addressed. Then, a fuzzy decision support scheme for a multi-criteria decision-making problem is proposed for estimating, ranking and selecting possible manufacturing processes, materials and their combinations. A Web-based prototype advisory system for the MEMS manufacturing process and material selection, WebMEMS-MASS, is developed based on the client-knowledge server architecture and framework to help the designer find good processes and materials for MEMS devices. The system, as one of the important parts of an advanced simulation and modeling tool for MEMS design, is a concept level process and material selection tool, which can be used as a standalone application or a Java applet via the Web. The running sessions of the system are inter-linked with webpages of tutorials and reference pages to explain the facets, fabrication processes and material choices, and calculations and reasoning in selection are performed using process capability and material property data from a remote Web-based database and interactive knowledge base that can be maintained and updated via the Internet. The use of the developed system including operation scenario, use support, and integration with an MEMS collaborative design system is presented. Finally, an illustration example is provided.

Natural selection in chemical evolution.

PubMed

Fernando, Chrisantha; Rowe, Jonathan

2007-07-07

We propose that chemical evolution can take place by natural selection if a geophysical process is capable of heterotrophic formation of liposomes that grow at some base rate, divide by external agitation, and are subject to stochastic chemical avalanches, in the absence of nucleotides or any monomers capable of modular heredity. We model this process using a simple hill-climbing algorithm, and an artificial chemistry that is unique in exhibiting conservation of mass and energy in an open thermodynamic system. Selection at the liposome level results in the stabilization of rarely occurring molecular autocatalysts that either catalyse or are consumed in reactions that confer liposome level fitness; typically they contribute in parallel to an increasingly conserved intermediary metabolism. Loss of competing autocatalysts can sometimes be adaptive. Steady-state energy flux by the individual increases due to the energetic demands of growth, but also of memory, i.e. maintaining variations in the chemical network. Self-organizing principles such as those proposed by Kauffman, Fontana, and Morowitz have been hypothesized as an ordering principle in chemical evolution, rather than chemical evolution by natural selection. We reject those notions as either logically flawed or at best insufficient in the absence of natural selection. Finally, a finite population model without elitism shows the practical evolutionary constraints for achieving chemical evolution by natural selection in the lab.

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

2017-12-01

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrated model for assessing the cost and CO2 emission (IMACC) for sustainable structural design in ready-mix concrete.

PubMed

Hong, Taehoon; Ji, Changyoon; Park, Hyoseon

2012-07-30

Cost has traditionally been considered the most important factor in the decision-making process. Recently, along with the consistent interest in environmental problems, environmental impact has also become a key factor. Accordingly, there is a need to develop a method that simultaneously reflects the cost and environmental impact in the decision-making process. This study proposed an integrated model for assessing the cost and CO(2) emission (IMACC) at the same time. IMACC is a model that assesses the cost and CO(2) emission of the various structural-design alternatives proposed in the structural-design process. To develop the IMACC, a standard on assessing the cost and CO(2) emission generated in the construction stage was proposed, along with the CO(2) emission factors in the structural materials, based on such materials' strengths. Moreover, using the economic and environmental scores that signify the cost and CO(2) emission reduction ratios, respectively, a method of selecting the best design alternative was proposed. To verify the applicability of IMACC, practical application was carried out. Structural designs were assessed, each of which used 21, 24, 27, and 30 MPa ready-mix concrete (RMC). The use of IMACC makes it easy to verify what the best design is. Results show the one that used 27 MPa RMC was the best design. Therefore, the proposed IMACC can be used as a tool for supporting the decision-making process in selecting the best design alternative. Copyright © 2012 Elsevier Ltd. All rights reserved.

A non-volatile flip-flop based on diode-selected PCM for ultra-low power systems

NASA Astrophysics Data System (ADS)

Ye, Yong; Du, Yuan; Gao, Dan; Kang, Yong; Song, Zhitang; Chen, Bomy

2016-10-01

As the process technology is continuously shrinking, low power consumption is a major issue in VLSI Systems-on-Chip (SoCs), especially for standby-power-critical applications. Recently, the emerging CMOS-compatible non-volatile memories (NVMs), such as Phase Change Memory (PCM), have been used as on-chip storage elements, which can obtain non-volatile processing, nearly-zero standby power and instant-on capability. PCM has been considered as the best candidate for the next generation of NVMs for its low cost, high density and high resistance transformation ratio. In this paper, for the first time, we present a diode-selected PCM based non-volatile flip-flop (NVFF) which is optimized for better power consumption and process variation tolerance. With dual trench isolation process, the diode-selected PCM realizes ultra small area, which is very suitable for multi-context configuration and large scale flip-flops matrix. Since the MOS-selected PCM is hard to shrink further due to large amount of PCM write current, the proposed NVFF achieves higher power efficiency without loss of current driving capability. Using the 40nm manufacturing process, the area of the cell (1D1R) is as small as 0.016 μm2. Simulation results show that the energy consumption during the recall operation is 62 fJ with 1.1 standard supply voltage, which is reduced by 54.9% compared to the previous 2T2R based NVFF. When the supply voltage reduces to 0.7 V, the recall energy is as low as 17 fJ. With the great advantages in cell size and energy, the proposed diode-selected NVFF is very applicable and cost-effective for ULP systems.

Joint L2,1 Norm and Fisher Discrimination Constrained Feature Selection for Rational Synthesis of Microporous Aluminophosphates.

PubMed

Qi, Miao; Wang, Ting; Yi, Yugen; Gao, Na; Kong, Jun; Wang, Jianzhong

2017-04-01

Feature selection has been regarded as an effective tool to help researchers understand the generating process of data. For mining the synthesis mechanism of microporous AlPOs, this paper proposes a novel feature selection method by joint l 2,1 norm and Fisher discrimination constraints (JNFDC). In order to obtain more effective feature subset, the proposed method can be achieved in two steps. The first step is to rank the features according to sparse and discriminative constraints. The second step is to establish predictive model with the ranked features, and select the most significant features in the light of the contribution of improving the predictive accuracy. To the best of our knowledge, JNFDC is the first work which employs the sparse representation theory to explore the synthesis mechanism of six kinds of pore rings. Numerical simulations demonstrate that our proposed method can select significant features affecting the specified structural property and improve the predictive accuracy. Moreover, comparison results show that JNFDC can obtain better predictive performances than some other state-of-the-art feature selection methods. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Aesthetic evolution by mate choice: Darwin's really dangerous idea.

PubMed

Prum, Richard O

2012-08-19

Darwin proposed an explicitly aesthetic theory of sexual selection in which he described mate preferences as a 'taste for the beautiful', an 'aesthetic capacity', etc. These statements were not merely colourful Victorian mannerisms, but explicit expressions of Darwin's hypothesis that mate preferences can evolve for arbitrarily attractive traits that do not provide any additional benefits to mate choice. In his critique of Darwin, A. R. Wallace proposed an entirely modern mechanism of mate preference evolution through the correlation of display traits with male vigour or viability, but he called this mechanism natural selection. Wallace's honest advertisement proposal was stridently anti-Darwinian and anti-aesthetic. Most modern sexual selection research relies on essentially the same Neo-Wallacean theory renamed as sexual selection. I define the process of aesthetic evolution as the evolution of a communication signal through sensory/cognitive evaluation, which is most elaborated through coevolution of the signal and its evaluation. Sensory evaluation includes the possibility that display traits do not encode information that is being assessed, but are merely preferred. A genuinely Darwinian, aesthetic theory of sexual selection requires the incorporation of the Lande-Kirkpatrick null model into sexual selection research, but also encompasses the possibility of sensory bias, good genes and direct benefits mechanisms.

Energy reduction for the spot welding process in the automotive industry

NASA Astrophysics Data System (ADS)

Cullen, J. D.; Athi, N.; Al-Jader, M. A.; Shaw, A.; Al-Shamma'a, A. I.

2007-07-01

When performing spot welding on galvanised metals, higher welding force and current are required than on uncoated steels. This has implications for the energy usage when creating each spot weld, of which there are approximately 4300 in each passenger car. The paper presented is an overview of electrode current selection and its variance over the lifetime of the electrode tip. This also describes the proposed analysis system for the selection of welding parameters for the spot welding process, as the electrode tip wears.

Hybrid approach of selecting hyperparameters of support vector machine for regression.

PubMed

Jeng, Jin-Tsong

2006-06-01

To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.

A model of two-way selection system for human behavior.

PubMed

Zhou, Bin; Qin, Shujia; Han, Xiao-Pu; He, Zhe; Xie, Jia-Rong; Wang, Bing-Hong

2014-01-01

Two-way selection is a common phenomenon in nature and society. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytical solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller group's people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two-way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Chatter detection in milling process based on VMD and energy entropy

NASA Astrophysics Data System (ADS)

Liu, Changfu; Zhu, Lida; Ni, Chenbing

2018-05-01

This paper presents a novel approach to detect the milling chatter based on Variational Mode Decomposition (VMD) and energy entropy. VMD has already been employed in feature extraction from non-stationary signals. The parameters like number of modes (K) and the quadratic penalty (α) need to be selected empirically when raw signal is decomposed by VMD. Aimed at solving the problem how to select K and α, the automatic selection method of VMD's based on kurtosis is proposed in this paper. When chatter occurs in the milling process, energy will be absorbed to chatter frequency bands. To detect the chatter frequency bands automatically, the chatter detection method based on energy entropy is presented. The vibration signal containing chatter frequency is simulated and three groups of experiments which represent three cutting conditions are conducted. To verify the effectiveness of method presented by this paper, chatter feather extraction has been successfully employed on simulation signals and experimental signals. The simulation and experimental results show that the proposed method can effectively detect the chatter.

Overview of NASA's Microgravity Materials Science Program

NASA Technical Reports Server (NTRS)

Downey, James Patton

2012-01-01

The microgravity materials program was nearly eliminated in the middle of the aughts due to budget constraints. Hardware developments were eliminated. Some investigators with experiments that could be performed using ISS partner hardware received continued funding. Partnerships were established between US investigators and ESA science teams for several investigations. ESA conducted peer reviews on the proposals of various science teams as part of an ESA AO process. Assuming he or she was part of a science team that was selected by the ESA process, a US investigator would submit a proposal to NASA for grant funding to support their part of the science team effort. In a similar manner, a US materials investigator (Dr. Rohit Trivedi) is working as a part of a CNES selected science team. As funding began to increase another seven materials investigators were selected in 2010 through an NRA mechanism to perform research related to development of Materials Science Research Rack investigations. One of these has since been converted to a Glovebox investigation.

Multi-Criteria selection of technology for processing ore raw materials

NASA Astrophysics Data System (ADS)

Gorbatova, E. A.; Emelianenko, E. A.; Zaretckii, M. V.

2017-10-01

The development of Computer-Aided Process Planning (CAPP) for the Ore Beneficiation process is considered. The set of parameters to define the quality of the Ore Beneficiation process is identified. The ontological model of CAPP for the Ore Beneficiation process is described. The hybrid choice method of the most appropriate variant of the Ore Beneficiation process based on the Logical Conclusion Rules and the Fuzzy Multi-Criteria Decision Making (MCDM) approach is proposed.

Microfabrication Method using a Combination of Local Ion Implantation and Magnetorheological Finishing

NASA Astrophysics Data System (ADS)

Han, Jin; Kim, Jong-Wook; Lee, Hiwon; Min, Byung-Kwon; Lee, Sang Jo

2009-02-01

A new microfabrication method that combines localized ion implantation and magnetorheological finishing is proposed. The proposed technique involves two steps. First, selected regions of a silicon wafer are irradiated with gallium ions by using a focused ion beam system. The mechanical properties of the irradiated regions are altered as a result of the ion implantation. Second, the wafer is processed by using a magnetorheological finishing method. During the finishing process, the regions not implanted with ion are preferentially removed. The material removal rate difference is utilized for microfabrication. The mechanisms of the proposed method are discussed, and applications are presented.

Small Business Innovation Research. Program solicitation. Closing date: July 22, 1988

NASA Technical Reports Server (NTRS)

1988-01-01

The sixth annual Small Business Innovation Research (SBIR) solicitation by NASA, describes the program, identifies eligibility requirements, outlines proposal preparation and submission requirements, describes the proposal evaluation and award selection process, and provides other information to assist those interested in participating in the SBIR program. It also identifies in Section 8.0 and Appendix D, the specific technical topics and subtopics in which SBIR Phase 1 proposals are solicited in 1988.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

The Interview and Personnel Selection: Is the Process Valid and Reliable?

ERIC Educational Resources Information Center

Niece, Richard

1983-01-01

Reviews recent literature concerning the job interview. Concludes that such interviews are generally ineffective and proposes that school administrators devise techniques for improving their interviewing systems. (FL)

10 CFR 434.601 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... a conventional simulation tool, of the Proposed Design. A life cycle cost analysis shall be used to select the fuel source for the HVAC systems, service hot water, and process loads from available...

System for Processing Coded OFDM Under Doppler and Fading

NASA Technical Reports Server (NTRS)

Tsou, Haiping; Darden, Scott; Lee, Dennis; Yan, Tsun-Yee

2005-01-01

An advanced communication system has been proposed for transmitting and receiving coded digital data conveyed as a form of quadrature amplitude modulation (QAM) on orthogonal frequency-division multiplexing (OFDM) signals in the presence of such adverse propagation-channel effects as large dynamic Doppler shifts and frequency-selective multipath fading. Such adverse channel effects are typical of data communications between mobile units or between mobile and stationary units (e.g., telemetric transmissions from aircraft to ground stations). The proposed system incorporates novel signal processing techniques intended to reduce the losses associated with adverse channel effects while maintaining compatibility with the high-speed physical layer specifications defined for wireless local area networks (LANs) as the standard 802.11a of the Institute of Electrical and Electronics Engineers (IEEE 802.11a). OFDM is a multi-carrier modulation technique that is widely used for wireless transmission of data in LANs and in metropolitan area networks (MANs). OFDM has been adopted in IEEE 802.11a and some other industry standards because it affords robust performance under frequency-selective fading. However, its intrinsic frequency-diversity feature is highly sensitive to synchronization errors; this sensitivity poses a challenge to preserve coherence between the component subcarriers of an OFDM system in order to avoid intercarrier interference in the presence of large dynamic Doppler shifts as well as frequency-selective fading. As a result, heretofore, the use of OFDM has been limited primarily to applications involving small or zero Doppler shifts. The proposed system includes a digital coherent OFDM communication system that would utilize enhanced 802.1la-compatible signal-processing algorithms to overcome effects of frequency-selective fading and large dynamic Doppler shifts. The overall transceiver design would implement a two-frequency-channel architecture (see figure) that would afford frequency diversity for reducing the adverse effects of multipath fading. By using parallel concatenated convolutional codes (also known as Turbo codes) across the dual-channel and advanced OFDM signal processing within each channel, the proposed system is intended to achieve at least an order of magnitude improvement in received signal-to-noise ratio under adverse channel effects while preserving spectral efficiency.

Self-Interest and the Design of Rules.

PubMed

Singh, Manvir; Wrangham, Richard; Glowacki, Luke

2017-12-01

Rules regulating social behavior raise challenging questions about cultural evolution in part because they frequently confer group-level benefits. Current multilevel selection theories contend that between-group processes interact with within-group processes to produce norms and institutions, but within-group processes have remained underspecified, leading to a recent emphasis on cultural group selection as the primary driver of cultural design. Here we present the self-interested enforcement (SIE) hypothesis, which proposes that the design of rules importantly reflects the relative enforcement capacities of competing parties. We show that, in addition to explaining patterns in cultural change and stability, SIE can account for the emergence of much group-functional culture. We outline how this process can stifle or accelerate cultural group selection, depending on various social conditions. Self-interested enforcement has important bearings on the emergence, stability, and change of rules.

Active control of impulsive noise with symmetric α-stable distribution based on an improved step-size normalized adaptive algorithm

NASA Astrophysics Data System (ADS)

Zhou, Yali; Zhang, Qizhi; Yin, Yixin

2015-05-01

In this paper, active control of impulsive noise with symmetric α-stable (SαS) distribution is studied. A general step-size normalized filtered-x Least Mean Square (FxLMS) algorithm is developed based on the analysis of existing algorithms, and the Gaussian distribution function is used to normalize the step size. Compared with existing algorithms, the proposed algorithm needs neither the parameter selection and thresholds estimation nor the process of cost function selection and complex gradient computation. Computer simulations have been carried out to suggest that the proposed algorithm is effective for attenuating SαS impulsive noise, and then the proposed algorithm has been implemented in an experimental ANC system. Experimental results show that the proposed scheme has good performance for SαS impulsive noise attenuation.

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model

PubMed Central

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. PMID:26871694

An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process

PubMed Central

Tan, Chao; Xu, Rongxin; Wang, Zhongbin; Si, Lei; Liu, Xinhua

2016-01-01

In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient. PMID:27217824

Five Guidelines for Selecting Hydrological Signatures

NASA Astrophysics Data System (ADS)

McMillan, H. K.; Westerberg, I.; Branger, F.

2017-12-01

Hydrological signatures are index values derived from observed or modeled series of hydrological data such as rainfall, flow or soil moisture. They are designed to extract relevant information about hydrological behavior, such as to identify dominant processes, and to determine the strength, speed and spatiotemporal variability of the rainfall-runoff response. Hydrological signatures play an important role in model evaluation. They allow us to test whether particular model structures or parameter sets accurately reproduce the runoff generation processes within the watershed of interest. Most modeling studies use a selection of different signatures to capture different aspects of the catchment response, for example evaluating overall flow distribution as well as high and low flow extremes and flow timing. Such studies often choose their own set of signatures, or may borrow subsets of signatures used in multiple other works. The link between signature values and hydrological processes is not always straightforward, leading to uncertainty and variability in hydrologists' signature choices. In this presentation, we aim to encourage a more rigorous approach to hydrological signature selection, which considers the ability of signatures to represent hydrological behavior and underlying processes for the catchment and application in question. To this end, we propose a set of guidelines for selecting hydrological signatures. We describe five criteria that any hydrological signature should conform to: Identifiability, Robustness, Consistency, Representativeness, and Discriminatory Power. We describe an example of the design process for a signature, assessing possible signature designs against the guidelines above. Due to their ubiquity, we chose a signature related to the Flow Duration Curve, selecting the FDC mid-section slope as a proposed signature to quantify catchment overall behavior and flashiness. We demonstrate how assessment against each guideline could be used to compare or choose between alternative signature definitions. We believe that reaching a consensus on selection criteria for hydrological signatures will assist modelers to choose between competing signatures, facilitate comparison between hydrological studies, and help hydrologists to fully evaluate their models.

Image search engine with selective filtering and feature-element-based classification

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

2015-11-01

We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

A Conceptual Framework for Procurement Decision Making Model to Optimize Supplier Selection: The Case of Malaysian Construction Industry

NASA Astrophysics Data System (ADS)

Chuan, Ngam Min; Thiruchelvam, Sivadass; Nasharuddin Mustapha, Kamal; Che Muda, Zakaria; Mat Husin, Norhayati; Yong, Lee Choon; Ghazali, Azrul; Ezanee Rusli, Mohd; Itam, Zarina Binti; Beddu, Salmia; Liyana Mohd Kamal, Nur

2016-03-01

This paper intends to fathom the current state of procurement system in Malaysia specifically in the construction industry in the aspect of supplier selection. This paper propose a comprehensive study on the supplier selection metrics for infrastructure building, weight the importance of each metrics assigned and to find the relationship between the metrics among initiators, decision makers, buyers and users. With the metrics hierarchy of criteria importance, a supplier selection process can be defined, repeated and audited with lesser complications or difficulties. This will help the field of procurement to improve as this research is able to develop and redefine policies and procedures that have been set in supplier selection. Developing this systematic process will enable optimization of supplier selection and thus increasing the value for every stakeholders as the process of selection is greatly simplified. With a new redefined policy and procedure, it does not only increase the company’s effectiveness and profit, but also make it available for the company to reach greater heights in the advancement of procurement in Malaysia.

Managing for resilience

EPA Science Inventory

Early efforts in wildlife management focused on reducing population variability and maximizing yields of select species. Aldo Leopold proposed the concept of habitat management as superior to population management. More recently, ecosystem management, whereby ecological processes...

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

Computation-aware algorithm selection approach for interlaced-to-progressive conversion

NASA Astrophysics Data System (ADS)

Park, Sang-Jun; Jeon, Gwanggil; Jeong, Jechang

2010-05-01

We discuss deinterlacing results in a computationally constrained and varied environment. The proposed computation-aware algorithm selection approach (CASA) for fast interlaced to progressive conversion algorithm consists of three methods: the line-averaging (LA) method for plain regions, the modified edge-based line-averaging (MELA) method for medium regions, and the proposed covariance-based adaptive deinterlacing (CAD) method for complex regions. The proposed CASA uses two criteria, mean-squared error (MSE) and CPU time, for assigning the method. We proposed a CAD method. The principle idea of CAD is based on the correspondence between the high and low-resolution covariances. We estimated the local covariance coefficients from an interlaced image using Wiener filtering theory and then used these optimal minimum MSE interpolation coefficients to obtain a deinterlaced image. The CAD method, though more robust than most known methods, was not found to be very fast compared to the others. To alleviate this issue, we proposed an adaptive selection approach using a fast deinterlacing algorithm rather than using only one CAD algorithm. The proposed hybrid approach of switching between the conventional schemes (LA and MELA) and our CAD was proposed to reduce the overall computational load. A reliable condition to be used for switching the schemes was presented after a wide set of initial training processes. The results of computer simulations showed that the proposed methods outperformed a number of methods presented in the literature.

Serial and parallel attentive visual searches: evidence from cumulative distribution functions of response times.

PubMed

Sung, Kyongje

2008-12-01

Participants searched a visual display for a target among distractors. Each of 3 experiments tested a condition proposed to require attention and for which certain models propose a serial search. Serial versus parallel processing was tested by examining effects on response time means and cumulative distribution functions. In 2 conditions, the results suggested parallel rather than serial processing, even though the tasks produced significant set-size effects. Serial processing was produced only in a condition with a difficult discrimination and a very large set-size effect. The results support C. Bundesen's (1990) claim that an extreme set-size effect leads to serial processing. Implications for parallel models of visual selection are discussed.

Sustainable Supplier Performance Evaluation and Selection with Neofuzzy TOPSIS Method

PubMed Central

Chaharsooghi, S. K.; Ashrafi, Mehdi

2014-01-01

Supplier selection plays an important role in the supply chain management and traditional criteria such as price, quality, and flexibility are considered for supplier performance evaluation in researches. In recent years sustainability has received more attention in the supply chain management literature with triple bottom line (TBL) describing the sustainability in supply chain management with social, environmental, and economic initiatives. This paper explores sustainability in supply chain management and examines the problem of identifying a new model for supplier selection based on extended model of TBL approach in supply chain by presenting fuzzy multicriteria method. Linguistic values of experts' subjective preferences are expressed with fuzzy numbers and Neofuzzy TOPSIS is proposed for finding the best solution of supplier selection problem. Numerical results show that the proposed model is efficient for integrating sustainability in supplier selection problem. The importance of using complimentary aspects of sustainability and Neofuzzy TOPSIS concept in sustainable supplier selection process is shown with sensitivity analysis. PMID:27379267

Low-Complexity User Selection for Rate Maximization in MIMO Broadcast Channels with Downlink Beamforming

PubMed Central

Silva, Adão; Gameiro, Atílio

2014-01-01

We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users. PMID:24574928

Sustainable Supplier Performance Evaluation and Selection with Neofuzzy TOPSIS Method.

PubMed

Chaharsooghi, S K; Ashrafi, Mehdi

2014-01-01

Supplier selection plays an important role in the supply chain management and traditional criteria such as price, quality, and flexibility are considered for supplier performance evaluation in researches. In recent years sustainability has received more attention in the supply chain management literature with triple bottom line (TBL) describing the sustainability in supply chain management with social, environmental, and economic initiatives. This paper explores sustainability in supply chain management and examines the problem of identifying a new model for supplier selection based on extended model of TBL approach in supply chain by presenting fuzzy multicriteria method. Linguistic values of experts' subjective preferences are expressed with fuzzy numbers and Neofuzzy TOPSIS is proposed for finding the best solution of supplier selection problem. Numerical results show that the proposed model is efficient for integrating sustainability in supplier selection problem. The importance of using complimentary aspects of sustainability and Neofuzzy TOPSIS concept in sustainable supplier selection process is shown with sensitivity analysis.

Mobil plans methanol plant in Nigeria

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

Alperowicz, N.

1992-08-12

Mobil Chemical (Houston) is in discussions with Nigerian National Petroleum Corp. (NNPC; Lagos) on a joint venture methanol plant at Port Harcourt, Nigeria. The U.S. firm has invited process owners to submit proposals for a 1-million m.t./year unit and hopes to select the technology by the end of this year. Three proposals have been submitted: Lurgi, offering its own low-pressure process; John Brown/Davy, with the ICI process; and M.W. Kellogg, proposing its own technology. Shareholding in the joint venture is yet to be decided, but it is likely to be a 50/50 tie-up. Marketing of Mobil's share or of themore » entire tonnage would be handled by Mobil Petrochemical International (Brussels). The plant could be onstream in late 1996.« less

Green supply chain management strategy selection using analytic network process: case study at PT XYZ

NASA Astrophysics Data System (ADS)

Adelina, W.; Kusumastuti, R. D.

2017-01-01

This study is about business strategy selection for green supply chain management (GSCM) for PT XYZ by using Analytic Network Process (ANP). GSCM is initiated as a response to reduce environmental impacts from industrial activities. The purposes of this study are identifying criteria and sub criteria in selecting GSCM Strategy, and analysing a suitable GSCM strategy for PT XYZ. This study proposes ANP network with 6 criteria and 29 sub criteria, which are obtained from the literature and experts’ judgements. One of the six criteria contains GSCM strategy options, namely risk-based strategy, efficiency-based strategy, innovation-based strategy, and closed loop strategy. ANP solves complex GSCM strategy-selection by using a more structured process and considering green perspectives from experts. The result indicates that innovation-based strategy is the most suitable green supply chain management strategy for PT XYZ.

Evidence accumulation as a model for lexical selection.

PubMed

Anders, R; Riès, S; van Maanen, L; Alario, F X

2015-11-01

We propose and demonstrate evidence accumulation as a plausible theoretical and/or empirical model for the lexical selection process of lexical retrieval. A number of current psycholinguistic theories consider lexical selection as a process related to selecting a lexical target from a number of alternatives, which each have varying activations (or signal supports), that are largely resultant of an initial stimulus recognition. We thoroughly present a case for how such a process may be theoretically explained by the evidence accumulation paradigm, and we demonstrate how this paradigm can be directly related or combined with conventional psycholinguistic theory and their simulatory instantiations (generally, neural network models). Then with a demonstrative application on a large new real data set, we establish how the empirical evidence accumulation approach is able to provide parameter results that are informative to leading psycholinguistic theory, and that motivate future theoretical development. Copyright © 2015 Elsevier Inc. All rights reserved.

Modified Bat Algorithm for Feature Selection with the Wisconsin Diagnosis Breast Cancer (WDBC) Dataset

PubMed

Jeyasingh, Suganthi; Veluchamy, Malathi

2017-05-01

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE). Creative Commons Attribution License

Selective heteroepitaxy on deeply grooved substrate: A route to low cost semipolar GaN platforms of bulk quality

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

Tendille, Florian, E-mail: florian.tendille@crhea.cnrs.fr; Vennéguès, Philippe; De Mierry, Philippe

2016-08-22

Semipolar GaN crystal stripes larger than 100 μm with dislocation densities below 5 × 10{sup 6} cm{sup −2} are achieved using a low cost fabrication process. An original sapphire patterning procedure is proposed, enabling selective growth of semipolar oriented GaN stripes while confining the defects to specific areas. Radiative and non-radiative crystalline defects are investigated by cathodoluminescence and can be correlated to the development of crystal microstructure during the growth process. A dislocation reduction mechanism, supported by transmission electron microscopy, is proposed. This method represents a step forward toward low-cost quasi-bulk semipolar GaN epitaxial platforms with an excellent structural quality which will allowmore » for even more efficient III-nitride based devices.« less

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

PubMed

Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

2018-04-01

Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

An Ontology-Based Tourism Recommender System Based on Spreading Activation Model

NASA Astrophysics Data System (ADS)

Bahramian, Z.; Abbaspour, R. Ali

2015-12-01

A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user's preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user's preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontology-based recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User's feedback adapts the user's preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.

Process identification of the SCR system of coal-fired power plant for de-NOx based on historical operation data.

PubMed

Li, Jian; Shi, Raoqiao; Xu, Chuanlong; Wang, Shimin

2018-05-08

The selective catalytic reduction (SCR) system, as one principal flue gas treatment method employed for the NO x emission control of the coal-fired power plant, is nonlinear and time-varying with great inertia and large time delay. It is difficult for the present SCR control system to achieve satisfactory performance with the traditional feedback and feedforward control strategies. Although some improved control strategies, such as the Smith predictor control and the model predictive control, have been proposed for this issue, a well-matched identification model is essentially required to realize a superior control of the SCR system. Industrial field experiment is an alternative way to identify the SCR system model in the coal-fired power plant. But it undesirably disturbs the operation system and is costly in time and manpower. In this paper, a process identification model of the SCR system is proposed and developed by applying the asymptotic method to the sufficiently excited data, selected from the original historical operation database of a 350 MW coal-fired power plant according to the condition number of the Fisher information matrix. Numerical simulations are carried out based on the practical historical operation data to evaluate the performance of the proposed model. Results show that the proposed model can efficiently achieve the process identification of the SCR system.

LAC indicators: an evaluation of progress and list of proposed indicators

Treesearch

Alan E. Watson; David N. Cole

1992-01-01

One of the most critical, and difficult, steps in the Limits of Acceptable Change (LAC) process is the selection of indicators. To help with this step, this paper (I) briefly reviews some desirable characteristics of indicators and (2) lists indicators that have been proposed or adopted in LAC plans. From a comparison of this list of indicators and desirable...

Multichannel spectral mode of the ALOHA up-conversion interferometer

NASA Astrophysics Data System (ADS)

Lehmann, L.; Darré, P.; Boulogne, H.; Delage, L.; Grossard, L.; Reynaud, F.

2018-06-01

In this paper, we propose a multichannel spectral configuration of the Astronomical Light Optical Hybrid Analysis (ALOHA) instrument dedicated to high-resolution imaging. A frequency conversion process is implemented in each arm of an interferometer to transfer the astronomical light to a shorter wavelength domain. Exploiting the spectral selectivity of this non-linear optical process, we propose to use a set of independent pump lasers in order to simultaneously study multiple spectral channels. This principle is experimentally demonstrated with a dual-channel configuration as a proof-of-principle.

A diagnostic signal selection scheme for planetary gearbox vibration monitoring under non-stationary operational conditions

NASA Astrophysics Data System (ADS)

Feng, Ke; Wang, KeSheng; Zhang, Mian; Ni, Qing; Zuo, Ming J.

2017-03-01

The planetary gearbox, due to its unique mechanical structures, is an important rotating machine for transmission systems. Its engineering applications are often in non-stationary operational conditions, such as helicopters, wind energy systems, etc. The unique physical structures and working conditions make the vibrations measured from planetary gearboxes exhibit a complex time-varying modulation and therefore yield complicated spectral structures. As a result, traditional signal processing methods, such as Fourier analysis, and the selection of characteristic fault frequencies for diagnosis face serious challenges. To overcome this drawback, this paper proposes a signal selection scheme for fault-emphasized diagnostics based upon two order tracking techniques. The basic procedures for the proposed scheme are as follows. (1) Computed order tracking is applied to reveal the order contents and identify the order(s) of interest. (2) Vold-Kalman filter order tracking is used to extract the order(s) of interest—these filtered order(s) constitute the so-called selected vibrations. (3) Time domain statistic indicators are applied to the selected vibrations for faulty information-emphasized diagnostics. The proposed scheme is explained and demonstrated in a signal simulation model and experimental studies and the method proves to be effective for planetary gearbox fault diagnosis.

Excitatory and inhibitory STDP jointly tune feedforward neural circuits to selectively propagate correlated spiking activity

PubMed Central

Kleberg, Florence I.; Fukai, Tomoki; Gilson, Matthieu

2014-01-01

Spike-timing-dependent plasticity (STDP) has been well established between excitatory neurons and several computational functions have been proposed in various neural systems. Despite some recent efforts, however, there is a significant lack of functional understanding of inhibitory STDP (iSTDP) and its interplay with excitatory STDP (eSTDP). Here, we demonstrate by analytical and numerical methods that iSTDP contributes crucially to the balance of excitatory and inhibitory weights for the selection of a specific signaling pathway among other pathways in a feedforward circuit. This pathway selection is based on the high sensitivity of STDP to correlations in spike times, which complements a recent proposal for the role of iSTDP in firing-rate based selection. Our model predicts that asymmetric anti-Hebbian iSTDP exceeds asymmetric Hebbian iSTDP for supporting pathway-specific balance, which we show is useful for propagating transient neuronal responses. Furthermore, we demonstrate how STDPs at excitatory–excitatory, excitatory–inhibitory, and inhibitory–excitatory synapses cooperate to improve the pathway selection. We propose that iSTDP is crucial for shaping the network structure that achieves efficient processing of synchronous spikes. PMID:24847242

A Guide to the Selection of Cost-Effective Wastewater Treatment Systems. Technical Report.

ERIC Educational Resources Information Center

Van Note, Robert H.; And Others

The data within this publication provide guidelines for planners, engineers and decision-makers at all governmental levels to evaluate cost-effectiveness of alternative wastewater treatment proposals. The processes described include conventional and advanced treatment units as well as most sludge handling and processing units. Flow sheets, cost…

Discriminative dictionary learning for abdominal multi-organ segmentation.

PubMed

Tong, Tong; Wolz, Robin; Wang, Zehan; Gao, Qinquan; Misawa, Kazunari; Fujiwara, Michitaka; Mori, Kensaku; Hajnal, Joseph V; Rueckert, Daniel

2015-07-01

An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Visual and olfactory disruption of orientation by the western pine beetle to attractant-baited traps

Treesearch

B.L. Strom; R.A. Goyer; P.J. Shea

2001-01-01

Olfactory deterrents have been proposed as tree protectants against attack by bark beetles, but their development has been hindered by a lack of knowledge of host selection behavior. Among the primary tree-killing (aggressive) Dendroctonus, vision appears to be an integral part of the host selection process. We evaluated the importance of vision in...

Symbiogenesis, natural selection, and the dynamic Earth.

PubMed

Kutschera, U

2009-08-01

One century ago, Constantin S. Mereschkowsky introduced the symbiogenesis theory for the origin of chloroplasts from ancient cyanobacteria which was later supplemented by Ivan E. Wallin's proposal that mitochondria evolved from once free-living bacteria. Today, this Mereschkowsky-Wallin principle of symbiogenesis, which is also known as the serial primary endosymbiosis theory, explains the evolutionary origin of eukaryotic cells and hence the emergence of all eukaryotes (protists, fungi, animals and plants). In 1858, the concept of natural selection was described independently by Charles Darwin and Alfred R. Wallace. In the same year, Antonio Snider-Pellegrini proposed the idea of shifting continents, which was later expanded by Alfred Wegener, who published his theory of continental drift eight decades ago. Today, directional selection is accepted as the major cause of adaptive evolution within natural populations of micro- and macro-organisms and the theory of the dynamic Earth (plate tectonics) is well supported. In this article, I combine the processes and principles of symbiogenesis, natural selection and the dynamic Earth and propose an integrative 'synade-model' of macroevolution which takes into account organisms from all five Kingdoms of life.

An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

PubMed

Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

How to select combination operators for fuzzy expert systems using CRI

NASA Technical Reports Server (NTRS)

Turksen, I. B.; Tian, Y.

1992-01-01

A method to select combination operators for fuzzy expert systems using the Compositional Rule of Inference (CRI) is proposed. First, fuzzy inference processes based on CRI are classified into three categories in terms of their inference results: the Expansion Type Inference, the Reduction Type Inference, and Other Type Inferences. Further, implication operators under Sup-T composition are classified as the Expansion Type Operator, the Reduction Type Operator, and the Other Type Operators. Finally, the combination of rules or their consequences is investigated for inference processes based on CRI.

Aesthetic evolution by mate choice: Darwin's really dangerous idea

PubMed Central

Prum, Richard O.

2012-01-01

Darwin proposed an explicitly aesthetic theory of sexual selection in which he described mate preferences as a ‘taste for the beautiful’, an ‘aesthetic capacity’, etc. These statements were not merely colourful Victorian mannerisms, but explicit expressions of Darwin's hypothesis that mate preferences can evolve for arbitrarily attractive traits that do not provide any additional benefits to mate choice. In his critique of Darwin, A. R. Wallace proposed an entirely modern mechanism of mate preference evolution through the correlation of display traits with male vigour or viability, but he called this mechanism natural selection. Wallace's honest advertisement proposal was stridently anti-Darwinian and anti-aesthetic. Most modern sexual selection research relies on essentially the same Neo-Wallacean theory renamed as sexual selection. I define the process of aesthetic evolution as the evolution of a communication signal through sensory/cognitive evaluation, which is most elaborated through coevolution of the signal and its evaluation. Sensory evaluation includes the possibility that display traits do not encode information that is being assessed, but are merely preferred. A genuinely Darwinian, aesthetic theory of sexual selection requires the incorporation of the Lande–Kirkpatrick null model into sexual selection research, but also encompasses the possibility of sensory bias, good genes and direct benefits mechanisms. PMID:22777014

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

A parallel optimization method for product configuration and supplier selection based on interval

NASA Astrophysics Data System (ADS)

Zheng, Jian; Zhang, Meng; Li, Guoxi

2017-06-01

In the process of design and manufacturing, product configuration is an important way of product development, and supplier selection is an essential component of supply chain management. To reduce the risk of procurement and maximize the profits of enterprises, this study proposes to combine the product configuration and supplier selection, and express the multiple uncertainties as interval numbers. An integrated optimization model of interval product configuration and supplier selection was established, and NSGA-II was put forward to locate the Pareto-optimal solutions to the interval multiobjective optimization model.

Development of a Robust Identifier for NPPs Transients Combining ARIMA Model and EBP Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-08-01

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error backpropagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time series of the selected plant variables. In the third step, for identification the type of transients, the forecasted time series are fed to the modular identifier which has been developed using the latest advances of EBP learning algorithm. Bushehr nuclear power plant (BNPP) transients are probed to analyze the ability of the proposed identifier. Recognition of transient is based on similarity of its statistical properties to the reference one, rather than the values of input patterns. More robustness against noisy data and improvement balance between memorization and generalization are salient advantages of the proposed identifier. Reduction of false identification, sole dependency of identification on the sign of each output signal, selection of the plant variables for transients training independent of each other, and extendibility for identification of more transients without unfavorable effects are other merits of the proposed identifier.

Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

PubMed

Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

2015-01-01

The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with a low cognitive effort in contrast with the alternative of using an ad-hoc manual analysis of the selected descriptors. Graphical abstractVIDEAN allows the visual analysis of candidate subsets of descriptors for QSAR/QSPR. In the two panels on the top, users can interactively explore numerical correlations as well as co-occurrences in the candidate subsets through two interactive graphs.

Evaluation method of membrane performance in membrane distillation process for seawater desalination.

PubMed

Chung, Seungjoon; Seo, Chang Duck; Choi, Jae-Hoon; Chung, Jinwook

2014-01-01

Membrane distillation (MD) is an emerging desalination technology as an energy-saving alternative to conventional distillation and reverse osmosis method. The selection of appropriate membrane is a prerequisite for the design of an optimized MD process. We proposed a simple approximation method to evaluate the performance of membranes for MD process. Three hollow fibre-type commercial membranes with different thicknesses and pore sizes were tested. Experimental results showed that one membrane was advantageous due to the highest flux, whereas another membrane was due to the lowest feed temperature drop. Regression analyses and multi-stage calculations were used to account for the trade-offeffects of flux and feed temperature drop. The most desirable membrane was selected from tested membranes in terms of the mean flux in a multi-stage process. This method would be useful for the selection of the membranes without complicated simulation techniques.

78 FR 68092 - Notice of Lodging of Proposed Consent Decree Amendment Under the Comprehensive Environmental...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-13

... dechlorination as an active treatment process to address groundwater contamination, and selecting monitored... Packaging Inc.; Ethox Chemicals, LLC; Expert Management Inc. on behalf of National Starch and Chemical...

Mutual information estimation reveals global associations between stimuli and biological processes

PubMed Central

Suzuki, Taiji; Sugiyama, Masashi; Kanamori, Takafumi; Sese, Jun

2009-01-01

Background Although microarray gene expression analysis has become popular, it remains difficult to interpret the biological changes caused by stimuli or variation of conditions. Clustering of genes and associating each group with biological functions are often used methods. However, such methods only detect partial changes within cell processes. Herein, we propose a method for discovering global changes within a cell by associating observed conditions of gene expression with gene functions. Results To elucidate the association, we introduce a novel feature selection method called Least-Squares Mutual Information (LSMI), which computes mutual information without density estimaion, and therefore LSMI can detect nonlinear associations within a cell. We demonstrate the effectiveness of LSMI through comparison with existing methods. The results of the application to yeast microarray datasets reveal that non-natural stimuli affect various biological processes, whereas others are no significant relation to specific cell processes. Furthermore, we discover that biological processes can be categorized into four types according to the responses of various stimuli: DNA/RNA metabolism, gene expression, protein metabolism, and protein localization. Conclusion We proposed a novel feature selection method called LSMI, and applied LSMI to mining the association between conditions of yeast and biological processes through microarray datasets. In fact, LSMI allows us to elucidate the global organization of cellular process control. PMID:19208155

Heat transfer enhancement in triplex-tube latent thermal energy storage system with selected arrangements of fins

NASA Astrophysics Data System (ADS)

Zhao, Liang; Xing, Yuming; Liu, Xin; Rui, Zhoufeng

2018-01-01

The use of thermal energy storage systems can effectively reduce energy consumption and improve the system performance. One of the promising ways for thermal energy storage system is application of phase change materials (PCMs). In this study, a two-dimensional numerical model is presented to investigate the heat transfer enhancement during the melting/solidification process in a triplex tube heat exchanger (TTHX) by using fluent software. The thermal conduction and natural convection are all taken into account in the simulation of the melting/solidification process. As the volume fraction of fin is kept to be a constant, the influence of proposed fin arrangement on temporal profile of liquid fraction over the melting process is studied and reported. By rotating the unit with different angle, the simulation shows that the melting time varies a little, which means that the installation error can be reduced by the selected fin arrangement. The proposed fin arrangement also can effectively reduce time of the solidification of the PCM by investigating the solidification process. To summarize, this work presents a shape optimization for the improvement of the thermal energy storage system by considering both thermal energy charging and discharging process.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Selective mitochondrial autophagy, or mitophagy, as a targeted defense against oxidative stress, mitochondrial dysfunction, and aging.

PubMed

Lemasters, John J

2005-01-01

In autophagy, portions of cytoplasm are sequestered into autophagosomes and delivered to lysosomes for degradation. Long assumed to be a random process, increasing evidence suggests that autophagy of mitochondria, peroxisomes, and possibly other organelles is selective. A recent paper (Kissova et al., J. Biol. Chem. 2004;279:39068-39074) shows in yeast that a specific outer membrane protein, Uth1p, is required for efficient mitochondrial autophagy. For this selective autophagy of mitochondria, we propose the term "mitophagy" to emphasize the non-random nature of the process. Mitophagy may play a key role in retarding accumulation of somatic mutations of mtDNA with aging.

Edge enhancement of color images using a digital micromirror device.

PubMed

Di Martino, J Matías; Flores, Jorge L; Ayubi, Gastón A; Alonso, Julia R; Fernández, Ariel; Ferrari, José A

2012-06-01

A method for orientation-selective enhancement of edges in color images is proposed. The method utilizes the capacity of digital micromirror devices to generate a positive and a negative color replica of the image used as input. When both images are slightly displaced and imagined together, one obtains an image with enhanced edges. The proposed technique does not require a coherent light source or precise alignment. The proposed method could be potentially useful for processing large image sequences in real time. Validation experiments are presented.

Hierarchical and successive approximate registration of the non-rigid medical image based on thin-plate splines

NASA Astrophysics Data System (ADS)

Hu, Jinyan; Li, Li; Yang, Yunfeng

2017-06-01

The hierarchical and successive approximate registration method of non-rigid medical image based on the thin-plate splines is proposed in the paper. There are two major novelties in the proposed method. First, the hierarchical registration based on Wavelet transform is used. The approximate image of Wavelet transform is selected as the registered object. Second, the successive approximation registration method is used to accomplish the non-rigid medical images registration, i.e. the local regions of the couple images are registered roughly based on the thin-plate splines, then, the current rough registration result is selected as the object to be registered in the following registration procedure. Experiments show that the proposed method is effective in the registration process of the non-rigid medical images.

Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization.

PubMed

Ding, Jinliang; Chai, Tianyou; Wang, Hong

2011-03-01

This paper presents a novel offline modeling for product quality prediction of mineral processing which consists of a number of unit processes in series. The prediction of the product quality of the whole mineral process (i.e., the mixed concentrate grade) plays an important role and the establishment of its predictive model is a key issue for the plantwide optimization. For this purpose, a hybrid modeling approach of the mixed concentrate grade prediction is proposed, which consists of a linear model and a nonlinear model. The least-squares support vector machine is adopted to establish the nonlinear model. The inputs of the predictive model are the performance indices of each unit process, while the output is the mixed concentrate grade. In this paper, the model parameter selection is transformed into the shape control of the probability density function (PDF) of the modeling error. In this context, both the PDF-control-based and minimum-entropy-based model parameter selection approaches are proposed. Indeed, this is the first time that the PDF shape control idea is used to deal with system modeling, where the key idea is to turn model parameters so that either the modeling error PDF is controlled to follow a target PDF or the modeling error entropy is minimized. The experimental results using the real plant data and the comparison of the two approaches are discussed. The results show the effectiveness of the proposed approaches.

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method

PubMed Central

Zhang, Tingting; Kou, S. C.

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure. PMID:21258615

Nonparametric Inference of Doubly Stochastic Poisson Process Data via the Kernel Method.

PubMed

Zhang, Tingting; Kou, S C

2010-01-01

Doubly stochastic Poisson processes, also known as the Cox processes, frequently occur in various scientific fields. In this article, motivated primarily by analyzing Cox process data in biophysics, we propose a nonparametric kernel-based inference method. We conduct a detailed study, including an asymptotic analysis, of the proposed method, and provide guidelines for its practical use, introducing a fast and stable regression method for bandwidth selection. We apply our method to real photon arrival data from recent single-molecule biophysical experiments, investigating proteins' conformational dynamics. Our result shows that conformational fluctuation is widely present in protein systems, and that the fluctuation covers a broad range of time scales, highlighting the dynamic and complex nature of proteins' structure.

Intrusion recognition for optic fiber vibration sensor based on the selective attention mechanism

NASA Astrophysics Data System (ADS)

Xu, Haiyan; Xie, Yingjuan; Li, Min; Zhang, Zhuo; Zhang, Xuewu

2017-11-01

Distributed fiber-optic vibration sensors receive extensive investigation and play a significant role in the sensor panorama. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, an intrusion recognition based on the auditory selective attention mechanism is proposed. Firstly, considering the time-frequency of vibration, the spectrogram is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Based on these maps, the feature matrix is formed after normalization. The system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises. What's more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.

The use of pharmacoeconomic data in formulary selection.

PubMed

Hatoum, H T; Freeman, R A

1994-01-01

Pharmacists are encouraged to improve their knowledge and use of pharmacoeconomic data in formulary selection. The formulary selection process has changed significantly in recent years. Among its most significant uses is its potential for cost containment strategies. An overview is presented of the origin as well as the potential impact of pharmacoeconomic data. The need to balance the economic benefit with the clinical advantages for any proposed new drug for formulary inclusion remains the most critical decision to be made by pharmacists.

Quantitative genetic models of sexual conflict based on interacting phenotypes.

PubMed

Moore, Allen J; Pizzari, Tommaso

2005-05-01

Evolutionary conflict arises between reproductive partners when alternative reproductive opportunities are available. Sexual conflict can generate sexually antagonistic selection, which mediates sexual selection and intersexual coevolution. However, despite intense interest, the evolutionary implications of sexual conflict remain unresolved. We propose a novel theoretical approach to study the evolution of sexually antagonistic phenotypes based on quantitative genetics and the measure of social selection arising from male-female interactions. We consider the phenotype of one sex as both a genetically influenced evolving trait as well as the (evolving) social environment in which the phenotype of the opposite sex evolves. Several important points emerge from our analysis, including the relationship between direct selection on one sex and indirect effects through selection on the opposite sex. We suggest that the proposed approach may be a valuable tool to complement other theoretical approaches currently used to study sexual conflict. Most importantly, our approach highlights areas where additional empirical data can help clarify the role of sexual conflict in the evolutionary process.

The Medical School Admissions Process and Meeting the Public's Health Care Needs: Never the Twain Shall Meet?

PubMed

Cleland, Jennifer

2017-12-19

Medical schools typically assess how good their selection process is using metrics such as students' assessment performance and the academic success of alumni on later indicators of academic ability and clinical competence, such as Royal College of Physicians or specialty board examinations. To address global issues with the maldistribution of doctors and increasing numbers of new medical school graduates choosing not to work in a clinical context requires different measurements of medical school admissions processes, like those related to graduates' career outcomes (e.g., working in underserved regions and/or working in certain specialties). This shift in focus is not straightforward. Medical education is a complex social system where, intentionally or not, medical schools focus on reproducing cultural, historical, and social norms. Simple solutions are often proposed but they are insufficient to address these complex drivers. Instead it is time to step back and think very differently about medical school admissions. In this Invited Commentary, the author proposes new solutions to address these issues, including: bringing in to the medical school selection process the perspectives of other key stakeholders; increasing collaboration and dialogue across these stakeholder groups; changing the performance metrics by which medical schools are assessed in the global education marketplace; and developing and evaluating new selection processes and tools. Medical schools must engage more reflectively and collaboratively in debates about how to align medical school admissions and meeting the health care needs of the public.

The influence of expert opinions on the selection of wastewater treatment alternatives: a group decision-making approach.

PubMed

Kalbar, Pradip P; Karmakar, Subhankar; Asolekar, Shyam R

2013-10-15

The application of multiple-attribute decision-making (MADM) to real life decision problems suggests that avoiding the loss of information through scenario-based approaches and including expert opinions in the decision-making process are two major challenges that require more research efforts. Recently, a wastewater treatment technology selection effort has been made with a 'scenario-based' method of MADM. This paper focuses on a novel approach to incorporate expert opinions into the scenario-based decision-making process, as expert opinions play a major role in the selection of treatment technologies. The sets of criteria and the indicators that are used consist of both qualitative and quantitative criteria. The group decision-making (GDM) approach that is implemented for aggregating expert opinions is based on an analytical hierarchy process (AHP), which is the most widely used MADM method. The pairwise comparison matrices (PCMs) for qualitative criteria are formed based on expert opinions, whereas, a novel approach is proposed for generating PCMs for quantitative criteria. It has been determined that the experts largely prefer natural treatment systems because they are more sustainable in any scenario. However, PCMs based on expert opinions suggest that advanced technologies such as the sequencing batch reactor (SBR) can also be appropriate for a given decision scenario. The proposed GDM approach is a rationalized process that will be more appropriate in realistic scenarios where multiple stakeholders with local and regional societal priorities are involved in the selection of treatment technology. Copyright © 2013 Elsevier Ltd. All rights reserved.

SEIPS-based process modeling in primary care.

PubMed

Wooldridge, Abigail R; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter L T

2017-04-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. Copyright © 2016 Elsevier Ltd. All rights reserved.

SEIPS-Based Process Modeling in Primary Care

PubMed Central

Wooldridge, Abigail R.; Carayon, Pascale; Hundt, Ann Schoofs; Hoonakker, Peter

2016-01-01

Process mapping, often used as part of the human factors and systems engineering approach to improve care delivery and outcomes, should be expanded to represent the complex, interconnected sociotechnical aspects of health care. Here, we propose a new sociotechnical process modeling method to describe and evaluate processes, using the SEIPS model as the conceptual framework. The method produces a process map and supplementary table, which identify work system barriers and facilitators. In this paper, we present a case study applying this method to three primary care processes. We used purposeful sampling to select staff (care managers, providers, nurses, administrators and patient access representatives) from two clinics to observe and interview. We show the proposed method can be used to understand and analyze healthcare processes systematically and identify specific areas of improvement. Future work is needed to assess usability and usefulness of the SEIPS-based process modeling method and further refine it. PMID:28166883

Screened selection design for randomised phase II oncology trials: an example in chronic lymphocytic leukaemia

PubMed Central

2013-01-01

Background As there are limited patients for chronic lymphocytic leukaemia trials, it is important that statistical methodologies in Phase II efficiently select regimens for subsequent evaluation in larger-scale Phase III trials. Methods We propose the screened selection design (SSD), which is a practical multi-stage, randomised Phase II design for two experimental arms. Activity is first evaluated by applying Simon’s two-stage design (1989) on each arm. If both are active, the play-the-winner selection strategy proposed by Simon, Wittes and Ellenberg (SWE) (1985) is applied to select the superior arm. A variant of the design, Modified SSD, also allows the arm with the higher response rates to be recommended only if its activity rate is greater by a clinically-relevant value. The operating characteristics are explored via a simulation study and compared to a Bayesian Selection approach. Results Simulations showed that with the proposed SSD, it is possible to retain the sample size as required in SWE and obtain similar probabilities of selecting the correct superior arm of at least 90%; with the additional attractive benefit of reducing the probability of selecting ineffective arms. This approach is comparable to a Bayesian Selection Strategy. The Modified SSD performs substantially better than the other designs in selecting neither arm if the underlying rates for both arms are desirable but equivalent, allowing for other factors to be considered in the decision making process. Though its probability of correctly selecting a superior arm might be reduced, it still performs reasonably well. It also reduces the probability of selecting an inferior arm. Conclusions SSD provides an easy to implement randomised Phase II design that selects the most promising treatment that has shown sufficient evidence of activity, with available R codes to evaluate its operating characteristics. PMID:23819695

Screened selection design for randomised phase II oncology trials: an example in chronic lymphocytic leukaemia.

PubMed

Yap, Christina; Pettitt, Andrew; Billingham, Lucinda

2013-07-03

As there are limited patients for chronic lymphocytic leukaemia trials, it is important that statistical methodologies in Phase II efficiently select regimens for subsequent evaluation in larger-scale Phase III trials. We propose the screened selection design (SSD), which is a practical multi-stage, randomised Phase II design for two experimental arms. Activity is first evaluated by applying Simon's two-stage design (1989) on each arm. If both are active, the play-the-winner selection strategy proposed by Simon, Wittes and Ellenberg (SWE) (1985) is applied to select the superior arm. A variant of the design, Modified SSD, also allows the arm with the higher response rates to be recommended only if its activity rate is greater by a clinically-relevant value. The operating characteristics are explored via a simulation study and compared to a Bayesian Selection approach. Simulations showed that with the proposed SSD, it is possible to retain the sample size as required in SWE and obtain similar probabilities of selecting the correct superior arm of at least 90%; with the additional attractive benefit of reducing the probability of selecting ineffective arms. This approach is comparable to a Bayesian Selection Strategy. The Modified SSD performs substantially better than the other designs in selecting neither arm if the underlying rates for both arms are desirable but equivalent, allowing for other factors to be considered in the decision making process. Though its probability of correctly selecting a superior arm might be reduced, it still performs reasonably well. It also reduces the probability of selecting an inferior arm. SSD provides an easy to implement randomised Phase II design that selects the most promising treatment that has shown sufficient evidence of activity, with available R codes to evaluate its operating characteristics.

Fundamental Vocabulary Selection Based on Word Familiarity

NASA Astrophysics Data System (ADS)

Sato, Hiroshi; Kasahara, Kaname; Kanasugi, Tomoko; Amano, Shigeaki

This paper proposes a new method for selecting fundamental vocabulary. We are presently constructing the Fundamental Vocabulary Knowledge-base of Japanese that contains integrated information on syntax, semantics and pragmatics, for the purposes of advanced natural language processing. This database mainly consists of a lexicon and a treebank: Lexeed (a Japanese Semantic Lexicon) and the Hinoki Treebank. Fundamental vocabulary selection is the first step in the construction of Lexeed. The vocabulary should include sufficient words to describe general concepts for self-expandability, and should not be prohibitively large to construct and maintain. There are two conventional methods for selecting fundamental vocabulary. The first is intuition-based selection by experts. This is the traditional method for making dictionaries. A weak point of this method is that the selection strongly depends on personal intuition. The second is corpus-based selection. This method is superior in objectivity to intuition-based selection, however, it is difficult to compile a sufficiently balanced corpora. We propose a psychologically-motivated selection method that adopts word familiarity as the selection criterion. Word familiarity is a rating that represents the familiarity of a word as a real number ranging from 1 (least familiar) to 7 (most familiar). We determined the word familiarity ratings statistically based on psychological experiments over 32 subjects. We selected about 30,000 words as the fundamental vocabulary, based on a minimum word familiarity threshold of 5. We also evaluated the vocabulary by comparing its word coverage with conventional intuition-based and corpus-based selection over dictionary definition sentences and novels, and demonstrated the superior coverage of our lexicon. Based on this, we conclude that the proposed method is superior to conventional methods for fundamental vocabulary selection.

The Artist-Led Pedagogic Process in the Contemporary Art Gallery: Developing a Meaning Making Framework

ERIC Educational Resources Information Center

Pringle, Emily

2009-01-01

Drawing on recent research which examined how selected artist educators perceive themselves as arts practitioners and analysed how these constructions inform their pedagogy, this article proposes a framework of meaning making in the art gallery. Art practice is defined as a process of conceptual and experiential enquiry which embraces inspiration,…

A Proposal for a Prevalence Study of the Use of Alcohol among Women Attending Selected United States Air Force Prenatal Clinics.

DTIC Science & Technology

1985-06-01

and ptosis 7. epicanthal folds 8. cleft lip or cleft palate 9. hirsuitism APPENDIX 2 PROCESSION PLAN Stage Activit Time Required Phase I step 1 Select...thin upper lip , and/or flattening of the maxillary area II. FETAL ALCOHOL EFFECTS: Any congenital abnormality seen in children as a result of maternal

Direct selective growth of ZnO nanowire arrays from inkjet-printed zinc acetate precursor on a heated substrate

PubMed Central

2013-01-01

Inkjet printing of functional materials has drawn tremendous interest as an alternative to the conventional photolithography-based microelectronics fabrication process development. We introduce direct selective nanowire array growth by inkjet printing of Zn acetate precursor ink patterning and subsequent hydrothermal ZnO local growth without nozzle clogging problem which frequently happens in nanoparticle inkjet printing. The proposed process can directly grow ZnO nanowires in any arbitrary patterned shape, and it is basically very fast, low cost, environmentally benign, and low temperature. Therefore, Zn acetate precursor inkjet printing-based direct nanowire local growth is expected to give extremely high flexibility in nanomaterial patterning for high-performance electronics fabrication especially at the development stage. As a proof of concept of the proposed method, ZnO nanowire network-based field effect transistors and ultraviolet photo-detectors were demonstrated by direct patterned grown ZnO nanowires as active layer. PMID:24252130

Creative thought as blind-variation and selective-retention: Combinatorial models of exceptional creativity

NASA Astrophysics Data System (ADS)

Simonton, Dean Keith

2010-06-01

Campbell (1960) proposed that creative thought should be conceived as a blind-variation and selective-retention process (BVSR). This article reviews the developments that have taken place in the half century that has elapsed since his proposal, with special focus on the use of combinatorial models as formal representations of the general theory. After defining the key concepts of blind variants, creative thought, and disciplinary context, the combinatorial models are specified in terms of individual domain samples, variable field size, ideational combination, and disciplinary communication. Empirical implications are then derived with respect to individual, domain, and field systems. These abstract combinatorial models are next provided substantive reinforcement with respect to findings concerning the cognitive processes, personality traits, developmental factors, and social contexts that contribute to creativity. The review concludes with some suggestions regarding future efforts to explicate creativity according to BVSR theory.

Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

PubMed Central

Kaliappan, Jayakumar; Thiagarajan, Revathi; Sundararajan, Karpagam

2015-01-01

An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate. PMID:26295058

Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection.

PubMed

Kaliappan, Jayakumar; Thiagarajan, Revathi; Sundararajan, Karpagam

2015-01-01

An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.

Development of Advanced Nuclide Separation and Recovery Methods using Ion-Exchanhge Techniques in Nuclear Backend

NASA Astrophysics Data System (ADS)

Miura, Hitoshi

The development of compact separation and recovery methods using selective ion-exchange techniques is very important for the reprocessing and high-level liquid wastes (HLLWs) treatment in the nuclear backend field. The selective nuclide separation techniques are effective for the volume reduction of wastes and the utilization of valuable nuclides, and expected for the construction of advanced nuclear fuel cycle system and the rationalization of waste treatment. In order to accomplish the selective nuclide separation, the design and synthesis of novel adsorbents are essential for the development of compact and precise separation processes. The present paper deals with the preparation of highly functional and selective hybrid microcapsules enclosing nano-adsorbents in the alginate gel polymer matrices by sol-gel methods, their characterization and the clarification of selective adsorption properties by batch and column methods. The selective separation of Cs, Pd and Re in real HLLW was further accomplished by using novel microcapsules, and an advanced nuclide separation system was proposed by the combination of selective processes using microcapsules.

Framework for adaptive multiscale analysis of nonhomogeneous point processes.

PubMed

Helgason, Hannes; Bartroff, Jay; Abry, Patrice

2011-01-01

We develop the methodology for hypothesis testing and model selection in nonhomogeneous Poisson processes, with an eye toward the application of modeling and variability detection in heart beat data. Modeling the process' non-constant rate function using templates of simple basis functions, we develop the generalized likelihood ratio statistic for a given template and a multiple testing scheme to model-select from a family of templates. A dynamic programming algorithm inspired by network flows is used to compute the maximum likelihood template in a multiscale manner. In a numerical example, the proposed procedure is nearly as powerful as the super-optimal procedures that know the true template size and true partition, respectively. Extensions to general history-dependent point processes is discussed.

Attentional neurocomputing

NASA Astrophysics Data System (ADS)

Speidel, Steven

1992-08-01

Our ultimate goal is to develop neural-like cognitive sensory processing within non-neuronal systems. Toward this end, computational models are being developed for selectivity attending the task-relevant parts of composite sensory excitations in an example sound processing application. Significant stimuli partials are selectively attended through the use of generalized neural adaptive beamformers. Computational components are being tested by experiment in the laboratory and also by use of recordings from sensor deployments in the ocean. Results will be presented. These computational components are being integrated into a comprehensive processing architecture that simultaneously attends memory according to stimuli, attends stimuli according to memory, and attends stimuli and memory according to an ongoing thought process. The proposed neural architecture is potentially very fast when implemented in special hardware.

Arc-Welding Spectroscopic Monitoring based on Feature Selection and Neural Networks.

PubMed

Garcia-Allende, P Beatriz; Mirapeix, Jesus; Conde, Olga M; Cobo, Adolfo; Lopez-Higuera, Jose M

2008-10-21

A new spectral processing technique designed for application in the on-line detection and classification of arc-welding defects is presented in this paper. A noninvasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed in two consecutive stages. A compression algorithm is first applied to the data, allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in previous works, giving rise to an improvement in the performance of the monitoring system.

Data-driven process decomposition and robust online distributed modelling for large-scale processes

NASA Astrophysics Data System (ADS)

Shu, Zhang; Lijuan, Li; Lijuan, Yao; Shipin, Yang; Tao, Zou

2018-02-01

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Vibration and acoustic frequency spectra for industrial process modeling using selective fusion multi-condition samples and multi-source features

NASA Astrophysics Data System (ADS)

Tang, Jian; Qiao, Junfei; Wu, ZhiWei; Chai, Tianyou; Zhang, Jian; Yu, Wen

2018-01-01

Frequency spectral data of mechanical vibration and acoustic signals relate to difficult-to-measure production quality and quantity parameters of complex industrial processes. A selective ensemble (SEN) algorithm can be used to build a soft sensor model of these process parameters by fusing valued information selectively from different perspectives. However, a combination of several optimized ensemble sub-models with SEN cannot guarantee the best prediction model. In this study, we use several techniques to construct mechanical vibration and acoustic frequency spectra of a data-driven industrial process parameter model based on selective fusion multi-condition samples and multi-source features. Multi-layer SEN (MLSEN) strategy is used to simulate the domain expert cognitive process. Genetic algorithm and kernel partial least squares are used to construct the inside-layer SEN sub-model based on each mechanical vibration and acoustic frequency spectral feature subset. Branch-and-bound and adaptive weighted fusion algorithms are integrated to select and combine outputs of the inside-layer SEN sub-models. Then, the outside-layer SEN is constructed. Thus, "sub-sampling training examples"-based and "manipulating input features"-based ensemble construction methods are integrated, thereby realizing the selective information fusion process based on multi-condition history samples and multi-source input features. This novel approach is applied to a laboratory-scale ball mill grinding process. A comparison with other methods indicates that the proposed MLSEN approach effectively models mechanical vibration and acoustic signals.

24 CFR 983.51 - Owner proposal selection procedures.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Owner proposal selection procedures... proposal selection procedures. (a) Procedures for selecting PBV proposals. The PHA administrative plan must describe the procedures for owner submission of PBV proposals and for PHA selection of PBV proposals...

Instances selection algorithm by ensemble margin

NASA Astrophysics Data System (ADS)

Saidi, Meryem; Bechar, Mohammed El Amine; Settouti, Nesma; Chikh, Mohamed Amine

2018-05-01

The main limit of data mining algorithms is their inability to deal with the huge amount of available data in a reasonable processing time. A solution of producing fast and accurate results is instances and features selection. This process eliminates noisy or redundant data in order to reduce the storage and computational cost without performances degradation. In this paper, a new instance selection approach called Ensemble Margin Instance Selection (EMIS) algorithm is proposed. This approach is based on the ensemble margin. To evaluate our approach, we have conducted several experiments on different real-world classification problems from UCI Machine learning repository. The pixel-based image segmentation is a field where the storage requirement and computational cost of applied model become higher. To solve these limitations we conduct a study based on the application of EMIS and other instance selection techniques for the segmentation and automatic recognition of white blood cells WBC (nucleus and cytoplasm) in cytological images.

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

Apple Image Processing Educator

NASA Technical Reports Server (NTRS)

Gunther, F. J.

1981-01-01

A software system design is proposed and demonstrated with pilot-project software. The system permits the Apple II microcomputer to be used for personalized computer-assisted instruction in the digital image processing of LANDSAT images. The programs provide data input, menu selection, graphic and hard-copy displays, and both general and detailed instructions. The pilot-project results are considered to be successful indicators of the capabilities and limits of microcomputers for digital image processing education.

An open-loop system design for deep space signal processing applications

NASA Astrophysics Data System (ADS)

Tang, Jifei; Xia, Lanhua; Mahapatra, Rabi

2018-06-01

A novel open-loop system design with high performance is proposed for space positioning and navigation signal processing. Divided by functions, the system has four modules, bandwidth selectable data recorder, narrowband signal analyzer, time-delay difference of arrival estimator and ANFIS supplement processor. A hardware-software co-design approach is made to accelerate computing capability and improve system efficiency. Embedded with the proposed signal processing algorithms, the designed system is capable of handling tasks with high accuracy over long period of continuous measurements. The experiment results show the Doppler frequency tracking root mean square error during 3 h observation is 0.0128 Hz, while the TDOA residue analysis in correlation power spectrum is 0.1166 rad.

Small business innovation research: Program solicitation

NASA Technical Reports Server (NTRS)

1989-01-01

This, the seventh annual SBIR solicitation by NASA, describes the program, identifies eligibility requirements, outlines the required proposal format and content, states proposal preparation and submission requirements, describes the proposal evaluation and award selection process, and provides other information to assist those interested in participating in NASA's SBIR program. It also identifies the Technical Topics and Subtopics in which SBIR Phase 1 proposals are solicited in 1989. These Topics and Subtopics cover a broad range of current NASA interests, but do not necessarily include all areas in which NASA plans or currently conducts research. High-risk high pay-off innovations are desired.

Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

PubMed

Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; Liu, Feng

2017-09-01

Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences. Copyright © 2016 John Wiley & Sons, Ltd.

A Rotational Motion Perception Neural Network Based on Asymmetric Spatiotemporal Visual Information Processing.

PubMed

Hu, Bin; Yue, Shigang; Zhang, Zhuhong

All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.All complex motion patterns can be decomposed into several elements, including translation, expansion/contraction, and rotational motion. In biological vision systems, scientists have found that specific types of visual neurons have specific preferences to each of the three motion elements. There are computational models on translation and expansion/contraction perceptions; however, little has been done in the past to create computational models for rotational motion perception. To fill this gap, we proposed a neural network that utilizes a specific spatiotemporal arrangement of asymmetric lateral inhibited direction selective neural networks (DSNNs) for rotational motion perception. The proposed neural network consists of two parts-presynaptic and postsynaptic parts. In the presynaptic part, there are a number of lateral inhibited DSNNs to extract directional visual cues. In the postsynaptic part, similar to the arrangement of the directional columns in the cerebral cortex, these direction selective neurons are arranged in a cyclic order to perceive rotational motion cues. In the postsynaptic network, the delayed excitation from each direction selective neuron is multiplied by the gathered excitation from this neuron and its unilateral counterparts depending on which rotation, clockwise (cw) or counter-cw (ccw), to perceive. Systematic experiments under various conditions and settings have been carried out and validated the robustness and reliability of the proposed neural network in detecting cw or ccw rotational motion. This research is a critical step further toward dynamic visual information processing.

Writing a Research Proposal to The Research Council of Oman.

PubMed

Al-Shukaili, Ahmed; Al-Maniri, Abdullah

2017-05-01

Writing a research proposal can be a challenging task for young researchers. This article explains how to write a strong research proposal to apply for funding, specifically, a proposal for The Research Council (TRC) of Oman. Three different research proposal application forms are currently used in TRC, including Open Research Grant (ORG), Graduate Research Support Program (GRSP), and Faculty-mentored Undergraduate Research Award Program (FURAP). The application forms are filled and submitted electronically on TRC website. Each of the proposals submitted to TRC is selected through a rigorous reviewing and screening process. Novelty and originality of the research idea is the most crucial element in writing a research proposal. Performing an in-depth review of the literature will assist you to compose a good researchable question and generate a strong hypothesis. The development of a good hypothesis will offer insight into the specific objectives of a study. Research objectives should be focused, measurable, and achievable by a specific time using the most appropriate methodology. Moreover, it is essential to select a proper study design in-line with the purpose of the study and the hypothesis. Furthermore, social/economic impact and reasonable budget of proposed research are important criteria in research proposal evaluation by TRC. Finally, ethical principles should be observed before writing a research proposal involving human or animal subjects.

A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks

NASA Astrophysics Data System (ADS)

Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon

In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

Combining LCT tools for the optimization of an industrial process: material and energy flow analysis and best available techniques.

PubMed

Rodríguez, M T Torres; Andrade, L Cristóbal; Bugallo, P M Bello; Long, J J Casares

2011-09-15

Life cycle thinking (LCT) is one of the philosophies that has recently appeared in the context of the sustainable development. Some of the already existing tools and methods, as well as some of the recently emerged ones, which seek to understand, interpret and design the life of a product, can be included into the scope of the LCT philosophy. That is the case of the material and energy flow analysis (MEFA), a tool derived from the industrial metabolism definition. This paper proposes a methodology combining MEFA with another technique derived from sustainable development which also fits the LCT philosophy, the BAT (best available techniques) analysis. This methodology, applied to an industrial process, seeks to identify the so-called improvable flows by MEFA, so that the appropriate candidate BAT can be selected by BAT analysis. Material and energy inputs, outputs and internal flows are quantified, and sustainable solutions are provided on the basis of industrial metabolism. The methodology has been applied to an exemplary roof tile manufacture plant for validation. 14 Improvable flows have been identified and 7 candidate BAT have been proposed aiming to reduce these flows. The proposed methodology provides a way to detect improvable material or energy flows in a process and selects the most sustainable options to enhance them. Solutions are proposed for the detected improvable flows, taking into account their effectiveness on improving such flows. Copyright © 2011 Elsevier B.V. All rights reserved.

Proposal Auto-Categorizer and Manager for Time Allocation Review at Space Telescope Science Institute

NASA Astrophysics Data System (ADS)

Porter, Sophia; Strolger, Louis-Gregory; Lagerstrom, Jill; Weissman, Sarah

2016-01-01

The Space Telescope Science Institute annually receives more than one thousand formal proposals for Hubble Space Telescope time, exceeding the available time with the observatory by a factor of over four. With JWST, the proposal pressure will only increase, straining our ability to provide rigorous peer review of each proposal's scientific merit. Significant hurdles in this process include the proper categorization of proposals, to ensure Time Allocation Committees (TACs) have the required and desired expertise to fairly and appropriately judge each proposal, and the selection of reviewers themselves, to establish diverse and well-qualified TACs. The Panel Auto-Categorizer and Manager (PACMan; a naive Bayesian classifier) was developed to automatically sort new proposals into their appropriate science categories and, similarly, to appoint panel reviewers with the best qualifications to serve on the corresponding TACs. We will provide an overview of PACMan and present the results of its testing on five previous cycles of proposals. PACMan will be implemented in upcoming cycles to support and eventually replace the process for constructing the time allocation reviews.

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification

PubMed Central

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice. PMID:25823003

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2015-01-01

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Selection of infectious medical waste disposal firms by using the analytic hierarchy process and sensitivity analysis.

PubMed

Hsu, Pi-Fang; Wu, Cheng-Ru; Li, Ya-Ting

2008-01-01

While Taiwanese hospitals dispose of large amounts of medical waste to ensure sanitation and personal hygiene, doing so inefficiently creates potential environmental hazards and increases operational expenses. However, hospitals lack objective criteria to select the most appropriate waste disposal firm and evaluate its performance, instead relying on their own subjective judgment and previous experiences. Therefore, this work presents an analytic hierarchy process (AHP) method to objectively select medical waste disposal firms based on the results of interviews with experts in the field, thus reducing overhead costs and enhancing medical waste management. An appropriate weight criterion based on AHP is derived to assess the effectiveness of medical waste disposal firms. The proposed AHP-based method offers a more efficient and precise means of selecting medical waste firms than subjective assessment methods do, thus reducing the potential risks for hospitals. Analysis results indicate that the medical sector selects the most appropriate infectious medical waste disposal firm based on the following rank: matching degree, contractor's qualifications, contractor's service capability, contractor's equipment and economic factors. By providing hospitals with an effective means of evaluating medical waste disposal firms, the proposed AHP method can reduce overhead costs and enable medical waste management to understand the market demand in the health sector. Moreover, performed through use of Expert Choice software, sensitivity analysis can survey the criterion weight of the degree of influence with an alternative hierarchy.

A universal deep learning approach for modeling the flow of patients under different severities.

PubMed

Jiang, Shancheng; Chin, Kwai-Sang; Tsui, Kwok L

2018-02-01

The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure. Based on this motivation, the objective of this study is to explore an integrated framework with high accuracy for predicting A&ED patient flow under different triage levels, by combining a novel feature selection process with deep neural networks. Administrative data is collected from an actual A&ED and categorized into five groups based on different triage levels. A genetic algorithm (GA)-based feature selection algorithm is improved and implemented as a pre-processing step for this time-series prediction problem, in order to explore key features affecting patient flow. In our improved GA, a fitness-based crossover is proposed to maintain the joint information of multiple features during iterative process, instead of traditional point-based crossover. Deep neural networks (DNN) is employed as the prediction model to utilize their universal adaptability and high flexibility. In the model-training process, the learning algorithm is well-configured based on a parallel stochastic gradient descent algorithm. Two effective regularization strategies are integrated in one DNN framework to avoid overfitting. All introduced hyper-parameters are optimized efficiently by grid-search in one pass. As for feature selection, our improved GA-based feature selection algorithm has outperformed a typical GA and four state-of-the-art feature selection algorithms (mRMR, SAFS, VIFR, and CFR). As for the prediction accuracy of proposed integrated framework, compared with other frequently used statistical models (GLM, seasonal-ARIMA, ARIMAX, and ANN) and modern machine models (SVM-RBF, SVM-linear, RF, and R-LASSO), the proposed integrated "DNN-I-GA" framework achieves higher prediction accuracy on both MAPE and RMSE metrics in pairwise comparisons. The contribution of our study is two-fold. Theoretically, the traditional GA-based feature selection process is improved to have less hyper-parameters and higher efficiency, and the joint information of multiple features is maintained by fitness-based crossover operator. The universal property of DNN is further enhanced by merging different regularization strategies. Practically, features selected by our improved GA can be used to acquire an underlying relationship between patient flows and input features. Predictive values are significant indicators of patients' demand and can be used by A&ED managers to make resource planning and allocation. High accuracy achieved by the present framework in different cases enhances the reliability of downstream decision makings. Copyright © 2017 Elsevier B.V. All rights reserved.

Medical student selection and society: Lessons we learned from sociological theories.

PubMed

Yaghmaei, Minoo; Yazdani, Shahram; Ahmady, Soleiman

2016-01-01

The aim of this study was to show the interaction between the society, applicants and medical schools in terms of medical student selection. In this study, the trends to implement social factors in the selection process were highlighted. These social factors were explored through functionalism and conflict theories, each focusing on different categories of social factors. While functionalist theorists pay attention to diversity in the selection process, conflict theorists highlight the importance of socio-economic class. Although both theories believe in sorting, their different views are reflected in their sorting strategies. Both theories emphasize the importance of the person-society relationship in motivation to enter university. Furthermore, the impacts of social goals on the selection policies are derived from both theories. Theories in the sociology of education offer an approach to student selection that acknowledges and supports complexity, plurality of approaches and innovative means of selection. Medical student selection does not solely focus on the individual assessment and qualification, but it focuses on a social and collective process, which includes all the influences and interactions between the medical schools and the society. Sociological perspective of medical student selection proposes a model that envelops the individual and the society. In this model, the selection methods should meet the criteria of merit at the individual level, while the selection policies should aim at the society goals at the institutional level.

Range Process Simulation Tool

NASA Technical Reports Server (NTRS)

Phillips, Dave; Haas, William; Barth, Tim; Benjamin, Perakath; Graul, Michael; Bagatourova, Olga

2005-01-01

Range Process Simulation Tool (RPST) is a computer program that assists managers in rapidly predicting and quantitatively assessing the operational effects of proposed technological additions to, and/or upgrades of, complex facilities and engineering systems such as the Eastern Test Range. Originally designed for application to space transportation systems, RPST is also suitable for assessing effects of proposed changes in industrial facilities and large organizations. RPST follows a model-based approach that includes finite-capacity schedule analysis and discrete-event process simulation. A component-based, scalable, open architecture makes RPST easily and rapidly tailorable for diverse applications. Specific RPST functions include: (1) definition of analysis objectives and performance metrics; (2) selection of process templates from a processtemplate library; (3) configuration of process models for detailed simulation and schedule analysis; (4) design of operations- analysis experiments; (5) schedule and simulation-based process analysis; and (6) optimization of performance by use of genetic algorithms and simulated annealing. The main benefits afforded by RPST are provision of information that can be used to reduce costs of operation and maintenance, and the capability for affordable, accurate, and reliable prediction and exploration of the consequences of many alternative proposed decisions.

Enhancement of the Comb Filtering Selectivity Using Iterative Moving Average for Periodic Waveform and Harmonic Elimination

PubMed Central

Wu, Yan; Aarts, Ronald M.

2018-01-01

A recurring problem regarding the use of conventional comb filter approaches for elimination of periodic waveforms is the degree of selectivity achieved by the filtering process. Some applications, such as the gradient artefact correction in EEG recordings during coregistered EEG-fMRI, require a highly selective comb filtering that provides effective attenuation in the stopbands and gain close to unity in the pass-bands. In this paper, we present a novel comb filtering implementation whereby the iterative filtering application of FIR moving average-based approaches is exploited in order to enhance the comb filtering selectivity. Our results indicate that the proposed approach can be used to effectively approximate the FIR moving average filter characteristics to those of an ideal filter. A cascaded implementation using the proposed approach shows to further increase the attenuation in the filter stopbands. Moreover, broadening of the bandwidth of the comb filtering stopbands around −3 dB according to the fundamental frequency of the stopband can be achieved by the novel method, which constitutes an important characteristic to account for broadening of the harmonic gradient artefact spectral lines. In parallel, the proposed filtering implementation can also be used to design a novel notch filtering approach with enhanced selectivity as well. PMID:29599955

Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease.

PubMed

Vivekanandan, T; Sriman Narayana Iyengar, N Ch

2017-11-01

Enormous data growth in multiple domains has posed a great challenge for data processing and analysis techniques. In particular, the traditional record maintenance strategy has been replaced in the healthcare system. It is vital to develop a model that is able to handle the huge amount of e-healthcare data efficiently. In this paper, the challenging tasks of selecting critical features from the enormous set of available features and diagnosing heart disease are carried out. Feature selection is one of the most widely used pre-processing steps in classification problems. A modified differential evolution (DE) algorithm is used to perform feature selection for cardiovascular disease and optimization of selected features. Of the 10 available strategies for the traditional DE algorithm, the seventh strategy, which is represented by DE/rand/2/exp, is considered for comparative study. The performance analysis of the developed modified DE strategy is given in this paper. With the selected critical features, prediction of heart disease is carried out using fuzzy AHP and a feed-forward neural network. Various performance measures of integrating the modified differential evolution algorithm with fuzzy AHP and a feed-forward neural network in the prediction of heart disease are evaluated in this paper. The accuracy of the proposed hybrid model is 83%, which is higher than that of some other existing models. In addition, the prediction time of the proposed hybrid model is also evaluated and has shown promising results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Economic feasibility of solar thermal industrial applications and selected case studies

NASA Astrophysics Data System (ADS)

Montelione, A.; Boyd, D.; Branz, M.

1981-12-01

The economic feasibility is assessed of utilizing solar energy to augment an existing fossil fuel system to generate industrial process heat. Several case studies in the textile and food processing industries in the southern United States were analyzed. Sensitivity analyses were performed, and comparisons illustrating the effects of the Economic Recovery Tax Act of 1981 were made. The economic desirability of the proposed solar systems varied with the type of system selected, location of the facility, state tax credits, and type of fuel displaced. For those systems presently not economical, the projected time to economic feasibility was ascertained.

Promotion of cooperation in evolutionary game dynamics with local information.

PubMed

Liu, Xuesong; Pan, Qiuhui; He, Mingfeng

2018-01-21

In this paper, we propose a strategy-updating rule driven by local information, which is called Local process. Unlike the standard Moran process, the Local process does not require global information about the strategic environment. By analyzing the dynamical behavior of the system, we explore how the local information influences the fixation of cooperation in two-player evolutionary games. Under weak selection, the decreasing local information leads to an increase of the fixation probability when natural selection does not favor cooperation replacing defection. In the limit of sufficiently large selection, the analytical results indicate that the fixation probability increases with the decrease of the local information, irrespective of the evolutionary games. Furthermore, for the dominance of defection games under weak selection and for coexistence games, the decreasing of local information will lead to a speedup of a single cooperator taking over the population. Overall, to some extent, the local information is conducive to promoting the cooperation. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing

PubMed Central

Long, Chengjiang; Hua, Gang; Kapoor, Ashish

2015-01-01

We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. PMID:26924892

Fitness consequences of plants growing with siblings: reconciling kin selection, niche partitioning and competitive ability

PubMed Central

File, Amanda L.; Murphy, Guillermo P.; Dudley, Susan A.

2012-01-01

Plant studies that have investigated the fitness consequences of growing with siblings have found conflicting evidence that can support different theoretical frameworks. Depending on whether siblings or strangers have higher fitness in competition, kin selection, niche partitioning and competitive ability have been invoked. Here, we bring together these processes in a conceptual synthesis and argue that they can be co-occurring. We propose that these processes can be reconciled and argue for a trait-based approach of measuring natural selection instead of the fitness-based approach to the study of sibling competition. This review will improve the understanding of how plants interact socially under competitive situations, and provide a framework for future studies. PMID:22072602

Dynamic video encryption algorithm for H.264/AVC based on a spatiotemporal chaos system.

PubMed

Xu, Hui; Tong, Xiao-Jun; Zhang, Miao; Wang, Zhu; Li, Ling-Hao

2016-06-01

Video encryption schemes mostly employ the selective encryption method to encrypt parts of important and sensitive video information, aiming to ensure the real-time performance and encryption efficiency. The classic block cipher is not applicable to video encryption due to the high computational overhead. In this paper, we propose the encryption selection control module to encrypt video syntax elements dynamically which is controlled by the chaotic pseudorandom sequence. A novel spatiotemporal chaos system and binarization method is used to generate a key stream for encrypting the chosen syntax elements. The proposed scheme enhances the resistance against attacks through the dynamic encryption process and high-security stream cipher. Experimental results show that the proposed method exhibits high security and high efficiency with little effect on the compression ratio and time cost.

Improving tablet coating robustness by selecting critical process parameters from retrospective data.

PubMed

Galí, A; García-Montoya, E; Ascaso, M; Pérez-Lozano, P; Ticó, J R; Miñarro, M; Suñé-Negre, J M

2016-09-01

Although tablet coating processes are widely used in the pharmaceutical industry, they often lack adequate robustness. Up-scaling can be challenging as minor changes in parameters can lead to varying quality results. To select critical process parameters (CPP) using retrospective data of a commercial product and to establish a design of experiments (DoE) that would improve the robustness of the coating process. A retrospective analysis of data from 36 commercial batches. Batches were selected based on the quality results generated during batch release, some of which revealed quality deviations concerning the appearance of the coated tablets. The product is already marketed and belongs to the portfolio of a multinational pharmaceutical company. The Statgraphics 5.1 software was used for data processing to determine critical process parameters in order to propose new working ranges. This study confirms that it is possible to determine the critical process parameters and create design spaces based on retrospective data of commercial batches. This type of analysis is thus converted into a tool to optimize the robustness of existing processes. Our results show that a design space can be established with minimum investment in experiments, since current commercial batch data are processed statistically.

How operational issues impact science peer review

NASA Astrophysics Data System (ADS)

Blacker, Brett S.; Golombek, Daniel; Macchetto, Duccio

2006-06-01

In some eyes, the Phase I proposal selection process is the most important activity handled by the Space Telescope Science Institute (STScI). Proposing for HST and other missions consists of requesting observing time and/or archival research funding. This step is called Phase I, where the scientific merit of a proposal is considered by a community based peer-review process. Accepted proposals then proceed thru Phase II, where the observations are specified in sufficient detail to enable scheduling on the telescope. Each cycle the Hubble Space Telescope (HST) Telescope Allocation Committee (TAC) reviews proposals and awards observing time that is valued at $0.5B, when the total expenditures for HST over its lifetime are figured on an annual basis. This is in fact a very important endeavor that we continue to fine-tune and tweak. This process is open to the science community and we constantly receive comments and praise for this process. In this last year we have had to deal with the loss of the Space Telescope Imaging Spectrograph (STIS) and move from 3-gyro operations to 2-gyro operations. This paper will outline how operational issues impact the HST science peer review process. We will discuss the process that was used to recover from the loss of the STIS instrument and how we dealt with the loss of 1/3 of the current science observations. We will also discuss the issues relating to 3-gyro vs. 2-gyro operations and how that changes impacted Proposers, our in-house processing and the TAC.

Extraction and characterization of holocellulose fibers by microwave-assisted selective liquefaction of bamboo

Treesearch

Jiulong Xie; Chung Hse; Todd F. Shupe; Hui Pan; Tingxing Hu

2016-01-01

Microwave-assisted selective liquefaction was proposed and used as a novel method for the isolation of holocellulose fibers. The results showed that the bamboo lignin component and extractives were almost completely removed by using a liquefaction process at 120 8C for 9 min, and the residual lignin and extractives in the solid residue were as low as 0.65% and 0.49%,...

Efficient least angle regression for identification of linear-in-the-parameters models

PubMed Central

Beach, Thomas H.; Rezgui, Yacine

2017-01-01

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm. PMID:28293140

Fast Video Encryption Using the H.264 Error Propagation Property for Smart Mobile Devices

PubMed Central

Chung, Yongwha; Lee, Sungju; Jeon, Taewoong; Park, Daihee

2015-01-01

In transmitting video data securely over Video Sensor Networks (VSNs), since mobile handheld devices have limited resources in terms of processor clock speed and battery size, it is necessary to develop an efficient method to encrypt video data to meet the increasing demand for secure connections. Selective encryption methods can reduce the amount of computation needed while satisfying high-level security requirements. This is achieved by selecting an important part of the video data and encrypting it. In this paper, to ensure format compliance and security, we propose a special encryption method for H.264, which encrypts only the DC/ACs of I-macroblocks and the motion vectors of P-macroblocks. In particular, the proposed new selective encryption method exploits the error propagation property in an H.264 decoder and improves the collective performance by analyzing the tradeoff between the visual security level and the processing speed compared to typical selective encryption methods (i.e., I-frame, P-frame encryption, and combined I-/P-frame encryption). Experimental results show that the proposed method can significantly reduce the encryption workload without any significant degradation of visual security. PMID:25850068

A Planetary Protection Strategy for the Mars Aerial Regional-Scale Environmental Survey (ARES) Mission Concept

NASA Technical Reports Server (NTRS)

Kuhl, Christopher A.

2008-01-01

The Aerial Regional-scale Environmental Survey (ARES) is a Mars exploration mission concept designed to send an airplane to fly through the lower atmosphere of Mars, with the goal of taking scientific measurements of the atmosphere, surface, and subsurface phenomenon. ARES was first proposed to the Mars Scout program in December 2002 for a 2007 launch opportunity and was selected to proceed with a Phase A study, step-2 proposal which was submitted in May 2003. ARES was not selected for the Scout mission, but efforts continued on risk reduction of the atmospheric flight system in preparation for the next Mars Scout opportunity in 2006. The ARES concept was again proposed in July 2006 to the Mars Scout program but was not selected to proceed into Phase A. This document describes the Planetary Protection strategy that was developed in ARES Pre Phase-A activities to help identify, early in the design process, certain hardware, assemblies, and/or subsystems that will require unique design considerations based on constraints imposed by Planetary Protection requirements. Had ARES been selected as an exploration project, information in this document would make up the ARES Project Planetary Protection Plan.

A new web-based framework development for fuzzy multi-criteria group decision-making.

PubMed

Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

2016-01-01

Fuzzy multi-criteria group decision making (FMCGDM) process is usually used when a group of decision-makers faces imprecise data or linguistic variables to solve the problems. However, this process contains many methods that require many time-consuming calculations depending on the number of criteria, alternatives and decision-makers in order to reach the optimal solution. In this study, a web-based FMCGDM framework that offers decision-makers a fast and reliable response service is proposed. The proposed framework includes commonly used tools for multi-criteria decision-making problems such as fuzzy Delphi, fuzzy AHP and fuzzy TOPSIS methods. The integration of these methods enables taking advantages of the strengths and complements each method's weakness. Finally, a case study of location selection for landfill waste in Morocco is performed to demonstrate how this framework can facilitate decision-making process. The results demonstrate that the proposed framework can successfully accomplish the goal of this study.

The new millennium program: Fast-track procurements

NASA Astrophysics Data System (ADS)

Metzger, Robert M.

1996-11-01

The National Aeronautics and Space Administration's (NASA's) New Millennium Program (NMP) has embarked on a technology flight-validation demonstration program to enable the kinds of missions that NASA envisions for the 21st century. Embedded in this program is the concept of rapid mission development supported by a fast-track procurement process. This process begins with the decision to initiate a procurement very early in the program along with the formation of a technical acquisition team. A close working relationship among the team members is essential to avoiding delays and developing a clear acquisition plan. The request for proposal (RFP) that is subsequently issued seeks a company with proven capabilities, so that the time allotted for responses from proposers and the length of proposals they submit can be shortened. The fast-track procurement process has been demonstrated during selection of NMP's industrial partners and has been proven to work.

10 CFR 1005.7 - How does the Secretary communicate with state and local officials concerning the Department's...

Code of Federal Regulations, 2010 CFR

2010-01-01

..., areawide, regional, and local entities in a state of proposed Federal financial assistance or direct Federal development if: (1) The state has not adopted a process under the Order; or (2) The assistance or development involves a program or activity not selected for the state process. This notice may be made by...

39 CFR 778.7 - How does the Postal Service communicate with state and local officials concerning the Postal...

Code of Federal Regulations, 2010 CFR

2010-07-01

... notice directly to affected state, areawide, regional, and local entities in a state of a proposed direct Federal development project if: (1) The state has not adopted a process under the Order; or (2) The development project involves a facility project action category not selected for the state process. This...

34 CFR 79.7 - How does the Secretary communicate with State and local officials concerning the Department's...

Code of Federal Regulations, 2010 CFR

2010-07-01

... state, areawide, regional, and local entities in a state of proposed federal financial assistance if: (i) The state has not adopted a process under the Order; or (ii) The assistance involves a program or activity not selected for the state process. (2) This notice may be made by publication in the Federal...

34 CFR 79.7 - How does the Secretary communicate with State and local officials concerning the Department's...

Code of Federal Regulations, 2014 CFR

2014-07-01

... state, areawide, regional, and local entities in a state of proposed federal financial assistance if: (i) The state has not adopted a process under the Order; or (ii) The assistance involves a program or activity not selected for the state process. (2) This notice may be made by publication in the Federal...

Evaluation Model for Applying an E-Learning System in a Course: An Analytic Hierarchy Process-Multi-Choice Goal Programming Approach

ERIC Educational Resources Information Center

Lin, Teng-Chiao; Ho, Hui-Ping; Chang, Ching-Ter

2014-01-01

With the widespread use of the Internet, adopting e-learning systems in courses has gradually become more and more important in universities in Taiwan. However, because of limitations of teachers' time, selecting suitable online IT tools has become very important. This study proposes an analytic hierarchy process (AHP)-multi-choice goal…

Universal Darwinism As a Process of Bayesian Inference.

PubMed

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

Universal Darwinism As a Process of Bayesian Inference

PubMed Central

Campbell, John O.

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an “experiment” in the external world environment, and the results of that “experiment” or the “surprise” entailed by predicted and actual outcomes of the “experiment.” Minimization of free energy implies that the implicit measure of “surprise” experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature. PMID:27375438

A competitive interaction theory of attentional selection and decision making in brief, multielement displays.

PubMed

Smith, Philip L; Sewell, David K

2013-07-01

We generalize the integrated system model of Smith and Ratcliff (2009) to obtain a new theory of attentional selection in brief, multielement visual displays. The theory proposes that attentional selection occurs via competitive interactions among detectors that signal the presence of task-relevant features at particular display locations. The outcome of the competition, together with attention, determines which stimuli are selected into visual short-term memory (VSTM). Decisions about the contents of VSTM are made by a diffusion-process decision stage. The selection process is modeled by coupled systems of shunting equations, which perform gated where-on-what pathway VSTM selection. The theory provides a computational account of key findings from attention tasks with near-threshold stimuli. These are (a) the success of the MAX model of visual search and spatial cuing, (b) the distractor homogeneity effect, (c) the double-target detection deficit, (d) redundancy costs in the post-stimulus probe task, (e) the joint item and information capacity limits of VSTM, and (f) the object-based nature of attentional selection. We argue that these phenomena are all manifestations of an underlying competitive VSTM selection process, which arise as a natural consequence of our theory. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Parameter estimation procedure for complex non-linear systems: calibration of ASM No. 1 for N-removal in a full-scale oxidation ditch.

PubMed

Abusam, A; Keesman, K J; van Straten, G; Spanjers, H; Meinema, K

2001-01-01

When applied to large simulation models, the process of parameter estimation is also called calibration. Calibration of complex non-linear systems, such as activated sludge plants, is often not an easy task. On the one hand, manual calibration of such complex systems is usually time-consuming, and its results are often not reproducible. On the other hand, conventional automatic calibration methods are not always straightforward and often hampered by local minima problems. In this paper a new straightforward and automatic procedure, which is based on the response surface method (RSM) for selecting the best identifiable parameters, is proposed. In RSM, the process response (output) is related to the levels of the input variables in terms of a first- or second-order regression model. Usually, RSM is used to relate measured process output quantities to process conditions. However, in this paper RSM is used for selecting the dominant parameters, by evaluating parameters sensitivity in a predefined region. Good results obtained in calibration of ASM No. 1 for N-removal in a full-scale oxidation ditch proved that the proposed procedure is successful and reliable.

Design and Optimization of Composite Automotive Hatchback Using Integrated Material-Structure-Process-Performance Method

NASA Astrophysics Data System (ADS)

Yang, Xudong; Sun, Lingyu; Zhang, Cheng; Li, Lijun; Dai, Zongmiao; Xiong, Zhenkai

2018-03-01

The application of polymer composites as a substitution of metal is an effective approach to reduce vehicle weight. However, the final performance of composite structures is determined not only by the material types, structural designs and manufacturing process, but also by their mutual restrict. Hence, an integrated "material-structure-process-performance" method is proposed for the conceptual and detail design of composite components. The material selection is based on the principle of composite mechanics such as rule of mixture for laminate. The design of component geometry, dimension and stacking sequence is determined by parametric modeling and size optimization. The selection of process parameters are based on multi-physical field simulation. The stiffness and modal constraint conditions were obtained from the numerical analysis of metal benchmark under typical load conditions. The optimal design was found by multi-discipline optimization. Finally, the proposed method was validated by an application case of automotive hatchback using carbon fiber reinforced polymer. Compared with the metal benchmark, the weight of composite one reduces 38.8%, simultaneously, its torsion and bending stiffness increases 3.75% and 33.23%, respectively, and the first frequency also increases 44.78%.

Feature-selective attention in healthy old age: a selective decline in selective attention?

PubMed

Quigley, Cliodhna; Müller, Matthias M

2014-02-12

Deficient selection against irrelevant information has been proposed to underlie age-related cognitive decline. We recently reported evidence for maintained early sensory selection when older and younger adults used spatial selective attention to perform a challenging task. Here we explored age-related differences when spatial selection is not possible and feature-selective attention must be deployed. We additionally compared the integrity of feedforward processing by exploiting the well established phenomenon of suppression of visual cortical responses attributable to interstimulus competition. Electroencephalogram was measured while older and younger human adults responded to brief occurrences of coherent motion in an attended stimulus composed of randomly moving, orientation-defined, flickering bars. Attention was directed to horizontal or vertical bars by a pretrial cue, after which two orthogonally oriented, overlapping stimuli or a single stimulus were presented. Horizontal and vertical bars flickered at different frequencies and thereby elicited separable steady-state visual-evoked potentials, which were used to examine the effect of feature-based selection and the competitive influence of a second stimulus on ongoing visual processing. Age differences were found in feature-selective attentional modulation of visual responses: older adults did not show consistent modulation of magnitude or phase. In contrast, the suppressive effect of a second stimulus was robust and comparable in magnitude across age groups, suggesting that bottom-up processing of the current stimuli is essentially unchanged in healthy old age. Thus, it seems that visual processing per se is unchanged, but top-down attentional control is compromised in older adults when space cannot be used to guide selection.

Extremely Robust and Patternable Electrodes for Copy-Paper-Based Electronics.

PubMed

Ahn, Jaeho; Seo, Ji-Won; Lee, Tae-Ik; Kwon, Donguk; Park, Inkyu; Kim, Taek-Soo; Lee, Jung-Yong

2016-07-27

We propose a fabrication process for extremely robust and easily patternable silver nanowire (AgNW) electrodes on paper. Using an auxiliary donor layer and a simple laminating process, AgNWs can be easily transferred to copy paper as well as various other substrates using a dry process. Intercalating a polymeric binder between the AgNWs and the substrate through a simple printing technique enhances adhesion, not only guaranteeing high foldability of the electrodes, but also facilitating selective patterning of the AgNWs. Using the proposed process, extremely crease-tolerant electronics based on copy paper can be fabricated, such as a printed circuit board for a 7-segment display, portable heater, and capacitive touch sensor, demonstrating the applicability of the AgNWs-based electrodes to paper electronics.

Software component quality evaluation

NASA Technical Reports Server (NTRS)

Clough, A. J.

1991-01-01

The paper describes a software inspection process that can be used to evaluate the quality of software components. Quality criteria, process application, independent testing of the process and proposed associated tool support are covered. Early results indicate that this technique is well suited for assessing software component quality in a standardized fashion. With automated machine assistance to facilitate both the evaluation and selection of software components, such a technique should promote effective reuse of software components.

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Model-based design of an intermittent simulated moving bed process for recovering lactic acid from ternary mixture.

PubMed

Song, Mingkai; Cui, Linlin; Kuang, Han; Zhou, Jingwei; Yang, Pengpeng; Zhuang, Wei; Chen, Yong; Liu, Dong; Zhu, Chenjie; Chen, Xiaochun; Ying, Hanjie; Wu, Jinglan

2018-08-10

An intermittent simulated moving bed (3F-ISMB) operation scheme, the extension of the 3W-ISMB to the non-linear adsorption region, has been introduced for separation of glucose, lactic acid and acetic acid ternary-mixture. This work focuses on exploring the feasibility of the proposed process theoretically and experimentally. Firstly, the real 3F-ISMB model coupled with the transport dispersive model (TDM) and the Modified-Langmuir isotherm was established to build up the separation parameter plane. Subsequently, three operating conditions were selected from the plane to run the 3F-ISMB unit. The experimental results were used to verify the model. Afterwards, the influences of the various flow rates on the separation performances were investigated systematically by means of the validated 3F-ISMB model. The intermittent-retained component lactic acid was finally obtained with the purity of 98.5%, recovery of 95.5% and the average concentration of 38 g/L. The proposed 3F-ISMB process can efficiently separate the mixture with low selectivity into three fractions. Copyright © 2018 Elsevier B.V. All rights reserved.

Adaptive Skeletal Muscle Action Requires Anticipation and “Conscious Broadcasting”

PubMed Central

Poehlman, T. Andrew; Jantz, Tiffany K.; Morsella, Ezequiel

2012-01-01

Historically, the conscious and anticipatory processes involved in voluntary action have been associated with the loftiest heights of nervous function. Concepts like mental time travel, “theory of mind,” and the formation of “the self” have been at the center of many attempts to determine the purpose of consciousness. Eventually, more reductionistic accounts of consciousness emerged, proposing rather that conscious states play a much more basic role in nervous function. Though the widely held integration consensus proposes that conscious states integrate information-processing structures and events that would otherwise be independent, Supramodular Interaction Theory (SIT) argues that conscious states are necessary for the integration of only certain kinds of information. As revealed in this selective review, this integration is related to what is casually referred to as “voluntary” action, which is intimately related to the skeletal muscle output system. Through a peculiar form of broadcasting, conscious integration often controls and guides action via “ideomotor” mechanisms, where anticipatory processes play a central role. Our selective review covers evidence (including findings from anesthesia research) for the integration consensus, SIT, and ideomotor theory. PMID:23264766

Proposed standards for peer-reviewed publication of computer code

USDA-ARS?s Scientific Manuscript database

Computer simulation models are mathematical abstractions of physical systems. In the area of natural resources and agriculture, these physical systems encompass selected interacting processes in plants, soils, animals, or watersheds. These models are scientific products and have become important i...

Investigation of proposed process sequence for the array automated assembly task, phases 1 and 2

NASA Technical Reports Server (NTRS)

Mardesich, N.; Garcia, A.; Eskenas, K.

1980-01-01

Progress was made on the process sequence for module fabrication. A shift from bonding with a conformal coating to laminating with ethylene vinyl acetate and a glass superstrate is recommended for further module fabrication. The processes that were retained for the selected process sequence, spin-on diffusion, print and fire aluminum p+ back, clean, print and fire silver front contact and apply tin pad to aluminum back, were evaluated for their cost contribution.

A cognitive model for multidigit number reading: Inferences from individuals with selective impairments.

PubMed

Dotan, Dror; Friedmann, Naama

2018-04-01

We propose a detailed cognitive model of multi-digit number reading. The model postulates separate processes for visual analysis of the digit string and for oral production of the verbal number. Within visual analysis, separate sub-processes encode the digit identities and the digit order, and additional sub-processes encode the number's decimal structure: its length, the positions of 0, and the way it is parsed into triplets (e.g., 314987 → 314,987). Verbal production consists of a process that generates the verbal structure of the number, and another process that retrieves the phonological forms of each number word. The verbal number structure is first encoded in a tree-like structure, similarly to syntactic trees of sentences, and then linearized to a sequence of number-word specifiers. This model is based on an investigation of the number processing abilities of seven individuals with different selective deficits in number reading. We report participants with impairment in specific sub-processes of the visual analysis of digit strings - in encoding the digit order, in encoding the number length, or in parsing the digit string to triplets. Other participants were impaired in verbal production, making errors in the number structure (shifts of digits to another decimal position, e.g., 3,040 → 30,004). Their selective deficits yielded several dissociations: first, we found a double dissociation between visual analysis deficits and verbal production deficits. Second, several dissociations were found within visual analysis: a double dissociation between errors in digit order and errors in the number length; a dissociation between order/length errors and errors in parsing the digit string into triplets; and a dissociation between the processing of different digits - impaired order encoding of the digits 2-9, without errors in the 0 position. Third, within verbal production, a dissociation was found between digit shifts and substitutions of number words. A selective deficit in any of the processes described by the model would cause difficulties in number reading, which we propose to term "dysnumeria". Copyright © 2017 Elsevier Ltd. All rights reserved.

Arc-welding quality assurance by means of embedded fiber sensor and spectral processing combining feature selection and neural networks

NASA Astrophysics Data System (ADS)

Mirapeix, J.; García-Allende, P. B.; Cobo, A.; Conde, O.; López-Higuera, J. M.

2007-07-01

A new spectral processing technique designed for its application in the on-line detection and classification of arc-welding defects is presented in this paper. A non-invasive fiber sensor embedded within a TIG torch collects the plasma radiation originated during the welding process. The spectral information is then processed by means of two consecutive stages. A compression algorithm is first applied to the data allowing real-time analysis. The selected spectral bands are then used to feed a classification algorithm, which will be demonstrated to provide an efficient weld defect detection and classification. The results obtained with the proposed technique are compared to a similar processing scheme presented in a previous paper, giving rise to an improvement in the performance of the monitoring system.

Development of spent fuel reprocessing process based on selective sulfurization: Study on the Pu, Np and Am sulfurization

NASA Astrophysics Data System (ADS)

Kirishima, Akira; Amano, Yuuki; Nihei, Toshifumi; Mitsugashira, Toshiaki; Sato, Nobuaki

2010-03-01

For the recovery of fissile materials from spent nuclear fuel, we have proposed a novel reprocessing process based on selective sulfurization of fission products (FPs). The key concept of this process is utilization of unique chemical property of carbon disulfide (CS2), i.e., it works as a reductant for U3O8 but works as a sulfurizing agent for minor actinides and lanthanides. Sulfurized FPs and minor actinides (MA) are highly soluble to dilute nitric acid while UO2 and PuO2 are hardly soluble, therefore, FPs and MA can be removed from Uranium and Plutonium matrix by selective dissolution. As a feasibility study of this new concept, the sulfurization behaviours of U, Pu, Np, Am and Eu are investigated in this paper by the thermodynamical calculation, phase analysis of chemical analogue elements and tracer experiments.

Site-selective substitutional doping with atomic precision on stepped Al (111) surface by single-atom manipulation

PubMed Central

2014-01-01

In fabrication of nano- and quantum devices, it is sometimes critical to position individual dopants at certain sites precisely to obtain the specific or enhanced functionalities. With first-principles simulations, we propose a method for substitutional doping of individual atom at a certain position on a stepped metal surface by single-atom manipulation. A selected atom at the step of Al (111) surface could be extracted vertically with an Al trimer-apex tip, and then the dopant atom will be positioned to this site. The details of the entire process including potential energy curves are given, which suggests the reliability of the proposed single-atom doping method. PMID:24899871

Site-selective substitutional doping with atomic precision on stepped Al (111) surface by single-atom manipulation.

PubMed

Chen, Chang; Zhang, Jinhu; Dong, Guofeng; Shao, Hezhu; Ning, Bo-Yuan; Zhao, Li; Ning, Xi-Jing; Zhuang, Jun

2014-01-01

In fabrication of nano- and quantum devices, it is sometimes critical to position individual dopants at certain sites precisely to obtain the specific or enhanced functionalities. With first-principles simulations, we propose a method for substitutional doping of individual atom at a certain position on a stepped metal surface by single-atom manipulation. A selected atom at the step of Al (111) surface could be extracted vertically with an Al trimer-apex tip, and then the dopant atom will be positioned to this site. The details of the entire process including potential energy curves are given, which suggests the reliability of the proposed single-atom doping method.

Wavelength selective switch array employing silica-based waveguide frontend with integrated polarization diversity optics.

PubMed

Sakamaki, Yohei; Shikama, Kota; Ikuma, Yuichiro; Suzuki, Kenya

2017-08-21

We propose a waveguide frontend with integrated polarization diversity optics for a wavelength selective switch (WSS) array with a liquid crystal on silicon switching engine to simplify the free space optics configuration and the alignment process in optical modules. The polarization diversity function is realized by the integration of a waveguide-type polarization beam splitter and a polarization rotating half-wave plate in a beam launcher using silica-based planar lightwave circuit technology. We confirmed experimentally the feasibility of using our proposed waveguide frontend in a two-in-one 1 × 20 WSS. The experimental results show that the fabricated waveguide frontend provides a polarization diversity function without any degradation in optical performance.

An Analytic Network Process approach for the environmental aspect selection problem — A case study for a hand blender

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

Bereketli Zafeirakopoulos, Ilke, E-mail: ibereketli@gsu.edu.tr; Erol Genevois, Mujde, E-mail: merol@gsu.edu.tr

Life Cycle Assessment is a tool to assess, in a systematic way, the environmental aspects and its potential environmental impacts and resources used throughout a product's life cycle. It is widely accepted and considered as one of the most powerful tools to support decision-making processes used in ecodesign and sustainable production in order to learn about the most problematic parts and life cycle phases of a product and to have a projection for future improvements. However, since Life Cycle Assessment is a cost and time intensive method, companies do not intend to carry out a full version of it, exceptmore » for large corporate ones. Especially for small and medium sized enterprises, which do not have enough budget for and knowledge on sustainable production and ecodesign approaches, focusing only on the most important possible environmental aspect is unavoidable. In this direction, finding the right environmental aspect to work on is crucial for the companies. In this study, a multi-criteria decision-making methodology, Analytic Network Process is proposed to select the most relevant environmental aspect. The proposed methodology aims at providing a simplified environmental assessment to producers. It is applied for a hand blender, which is a member of the Electrical and Electronic Equipment family. The decision criteria for the environmental aspects and relations of dependence are defined. The evaluation is made by the Analytic Network Process in order to create a realistic approach to inter-dependencies among the criteria. The results are computed via the Super Decisions software. Finally, it is observed that the procedure is completed in less time, with less data, with less cost and in a less subjective way than conventional approaches. - Highlights: • We present a simplified environmental assessment methodology to support LCA. • ANP is proposed to select the most relevant environmental aspect. • ANP deals well with the interdependencies between aspects and impacts. • The methodology is less subjective, less complicated, and less time–money consuming. • The proposed methodology is suitable for use by SMEs.« less

A production planning model considering uncertain demand using two-stage stochastic programming in a fresh vegetable supply chain context.

PubMed

Mateo, Jordi; Pla, Lluis M; Solsona, Francesc; Pagès, Adela

2016-01-01

Production planning models are achieving more interest for being used in the primary sector of the economy. The proposed model relies on the formulation of a location model representing a set of farms susceptible of being selected by a grocery shop brand to supply local fresh products under seasonal contracts. The main aim is to minimize overall procurement costs and meet future demand. This kind of problem is rather common in fresh vegetable supply chains where producers are located in proximity either to processing plants or retailers. The proposed two-stage stochastic model determines which suppliers should be selected for production contracts to ensure high quality products and minimal time from farm-to-table. Moreover, Lagrangian relaxation and parallel computing algorithms are proposed to solve these instances efficiently in a reasonable computational time. The results obtained show computational gains from our algorithmic proposals in front of the usage of plain CPLEX solver. Furthermore, the results ensure the competitive advantages of using the proposed model by purchase managers in the fresh vegetables industry.

A Study on Real-Time Scheduling Methods in Holonic Manufacturing Systems

NASA Astrophysics Data System (ADS)

Iwamura, Koji; Taimizu, Yoshitaka; Sugimura, Nobuhiro

Recently, new architectures of manufacturing systems have been proposed to realize flexible control structures of the manufacturing systems, which can cope with the dynamic changes in the volume and the variety of the products and also the unforeseen disruptions, such as failures of manufacturing resources and interruptions by high priority jobs. They are so called as the autonomous distributed manufacturing system, the biological manufacturing system and the holonic manufacturing system. Rule-based scheduling methods were proposed and applied to the real-time production scheduling problems of the HMS (Holonic Manufacturing System) in the previous report. However, there are still remaining problems from the viewpoint of the optimization of the whole production schedules. New procedures are proposed, in the present paper, to select the production schedules, aimed at generating effective production schedules in real-time. The proposed methods enable the individual holons to select suitable machining operations to be carried out in the next time period. Coordination process among the holons is also proposed to carry out the coordination based on the effectiveness values of the individual holons.

Adsorption detection for polylysine biomolecules based on high-Q silica capillary whispering gallery mode microresonator

NASA Astrophysics Data System (ADS)

Wu, Jixuan; Liu, Bo; Zhang, Hao; Song, Binbin

2017-11-01

A silica-capillary-based whispering gallery mode (WGM) microresonator has been proposed and experimentally demonstrated for the real-time monitoring of the polylysine adsorption process. The spectral characteristics of the WGM resonance dips with high quality factor and good wavelength selectivity have been investigated to evaluate the dynamic process for the binding of polylysine with a capillary surface. The WGM transmission spectrum shows a regular shift with increments of observation time, which could be exploited for the analysis of the polylysine adsorption process. The proposed WGM microresonator system possesses desirable qualities such as high sensitivity, fast response, label-free method, high detection resolution and compactness, which could find promising applications in histology and related bioengineering areas.

Applying Qualitative Hazard Analysis to Support Quantitative Safety Analysis for Proposed Reduced Wake Separation Conops

NASA Technical Reports Server (NTRS)

Shortle, John F.; Allocco, Michael

2005-01-01

This paper describes a scenario-driven hazard analysis process to identify, eliminate, and control safety-related risks. Within this process, we develop selective criteria to determine the applicability of applying engineering modeling to hypothesized hazard scenarios. This provides a basis for evaluating and prioritizing the scenarios as candidates for further quantitative analysis. We have applied this methodology to proposed concepts of operations for reduced wake separation for closely spaced parallel runways. For arrivals, the process identified 43 core hazard scenarios. Of these, we classified 12 as appropriate for further quantitative modeling, 24 that should be mitigated through controls, recommendations, and / or procedures (that is, scenarios not appropriate for quantitative modeling), and 7 that have the lowest priority for further analysis.

Risk analysis within environmental impact assessment of proposed construction activity

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

Zeleňáková, Martina; Zvijáková, Lenka

Environmental impact assessment is an important process, prior to approval of the investment plan, providing a detailed examination of the likely and foreseeable impacts of proposed construction activity on the environment. The objective of this paper is to develop a specific methodology for the analysis and evaluation of environmental impacts of selected constructions – flood protection structures using risk analysis methods. The application of methodology designed for the process of environmental impact assessment will develop assumptions for further improvements or more effective implementation and performance of this process. The main objective of the paper is to improve the implementation ofmore » the environmental impact assessment process. Through the use of risk analysis methods in environmental impact assessment process, the set objective has been achieved. - Highlights: This paper is informed by an effort to develop research with the aim of: • Improving existing qualitative and quantitative methods for assessing the impacts • A better understanding of relations between probabilities and consequences • Methodology for the EIA of flood protection constructions based on risk analysis • Creative approaches in the search for environmentally friendly proposed activities.« less

Surviving an Information Systems Conversion.

ERIC Educational Resources Information Center

Neel, Don

1999-01-01

Prompted by the "millennium bug," many school districts are in the process of replacing non-Y2K-compliant information systems. Planners should establish a committee to develop performance criteria and select the winning proposal, estimate time requirements, and schedule retraining during low-activity periods. (MLH)

Online machining error estimation method of numerical control gear grinding machine tool based on data analysis of internal sensors

NASA Astrophysics Data System (ADS)

Zhao, Fei; Zhang, Chi; Yang, Guilin; Chen, Chinyin

2016-12-01

This paper presents an online estimation method of cutting error by analyzing of internal sensor readings. The internal sensors of numerical control (NC) machine tool are selected to avoid installation problem. The estimation mathematic model of cutting error was proposed to compute the relative position of cutting point and tool center point (TCP) from internal sensor readings based on cutting theory of gear. In order to verify the effectiveness of the proposed model, it was simulated and experimented in gear generating grinding process. The cutting error of gear was estimated and the factors which induce cutting error were analyzed. The simulation and experiments verify that the proposed approach is an efficient way to estimate the cutting error of work-piece during machining process.

Analysis of Selection Process for Management Education: Korean Military Case.

DTIC Science & Technology

1984-06-01

applicable to selecting Korean military officers for postgraduate education in management, is proposed. 1! 6! 6i N 0102. L r * 014- 6601 2 Unclassified...officers uhc can manage the ailitary Fersctrel Ard modern uea~ons efficiently. As a result, the military has begun to educate some of its cfficers in the...referal, advisinj 4 aiplicant- about alternatives in employment, and furthering public relations. Oijectives cf group interview might te to assess

Outlining a selection procedure for Saccharomyces cerevisiae isolated from grape marc to improve fermentation process and distillate quality.

PubMed

Bovo, Barbara; Carlot, Milena; Fontana, Federico; Lombardi, Angiolella; Soligo, Stefano; Giacomini, Alessio; Corich, Viviana

2015-04-01

Nowadays grape marc represents one of the main by-product of winemaking. Many South Europe countries valorize this ligno-cellulosic waste through fermentation and distillation for industrial alcoholic beverage production. The storage of marcs is a crucial phase in the distillation process, due to the physicochemical transformations ascribed to microbial activity. Among the methods adopted by distillers to improve the quality of spirits, the use of selected yeasts has not been explored so far, therefore in this work we evaluated the selection criteria of Saccharomyces cerevisiae strains for grape marc fermentation. The proposed selection procedure included three steps: characterization of phenotypical traits, evaluation of selected strains on pasteurised grape marc at lab-scale (100 g) and pilot-scale fermentation (350 kg). This selection process was applied on 104 strains isolated from grape marcs of different origins and technological treatment. Among physiological traits, β-glucosidase activity level as quality trait seems to be only partially involved in increasing varietal flavour. More effective in describing yeast impact on distillate quality is the ratio higher alcohols/esters that indicates strain ability to increase positive flavours. Finally, evaluating grape marc as source of selected yeasts, industrial treatment rather than varietal origin seems to shape strain technological and quality traits. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling of laser welding of steel and titanium plates with a composite insert

NASA Astrophysics Data System (ADS)

Isaev, V. I.; Cherepanov, A. N.; Shapeev, V. P.

2017-10-01

A 3D model of laser welding proposed before by the authors was extended to the case of welding of metallic plates made of dissimilar materials with a composite multilayer intermediate insert. The model simulates heat transfer in the welded plates and takes into account phase transitions. It was proposed to select the composition of several metals and dimensions of the insert to avoid the formation of brittle intermetallic phases in the weld joint negatively affecting its strength properties. The model accounts for key physical phenomena occurring during the complex process of laser welding. It is capable to calculate temperature regimes at each point of the plates. The model can be used to select the welding parameters reducing the risk of formation of intermetallic plates. It can forecast the dimensions and crystalline structure of the solidified melt. Based on the proposed model a numerical algorithm was constructed. Simulations were carried out for the welding of titanium and steel plates with a composite insert comprising four different metals: copper and niobium (intermediate plates) with steel and titanium (outer plates). The insert is produced by explosion welding. Temperature fields and the processes of melting, evaporation, and solidification were studied.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

PubMed Central

Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection.

PubMed

Garcia-Allende, P Beatriz; Mirapeix, Jesus; Conde, Olga M; Cobo, Adolfo; Lopez-Higuera, Jose M

2009-01-01

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution.

The Role of Attention in Information Processing Implications for the Design of Displays

DTIC Science & Technology

1989-12-01

processing system. Psychological Review, J, 214-255. Neisser , U . (1967). Cognitive Rsycholo&X. New York, NY: Appleton- Century-Crofts. Neisser , U . (1969...in the visual display is now an important part of a number of attention models. A related model suggested by Neisser (1967) is that successful...to filter attenuation theory have been proposed by Neisser (1967, 1969). According to Neisser’s theory, selective attention is an active process of

Writing a Research Proposal to The Research Council of Oman

PubMed Central

Al-Shukaili, Ahmed; Al-Maniri, Abdullah

2017-01-01

Writing a research proposal can be a challenging task for young researchers. This article explains how to write a strong research proposal to apply for funding, specifically, a proposal for The Research Council (TRC) of Oman. Three different research proposal application forms are currently used in TRC, including Open Research Grant (ORG), Graduate Research Support Program (GRSP), and Faculty-mentored Undergraduate Research Award Program (FURAP). The application forms are filled and submitted electronically on TRC website. Each of the proposals submitted to TRC is selected through a rigorous reviewing and screening process. Novelty and originality of the research idea is the most crucial element in writing a research proposal. Performing an in-depth review of the literature will assist you to compose a good researchable question and generate a strong hypothesis. The development of a good hypothesis will offer insight into the specific objectives of a study. Research objectives should be focused, measurable, and achievable by a specific time using the most appropriate methodology. Moreover, it is essential to select a proper study design in-line with the purpose of the study and the hypothesis. Furthermore, social/economic impact and reasonable budget of proposed research are important criteria in research proposal evaluation by TRC. Finally, ethical principles should be observed before writing a research proposal involving human or animal subjects. PMID:28584597

Adaptive illumination source for multispectral vision system applied to material discrimination

NASA Astrophysics Data System (ADS)

Conde, Olga M.; Cobo, Adolfo; Cantero, Paulino; Conde, David; Mirapeix, Jesús; Cubillas, Ana M.; López-Higuera, José M.

2008-04-01

A multispectral system based on a monochrome camera and an adaptive illumination source is presented in this paper. Its preliminary application is focused on material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach, in which the relevant wavelengths for the required discrimination task are selected in advance using a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based on Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illuminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illumination produces multispectral imaging with a simple monochrome camera, and cold illumination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry.

Setting Priorities in Global Child Health Research Investments: Guidelines for Implementation of the CHNRI Method

PubMed Central

Rudan, Igor; Gibson, Jennifer L.; Ameratunga, Shanthi; El Arifeen, Shams; Bhutta, Zulfiqar A.; Black, Maureen; Black, Robert E.; Brown, Kenneth H.; Campbell, Harry; Carneiro, Ilona; Chan, Kit Yee; Chandramohan, Daniel; Chopra, Mickey; Cousens, Simon; Darmstadt, Gary L.; Gardner, Julie Meeks; Hess, Sonja Y.; Hyder, Adnan A.; Kapiriri, Lydia; Kosek, Margaret; Lanata, Claudio F.; Lansang, Mary Ann; Lawn, Joy; Tomlinson, Mark; Tsai, Alexander C.; Webster, Jayne

2008-01-01

This article provides detailed guidelines for the implementation of systematic method for setting priorities in health research investments that was recently developed by Child Health and Nutrition Research Initiative (CHNRI). The target audience for the proposed method are international agencies, large research funding donors, and national governments and policy-makers. The process has the following steps: (i) selecting the managers of the process; (ii) specifying the context and risk management preferences; (iii) discussing criteria for setting health research priorities; (iv) choosing a limited set of the most useful and important criteria; (v) developing means to assess the likelihood that proposed health research options will satisfy the selected criteria; (vi) systematic listing of a large number of proposed health research options; (vii) pre-scoring check of all competing health research options; (viii) scoring of health research options using the chosen set of criteria; (ix) calculating intermediate scores for each health research option; (x) obtaining further input from the stakeholders; (xi) adjusting intermediate scores taking into account the values of stakeholders; (xii) calculating overall priority scores and assigning ranks; (xiii) performing an analysis of agreement between the scorers; (xiv) linking computed research priority scores with investment decisions; (xv) feedback and revision. The CHNRI method is a flexible process that enables prioritizing health research investments at any level: institutional, regional, national, international, or global. PMID:19090596

Landfill Site Selection by AHP Based Multi-criteria Decision Making Tool: A Case Study in Kolkata, India

NASA Astrophysics Data System (ADS)

Majumdar, Ankush; Hazra, Tumpa; Dutta, Amit

2017-09-01

This work presents a Multi-criteria Decision Making (MCDM) tool to select a landfill site from three candidate sites proposed for Kolkata Municipal Corporation (KMC) area that complies with accessibility, receptor, environment, public acceptability, geological and economic criteria. Analytical Hierarchy Process has been used to solve the MCDM problem. Suitability of the three sites (viz. Natagachi, Gangajoara and Kharamba) as landfills as proposed by KMC has been checked by Landfill Site Sensitivity Index (LSSI) as well as Economic Viability Index (EVI). Land area availability for disposing huge quantity of Municipal Solid Waste for the design period has been checked. Analysis of the studied sites show that they are moderately suitable for landfill facility construction as both LSSI and EVI scores lay between 300 and 750. The proposed approach represents an effective MCDM tool for siting sanitary landfill in growing metropolitan cities of developing countries like India.

Efficient Assignment of Multiple E-MBMS Sessions towards LTE

NASA Astrophysics Data System (ADS)

Alexiou, Antonios; Bouras, Christos; Kokkinos, Vasileios

One of the major prerequisites for Long Term Evolution (LTE) networks is the mass provision of multimedia services to mobile users. To this end, Evolved - Multimedia Broadcast/Multicast Service (E-MBMS) is envisaged to play an instrumental role during LTE standardization process and ensure LTE’s proliferation in mobile market. E-MBMS targets at the economic delivery, in terms of power and spectral efficiency, of multimedia data from a single source entity to multiple destinations. This paper proposes a novel mechanism for efficient radio bearer selection during E-MBMS transmissions in LTE networks. The proposed mechanism is based on the concept of transport channels combination in any cell of the network. Most significantly, the mechanism manages to efficiently deliver multiple E-MBMS sessions. The performance of the proposed mechanism is evaluated and compared with several radio bearer selection mechanisms in order to highlight the enhancements that it provides.

Proposed Project Selection Method for Human Support Research and Technology Development (HSR&TD)

NASA Technical Reports Server (NTRS)

Jones, Harry

2005-01-01

The purpose of HSR&TD is to deliver human support technologies to the Exploration Systems Mission Directorate (ESMD) that will be selected for future missions. This requires identifying promising candidate technologies and advancing them in technology readiness until they are acceptable. HSR&TD must select an may of technology development projects, guide them, and either terminate or continue them, so as to maximize the resulting number of usable advanced human support technologies. This paper proposes an effective project scoring methodology to support managing the HSR&TD project portfolio. Researchers strongly disagree as to what are the best technology project selection methods, or even if there are any proven ones. Technology development is risky and outstanding achievements are rare and unpredictable. There is no simple formula for success. Organizations that are satisfied with their project selection approach typically use a mix of financial, strategic, and scoring methods in an open, established, explicit, formal process. This approach helps to build consensus and develop management insight. It encourages better project proposals by clarifying the desired project attributes. We propose a project scoring technique based on a method previously used in a federal laboratory and supported by recent research. Projects are ranked by their perceived relevance, risk, and return - a new 3 R's. Relevance is the degree to which the project objective supports the HSR&TD goal of developing usable advanced human support technologies. Risk is the estimated probability that the project will achieve its specific objective. Return is the reduction in mission life cycle cost obtained if the project is successful. If the project objective technology performs a new function with no current cost, its return is the estimated cash value of performing the new function. The proposed project selection scoring method includes definitions of the criteria, a project evaluation questionnaire, and a scoring formula.

Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening.

PubMed

R, GeethaRamani; Balasubramanian, Lakshmi

2018-07-01

Macula segmentation and fovea localization is one of the primary tasks in retinal analysis as they are responsible for detailed vision. Existing approaches required segmentation of retinal structures viz. optic disc and blood vessels for this purpose. This work avoids knowledge of other retinal structures and attempts data mining techniques to segment macula. Unsupervised clustering algorithm is exploited for this purpose. Selection of initial cluster centres has a great impact on performance of clustering algorithms. A heuristic based clustering in which initial centres are selected based on measures defining statistical distribution of data is incorporated in the proposed methodology. The initial phase of proposed framework includes image cropping, green channel extraction, contrast enhancement and application of mathematical closing. Then, the pre-processed image is subjected to heuristic based clustering yielding a binary map. The binary image is post-processed to eliminate unwanted components. Finally, the component which possessed the minimum intensity is finalized as macula and its centre constitutes the fovea. The proposed approach outperforms existing works by reporting that 100%,of HRF, 100% of DRIVE, 96.92% of DIARETDB0, 97.75% of DIARETDB1, 98.81% of HEI-MED, 90% of STARE and 99.33% of MESSIDOR images satisfy the 1R criterion, a standard adopted for evaluating performance of macula and fovea identification. The proposed system thus helps the ophthalmologists in identifying the macula thereby facilitating to identify if any abnormality is present within the macula region. Copyright © 2018 Elsevier B.V. All rights reserved.

Propulsion for the lunar mission

NASA Technical Reports Server (NTRS)

Jones, Lee W.; Champion, Robert H., Jr.

1990-01-01

The paper describes the selection process utilized by NASA during the conduct of the 90-day study of the mission set that is known as the Space Exploration Initiative (SEI). It is directed specifically toward propulsion system definition and selection, with emphasis on the proposed Lunar Transfer Vehicle and the Lunar Exploration Vehicle. Results of trade studies show that selection cannot be readily made on the basis of engine performance alone, because the cost of launching hardware elements and the required propellant are very high. A decision must be made to use either life-cycle costs or annual program costs as the economic figure of merit, because they drive the selection in opposite directions.

Knowledge-Based Manufacturing and Structural Design for a High Speed Civil Transport

NASA Technical Reports Server (NTRS)

Marx, William J.; Mavris, Dimitri N.; Schrage, Daniel P.

1994-01-01

The aerospace industry is currently addressing the problem of integrating manufacturing and design. To address the difficulties associated with using many conventional procedural techniques and algorithms, one feasible way to integrate the two concepts is with the development of an appropriate Knowledge-Based System (KBS). The authors present their reasons for selecting a KBS to integrate design and manufacturing. A methodology for an aircraft producibility assessment is proposed, utilizing a KBS for manufacturing process selection, that addresses both procedural and heuristic aspects of designing and manufacturing of a High Speed Civil Transport (HSCT) wing. A cost model is discussed that would allow system level trades utilizing information describing the material characteristics as well as the manufacturing process selections. Statements of future work conclude the paper.

OligoIS: Scalable Instance Selection for Class-Imbalanced Data Sets.

PubMed

García-Pedrajas, Nicolás; Perez-Rodríguez, Javier; de Haro-García, Aida

2013-02-01

In current research, an enormous amount of information is constantly being produced, which poses a challenge for data mining algorithms. Many of the problems in extremely active research areas, such as bioinformatics, security and intrusion detection, or text mining, share the following two features: large data sets and class-imbalanced distribution of samples. Although many methods have been proposed for dealing with class-imbalanced data sets, most of these methods are not scalable to the very large data sets common to those research fields. In this paper, we propose a new approach to dealing with the class-imbalance problem that is scalable to data sets with many millions of instances and hundreds of features. This proposal is based on the divide-and-conquer principle combined with application of the selection process to balanced subsets of the whole data set. This divide-and-conquer principle allows the execution of the algorithm in linear time. Furthermore, the proposed method is easy to implement using a parallel environment and can work without loading the whole data set into memory. Using 40 class-imbalanced medium-sized data sets, we will demonstrate our method's ability to improve the results of state-of-the-art instance selection methods for class-imbalanced data sets. Using three very large data sets, we will show the scalability of our proposal to millions of instances and hundreds of features.

Distributed processing of a GPS receiver network for a regional ionosphere map

NASA Astrophysics Data System (ADS)

Choi, Kwang Ho; Hoo Lim, Joon; Yoo, Won Jae; Lee, Hyung Keun

2018-01-01

This paper proposes a distributed processing method applicable to GPS receivers in a network to generate a regional ionosphere map accurately and reliably. For accuracy, the proposed method is operated by multiple local Kalman filters and Kriging estimators. Each local Kalman filter is applied to a dual-frequency receiver to estimate the receiver’s differential code bias and vertical ionospheric delays (VIDs) at different ionospheric pierce points. The Kriging estimator selects and combines several VID estimates provided by the local Kalman filters to generate the VID estimate at each ionospheric grid point. For reliability, the proposed method uses receiver fault detectors and satellite fault detectors. Each receiver fault detector compares the VID estimates of the same local area provided by different local Kalman filters. Each satellite fault detector compares the VID estimate of each local area with that projected from the other local areas. Compared with the traditional centralized processing method, the proposed method is advantageous in that it considerably reduces the computational burden of each single Kalman filter and enables flexible fault detection, isolation, and reconfiguration capability. To evaluate the performance of the proposed method, several experiments with field collected measurements were performed.

A neural model of the temporal dynamics of figure-ground segregation in motion perception.

PubMed

Raudies, Florian; Neumann, Heiko

2010-03-01

How does the visual system manage to segment a visual scene into surfaces and objects and manage to attend to a target object? Based on psychological and physiological investigations, it has been proposed that the perceptual organization and segmentation of a scene is achieved by the processing at different levels of the visual cortical hierarchy. According to this, motion onset detection, motion-defined shape segregation, and target selection are accomplished by processes which bind together simple features into fragments of increasingly complex configurations at different levels in the processing hierarchy. As an alternative to this hierarchical processing hypothesis, it has been proposed that the processing stages for feature detection and segregation are reflected in different temporal episodes in the response patterns of individual neurons. Such temporal epochs have been observed in the activation pattern of neurons as low as in area V1. Here, we present a neural network model of motion detection, figure-ground segregation and attentive selection which explains these response patterns in an unifying framework. Based on known principles of functional architecture of the visual cortex, we propose that initial motion and motion boundaries are detected at different and hierarchically organized stages in the dorsal pathway. Visual shapes that are defined by boundaries, which were generated from juxtaposed opponent motions, are represented at different stages in the ventral pathway. Model areas in the different pathways interact through feedforward and modulating feedback, while mutual interactions enable the communication between motion and form representations. Selective attention is devoted to shape representations by sending modulating feedback signals from higher levels (working memory) to intermediate levels to enhance their responses. Areas in the motion and form pathway are coupled through top-down feedback with V1 cells at the bottom end of the hierarchy. We propose that the different temporal episodes in the response pattern of V1 cells, as recorded in recent experiments, reflect the strength of modulating feedback signals. This feedback results from the consolidated shape representations from coherent motion patterns and the attentive modulation of responses along the cortical hierarchy. The model makes testable predictions concerning the duration and delay of the temporal episodes of V1 cell responses as well as their response variations that were caused by modulating feedback signals. Copyright 2009 Elsevier Ltd. All rights reserved.

Punishment induced behavioural and neurophysiological variability reveals dopamine-dependent selection of kinematic movement parameters

PubMed Central

Galea, Joseph M.; Ruge, Diane; Buijink, Arthur; Bestmann, Sven; Rothwell, John C.

2013-01-01

Action selection describes the high-level process which selects between competing movements. In animals, behavioural variability is critical for the motor exploration required to select the action which optimizes reward and minimizes cost/punishment, and is guided by dopamine (DA). The aim of this study was to test in humans whether low-level movement parameters are affected by punishment and reward in ways similar to high-level action selection. Moreover, we addressed the proposed dependence of behavioural and neurophysiological variability on DA, and whether this may underpin the exploration of kinematic parameters. Participants performed an out-and-back index finger movement and were instructed that monetary reward and punishment were based on its maximal acceleration (MA). In fact, the feedback was not contingent on the participant’s behaviour but pre-determined. Blocks highly-biased towards punishment were associated with increased MA variability relative to blocks with either reward or without feedback. This increase in behavioural variability was positively correlated with neurophysiological variability, as measured by changes in cortico-spinal excitability with transcranial magnetic stimulation over the primary motor cortex. Following the administration of a DA-antagonist, the variability associated with punishment diminished and the correlation between behavioural and neurophysiological variability no longer existed. Similar changes in variability were not observed when participants executed a pre-determined MA, nor did DA influence resting neurophysiological variability. Thus, under conditions of punishment, DA-dependent processes influence the selection of low-level movement parameters. We propose that the enhanced behavioural variability reflects the exploration of kinematic parameters for less punishing, or conversely more rewarding, outcomes. PMID:23447607

Dissociated neural basis of two behavioral hallmarks of holistic face processing: The whole-part effect and composite-face effect.

PubMed

Li, Jin; Huang, Lijie; Song, Yiying; Liu, Jia

2017-07-28

It has been long proposed that our extraordinary face recognition ability stems from holistic face processing. Two widely-used behavioral hallmarks of holistic face processing are the whole-part effect (WPE) and composite-face effect (CFE). However, it remains unknown whether these two effects reflect similar or different aspects of holistic face processing. Here we investigated this question by examining whether the WPE and CFE involved shared or distinct neural substrates in a large sample of participants (N=200). We found that the WPE and CFE showed hemispheric dissociation in the fusiform face area (FFA), that is, the WPE was correlated with face selectivity in the left FFA, while the CFE was correlated with face selectivity in the right FFA. Further, the correlation between the WPE and face selectivity was largely driven by the FFA response to faces, whereas the association between the CFE and face selectivity resulted from suppressed response to objects in the right FFA. Finally, we also observed dissociated correlation patterns of the WPE and CFE in other face-selective regions and across the whole brain. These results suggest that the WPE and CFE may reflect different aspects of holistic face processing, which shed new light on the behavioral dissociations of these two effects demonstrated in literature. Copyright © 2017 Elsevier Ltd. All rights reserved.

Selection of infectious medical waste disposal firms by using the analytic hierarchy process and sensitivity analysis

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

Hsu, P.-F.; Wu, C.-R.; Li, Y.-T.

2008-07-01

While Taiwanese hospitals dispose of large amounts of medical waste to ensure sanitation and personal hygiene, doing so inefficiently creates potential environmental hazards and increases operational expenses. However, hospitals lack objective criteria to select the most appropriate waste disposal firm and evaluate its performance, instead relying on their own subjective judgment and previous experiences. Therefore, this work presents an analytic hierarchy process (AHP) method to objectively select medical waste disposal firms based on the results of interviews with experts in the field, thus reducing overhead costs and enhancing medical waste management. An appropriate weight criterion based on AHP is derivedmore » to assess the effectiveness of medical waste disposal firms. The proposed AHP-based method offers a more efficient and precise means of selecting medical waste firms than subjective assessment methods do, thus reducing the potential risks for hospitals. Analysis results indicate that the medical sector selects the most appropriate infectious medical waste disposal firm based on the following rank: matching degree, contractor's qualifications, contractor's service capability, contractor's equipment and economic factors. By providing hospitals with an effective means of evaluating medical waste disposal firms, the proposed AHP method can reduce overhead costs and enable medical waste management to understand the market demand in the health sector. Moreover, performed through use of Expert Choice software, sensitivity analysis can survey the criterion weight of the degree of influence with an alternative hierarchy.« less

Partial Verbal Redundancy in Multimedia Presentations for Writing Strategy Instruction

ERIC Educational Resources Information Center

Roscoe, Rod D.; Jacovina, Matthew E.; Harry, Danielle; Russell, Devin G.; McNamara, Danielle S.

2015-01-01

Multimedia instructional materials require learners to select, organize, and integrate information across multiple modalities. To facilitate these comprehension processes, a variety of multimedia design principles have been proposed. This study further explores the redundancy principle by manipulating the degree of partial redundancy between…

Vibrational monitor of early demineralization in tooth enamel after in vitro exposure to phosphoridic liquid

NASA Astrophysics Data System (ADS)

Pezzotti, Giuseppe; Adachi, Tetsuya; Gasparutti, Isabella; Vincini, Giulio; Zhu, Wenliang; Boffelli, Marco; Rondinella, Alfredo; Marin, Elia; Ichioka, Hiroaki; Yamamoto, Toshiro; Marunaka, Yoshinori; Kanamura, Narisato

2017-02-01

The Raman spectroscopic method has been applied to quantitatively assess the in vitro degree of demineralization in healthy human teeth. Based on previous evaluations of Raman selection rules (empowered by an orientation distribution function (ODF) statistical algorithm) and on a newly proposed analysis of phonon density of states (PDOS) for selected vibrational modes of the hexagonal structure of hydroxyapatite, a molecular-scale evaluation of the demineralization process upon in vitro exposure to a highly acidic beverage (i.e., CocaCola™ Classic, pH = 2.5) could be obtained. The Raman method proved quite sensitive and spectroscopic features could be directly related to an increase in off-stoichiometry of the enamel surface structure since the very early stage of the demineralization process (i.e., when yet invisible to other conventional analytical techniques). The proposed Raman spectroscopic algorithm might possess some generality for caries risk assessment, allowing a prompt non-contact diagnostic practice in dentistry.

Selective population rate coding: a possible computational role of gamma oscillations in selective attention.

PubMed

Masuda, Naoki

2009-12-01

Selective attention is often accompanied by gamma oscillations in local field potentials and spike field coherence in brain areas related to visual, motor, and cognitive information processing. Gamma oscillations are implicated to play an important role in, for example, visual tasks including object search, shape perception, and speed detection. However, the mechanism by which gamma oscillations enhance cognitive and behavioral performance of attentive subjects is still elusive. Using feedforward fan-in networks composed of spiking neurons, we examine a possible role for gamma oscillations in selective attention and population rate coding of external stimuli. We implement the concept proposed by Fries ( 2005 ) that under dynamic stimuli, neural populations effectively communicate with each other only when there is a good phase relationship among associated gamma oscillations. We show that the downstream neural population selects a specific dynamic stimulus received by an upstream population and represents it by population rate coding. The encoded stimulus is the one for which gamma rhythm in the corresponding upstream population is resonant with the downstream gamma rhythm. The proposed role for gamma oscillations in stimulus selection is to enable top-down control, a neural version of time division multiple access used in communication engineering.

Selection of the open pit mining cut-off grade strategy under price uncertainty using a risk based multi-criteria ranking system / Wybór strategii określania warunku opłacalności wydobycia w kopalniach odkrywkowych w warunkach niepewności cen w oparciu o wielokryterialny system rankingowy z uwzględnieniem czynników ryzyka

NASA Astrophysics Data System (ADS)

Azimi, Yousue; Osanloo, Montza; Esfahanipour, Akbar

2012-12-01

Cut-off Grade Strategy (COGS) is a concept that directly influences the financial, technical, economic, environmental, and legal issues in relation to exploitation of a mineral resource. A decision making system is proposed to select the best technically feasible COGS under price uncertainty. In the proposed system both the conventional discounted cash flow and modern simulation based real option valuations are used to evaluate the alternative strategies. Then the conventional expected value criterion and a multiple criteria ranking system were used to rank the strategies based on the two valuation methods. In the multiple criteria ranking system besides the expected value other stochastic orders expressing abilities of strategies in producing extra profits, minimizing losses and achieving the predefined goals of the exploitation strategy are considered. Finally, the best strategy is selected based on the overall average rank of strategies through all ranking systems. The proposed system was examined using the data of Sungun Copper Mine. To assess the merits of the alternatives better, ranking process was done at both high (prevailing economic condition) and low price conditions. Ranking results revealed that at different price conditions and valuation methods, different results would be obtained. It is concluded that these differences are due to the different behavior of the embedded option to close the mine early, which is more likely to be exercised under low price condition rather than high price condition. The proposed system would enhance the quality of decision making process by providing a more informative and certain platform for project evaluation.

Uncertainties in Eddy Covariance fluxes due to post-field data processing: a multi-site, full factorial analysis

NASA Astrophysics Data System (ADS)

Sabbatini, S.; Fratini, G.; Arriga, N.; Papale, D.

2012-04-01

Eddy Covariance (EC) is the only technologically available direct method to measure carbon and energy fluxes between ecosystems and atmosphere. However, uncertainties related to this method have not been exhaustively assessed yet, including those deriving from post-field data processing. The latter arise because there is no exact processing sequence established for any given situation, and the sequence itself is long and complex, with many processing steps and options available. However, the consistency and inter-comparability of flux estimates may be largely affected by the adoption of different processing sequences. The goal of our work is to quantify the uncertainty introduced in each processing step by the fact that different options are available, and to study how the overall uncertainty propagates throughout the processing sequence. We propose an easy-to-use methodology to assign a confidence level to the calculated fluxes of energy and mass, based on the adopted processing sequence, and on available information such as the EC system type (e.g. open vs. closed path), the climate and the ecosystem type. The proposed methodology synthesizes the results of a massive full-factorial experiment. We use one year of raw data from 15 European flux stations and process them so as to cover all possible combinations of the available options across a selection of the most relevant processing steps. The 15 sites have been selected to be representative of different ecosystems (forests, croplands and grasslands), climates (mediterranean, nordic, arid and humid) and instrumental setup (e.g. open vs. closed path). The software used for this analysis is EddyPro™ 3.0 (www.licor.com/eddypro). The critical processing steps, selected on the basis of the different options commonly used in the FLUXNET community, are: angle of attack correction; coordinate rotation; trend removal; time lag compensation; low- and high- frequency spectral correction; correction for air density fluctuations; and length of the flux averaging interval. We illustrate the results of the full-factorial combination relative to a subset of the selected sites with particular emphasis on the total uncertainty at different time scales and aggregations, as well as a preliminary analysis of the most critical steps for their contribution to the total uncertainties and their potential relation with site set-up characteristics and ecosystem type.

Agency Innovation Mission with Dava Newman

NASA Image and Video Library

2016-11-01

Dr. Dava Newman, NASA's deputy administrator, speaks to employees at the Florida spaceport during the annual KickStart Innovation Expo. The event gives employees an opportunity to present proposals for new ideas and processes. A small amount of funding is awarded to those selected allowing individuals or teams to procure needed items to implement their projects. Kennedy employees are encouraged to look for ways to do their work better and to propose concepts for tackling future mission needs.

Agency Innovation Mission with Dava Newman

NASA Image and Video Library

2016-11-01

Kennedy Space Center Director Bob Cabana speaks to employees at the Florida spaceport during the annual KickStart Innovation Expo. The event gives employees an opportunity to present proposals for new ideas and processes. A small amount of funding is awarded to those selected allowing individuals or teams to procure needed items to implement their projects. Kennedy employees are encouraged to look for ways to do their work better and to propose concepts for tackling future mission needs.

Agency Innovation Mission with Dava Newman

NASA Image and Video Library

2016-11-01

Dr. Dava Newman, NASA's deputy administrator, speaks to employees at the Florida spaceport during the annual KickStart Innovation Expo The event gives employees an opportunity to present proposals for new ideas and processes. A small amount of funding is awarded to those selected allowing individuals or teams to procure needed items to implement their projects. Kennedy employees are encouraged to look for ways to do their work better and to propose concepts for tackling future mission needs.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

Tumor or abnormality identification from magnetic resonance images using statistical region fusion based segmentation.

PubMed

Subudhi, Badri Narayan; Thangaraj, Veerakumar; Sankaralingam, Esakkirajan; Ghosh, Ashish

2016-11-01

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures. Copyright © 2016 Elsevier Inc. All rights reserved.

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion

PubMed Central

Ma, Rui; Guo, Qiang; Hu, Changzhen; Xue, Jingfeng

2015-01-01

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy. PMID:26334278

An Improved WiFi Indoor Positioning Algorithm by Weighted Fusion.

PubMed

Ma, Rui; Guo, Qiang; Hu, Changzhen; Xue, Jingfeng

2015-08-31

The rapid development of mobile Internet has offered the opportunity for WiFi indoor positioning to come under the spotlight due to its low cost. However, nowadays the accuracy of WiFi indoor positioning cannot meet the demands of practical applications. To solve this problem, this paper proposes an improved WiFi indoor positioning algorithm by weighted fusion. The proposed algorithm is based on traditional location fingerprinting algorithms and consists of two stages: the offline acquisition and the online positioning. The offline acquisition process selects optimal parameters to complete the signal acquisition, and it forms a database of fingerprints by error classification and handling. To further improve the accuracy of positioning, the online positioning process first uses a pre-match method to select the candidate fingerprints to shorten the positioning time. After that, it uses the improved Euclidean distance and the improved joint probability to calculate two intermediate results, and further calculates the final result from these two intermediate results by weighted fusion. The improved Euclidean distance introduces the standard deviation of WiFi signal strength to smooth the WiFi signal fluctuation and the improved joint probability introduces the logarithmic calculation to reduce the difference between probability values. Comparing the proposed algorithm, the Euclidean distance based WKNN algorithm and the joint probability algorithm, the experimental results indicate that the proposed algorithm has higher positioning accuracy.

PRP Comments for ICF Q1/Q2 FY17 Experiments 3/10/16

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

Kauffman, R.

2016-04-14

The PRP generally endorsed the Program plan during the short time for discussions. We agree that the strategy to develop a hohlraum that is symmetric and has low laser-plasma instabilities and to develop an alternative method for supporting the capsule is the best path forward for making progress in understanding ignition performance. The Program is oriented toward a milestone in 2020 for “determining the efficacy of NIF for ignition and credible physics-scaling to multi-megajoule yields for all ICF approaches.” We are concerned that the time and resources are not sufficient to vet all of the various approaches that are beingmore » pursued to make an informed decision by this date. For NIF to meet this goal, a process will be needed to to select the most promising paths forward. We recommend that the Program develop this process for selecting the path forward to optimize resources. We were glad to see that the direct drive program took our comments under consideration. We think that the proposed experiments have the program headed in a better direction. The PRP had only a short time to discuss the detailed experimental proposals. The following are comments on the detailed proposals. We did not have time to discuss them as a group. They represent individual opinions and provided to you as feedback to your proposals.« less

Evolution of ribonuclease in relation to polypeptide folding mechanisms.

NASA Technical Reports Server (NTRS)

Barnard, E. A.; Cohen, M. S.; Gold, M. H.; Kim, J.-K.

1972-01-01

Comparisons of the N-terminal region of pancreatic RNAase in seven species are presented, taking into account cow, bison, deer, rat, pig, kangaroo, and turtle. The available limited evidence on hypervariable regions indicates that there is still an evolutionary constraint on them. It is proposed that there is a selection pressure acting on all regions of a protein sequence in evolution. Mutations that tend to obstruct the folding process can lead to various intensities of selection pressure.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PubMed Central

2018-01-01

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. PMID:29304512

Evaluating and selecting an information system, Part 1.

PubMed

Neal, T

1993-01-01

Initial steps in the process of evaluating and selecting a computerized information system for the pharmacy department are described. The first step in the selection process is to establish a steering committee and a project committee. The steering committee oversees the project, providing policy guidance, making major decisions, and allocating budgeted expenditures. The project committee conducts the departmental needs assessment, identifies system requirements, performs day-to-day functions, evaluates vendor proposals, trains personnel, and implements the system chosen. The second step is the assessment of needs in terms of personnel, workload, physical layout, and operating requirements. The needs assessment should be based on the department's mission statement and strategic plan. The third step is the development of a request for information (RFI) and a request for proposal (RFP). The RFI is a document designed for gathering preliminary information from a wide range of vendors; this general information is used in deciding whether to send the RFP to a given vendor. The RFP requests more detailed information and gives the purchaser's exact specifications for a system; the RFP also includes contractual information. To help ensure project success, many institutions turn to computer consultants for guidance. The initial steps in selecting a computerized pharmacy information system are establishing computerization committees, conducting a needs assessment, and writing an RFI and an RFP. A crucial early decision is whether to seek a consultant's expertise.

Materials requirements for optical processing and computing devices

NASA Technical Reports Server (NTRS)

Tanguay, A. R., Jr.

1985-01-01

Devices for optical processing and computing systems are discussed, with emphasis on the materials requirements imposed by functional constraints. Generalized optical processing and computing systems are described in order to identify principal categories of requisite components for complete system implementation. Three principal device categories are selected for analysis in some detail: spatial light modulators, volume holographic optical elements, and bistable optical devices. The implications for optical processing and computing systems of the materials requirements identified for these device categories are described, and directions for future research are proposed.

NASA Institute for Advanced Concepts

NASA Technical Reports Server (NTRS)

Cassanova, Robert A.

1999-01-01

The purpose of NASA Institute for Advanced Concepts (NIAC) is to provide an independent, open forum for the external analysis and definition of space and aeronautics advanced concepts to complement the advanced concepts activities conducted within the NASA Enterprises. The NIAC will issue Calls for Proposals during each year of operation and will select revolutionary advanced concepts for grant or contract awards through a peer review process. Final selection of awards will be with the concurrence of NASA's Chief Technologist. The operation of the NIAC is reviewed biannually by the NIAC Science, Exploration and Technology Council (NSETC) whose members are drawn from the senior levels of industry and universities. The process of defining the technical scope of the initial Call for Proposals was begun with the NIAC "Grand Challenges" workshop conducted on May 21-22, 1998 in Columbia, Maryland. These "Grand Challenges" resulting from this workshop became the essence of the technical scope for the first Phase I Call for Proposals which was released on June 19, 1998 with a due date of July 31, 1998. The first Phase I Call for Proposals attracted 119 proposals. After a thorough peer review, prioritization by NIAC and technical concurrence by NASA, sixteen subgrants were awarded. The second Phase I Call for Proposals was released on November 23, 1998 with a due date of January 31, 1999. Sixty-three (63) proposals were received in response to this Call. On December 2-3, 1998, the NSETC met to review the progress and future plans of the NIAC. The next NSETC meeting is scheduled for August 5-6, 1999. The first Phase II Call for Proposals was released to the current Phase I grantees on February 3,1999 with a due date of May 31, 1999. Plans for the second year of the contract include a continuation of the sequence of Phase I and Phase II Calls for Proposals and hosting the first NIAC Annual Meeting and USRA/NIAC Technical Symposium in NASA HQ.

Fast processing of microscopic images using object-based extended depth of field.

PubMed

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This work presents a modification of the extended depth of field approach for efficiently enhancing microscopic images. This selective object processing scheme used in OEDoF can significantly reduce the overall processing time while maintaining the clarity of important image features. The empirical results from parasite-infected red cell images revealed that our proposed method efficiently and effectively produced in-focus composite images. With the speed improvement of OEDoF, this proposed algorithm is suitable for processing large numbers of microscope images, e.g., as required for medical diagnosis.

7 CFR 1485.14 - Application review and formation of agreements.

Code of Federal Regulations, 2013 CFR

2013-01-01

... passwords in accordance with USDA's information technology policies that CCC will provide to MAP... markets for U.S. agricultural commodities. The selection process, by its nature, involves the exercise of judgment. CCC's choice of Participants and proposed promotion projects requires that it consider and weigh...

7 CFR 1485.14 - Application review and formation of agreements.

Code of Federal Regulations, 2014 CFR

2014-01-01

... passwords in accordance with USDA's information technology policies that CCC will provide to MAP... markets for U.S. agricultural commodities. The selection process, by its nature, involves the exercise of judgment. CCC's choice of Participants and proposed promotion projects requires that it consider and weigh...

78 FR 69103 - 30-Day Notice of Proposed Information Collection: Quality Control for Rental Assistance Subsidy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-18

... Information Collection: Quality Control for Rental Assistance Subsidy Determinations AGENCY: Office of the... Collection Title of Information Collection: Quality Control for Rental Assistance Subsidy Determinations. OMB... Quality Control process involves selecting a nationally representative sample of assisted households to...

Impaired Filtering of Behaviourally Irrelevant Visual Information in Dyslexia

ERIC Educational Resources Information Center

Roach, Neil W.; Hogben, John H.

2007-01-01

A recent proposal suggests that dyslexic individuals suffer from attentional deficiencies, which impair the ability to selectively process incoming visual information. To investigate this possibility, we employed a spatial cueing procedure in conjunction with a single fixation visual search task measuring thresholds for discriminating the…

18 CFR 1311.11 - What are TVA's obligations in interstate situations?

Code of Federal Regulations, 2011 CFR

2011-04-01

... TENNESSEE VALLEY AUTHORITY INTERGOVERNMENTAL REVIEW OF TENNESSEE VALLEY AUTHORITY FEDERAL FINANCIAL... interstate situations? (a) TVA is responsible for: (1) Identifying proposed Federal financial assistance and... officials and entities in states which have adopted a process and which select TVA's program or activity; (3...

18 CFR 1311.11 - What are TVA's obligations in interstate situations?

Code of Federal Regulations, 2012 CFR

2012-04-01

... TENNESSEE VALLEY AUTHORITY INTERGOVERNMENTAL REVIEW OF TENNESSEE VALLEY AUTHORITY FEDERAL FINANCIAL... interstate situations? (a) TVA is responsible for: (1) Identifying proposed Federal financial assistance and... officials and entities in states which have adopted a process and which select TVA's program or activity; (3...

18 CFR 1311.11 - What are TVA's obligations in interstate situations?

Code of Federal Regulations, 2010 CFR

2010-04-01

... TENNESSEE VALLEY AUTHORITY INTERGOVERNMENTAL REVIEW OF TENNESSEE VALLEY AUTHORITY FEDERAL FINANCIAL... interstate situations? (a) TVA is responsible for: (1) Identifying proposed Federal financial assistance and... officials and entities in states which have adopted a process and which select TVA's program or activity; (3...

76 FR 59420 - Proposed Information Collection; Alaska Guide Service Evaluation

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-26

... Office of Management and Budget (OMB) to approve the information collection (IC) described below. As... lands, we issue permits for commercial guide services, including big game hunting, sport fishing... information during the competitive selection process for big game and sport fishing guide permits to evaluate...

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data. PMID:23593445

An Attempt of Formalizing the Selection Parameters for Settlements Generalization in Small-Scales

NASA Astrophysics Data System (ADS)

Karsznia, Izabela

2014-12-01

The paper covers one of the most important problems concerning context-sensitive settlement selection for the purpose of the small-scale maps. So far, no formal parameters for small-scale settlements generalization have been specified, hence the problem seems to be an important and innovative challenge. It is also crucial from the practical point of view as it is necessary to develop appropriate generalization algorithms for the purpose of the General Geographic Objects Database generalization which is the essential Spatial Data Infrastructure component in Poland. The author proposes and verifies quantitative generalization parameters for the purpose of the settlement selection process in small-scale maps. The selection of settlements was carried out in two research areas - in Lower Silesia and Łódź Province. Based on the conducted analysis appropriate contextual-sensitive settlements selection parameters have been defined. Particular effort has been made to develop a methodology of quantitative settlements selection which would be useful in the automation processes and that would make it possible to keep specifics of generalized objects unchanged.

Reactor technology assessment and selection utilizing systems engineering approach

NASA Astrophysics Data System (ADS)

Zolkaffly, Muhammed Zulfakar; Han, Ki-In

2014-02-01

The first Nuclear power plant (NPP) deployment in a country is a complex process that needs to consider technical, economic and financial aspects along with other aspects like public acceptance. Increased interest in the deployment of new NPPs, both among newcomer countries and those with expanding programs, necessitates the selection of reactor technology among commercially available technologies. This paper reviews the Systems Decision Process (SDP) of Systems Engineering and applies it in selecting the most appropriate reactor technology for the deployment in Malaysia. The integrated qualitative and quantitative analyses employed in the SDP are explored to perform reactor technology assessment and to select the most feasible technology whose design has also to comply with the IAEA standard requirements and other relevant requirements that have been established in this study. A quick Malaysian case study result suggests that the country reside with PWR (pressurized water reactor) technologies with more detailed study to be performed in the future for the selection of the most appropriate reactor technology for Malaysia. The demonstrated technology assessment also proposes an alternative method to systematically and quantitatively select the most appropriate reactor technology.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Unbiased feature selection in learning random forests for high-dimensional data.

PubMed

Nguyen, Thanh-Tung; Huang, Joshua Zhexue; Nguyen, Thuy Thi

2015-01-01

Random forests (RFs) have been widely used as a powerful classification method. However, with the randomization in both bagging samples and feature selection, the trees in the forest tend to select uninformative features for node splitting. This makes RFs have poor accuracy when working with high-dimensional data. Besides that, RFs have bias in the feature selection process where multivalued features are favored. Aiming at debiasing feature selection in RFs, we propose a new RF algorithm, called xRF, to select good features in learning RFs for high-dimensional data. We first remove the uninformative features using p-value assessment, and the subset of unbiased features is then selected based on some statistical measures. This feature subset is then partitioned into two subsets. A feature weighting sampling technique is used to sample features from these two subsets for building trees. This approach enables one to generate more accurate trees, while allowing one to reduce dimensionality and the amount of data needed for learning RFs. An extensive set of experiments has been conducted on 47 high-dimensional real-world datasets including image datasets. The experimental results have shown that RFs with the proposed approach outperformed the existing random forests in increasing the accuracy and the AUC measures.

The biometric-based module of smart grid system

NASA Astrophysics Data System (ADS)

Engel, E.; Kovalev, I. V.; Ermoshkina, A.

2015-10-01

Within Smart Grid concept the flexible biometric-based module base on Principal Component Analysis (PCA) and selective Neural Network is developed. The formation of the selective Neural Network the biometric-based module uses the method which includes three main stages: preliminary processing of the image, face localization and face recognition. Experiments on the Yale face database show that (i) selective Neural Network exhibits promising classification capability for face detection, recognition problems; and (ii) the proposed biometric-based module achieves near real-time face detection, recognition speed and the competitive performance, as compared to some existing subspaces-based methods.

Fast Image Restoration for Spatially Varying Defocus Blur of Imaging Sensor

PubMed Central

Cheong, Hejin; Chae, Eunjung; Lee, Eunsung; Jo, Gwanghyun; Paik, Joonki

2015-01-01

This paper presents a fast adaptive image restoration method for removing spatially varying out-of-focus blur of a general imaging sensor. After estimating the parameters of space-variant point-spread-function (PSF) using the derivative in each uniformly blurred region, the proposed method performs spatially adaptive image restoration by selecting the optimal restoration filter according to the estimated blur parameters. Each restoration filter is implemented in the form of a combination of multiple FIR filters, which guarantees the fast image restoration without the need of iterative or recursive processing. Experimental results show that the proposed method outperforms existing space-invariant restoration methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed to a wide area of image restoration applications, such as mobile imaging devices, robot vision, and satellite image processing. PMID:25569760

Intuitive and deliberate judgments are based on common principles.

PubMed

Kruglanski, Arie W; Gigerenzer, Gerd

2011-01-01

A popular distinction in cognitive and social psychology has been between intuitive and deliberate judgments. This juxtaposition has aligned in dual-process theories of reasoning associative, unconscious, effortless, heuristic, and suboptimal processes (assumed to foster intuitive judgments) versus rule-based, conscious, effortful, analytic, and rational processes (assumed to characterize deliberate judgments). In contrast, we provide convergent arguments and evidence for a unified theoretical approach to both intuitive and deliberative judgments. Both are rule-based, and in fact, the very same rules can underlie both intuitive and deliberate judgments. The important open question is that of rule selection, and we propose a 2-step process in which the task itself and the individual's memory constrain the set of applicable rules, whereas the individual's processing potential and the (perceived) ecological rationality of the rule for the task guide the final selection from that set. Deliberate judgments are not generally more accurate than intuitive judgments; in both cases, accuracy depends on the match between rule and environment: the rules' ecological rationality. Heuristics that are less effortful and in which parts of the information are ignored can be more accurate than cognitive strategies that have more information and computation. The proposed framework adumbrates a unified approach that specifies the critical dimensions on which judgmental situations may vary and the environmental conditions under which rules can be expected to be successful.

Inline Measurement of Particle Concentrations in Multicomponent Suspensions using Ultrasonic Sensor and Least Squares Support Vector Machines.

PubMed

Zhan, Xiaobin; Jiang, Shulan; Yang, Yili; Liang, Jian; Shi, Tielin; Li, Xiwen

2015-09-18

This paper proposes an ultrasonic measurement system based on least squares support vector machines (LS-SVM) for inline measurement of particle concentrations in multicomponent suspensions. Firstly, the ultrasonic signals are analyzed and processed, and the optimal feature subset that contributes to the best model performance is selected based on the importance of features. Secondly, the LS-SVM model is tuned, trained and tested with different feature subsets to obtain the optimal model. In addition, a comparison is made between the partial least square (PLS) model and the LS-SVM model. Finally, the optimal LS-SVM model with the optimal feature subset is applied to inline measurement of particle concentrations in the mixing process. The results show that the proposed method is reliable and accurate for inline measuring the particle concentrations in multicomponent suspensions and the measurement accuracy is sufficiently high for industrial application. Furthermore, the proposed method is applicable to the modeling of the nonlinear system dynamically and provides a feasible way to monitor industrial processes.

Locating the source of spreading in temporal networks

NASA Astrophysics Data System (ADS)

Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Yi, Dongyun

2017-02-01

The topological structure of many real networks changes with time. Thus, locating the sources of a temporal network is a creative and challenging problem, as the enormous size of many real networks makes it unfeasible to observe the state of all nodes. In this paper, we propose an algorithm to solve this problem, named the backward temporal diffusion process. The proposed algorithm calculates the shortest temporal distance to locate the transmission source. We assume that the spreading process can be modeled as a simple diffusion process and by consensus dynamics. To improve the location accuracy, we also adopt four strategies to select which nodes should be observed by ranking their importance in the temporal network. Our paper proposes a highly accurate method for locating the source in temporal networks and is, to the best of our knowledge, a frontier work in this field. Moreover, our framework has important significance for controlling the transmission of diseases or rumors and formulating immediate immunization strategies.

A molecularly imprinted polymer-coated CdTe quantum dot nanocomposite for tryptophan recognition based on the Förster resonance energy transfer process

NASA Astrophysics Data System (ADS)

Tirado-Guizar, Antonio; Paraguay-Delgado, Francisco; Pina-Luis, Georgina E.

2016-12-01

A new ‘turn-on’ Förster resonance energy transfer (FRET) nanosensor for l-tryptophan based on molecularly imprinted quantum dots (QDs) is proposed. The approach combines the advantages of the molecular imprinting technique, the fluorescent characteristics of the QDs and the energy transfer process. Silica-coated CdTe QDs were first synthesized and then molecularly imprinted using a sol-gel process without surfactants. The final composite presents stable fluorescence which increases with the addition of l-tryptophan. This ‘turn-on’ response is due to a FRET mechanism from the l-tryptophan as donor to the imprinted QD as acceptor. QDs are rarely applied as acceptors in FRET systems. The nanosensor shows selectivity towards l-tryptophan in the presence of other amino acids and interfering ions. The l-tryptophan nanosensor exhibits a linear range between 0 and 8 µM concentration, a detection limit of 350 nM and high selectivity. The proposed sensor was successfully applied for the detection of l-tryptophan in saliva. This novel sensor may offer an alternative approach to the design of a new generation of imprinted nanomaterials for the recognition of different analytes.

Medial-lateral organization of the orbitofrontal cortex.

PubMed

Rich, Erin L; Wallis, Jonathan D

2014-07-01

Emerging evidence suggests that specific cognitive functions localize to different subregions of OFC, but the nature of these functional distinctions remains unclear. One prominent theory, derived from human neuroimaging, proposes that different stimulus valences are processed in separate orbital regions, with medial and lateral OFC processing positive and negative stimuli, respectively. Thus far, neurophysiology data have not supported this theory. We attempted to reconcile these accounts by recording neural activity from the full medial-lateral extent of the orbital surface in monkeys receiving rewards and punishments via gain or loss of secondary reinforcement. We found no convincing evidence for valence selectivity in any orbital region. Instead, we report differences between neurons in central OFC and those on the inferior-lateral orbital convexity, in that they encoded different sources of value information provided by the behavioral task. Neurons in inferior convexity encoded the value of external stimuli, whereas those in OFC encoded value information derived from the structure of the behavioral task. We interpret these results in light of recent theories of OFC function and propose that these distinctions, not valence selectivity, may shed light on a fundamental organizing principle for value processing in orbital cortex.

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process

PubMed Central

Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-01-01

Background Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. PMID:28935617

Methodology for reducing energy and resource costs in construction of trenchless crossover of pipelines

NASA Astrophysics Data System (ADS)

Toropov, V. S.

2018-05-01

The paper suggests a set of measures to select the equipment and its components in order to reduce energy costs in the process of pulling the pipeline into the well in the constructing the trenchless pipeline crossings of various materials using horizontal directional drilling technology. A methodology for reducing energy costs has been developed by regulating the operation modes of equipment during the process of pulling the working pipeline into a drilled and pre-expanded well. Since the power of the drilling rig is the most important criterion in the selection of equipment for the construction of a trenchless crossover, an algorithm is proposed for calculating the required capacity of the rig when operating in different modes in the process of pulling the pipeline into the well.

A structured multi-stakeholder learning process for Sustainable Land Management.

PubMed

Schwilch, Gudrun; Bachmann, Felicitas; Valente, Sandra; Coelho, Celeste; Moreira, Jorge; Laouina, Abdellah; Chaker, Miloud; Aderghal, Mohamed; Santos, Patricia; Reed, Mark S

2012-09-30

There are many, often competing, options for Sustainable Land Management (SLM). Each must be assessed - and sometimes negotiated - prior to implementation. Participatory, multi-stakeholder approaches to identification and selection of SLM options are increasingly popular, often motivated by social learning and empowerment goals. Yet there are few practical tools for facilitating processes in which land managers may share, select, and decide on the most appropriate SLM options. The research presented here aims to close the gap between the theory and the practice of stakeholder participation/learning in SLM decision-making processes. The paper describes a three-part participatory methodology for selecting SLM options that was tested in 14 desertification-prone study sites within the EU-DESIRE project. Cross-site analysis and in-depth evaluation of the Moroccan and Portuguese sites were used to evaluate how well the proposed process facilitated stakeholder learning and selection of appropriate SLM options for local implementation. The structured nature of the process - starting with SLM goal setting - was found to facilitate mutual understanding and collaboration between stakeholders. The deliberation process led to a high degree of consensus over the outcome and, though not an initial aim, it fostered social learning in many cases. This solution-oriented methodology is applicable in a wide range of contexts and may be implemented with limited time and resources. Copyright © 2012 Elsevier Ltd. All rights reserved.

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity.

PubMed

Cazé, Romain D; Jarvis, Sarah; Foust, Amanda J; Schultz, Simon R

2017-09-01

Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

A game-based decision support methodology for competitive systems design

NASA Astrophysics Data System (ADS)

Briceno, Simon Ignacio

This dissertation describes the development of a game-based methodology that facilitates the exploration and selection of research and development (R&D) projects under uncertain competitive scenarios. The proposed method provides an approach that analyzes competitor positioning and formulates response strategies to forecast the impact of technical design choices on a project's market performance. A critical decision in the conceptual design phase of propulsion systems is the selection of the best architecture, centerline, core size, and technology portfolio. This selection can be challenging when considering evolving requirements from both the airframe manufacturing company and the airlines in the market. Furthermore, the exceedingly high cost of core architecture development and its associated risk makes this strategic architecture decision the most important one for an engine company. Traditional conceptual design processes emphasize performance and affordability as their main objectives. These areas alone however, do not provide decision-makers with enough information as to how successful their engine will be in a competitive market. A key objective of this research is to examine how firm characteristics such as their relative differences in completing R&D projects, differences in the degree of substitutability between different project types, and first/second-mover advantages affect their product development strategies. Several quantitative methods are investigated that analyze business and engineering strategies concurrently. In particular, formulations based on the well-established mathematical field of game theory are introduced to obtain insights into the project selection problem. The use of game theory is explored in this research as a method to assist the selection process of R&D projects in the presence of imperfect market information. The proposed methodology focuses on two influential factors: the schedule uncertainty of project completion times and the uncertainty associated with competitive reactions. A normal-form matrix is created to enumerate players, their moves and payoffs, and to formulate a process by which an optimal decision can be achieved. The non-cooperative model is tested using the concept of a Nash equilibrium to identify potential strategies that are robust to uncertain market fluctuations (e.g: uncertainty in airline demand, airframe requirements and competitor positioning). A first/second-mover advantage parameter is used as a scenario dial to adjust market rewards and firms' payoffs. The methodology is applied to a commercial aircraft engine selection study where engine firms must select an optimal engine project for development. An engine modeling and simulation framework is developed to generate a broad engine project portfolio. The creation of a customer value model enables designers to incorporate airline operation characteristics into the engine modeling and simulation process to improve the accuracy of engine/customer matching. Summary. Several key findings are made that provide recommendations on project selection strategies for firms uncertain as to when they will enter the market. The proposed study demonstrates that within a technical design environment, a rational and analytical means of modeling project development strategies is beneficial in high market risk situations.

Science Community Interface

NASA Technical Reports Server (NTRS)

Neupert, Werner M.

1991-01-01

The interface is described between NASA HQ, NASA Goddard, and the rocket Principal Investigators. The proposal selection process is described along with the cycle time to flight, constraints imposed by science objectives on operations, campaign modes, and coordination with ground based facilities. There were questions about the success rate of proposals and the primary sources of funding for the payloads program from the branches of the science divisions in OSSA, especially space physics, astrophysics, Earth sciences, and solar system exploration. The presentation is given in the form of viewgraphs.

Extraction of latent images from printed media

NASA Astrophysics Data System (ADS)

Sergeyev, Vladislav; Fedoseev, Victor

2015-12-01

In this paper we propose an automatic technology for extraction of latent images from printed media such as documents, banknotes, financial securities, etc. This technology includes image processing by adaptively constructed Gabor filter bank for obtaining feature images, as well as subsequent stages of feature selection, grouping and multicomponent segmentation. The main advantage of the proposed technique is versatility: it allows to extract latent images made by different texture variations. Experimental results showing performance of the method over another known system for latent image extraction are given.

Multiple attribute decision making model and application to food safety risk evaluation.

PubMed

Ma, Lihua; Chen, Hong; Yan, Huizhe; Yang, Lifeng; Wu, Lifeng

2017-01-01

Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the proposed model provides a reliable, accurate method for multiple attribute decision making.

Comparative muscle study fatigue with sEMG signals during the isotonic and isometric tasks for diagnostics purposes.

PubMed

Sarmiento, Jhon F; Benevides, Alessandro B; Moreira, Marcelo H; Elias, Arlindo; Bastos, Teodiano F; Silva, Ian V; Pelegrina, Claudinei C

2011-01-01

The study of fatigue is an important tool for diagnostics of disease, sports, ergonomics and robotics areas. This work deals with the analysis of sEMG most important fatigue muscle indicators with use of signal processing in isometric and isotonic tasks with the propose of standardizing fatigue protocol to select the data acquisition and processing with diagnostic proposes. As a result, the slope of the RMS, ARV and MNF indicators were successful to describe the fatigue behavior expected. Whereas that, MDF and AIF indicators failed in the description of fatigue. Similarly, the use of a constant load for sEMG data acquisition was the best strategy in both tasks.

Switches of stimulus tagging frequencies interact with the conflict-driven control of selective attention, but not with inhibitory control.

PubMed

Scherbaum, Stefan; Frisch, Simon; Dshemuchadse, Maja

2016-01-01

Selective attention and its adaptation by cognitive control processes are considered a core aspect of goal-directed action. Often, selective attention is studied behaviorally with conflict tasks, but an emerging neuroscientific method for the study of selective attention is EEG frequency tagging. It applies different flicker frequencies to the stimuli of interest eliciting steady state visual evoked potentials (SSVEPs) in the EEG. These oscillating SSVEPs in the EEG allow tracing the allocation of selective attention to each tagged stimulus continuously over time. The present behavioral investigation points to an important caveat of using tagging frequencies: The flicker of stimuli not only produces a useful neuroscientific marker of selective attention, but interacts with the adaptation of selective attention itself. Our results indicate that RT patterns of adaptation after response conflict (so-called conflict adaptation) are reversed when flicker frequencies switch at once. However, this effect of frequency switches is specific to the adaptation by conflict-driven control processes, since we find no effects of frequency switches on inhibitory control processes after no-go trials. We discuss the theoretical implications of this finding and propose precautions that should be taken into account when studying conflict adaptation using frequency tagging in order to control for the described confounds. Copyright © 2015 Elsevier B.V. All rights reserved.

Area-Selective Atomic Layer Deposition of Metal Oxides on Noble Metals through Catalytic Oxygen Activation

PubMed Central

2017-01-01

Area-selective atomic layer deposition (ALD) is envisioned to play a key role in next-generation semiconductor processing and can also provide new opportunities in the field of catalysis. In this work, we developed an approach for the area-selective deposition of metal oxides on noble metals. Using O2 gas as co-reactant, area-selective ALD has been achieved by relying on the catalytic dissociation of the oxygen molecules on the noble metal surface, while no deposition takes place on inert surfaces that do not dissociate oxygen (i.e., SiO2, Al2O3, Au). The process is demonstrated for selective deposition of iron oxide and nickel oxide on platinum and iridium substrates. Characterization by in situ spectroscopic ellipsometry, transmission electron microscopy, scanning Auger electron spectroscopy, and X-ray photoelectron spectroscopy confirms a very high degree of selectivity, with a constant ALD growth rate on the catalytic metal substrates and no deposition on inert substrates, even after 300 ALD cycles. We demonstrate the area-selective ALD approach on planar and patterned substrates and use it to prepare Pt/Fe2O3 core/shell nanoparticles. Finally, the approach is proposed to be extendable beyond the materials presented here, specifically to other metal oxide ALD processes for which the precursor requires a strong oxidizing agent for growth. PMID:29503508

Development and Validation of Cognitive Screening Instruments.

ERIC Educational Resources Information Center

Jarman, Ronald F.

The author suggests that most research on the early detection of learning disabilities is characterisized by an ineffective and a theoretical method of selecting and validating tasks. An alternative technique is proposed, based on a neurological theory of cognitive processes, whereby task analysis is a first step, with empirical analyses as…

View from OSERS. [Question-and-Answer Session.

ERIC Educational Resources Information Center

Kaufman, Martin J.

The paper describes the perspective of the Office of Special Education and Rehabilitative Services on special education research. The process for reviewing research proposals is explained, along with procedures for selecting experts in the field for creating registers of reviewers. Also discussed are the number of points allocated to different…

Inclusive Partnership: Enhancing Student Engagement in Geography

ERIC Educational Resources Information Center

Moore-Cherry, Niamh; Healey, Ruth; Nicholson, Dawn T.; Andrews, Will

2016-01-01

Partnership is currently the focus of much work within higher education and advocated as an important process to address a range of higher education goals. In this paper, we propose the term "inclusive partnership" to conceptualise a non-selective staff-student relationship. While recognising the challenges of inclusive partnership…

75 FR 64753 - Options Price Reporting Authority; Notice of Filing and Immediate Effectiveness of Proposed...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-20

... Securities Industry Automation Corporation as OPRA's Independent System Capacity Advisor October 14, 2010... would reflect the fact that OPRA has selected the Securities Industry Automation Corporation (``SIAC..., in that capacity, provided the data processing services needed to develop, operate and maintain the...

76 FR 45902 - Agency Information Collection Activities: Proposed Request and Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-01

... will allow our users to maintain one User ID, consisting of a self-selected Username and Password, to...) Registration and identity verification; (2) enhancement of the User ID; and (3) authentication. The...- person identification verification process for individuals who cannot or are not willing to register...

7 CFR 1780.17 - Selection priorities and process.

Code of Federal Regulations, 2011 CFR

2011-01-01

... the population to be served by the proposed project is: (1) Less than the poverty line if the poverty... than the poverty line and between 80% and 100%, inclusive, of the State's nonmetropolitan median... problems or natural disasters. The Administrator may delegate the authority to assign the 15 points to...

7 CFR 1780.17 - Selection priorities and process.

Code of Federal Regulations, 2010 CFR

2010-01-01

... the population to be served by the proposed project is: (1) Less than the poverty line if the poverty... than the poverty line and between 80% and 100%, inclusive, of the State's nonmetropolitan median... problems or natural disasters. The Administrator may delegate the authority to assign the 15 points to...

Writing the Right Contract: Getting What You Want.

ERIC Educational Resources Information Center

Finkel, Karen E.

1998-01-01

Outsourcing of school services creates a need for educators to learn how best to select a contractor. Contracted student transportation is used to illustrate flexibility and creativity in writing a request for proposal, the evaluation process, and the importance of contractors' willingness to work alongside the district as a genuine business…

Developmental Regulation across the Life Span: Toward a New Synthesis

ERIC Educational Resources Information Center

Haase, Claudia M.; Heckhausen, Jutta; Wrosch, Carsten

2013-01-01

How can individuals regulate their own development to live happy, healthy, and productive lives? Major theories of developmental regulation across the life span have been proposed (e.g., dual-process model of assimilation and accommodation; motivational theory of life-span development; model of selection, optimization, and compensation), but they…

Storage Optimization of Educational System Data

ERIC Educational Resources Information Center

Boja, Catalin

2006-01-01

There are described methods used to minimize data files dimension. There are defined indicators for measuring size of files and databases. The storage optimization process is based on selecting from a multitude of data storage models the one that satisfies the propose problem objective, maximization or minimization of the optimum criterion that is…

Intelligent Tutoring and the Development of Argumentative Competence

ERIC Educational Resources Information Center

Paneque, Juan J.; Cobo, Pedro; Fortuny, Josep M.

2017-01-01

This ethnographical study aims to interpret how an intelligent tutorial system, geogebraTUTOR, mediates to the student's argumentative processes. Data consisted of four geometrical problems proposed to a group of four students aged 16-17. Qualitative analysis of two selected cases led to the identification of the development of argumentative…

Does reproduction compromise defense in woody plants?

Treesearch

Daniel A. Herms; William J. Mattson

1991-01-01

A general principle of adaptive allocation was proposed by Cody (1966) who hypothesized that 1) all living organisms have finite resources to partition among growth and competing physiological processes such as reproduction and defense; and 2) natural selection results in the evolution of unique resource allocation patterns that maximize fitness in different...

Somatosensory Anticipatory Alpha Activity Increases to Suppress Distracting Input

ERIC Educational Resources Information Center

Haegens, Saskia; Luther, Lisa; Jensen, Ole

2012-01-01

Effective processing of sensory input in daily life requires attentional selection and amplification of relevant input and, just as importantly, attenuation of irrelevant information. It has been proposed that top-down modulation of oscillatory alpha band activity (8-14 Hz) serves to allocate resources to various regions, depending on task…

A Social Neuroscientific Model of Vocational Behavior

ERIC Educational Resources Information Center

Hansen, Jo-Ida C.; Sullivan, Brandon A.; Luciana, Monica

2011-01-01

In this article, the separate literatures of a neurobiologically based approach system and vocational interests are reviewed and integrated into a social neuroscientific model of the processes underlying interests, based upon the idea of selective approach motivation. The authors propose that vocational interests describe the types of stimuli that…

Therapeutic Academics for Behaviorally Disordered Adolescents: Content Selection and Instructional Guidelines.

ERIC Educational Resources Information Center

Graham, Marilyn Troth

Curriculum content for adolescents with behavioral disorders should emphasize academic learning as well as social and behavioral skill development. "Therapeutic academics" is proposed as a process of teaching academics that is therapeutic in its systematic focus on the behavioral and social needs of students. A therapeutic academic curriculum…

Enhancing Vocabulary Acquisition Through Reading: A Hierarchy of Text-Related Exercise Types.

ERIC Educational Resources Information Center

Paribakht, T. Sima; Wesche, Marjorie

1996-01-01

Presents a classification scheme for reading-related exercises advocated in English-as-a-Foreign-Language textbooks. The scheme proposes a hierarchy of the degree and type of mental processing required by various vocabulary exercises. The categories of classification are selective attention, recognition, manipulation, interpretation and…

Rationalization and Ritualism in Committee Decision Making.

ERIC Educational Resources Information Center

Parrillo, Vincent N.; And Others

1985-01-01

Suggests that quasi-theories may link symbolic interactionism and negotiated order theory. The proposal theory is grounded in a case study of a university sabbatical leave committee. Situational response is explained in regard to the microsocial processes of cure selection and cure justification, rather than relying on macrosocial issues.…

24 CFR 1003.303 - Project rating.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 24 Housing and Urban Development 4 2011-04-01 2011-04-01 false Project rating. 1003.303 Section... Application and Selection Process § 1003.303 Project rating. Each project included in an application that... problem. This factor will address the extent to which there is a need for the proposed project to address...

24 CFR 1003.303 - Project rating.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 4 2013-04-01 2013-04-01 false Project rating. 1003.303 Section... Application and Selection Process § 1003.303 Project rating. Each project included in an application that... problem. This factor will address the extent to which there is a need for the proposed project to address...

24 CFR 1003.303 - Project rating.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 24 Housing and Urban Development 4 2012-04-01 2012-04-01 false Project rating. 1003.303 Section... Application and Selection Process § 1003.303 Project rating. Each project included in an application that... problem. This factor will address the extent to which there is a need for the proposed project to address...

24 CFR 1003.303 - Project rating.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 24 Housing and Urban Development 4 2014-04-01 2014-04-01 false Project rating. 1003.303 Section... Application and Selection Process § 1003.303 Project rating. Each project included in an application that... problem. This factor will address the extent to which there is a need for the proposed project to address...

Simple communication using a SSVEP-based BCI

NASA Astrophysics Data System (ADS)

Sanchez, Guillermo; Diez, Pablo F.; Avila, Enrique; Laciar Leber, Eric

2011-12-01

Majority of Brain-Computer Interface (BCI) for communication purposes are speller, i.e., the user has to select letter by letter. In this work, is proposed a different approach where the user can select words from a word set designed in order to answer a wide range of questions. The word selection process is commanded by a Steady-state visual evoked potential (SSVEP) based-BCI that allows selecting a word in an average time of 26 s with accuracies of 92% on average. This BCI is focus in the first stages on rehabilitation or even in first moments of some diseases (such as stroke), when the person is eager to communicate with family and doctors.

A road map for multi-way calibration models.

PubMed

Escandar, Graciela M; Olivieri, Alejandro C

2017-08-07

A large number of experimental applications of multi-way calibration are known, and a variety of chemometric models are available for the processing of multi-way data. While the main focus has been directed towards three-way data, due to the availability of various instrumental matrix measurements, a growing number of reports are being produced on order signals of increasing complexity. The purpose of this review is to present a general scheme for selecting the appropriate data processing model, according to the properties exhibited by the multi-way data. In spite of the complexity of the multi-way instrumental measurements, simple criteria can be proposed for model selection, based on the presence and number of the so-called multi-linearity breaking modes (instrumental modes that break the low-rank multi-linearity of the multi-way arrays), and also on the existence of mutually dependent instrumental modes. Recent literature reports on multi-way calibration are reviewed, with emphasis on the models that were selected for data processing.

The Insertion of Human Factors Concerns into NextGen Programmatic Decisions

NASA Technical Reports Server (NTRS)

Beard, Bettina L.; Holbrook, Jon Brian; Seely, Rachel

2013-01-01

Since the costs of proposed improvements in air traffic management exceed available funding, FAA decision makers must select and prioritize what actually gets implemented. We discuss a set of methods to help forecast operational and human performance issues and benefits before new automation is introduced. This strategy could minimize the impact of politics, assist decision makers in selecting and prioritizing potential improvements, make the process more transparent and strengthen the link between the engineering and human factors domains.

Pareto-Zipf law in growing systems with multiplicative interactions

NASA Astrophysics Data System (ADS)

Ohtsuki, Toshiya; Tanimoto, Satoshi; Sekiyama, Makoto; Fujihara, Akihiro; Yamamoto, Hiroshi

2018-06-01

Numerical simulations of multiplicatively interacting stochastic processes with weighted selections were conducted. A feedback mechanism to control the weight w of selections was proposed. It becomes evident that when w is moderately controlled around 0, such systems spontaneously exhibit the Pareto-Zipf distribution. The simulation results are universal in the sense that microscopic details, such as parameter values and the type of control and weight, are irrelevant. The central ingredient of the Pareto-Zipf law is argued to be the mild control of interactions.

Recruiting post-doctoral fellows into global health research: selecting NIH Fogarty International Clinical Research Fellows.

PubMed

Heimburger, Douglas C; Warner, Tokesha L; Carothers, Catherine Lem; Blevins, Meridith; Thomas, Yolanda; Gardner, Pierce; Primack, Aron; Vermund, Sten H

2014-08-01

From 2008 to 2012, the National Institutes of Health (NIH) Fogarty International Clinical Research Fellows Program (FICRF) provided 1-year mentored research training at low- and middle-income country sites for American and international post-doctoral health professionals. We examined the FICRF applicant pool, proposed research topics, selection process, and characteristics of enrollees to assess trends in global health research interest and factors associated with applicant competitiveness. The majority (58%) of 67 US and 57 international Fellows were women, and 83% of Fellows had medical degrees. Most applicants were in clinical fellowships (41%) or residencies (24%). More applicants proposing infectious disease projects were supported (59%) than applicants proposing non-communicable disease (NCD) projects (41%), although projects that combined both topic areas were most successful (69%). The numbers of applicants proposing research on NCDs and the numbers of these applicants awarded fellowships rose dramatically over time. Funding provided to the FICRF varied significantly among NIH Institutes and Centers and was strongly associated with the research topics awarded. © The American Society of Tropical Medicine and Hygiene.

Interactive contour delineation and refinement in treatment planning of image‐guided radiation therapy

PubMed Central

Zhou, Wu

2014-01-01

The accurate contour delineation of the target and/or organs at risk (OAR) is essential in treatment planning for image‐guided radiation therapy (IGRT). Although many automatic contour delineation approaches have been proposed, few of them can fulfill the necessities of applications in terms of accuracy and efficiency. Moreover, clinicians would like to analyze the characteristics of regions of interests (ROI) and adjust contours manually during IGRT. Interactive tool for contour delineation is necessary in such cases. In this work, a novel approach of curve fitting for interactive contour delineation is proposed. It allows users to quickly improve contours by a simple mouse click. Initially, a region which contains interesting object is selected in the image, then the program can automatically select important control points from the region boundary, and the method of Hermite cubic curves is used to fit the control points. Hence, the optimized curve can be revised by moving its control points interactively. Meanwhile, several curve fitting methods are presented for the comparison. Finally, in order to improve the accuracy of contour delineation, the process of the curve refinement based on the maximum gradient magnitude is proposed. All the points on the curve are revised automatically towards the positions with maximum gradient magnitude. Experimental results show that Hermite cubic curves and the curve refinement based on the maximum gradient magnitude possess superior performance on the proposed platform in terms of accuracy, robustness, and time calculation. Experimental results of real medical images demonstrate the efficiency, accuracy, and robustness of the proposed process in clinical applications. PACS number: 87.53.Tf PMID:24423846

A polydimethylsiloxane (PDMS) sponge for the selective absorption of oil from water.

PubMed

Choi, Sung-Jin; Kwon, Tae-Hong; Im, Hwon; Moon, Dong-Il; Baek, David J; Seol, Myeong-Lok; Duarte, Juan P; Choi, Yang-Kyu

2011-12-01

We present a sugar-templated polydimethylsiloxane (PDMS) sponge for the selective absorption of oil from water. The process for fabricating the PDMS sponge does not require any intricate synthesis processes or equipment and it is not environmentally hazardous, thus promoting potential in environmental applications. The proposed PDMS sponge can be elastically deformed into any shape, and it can be compressed repeatedly in air or liquids without collapsing. Therefore, absorbed oils and organic solvents can be readily removed and reused by simply squeezing the PDMS sponge, enabling excellent recyclability. Furthermore, through appropriately combining various sugar particles, the absorption capacity of the PDMS sponge is favorably optimized. © 2011 American Chemical Society

A unified framework for group independent component analysis for multi-subject fMRI data

PubMed Central

Guo, Ying; Pagnoni, Giuseppe

2008-01-01

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT (Calhoun et al., 2001) and tensor PICA (Beckmann and Smith, 2005), make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation. PMID:18650105

Hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) and its application to predicting key process variables.

PubMed

He, Yan-Lin; Xu, Yuan; Geng, Zhi-Qiang; Zhu, Qun-Xiong

2016-03-01

In this paper, a hybrid robust model based on an improved functional link neural network integrating with partial least square (IFLNN-PLS) is proposed. Firstly, an improved functional link neural network with small norm of expanded weights and high input-output correlation (SNEWHIOC-FLNN) was proposed for enhancing the generalization performance of FLNN. Unlike the traditional FLNN, the expanded variables of the original inputs are not directly used as the inputs in the proposed SNEWHIOC-FLNN model. The original inputs are attached to some small norm of expanded weights. As a result, the correlation coefficient between some of the expanded variables and the outputs is enhanced. The larger the correlation coefficient is, the more relevant the expanded variables tend to be. In the end, the expanded variables with larger correlation coefficient are selected as the inputs to improve the performance of the traditional FLNN. In order to test the proposed SNEWHIOC-FLNN model, three UCI (University of California, Irvine) regression datasets named Housing, Concrete Compressive Strength (CCS), and Yacht Hydro Dynamics (YHD) are selected. Then a hybrid model based on the improved FLNN integrating with partial least square (IFLNN-PLS) was built. In IFLNN-PLS model, the connection weights are calculated using the partial least square method but not the error back propagation algorithm. Lastly, IFLNN-PLS was developed as an intelligent measurement model for accurately predicting the key variables in the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. Simulation results illustrated that the IFLNN-PLS could significant improve the prediction performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A fuzzy cost-benefit function to select economical products for processing in a closed-loop supply chain

NASA Astrophysics Data System (ADS)

Pochampally, Kishore K.; Gupta, Surendra M.; Cullinane, Thomas P.

2004-02-01

The cost-benefit analysis of data associated with re-processing of used products often involves the uncertainty feature of cash-flow modeling. The data is not objective because of uncertainties in supply, quality and disassembly times of used products. Hence, decision-makers must rely on "fuzzy" data for analysis. The same parties that are involved in the forward supply chain often carry out the collection and re-processing of used products. It is therefore important that the cost-benefit analysis takes the data of both new products and used products into account. In this paper, a fuzzy cost-benefit function is proposed that is used to perform a multi-criteria economic analysis to select the most economical products to process in a closed-loop supply chain. Application of the function is detailed through an illustrative example.

Intelligent agent-based intrusion detection system using enhanced multiclass SVM.

PubMed

Ganapathy, S; Yogesh, P; Kannan, A

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

PubMed Central

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

Accumulating pyramid spatial-spectral collaborative coding divergence for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Sun, Hao; Zou, Huanxin; Zhou, Shilin

2016-03-01

Detection of anomalous targets of various sizes in hyperspectral data has received a lot of attention in reconnaissance and surveillance applications. Many anomaly detectors have been proposed in literature. However, current methods are susceptible to anomalies in the processing window range and often make critical assumptions about the distribution of the background data. Motivated by the fact that anomaly pixels are often distinctive from their local background, in this letter, we proposed a novel hyperspectral anomaly detection framework for real-time remote sensing applications. The proposed framework consists of four major components, sparse feature learning, pyramid grid window selection, joint spatial-spectral collaborative coding and multi-level divergence fusion. It exploits the collaborative representation difference in the feature space to locate potential anomalies and is totally unsupervised without any prior assumptions. Experimental results on airborne recorded hyperspectral data demonstrate that the proposed methods adaptive to anomalies in a large range of sizes and is well suited for parallel processing.

An Update on the NASA Planetary Science Division Research and Analysis Program

NASA Astrophysics Data System (ADS)

Bernstein, Max; Richey, Christina; Rall, Jonathan

2015-11-01

Introduction: NASA’s Planetary Science Division (PSD) solicits its research and analysis (R&A) programs each year in Research Opportunities in Space and Earth Sciences (ROSES). Beginning with the 2014 ROSES solicitation, PSD changed the structure of the program elements under which the majority of planetary science R&A is done. Major changes included the creation of five core research program elements aligned with PSD’s strategic science questions, the introduction of several new R&A opportunities, new submission requirements, and a new timeline for proposal submission.ROSES and NSPIRES: ROSES contains the research announcements for all of SMD. Submission of ROSES proposals is done electronically via NSPIRES: http://nspires.nasaprs.com. We will present further details on the proposal submission process to help guide younger scientists. Statistical trends, including the average award size within the PSD programs, selections rates, and lessons learned, will be presented. Information on new programs will also be presented, if available.Review Process and Volunteering: The SARA website (http://sara.nasa.gov) contains information on all ROSES solicitations. There is an email address (SARA@nasa.gov) for inquiries and an area for volunteer reviewers to sign up. The peer review process is based on Scientific/Technical Merit, Relevance, and Level of Effort, and will be detailed within this presentation.ROSES 2015 submission changes: All PSD programs will continue to use a two-step proposal submission process. A Step-1 proposal is required and must be submitted electronically by the Step-1 due date. The Step-1 proposal should include a description of the science goals and objectives to be addressed by the proposal, a brief description of the methodology to be used to address the science goals and objectives, and the relevance of the proposed research to the call submitted to.

Progress Report on Landing Site Evaluation for the Next Japanese Lunar Exploration Project: SELENE-2

NASA Astrophysics Data System (ADS)

Saiki, K.; Arai, T.; Araki, H.; Ishihara, Y.; Ohtake, M.; Karouji, Y.; Kobayashi, N.; Sugihara, T.; Haruyama, J.; Honda, C.

2010-12-01

SELENE-2 is the next Japanese lunar exploration project that is planned to be launched by the end of fiscal year 2015. In order to select the landing site candidates which maximize the scientific return from the project, "SELENE-2 Landing Site Research Board" was organized in March, 2010. The board called for scientific proposals with landing site candidates from domestic researchers who are interested in lunar science and members of the Japanese Society for Planetary Sciences, Japan Association of Mineralogical Sciences, the Geochemical Society of Japan, Seismological society of Japan, or the Geodetic society of Japan. At present, we have 35 scientific proposals with over 70 landing site candidates submitted from 21 groups. The proposals were categorized into nine research subjects as follows: 1) Identification of mantle materials, 2) Temporal variation of igneous activity and thermal history of the moon, 3) Lava morphology, 4) Origin of swirl, 5) Crater formation mechanism, 6) Core size, 7) Internal structure (crust - mantle), 8) Origin of the region enriched in heat source elements, and 9) Origin of highland crust. We are evaluating the proposals with the landing sites, and discussing the scientific target of SELENE-2. Within 6 months, we will propose several model missions which execute the scientific exploration with the highest priority today. In our presentation, the present landing site candidates, the policy of the selection, and a plan of a further landing site selection process would be shown.

The Ability to Process Abstract Information.

DTIC Science & Technology

1983-09-01

Responses Associated with Stress . .. 8 2. Filter Theories: A. Broadbent’s filter model . . . . 12 B. Treisaman’s attentuation model . . . 12 3... model has been proposed by Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977). Unlike Broadbent’s filter models Schneider and Shiffrin...allows for processing to take place only on the input "selected". This filter model is shown in Figure 2A. According to this theory, any information

Site selection for managed aquifer recharge using fuzzy rules: integrating geographical information system (GIS) tools and multi-criteria decision making

NASA Astrophysics Data System (ADS)

Malekmohammadi, Bahram; Ramezani Mehrian, Majid; Jafari, Hamid Reza

2012-11-01

One of the most important water-resources management strategies for arid lands is managed aquifer recharge (MAR). In establishing a MAR scheme, site selection is the prime prerequisite that can be assisted by geographic information system (GIS) tools. One of the most important uncertainties in the site-selection process using GIS is finite ranges or intervals resulting from data classification. In order to reduce these uncertainties, a novel method has been developed involving the integration of multi-criteria decision making (MCDM), GIS, and a fuzzy inference system (FIS). The Shemil-Ashkara plain in the Hormozgan Province of Iran was selected as the case study; slope, geology, groundwater depth, potential for runoff, land use, and groundwater electrical conductivity have been considered as site-selection factors. By defining fuzzy membership functions for the input layers and the output layer, and by constructing fuzzy rules, a FIS has been developed. Comparison of the results produced by the proposed method and the traditional simple additive weighted (SAW) method shows that the proposed method yields more precise results. In conclusion, fuzzy-set theory can be an effective method to overcome associated uncertainties in classification of geographic information data.

Integrating Design and Manufacturing for a High Speed Civil Transport Wing

NASA Technical Reports Server (NTRS)

Marx, William J.; Mavris, Dimitri N.; Schrage, Daniel P.

1994-01-01

The aerospace industry is currently addressing the problem of integrating design and manufacturing. Because of the difficulties associated with using conventional, procedural techniques and algorithms, it is the authors' belief that the only feasible way to integrate the two concepts is with the development of an appropriate Knowledge-Based System (KBS). The authors propose a methodology for an aircraft producibility assessment, including a KBS, that addresses both procedural and heuristic aspects of integrating design and manufacturing of a High Speed Civil Transport (HSCT) wing. The HSCT was chosen as the focus of this investigation since it is a current NASA/aerospace industry initiative full of technological challenges involving many disciplines. The paper gives a brief background of selected previous supersonic transport studies followed by descriptions of key relevant design and manufacturing methodologies. Georgia Tech's Concurrent Engineering/Integrated Product and Process Development methodology is discussed with reference to this proposed conceptual producibility assessment. Evaluation criteria are presented that relate pertinent product and process parameters to overall product producibility. In addition, the authors' integration methodology and reasons for selecting a KBS to integrate design and manufacturing are presented in this paper. Finally, a proposed KBS is given, as well as statements of future work and overall investigation objectives.

Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method

PubMed Central

Alguliyev, Rasim M.; Aliguliyev, Ramiz M.; Mahmudova, Rasmiyya S.

2015-01-01

Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method. PMID:26516634

Reconceptualizing perceptual load as a rate problem: The role of time in the allocation of selective attention.

PubMed

Li, Zhi; Xin, Keyun; Li, Wei; Li, Yanzhe

2018-04-30

In the literature about allocation of selective attention, a widely studied question is when will attention be allocated to information that is clearly irrelevant to the task at hand. The present study, by using convergent evidence, demonstrated that there is a trade-off between quantity of information present in a display and the time allowed to process it. Specifically, whether or not there is interference from irrelevant distractors depends not only on the amount of information present, but also on the amount of time allowed to process that information. When processing time is calibrated to the amount of information present, irrelevant distractors can be selectively ignored successfully. These results suggest that the perceptual load in the load theory of selective attention (i.e., Lavie, 2005) should be thought about as a dynamic rate problem rather than a static capacity limitation. The authors thus propose that rather than conceiving of perceptual load as a quantity of information, they should consider it as a quantity of information per unit of time. In other words, it is the relationship between the quantity of information in the task and the time for processing the information that determines the allocation of selective attention. Thus, the present findings extended load theory, allowing it to explain findings that were previously considered as counter evidence of load theory. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

PubMed

Tan, Weng Chun; Mat Isa, Nor Ashidi

2016-01-01

In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm.

Impaired familiarity with preserved recollection after anterior temporal-lobe resection that spares the hippocampus.

PubMed

Bowles, Ben; Crupi, Carina; Mirsattari, Seyed M; Pigott, Susan E; Parrent, Andrew G; Pruessner, Jens C; Yonelinas, Andrew P; Köhler, Stefan

2007-10-09

It is well established that the medial-temporal lobe (MTL) is critical for recognition memory. The MTL is known to be composed of distinct structures that are organized in a hierarchical manner. At present, it remains controversial whether lower structures in this hierarchy, such as perirhinal cortex, support memory functions that are distinct from those of higher structures, in particular the hippocampus. Perirhinal cortex has been proposed to play a specific role in the assessment of familiarity during recognition, which can be distinguished from the selective contributions of the hippocampus to the recollection of episodic detail. Some researchers have argued, however, that the distinction between familiarity and recollection cannot capture functional specialization within the MTL and have proposed single-process accounts. Evidence supporting the dual-process view comes from demonstrations that selective hippocampal damage can produce isolated recollection impairments. It is unclear, however, whether temporal-lobe lesions that spare the hippocampus can produce selective familiarity impairments. Without this demonstration, single-process accounts cannot be ruled out. We examined recognition memory in NB, an individual who underwent surgical resection of left anterior temporal-lobe structures for treatment of intractable epilepsy. Her resection included a large portion of perirhinal cortex but spared the hippocampus. The results of four experiments based on three different experimental procedures (remember-know paradigm, receiver operating characteristics, and response-deadline procedure) indicate that NB exhibits impaired familiarity with preserved recollection. The present findings thus provide a crucial missing piece of support for functional specialization in the MTL.

Multiattribute selection of acute stroke imaging software platform for Extending the Time for Thrombolysis in Emergency Neurological Deficits (EXTEND) clinical trial.

PubMed

Churilov, Leonid; Liu, Daniel; Ma, Henry; Christensen, Soren; Nagakane, Yoshinari; Campbell, Bruce; Parsons, Mark W; Levi, Christopher R; Davis, Stephen M; Donnan, Geoffrey A

2013-04-01

The appropriateness of a software platform for rapid MRI assessment of the amount of salvageable brain tissue after stroke is critical for both the validity of the Extending the Time for Thrombolysis in Emergency Neurological Deficits (EXTEND) Clinical Trial of stroke thrombolysis beyond 4.5 hours and for stroke patient care outcomes. The objective of this research is to develop and implement a methodology for selecting the acute stroke imaging software platform most appropriate for the setting of a multi-centre clinical trial. A multi-disciplinary decision making panel formulated the set of preferentially independent evaluation attributes. Alternative Multi-Attribute Value Measurement methods were used to identify the best imaging software platform followed by sensitivity analysis to ensure the validity and robustness of the proposed solution. Four alternative imaging software platforms were identified. RApid processing of PerfusIon and Diffusion (RAPID) software was selected as the most appropriate for the needs of the EXTEND trial. A theoretically grounded generic multi-attribute selection methodology for imaging software was developed and implemented. The developed methodology assured both a high quality decision outcome and a rational and transparent decision process. This development contributes to stroke literature in the area of comprehensive evaluation of MRI clinical software. At the time of evaluation, RAPID software presented the most appropriate imaging software platform for use in the EXTEND clinical trial. The proposed multi-attribute imaging software evaluation methodology is based on sound theoretical foundations of multiple criteria decision analysis and can be successfully used for choosing the most appropriate imaging software while ensuring both robust decision process and outcomes. © 2012 The Authors. International Journal of Stroke © 2012 World Stroke Organization.

Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients.

PubMed

Park, Eunjeong; Chang, Hyuk-Jae; Nam, Hyo Suk

2017-04-18

The pronator drift test (PDT), a neurological examination, is widely used in clinics to measure motor weakness of stroke patients. The aim of this study was to develop a PDT tool with machine learning classifiers to detect stroke symptoms based on quantification of proximal arm weakness using inertial sensors and signal processing. We extracted features of drift and pronation from accelerometer signals of wearable devices on the inner wrists of 16 stroke patients and 10 healthy controls. Signal processing and feature selection approach were applied to discriminate PDT features used to classify stroke patients. A series of machine learning techniques, namely support vector machine (SVM), radial basis function network (RBFN), and random forest (RF), were implemented to discriminate stroke patients from controls with leave-one-out cross-validation. Signal processing by the PDT tool extracted a total of 12 PDT features from sensors. Feature selection abstracted the major attributes from the 12 PDT features to elucidate the dominant characteristics of proximal weakness of stroke patients using machine learning classification. Our proposed PDT classifiers had an area under the receiver operating characteristic curve (AUC) of .806 (SVM), .769 (RBFN), and .900 (RF) without feature selection, and feature selection improves the AUCs to .913 (SVM), .956 (RBFN), and .975 (RF), representing an average performance enhancement of 15.3%. Sensors and machine learning methods can reliably detect stroke signs and quantify proximal arm weakness. Our proposed solution will facilitate pervasive monitoring of stroke patients. ©Eunjeong Park, Hyuk-Jae Chang, Hyo Suk Nam. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.04.2017.

Attention-like processes in insects

PubMed Central

2016-01-01

Attention is fundamentally important for sensory systems to focus on behaviourally relevant stimuli. It has therefore been an important field of study in human psychology and neuroscience. Primates, however, are not the only animals that might benefit from attention-like processes. Other animals, including insects, also have to use their senses and select one among many stimuli to forage, avoid predators and find mates. They have evolved different mechanisms to reduce the information processed by their brains to focus on only relevant stimuli. What are the mechanisms used by insects to selectively attend to visual and auditory stimuli? Do these attention-like mechanisms achieve the same functions as they do in primates? To investigate these questions, I use an established framework for investigating attention in non-human animals that proposes four fundamental components of attention: salience filters, competitive selection, top-down sensitivity control and working memory. I discuss evidence for each of these component processes in insects and compare the characteristics of these processes in insects to what we know from primates. Finally, I highlight important outstanding questions about insect attention that need to be addressed for us to understand the differences and similarities between vertebrate and insect attention. PMID:27852803

Attention-like processes in insects.

PubMed

Nityananda, Vivek

2016-11-16

Attention is fundamentally important for sensory systems to focus on behaviourally relevant stimuli. It has therefore been an important field of study in human psychology and neuroscience. Primates, however, are not the only animals that might benefit from attention-like processes. Other animals, including insects, also have to use their senses and select one among many stimuli to forage, avoid predators and find mates. They have evolved different mechanisms to reduce the information processed by their brains to focus on only relevant stimuli. What are the mechanisms used by insects to selectively attend to visual and auditory stimuli? Do these attention-like mechanisms achieve the same functions as they do in primates? To investigate these questions, I use an established framework for investigating attention in non-human animals that proposes four fundamental components of attention: salience filters, competitive selection, top-down sensitivity control and working memory. I discuss evidence for each of these component processes in insects and compare the characteristics of these processes in insects to what we know from primates. Finally, I highlight important outstanding questions about insect attention that need to be addressed for us to understand the differences and similarities between vertebrate and insect attention. © 2016 The Author(s).

Small business innovation research program solicitation: Closing date July 16, 1990

NASA Technical Reports Server (NTRS)

1990-01-01

This is the eighth annual solicitation by NASA addressed to small business firms, inviting them to submit proposals for research, or research and development, activities in some of the science and engineering areas of interest to NASA. The solicitation describes the Small Business Innovative Research (SBIR) program, identifies eligibility requirements, outlines the required proposal format and content, states proposal preparation and submission requirements, describes the proposal evaluation and award selection process, and provides other information to assist those interested in participating in NASA's SBIR program. It also identifies the technical topics and subtopics for which SBIR proposals are solicited. These cover a broad range of current NASA interests, but do not necessarily include all areas in which NASA plans or currently conducts research. High-risk high pay-off innovations are desired.

The Proposal Auto-Categorizer and Manager for Time Allocation Review at the Space Telescope Science Institute

NASA Astrophysics Data System (ADS)

Strolger, Louis-Gregory; Porter, Sophia; Lagerstrom, Jill; Weissman, Sarah; Reid, I. Neill; Garcia, Michael

2017-04-01

The Proposal Auto-Categorizer and Manager (PACMan) tool was written to respond to concerns about subjective flaws and potential biases in some aspects of the proposal review process for time allocation for the Hubble Space Telescope (HST), and to partially alleviate some of the anticipated additional workload from the James Webb Space Telescope (JWST) proposal review. PACMan is essentially a mixed-method Naive Bayesian spam filtering routine, with multiple pools representing scientific categories, that utilizes the Robinson method for combining token (or word) probabilities. PACMan was trained to make similar programmatic decisions in science category sorting, panelist selection, and proposal-to-panelists assignments to those made by individuals and committees in the Science Policies Group (SPG) at the Space Telescope Science Institute. Based on training from the previous cycle’s proposals, at an average of 87%, PACMan made the same science category assignments for proposals in Cycle 24 as the SPG. Tests for similar science categorizations, based on training using proposals from additional cycles, show that this accuracy can be further improved, to the > 95 % level. This tool will be used to augment or replace key functions in the Time Allocation Committee review processes in future HST and JWST cycles.

Sample selection and preservation techniques for the Mars sample return mission

NASA Technical Reports Server (NTRS)

Tsay, Fun-Dow

1988-01-01

It is proposed that a miniaturized electron spin resonance (ESR) spectrometer be developed as an effective, nondestructivew sample selection and characterization instrument for the Mars Rover Sample Return mission. The ESR instrument can meet rover science payload requirements and yet has the capability and versatility to perform the following in situ Martian sample analyses: (1) detection of active oxygen species, and characterization of Martian surface chemistry and photocatalytic oxidation processes; (2) determination of paramagnetic Fe(3+) in clay silicate minerals, Mn(2+) in carbonates, and ferromagnetic centers of magnetite, maghemite and hematite; (3) search for organic compounds in the form of free radicals in subsoil, and detection of Martian fossil organic matter likely to be associated with carbonate and other sedimentary deposits. The proposed instrument is further detailed.

Estimate of within population incremental selection through branch imbalance in lineage trees

PubMed Central

Liberman, Gilad; Benichou, Jennifer I.C.; Maman, Yaakov; Glanville, Jacob; Alter, Idan; Louzoun, Yoram

2016-01-01

Incremental selection within a population, defined as limited fitness changes following mutation, is an important aspect of many evolutionary processes. Strongly advantageous or deleterious mutations are detected using the synonymous to non-synonymous mutations ratio. However, there are currently no precise methods to estimate incremental selection. We here provide for the first time such a detailed method and show its precision in multiple cases of micro-evolution. The proposed method is a novel mixed lineage tree/sequence based method to detect within population selection as defined by the effect of mutations on the average number of offspring. Specifically, we propose to measure the log of the ratio between the number of leaves in lineage trees branches following synonymous and non-synonymous mutations. The method requires a high enough number of sequences, and a large enough number of independent mutations. It assumes that all mutations are independent events. It does not require of a baseline model and is practically not affected by sampling biases. We show the method's wide applicability by testing it on multiple cases of micro-evolution. We show that it can detect genes and inter-genic regions using the selection rate and detect selection pressures in viral proteins and in the immune response to pathogens. PMID:26586802

Selection, integration, and conflict monitoring; assessing the nature and generality of prefrontal cognitive control mechanisms.

PubMed

Badre, David; Wagner, Anthony D

2004-02-05

Prefrontal cortex (PFC) supports flexible behavior by mediating cognitive control, though the elemental forms of control supported by PFC remain a central debate. Dorsolateral PFC (DLPFC) is thought to guide response selection under conditions of response conflict or, alternatively, may refresh recently active representations within working memory. Lateral frontopolar cortex (FPC) may also adjudicate response conflict, though others propose that FPC supports higher order control processes such as subgoaling and integration. Anterior cingulate cortex (ACC) is hypothesized to upregulate response selection by detecting response conflict; it remains unclear whether ACC functions generalize beyond monitoring response conflict. The present fMRI experiment directly tested these competing theories regarding the functional roles of DLPFC, FPC, and ACC. Results reveal dissociable control processes in PFC, with mid-DLPFC selectively mediating resolution of response conflict and FPC further mediating subgoaling/integration. ACC demonstrated a broad sensitivity to control demands, suggesting a generalized role in modulating cognitive control.

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

NASA Astrophysics Data System (ADS)

Zhang, Zhifen; Chen, Huabin; Xu, Yanling; Zhong, Jiyong; Lv, Na; Chen, Shanben

2015-08-01

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.

Post-processing of adaptive optics images based on frame selection and multi-frame blind deconvolution

NASA Astrophysics Data System (ADS)

Tian, Yu; Rao, Changhui; Wei, Kai

2008-07-01

The adaptive optics can only partially compensate the image blurred by atmospheric turbulence due to the observing condition and hardware restriction. A post-processing method based on frame selection and multi-frames blind deconvolution to improve images partially corrected by adaptive optics is proposed. The appropriate frames which are suitable for blind deconvolution from the recorded AO close-loop frames series are selected by the frame selection technique and then do the multi-frame blind deconvolution. There is no priori knowledge except for the positive constraint in blind deconvolution. It is benefit for the use of multi-frame images to improve the stability and convergence of the blind deconvolution algorithm. The method had been applied in the image restoration of celestial bodies which were observed by 1.2m telescope equipped with 61-element adaptive optical system at Yunnan Observatory. The results show that the method can effectively improve the images partially corrected by adaptive optics.

Advances in feature selection methods for hyperspectral image processing in food industry applications: a review.

PubMed

Dai, Qiong; Cheng, Jun-Hu; Sun, Da-Wen; Zeng, Xin-An

2015-01-01

There is an increased interest in the applications of hyperspectral imaging (HSI) for assessing food quality, safety, and authenticity. HSI provides abundance of spatial and spectral information from foods by combining both spectroscopy and imaging, resulting in hundreds of contiguous wavebands for each spatial position of food samples, also known as the curse of dimensionality. It is desirable to employ feature selection algorithms for decreasing computation burden and increasing predicting accuracy, which are especially relevant in the development of online applications. Recently, a variety of feature selection algorithms have been proposed that can be categorized into three groups based on the searching strategy namely complete search, heuristic search and random search. This review mainly introduced the fundamental of each algorithm, illustrated its applications in hyperspectral data analysis in the food field, and discussed the advantages and disadvantages of these algorithms. It is hoped that this review should provide a guideline for feature selections and data processing in the future development of hyperspectral imaging technique in foods.

Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach.

PubMed

Maity, Maitreya; Dhane, Dhiraj; Mungle, Tushar; Maiti, A K; Chakraborty, Chandan

2017-10-26

Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.

A hands-free region-of-interest selection interface for solo surgery with a wide-angle endoscope: preclinical proof of concept.

PubMed

Jung, Kyunghwa; Choi, Hyunseok; Hong, Hanpyo; Adikrishna, Arnold; Jeon, In-Ho; Hong, Jaesung

2017-02-01

A hands-free region-of-interest (ROI) selection interface is proposed for solo surgery using a wide-angle endoscope. A wide-angle endoscope provides images with a larger field of view than a conventional endoscope. With an appropriate selection interface for a ROI, surgeons can also obtain a detailed local view as if they moved a conventional endoscope in a specific position and direction. To manipulate the endoscope without releasing the surgical instrument in hand, a mini-camera is attached to the instrument, and the images taken by the attached camera are analyzed. When a surgeon moves the instrument, the instrument orientation is calculated by an image processing. Surgeons can select the ROI with this instrument movement after switching from 'task mode' to 'selection mode.' The accelerated KAZE algorithm is used to track the features of the camera images once the instrument is moved. Both the wide-angle and detailed local views are displayed simultaneously, and a surgeon can move the local view area by moving the mini-camera attached to the surgical instrument. Local view selection for a solo surgery was performed without releasing the instrument. The accuracy of camera pose estimation was not significantly different between camera resolutions, but it was significantly different between background camera images with different numbers of features (P < 0.01). The success rate of ROI selection diminished as the number of separated regions increased. However, separated regions up to 12 with a region size of 160 × 160 pixels were selected with no failure. Surgical tasks on a phantom model and a cadaver were attempted to verify the feasibility in a clinical environment. Hands-free endoscope manipulation without releasing the instruments in hand was achieved. The proposed method requires only a small, low-cost camera and an image processing. The technique enables surgeons to perform solo surgeries without a camera assistant.

Fast digital zooming system using directionally adaptive image interpolation and restoration.

PubMed

Kang, Wonseok; Jeon, Jaehwan; Yu, Soohwan; Paik, Joonki

2014-01-01

This paper presents a fast digital zooming system for mobile consumer cameras using directionally adaptive image interpolation and restoration methods. The proposed interpolation algorithm performs edge refinement along the initially estimated edge orientation using directionally steerable filters. Either the directionally weighted linear or adaptive cubic-spline interpolation filter is then selectively used according to the refined edge orientation for removing jagged artifacts in the slanted edge region. A novel image restoration algorithm is also presented for removing blurring artifacts caused by the linear or cubic-spline interpolation using the directionally adaptive truncated constrained least squares (TCLS) filter. Both proposed steerable filter-based interpolation and the TCLS-based restoration filters have a finite impulse response (FIR) structure for real time processing in an image signal processing (ISP) chain. Experimental results show that the proposed digital zooming system provides high-quality magnified images with FIR filter-based fast computational structure.

A Novel BA Complex Network Model on Color Template Matching

PubMed Central

Han, Risheng; Yue, Guangxue; Ding, Hui

2014-01-01

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching. PMID:25243235

A novel BA complex network model on color template matching.

PubMed

Han, Risheng; Shen, Shigen; Yue, Guangxue; Ding, Hui

2014-01-01

A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.

E-Services quality assessment framework for collaborative networks

NASA Astrophysics Data System (ADS)

Stegaru, Georgiana; Danila, Cristian; Sacala, Ioan Stefan; Moisescu, Mihnea; Mihai Stanescu, Aurelian

2015-08-01

In a globalised networked economy, collaborative networks (CNs) are formed to take advantage of new business opportunities. Collaboration involves shared resources and capabilities, such as e-Services that can be dynamically composed to automate CN participants' business processes. Quality is essential for the success of business process automation. Current approaches mostly focus on quality of service (QoS)-based service selection and ranking algorithms, overlooking the process of service composition which requires interoperable, adaptable and secure e-Services to ensure seamless collaboration, data confidentiality and integrity. Lack of assessment of these quality attributes can result in e-Service composition failure. The quality of e-Service composition relies on the quality of each e-Service and on the quality of the composition process. Therefore, there is the need for a framework that addresses quality from both views: product and process. We propose a quality of e-Service composition (QoESC) framework for quality assessment of e-Service composition for CNs which comprises of a quality model for e-Service evaluation and guidelines for quality of e-Service composition process. We implemented a prototype considering a simplified telemedicine use case which involves a CN in e-Healthcare domain. To validate the proposed quality-driven framework, we analysed service composition reliability with and without using the proposed framework.

Coupled Phases and Combinatorial Selection in Fluctuating Hydrothermal Pools: A Scenario to Guide Experimental Approaches to the Origin of Cellular Life

PubMed Central

Damer, Bruce; Deamer, David

2015-01-01

Hydrothermal fields on the prebiotic Earth are candidate environments for biogenesis. We propose a model in which molecular systems driven by cycles of hydration and dehydration in such sites undergo chemical evolution in dehydrated films on mineral surfaces followed by encapsulation and combinatorial selection in a hydrated bulk phase. The dehydrated phase can consist of concentrated eutectic mixtures or multilamellar liquid crystalline matrices. Both conditions organize and concentrate potential monomers and thereby promote polymerization reactions that are driven by reduced water activity in the dehydrated phase. In the case of multilamellar lipid matrices, polymers that have been synthesized are captured in lipid vesicles upon rehydration to produce a variety of molecular systems. Each vesicle represents a protocell, an “experiment” in a natural version of combinatorial chemistry. Two kinds of selective processes can then occur. The first is a physical process in which relatively stable molecular systems will be preferentially selected. The second is a chemical process in which rare combinations of encapsulated polymers form systems capable of capturing energy and nutrients to undergo growth by catalyzed polymerization. Given continued cycling over extended time spans, such combinatorial processes will give rise to molecular systems having the fundamental properties of life. PMID:25780958

An intelligent approach to welding robot selection

NASA Astrophysics Data System (ADS)

Milano, J.; Mauk, S. D.; Flitter, L.; Morris, R.

1993-10-01

In a shipyard where multiple stationary and mobile workcells are employed in the fabrication of components of complex sub-assemblies,efficient operation requires an intelligent method of scheduling jobs and selecting workcells based on optimum throughput and cost. The achievement of this global solution requires the successful organization of resource availability,process requirements,and process constraints. The Off-line Planner (OLP) of the Programmable Automated Weld Systemd (PAWS) is capable of advanced modeling of weld processes and environments as well as the generation of complete weld procedures. These capabilities involve the integration of advanced Computer Aided Design (CAD), path planning, and obstacle detection and avoidance techniques as well as the synthesis of complex design and process information. These existing capabilities provide the basis of the functionality required for the successful implementation of an intelligent weld robot selector and material flow planner. Current efforts are focused on robot selection via the dynamic routing of components to the appropriate work cells. It is proposed that this problem is a variant of the “Traveling Salesman Problem” (TSP) that has been proven to belong to a larger set of optimization problems termed nondeterministic polynomial complete (NP complete). In this paper, a heuristic approach utilizing recurrent neural networks is explored as a rapid means of producing a near optimal, if not optimal, bdweld robot selection.

Response terminated displays unload selective attention

PubMed Central

Roper, Zachary J. J.; Vecera, Shaun P.

2013-01-01

Perceptual load theory successfully replaced the early vs. late selection debate by appealing to adaptive control over the efficiency of selective attention. Early selection is observed unless perceptual load (p-Load) is sufficiently low to grant attentional “spill-over” to task-irrelevant stimuli. Many studies exploring load theory have used limited display durations that perhaps impose artificial limits on encoding processes. We extended the exposure duration in a classic p-Load task to alleviate temporal encoding demands that may otherwise tax mnemonic consolidation processes. If the load effect arises from perceptual demands alone, then freeing-up available mnemonic resources by extending the exposure duration should have little effect. The results of Experiment 1 falsify this prediction. We observed a reliable flanker effect under high p-Load, response-terminated displays. Next, we orthogonally manipulated exposure duration and task-relevance. Counter-intuitively, we found that the likelihood of observing the flanker effect under high p-Load resides with the duration of the task-relevant array, not the flanker itself. We propose that stimulus and encoding demands interact to produce the load effect. Our account clarifies how task parameters differentially impinge upon cognitive processes to produce attentional “spill-over” by appealing to visual short-term memory as an additional processing bottleneck when stimuli are briefly presented. PMID:24399983

Response terminated displays unload selective attention.

PubMed

Roper, Zachary J J; Vecera, Shaun P

2013-01-01

Perceptual load theory successfully replaced the early vs. late selection debate by appealing to adaptive control over the efficiency of selective attention. Early selection is observed unless perceptual load (p-Load) is sufficiently low to grant attentional "spill-over" to task-irrelevant stimuli. Many studies exploring load theory have used limited display durations that perhaps impose artificial limits on encoding processes. We extended the exposure duration in a classic p-Load task to alleviate temporal encoding demands that may otherwise tax mnemonic consolidation processes. If the load effect arises from perceptual demands alone, then freeing-up available mnemonic resources by extending the exposure duration should have little effect. The results of Experiment 1 falsify this prediction. We observed a reliable flanker effect under high p-Load, response-terminated displays. Next, we orthogonally manipulated exposure duration and task-relevance. Counter-intuitively, we found that the likelihood of observing the flanker effect under high p-Load resides with the duration of the task-relevant array, not the flanker itself. We propose that stimulus and encoding demands interact to produce the load effect. Our account clarifies how task parameters differentially impinge upon cognitive processes to produce attentional "spill-over" by appealing to visual short-term memory as an additional processing bottleneck when stimuli are briefly presented.

Quantitative genetic correlation between trait and preference supports a sexually selected sperm process

PubMed Central

Simmons, Leigh W.; Kotiaho, Janne S.

2007-01-01

Sperm show patterns of rapid and divergent evolution that are characteristic of sexual selection. Sperm competition has been proposed as an important selective agent in the evolution of sperm morphology. However, several comparative analyses have revealed evolutionary associations between sperm length and female reproductive tract morphology that suggest patterns of male–female coevolution. In the dung beetle Onthophagus taurus, males with short sperm have a fertilization advantage that depends on the size of the female's sperm storage organ, the spermatheca; large spermathecae select for short sperm. Sperm length is heritable and is genetically correlated with male condition. Here we report significant additive genetic variation and heritability for spermatheca size and genetic covariance between spermatheca size and sperm length predicted by both the “good-sperm” and “sexy-sperm” models of postcopulatory female preference. Our data thus provide quantitative genetic support for the role of a sexually selected sperm process in the evolutionary divergence of sperm morphology, in much the same manner as precopulatory female preferences drive the evolutionary divergence of male secondary sexual traits. PMID:17921254

A two-phased fuzzy decision making procedure for IT supplier selection

NASA Astrophysics Data System (ADS)

Shohaimay, Fairuz; Ramli, Nazirah; Mohamed, Siti Rosiah; Mohd, Ainun Hafizah

2013-09-01

In many studies on fuzzy decision making, linguistic terms are usually represented by corresponding fixed triangular or trapezoidal fuzzy numbers. However, the fixed fuzzy numbers used in decision making process may not explain the actual respondents' opinions. Hence, a two-phased fuzzy decision making procedure is proposed. First, triangular fuzzy numbers were built based on respondents' opinions on the appropriate range (0-100) for each seven-scale linguistic terms. Then, the fuzzy numbers were integrated into fuzzy decision making model. The applicability of the proposed method is demonstrated in a case study of supplier selection in Information Technology (IT) department. The results produced via the developed fuzzy numbers were consistent with the results obtained using fixed fuzzy numbers. However, with different set of fuzzy numbers based on respondents, there is a difference in the ranking of suppliers based on criterion X1 (background of supplier). Hopefully the proposed model which incorporates fuzzy numbers based on respondents will provide a more significant meaning towards future decision making.

Image steganalysis using Artificial Bee Colony algorithm

NASA Astrophysics Data System (ADS)

Sajedi, Hedieh

2017-09-01

Steganography is the science of secure communication where the presence of the communication cannot be detected while steganalysis is the art of discovering the existence of the secret communication. Processing a huge amount of information takes extensive execution time and computational sources most of the time. As a result, it is needed to employ a phase of preprocessing, which can moderate the execution time and computational sources. In this paper, we propose a new feature-based blind steganalysis method for detecting stego images from the cover (clean) images with JPEG format. In this regard, we present a feature selection technique based on an improved Artificial Bee Colony (ABC). ABC algorithm is inspired by honeybees' social behaviour in their search for perfect food sources. In the proposed method, classifier performance and the dimension of the selected feature vector depend on using wrapper-based methods. The experiments are performed using two large data-sets of JPEG images. Experimental results demonstrate the effectiveness of the proposed steganalysis technique compared to the other existing techniques.

A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues.

PubMed

Guo, Song; Liu, Chunhua; Zhou, Peng; Li, Yanling

2016-01-01

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields.

A Multifeatures Fusion and Discrete Firefly Optimization Method for Prediction of Protein Tyrosine Sulfation Residues

PubMed Central

Liu, Chunhua; Zhou, Peng; Li, Yanling

2016-01-01

Tyrosine sulfation is one of the ubiquitous protein posttranslational modifications, where some sulfate groups are added to the tyrosine residues. It plays significant roles in various physiological processes in eukaryotic cells. To explore the molecular mechanism of tyrosine sulfation, one of the prerequisites is to correctly identify possible protein tyrosine sulfation residues. In this paper, a novel method was presented to predict protein tyrosine sulfation residues from primary sequences. By means of informative feature construction and elaborate feature selection and parameter optimization scheme, the proposed predictor achieved promising results and outperformed many other state-of-the-art predictors. Using the optimal features subset, the proposed method achieved mean MCC of 94.41% on the benchmark dataset, and a MCC of 90.09% on the independent dataset. The experimental performance indicated that our new proposed method could be effective in identifying the important protein posttranslational modifications and the feature selection scheme would be powerful in protein functional residues prediction research fields. PMID:27034949

EMD self-adaptive selecting relevant modes algorithm for FBG spectrum signal

NASA Astrophysics Data System (ADS)

Chen, Yong; Wu, Chun-ting; Liu, Huan-lin

2017-07-01

Noise may reduce the demodulation accuracy of fiber Bragg grating (FBG) sensing signal so as to affect the quality of sensing detection. Thus, the recovery of a signal from observed noisy data is necessary. In this paper, a precise self-adaptive algorithm of selecting relevant modes is proposed to remove the noise of signal. Empirical mode decomposition (EMD) is first used to decompose a signal into a set of modes. The pseudo modes cancellation is introduced to identify and eliminate false modes, and then the Mutual Information (MI) of partial modes is calculated. MI is used to estimate the critical point of high and low frequency components. Simulation results show that the proposed algorithm estimates the critical point more accurately than the traditional algorithms for FBG spectral signal. While, compared to the similar algorithms, the signal noise ratio of the signal can be improved more than 10 dB after processing by the proposed algorithm, and correlation coefficient can be increased by 0.5, so it demonstrates better de-noising effect.

Application of Advanced Process Control techniques to a pusher type reheating furnace

NASA Astrophysics Data System (ADS)

Zanoli, S. M.; Pepe, C.; Barboni, L.

2015-11-01

In this paper an Advanced Process Control system aimed at controlling and optimizing a pusher type reheating furnace located in an Italian steel plant is proposed. The designed controller replaced the previous control system, based on PID controllers manually conducted by process operators. A two-layer Model Predictive Control architecture has been adopted that, exploiting a chemical, physical and economic modelling of the process, overcomes the limitations of plant operators’ mental model and knowledge. In addition, an ad hoc decoupling strategy has been implemented, allowing the selection of the manipulated variables to be used for the control of each single process variable. Finally, in order to improve the system flexibility and resilience, the controller has been equipped with a supervision module. A profitable trade-off between conflicting specifications, e.g. safety, quality and production constraints, energy saving and pollution impact, has been guaranteed. Simulation tests and real plant results demonstrated the soundness and the reliability of the proposed system.

Process Pharmacology: A Pharmacological Data Science Approach to Drug Development and Therapy.

PubMed

Lötsch, Jörn; Ultsch, Alfred

2016-04-01

A novel functional-genomics based concept of pharmacology that uses artificial intelligence techniques for mining and knowledge discovery in "big data" providing comprehensive information about the drugs' targets and their functional genomics is proposed. In "process pharmacology", drugs are associated with biological processes. This puts the disease, regarded as alterations in the activity in one or several cellular processes, in the focus of drug therapy. In this setting, the molecular drug targets are merely intermediates. The identification of drugs for therapeutic or repurposing is based on similarities in the high-dimensional space of the biological processes that a drug influences. Applying this principle to data associated with lymphoblastic leukemia identified a short list of candidate drugs, including one that was recently proposed as novel rescue medication for lymphocytic leukemia. The pharmacological data science approach provides successful selections of drug candidates within development and repurposing tasks. © 2016 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Search asymmetries: parallel processing of uncertain sensory information.

PubMed

Vincent, Benjamin T

2011-08-01

What is the mechanism underlying search phenomena such as search asymmetry? Two-stage models such as Feature Integration Theory and Guided Search propose parallel pre-attentive processing followed by serial post-attentive processing. They claim search asymmetry effects are indicative of finding pairs of features, one processed in parallel, the other in serial. An alternative proposal is that a 1-stage parallel process is responsible, and search asymmetries occur when one stimulus has greater internal uncertainty associated with it than another. While the latter account is simpler, only a few studies have set out to empirically test its quantitative predictions, and many researchers still subscribe to the 2-stage account. This paper examines three separate parallel models (Bayesian optimal observer, max rule, and a heuristic decision rule). All three parallel models can account for search asymmetry effects and I conclude that either people can optimally utilise the uncertain sensory data available to them, or are able to select heuristic decision rules which approximate optimal performance. Copyright © 2011 Elsevier Ltd. All rights reserved.

Designing and examining e-waste recycling process: methodology and case studies.

PubMed

Li, Jinhui; He, Xin; Zeng, Xianlai

2017-03-01

Increasing concerns on resource depletion and environmental pollution have largely obliged electrical and electronic waste (e-waste) should be tackled in an environmentally sound manner. Recycling process development is regarded as the most effective and fundamental to solve the e-waste problem. Based on global achievements related to e-waste recycling in the past 15 years, we first propose a theory to design an e-waste recycling process, including measuring e-waste recyclability and selection of recycling process. And we summarize the indicators and tools in terms of resource dimension, environmental dimension, and economic dimension, to examine the e-waste recycling process. Using the sophisticated experience and adequate information of e-waste management, spent lithium-ion batteries and waste printed circuit boards are chosen as case studies to implement and verify the proposed method. All the potential theory and obtained results in this work can contribute to future e-waste management toward best available techniques and best environmental practices.

A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

NASA Astrophysics Data System (ADS)

Wu, Bo; Yang, Minglei; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Wang, Tong; Lee, Chin-Hui

2017-12-01

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

NASA Astrophysics Data System (ADS)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

Make or buy analysis model based on tolerance allocation to minimize manufacturing cost and fuzzy quality loss

NASA Astrophysics Data System (ADS)

Rosyidi, C. N.; Puspitoingrum, W.; Jauhari, W. A.; Suhardi, B.; Hamada, K.

2016-02-01

The specification of tolerances has a significant impact on the quality of product and final production cost. The company should carefully pay attention to the component or product tolerance so they can produce a good quality product at the lowest cost. Tolerance allocation has been widely used to solve problem in selecting particular process or supplier. But before merely getting into the selection process, the company must first make a plan to analyse whether the component must be made in house (make), to be purchased from a supplier (buy), or used the combination of both. This paper discusses an optimization model of process and supplier selection in order to minimize the manufacturing costs and the fuzzy quality loss. This model can also be used to determine the allocation of components to the selected processes or suppliers. Tolerance, process capability and production capacity are three important constraints that affect the decision. Fuzzy quality loss function is used in this paper to describe the semantic of the quality, in which the product quality level is divided into several grades. The implementation of the proposed model has been demonstrated by solving a numerical example problem that used a simple assembly product which consists of three components. The metaheuristic approach were implemented to OptQuest software from Oracle Crystal Ball in order to obtain the optimal solution of the numerical example.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

Orbital selective pairing and gap structures of iron-based superconductors

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

Kreisel, Andreas; Andersen, Brian M.; Sprau, P. O.

We discuss the in uence on spin-fluctuation pairing theory of orbital selective strong correlation effects in Fe-based superconductors, particularly Fe chalcogenide systems. We propose that a key ingredient for an improved itinerant pairing theory is orbital selectivity, i.e., incorporating the reduced coherence of quasiparticles occupying specific orbital states. This modifies the usual spin-fluctuation via suppression of pair scattering processes involving those less coherent states and results in orbital selective Cooper pairing of electrons in the remaining states. We show that this paradigm yields remarkably good agreement with the experimentally observed anisotropic gap structures in both bulk and monolayer FeSe, asmore » well as LiFeAs, indicating that orbital selective Cooper pairing plays a key role in the more strongly correlated iron-based superconductors.« less

Orbital selective pairing and gap structures of iron-based superconductors

DOE PAGES

Kreisel, Andreas; Andersen, Brian M.; Sprau, P. O.; ...

2017-05-08

We discuss the in uence on spin-fluctuation pairing theory of orbital selective strong correlation effects in Fe-based superconductors, particularly Fe chalcogenide systems. We propose that a key ingredient for an improved itinerant pairing theory is orbital selectivity, i.e., incorporating the reduced coherence of quasiparticles occupying specific orbital states. This modifies the usual spin-fluctuation via suppression of pair scattering processes involving those less coherent states and results in orbital selective Cooper pairing of electrons in the remaining states. We show that this paradigm yields remarkably good agreement with the experimentally observed anisotropic gap structures in both bulk and monolayer FeSe, asmore » well as LiFeAs, indicating that orbital selective Cooper pairing plays a key role in the more strongly correlated iron-based superconductors.« less

Image quality enhancement for skin cancer optical diagnostics

NASA Astrophysics Data System (ADS)

Bliznuks, Dmitrijs; Kuzmina, Ilona; Bolocko, Katrina; Lihachev, Alexey

2017-12-01

The research presents image quality analysis and enhancement proposals in biophotonic area. The sources of image problems are reviewed and analyzed. The problems with most impact in biophotonic area are analyzed in terms of specific biophotonic task - skin cancer diagnostics. The results point out that main problem for skin cancer analysis is the skin illumination problems. Since it is often not possible to prevent illumination problems, the paper proposes image post processing algorithm - low frequency filtering. Practical results show diagnostic results improvement after using proposed filter. Along that, filter do not reduces diagnostic results' quality for images without illumination defects. Current filtering algorithm requires empirical tuning of filter parameters. Further work needed to test the algorithm in other biophotonic applications and propose automatic filter parameter selection.

Status report: Data management program algorithm evaluation activity at Marshall Space Flight Center

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1977-01-01

An algorithm evaluation activity was initiated to study the problems associated with image processing by assessing the independent and interdependent effects of registration, compression, and classification techniques on LANDSAT data for several discipline applications. The objective of the activity was to make recommendations on selected applicable image processing algorithms in terms of accuracy, cost, and timeliness or to propose alternative ways of processing the data. As a means of accomplishing this objective, an Image Coding Panel was established. The conduct of the algorithm evaluation is described.

Underground Mining Method Selection Using WPM and PROMETHEE

NASA Astrophysics Data System (ADS)

Balusa, Bhanu Chander; Singam, Jayanthu

2018-04-01

The aim of this paper is to represent the solution to the problem of selecting suitable underground mining method for the mining industry. It is achieved by using two multi-attribute decision making techniques. These two techniques are weighted product method (WPM) and preference ranking organization method for enrichment evaluation (PROMETHEE). In this paper, analytic hierarchy process is used for weight's calculation of the attributes (i.e. parameters which are used in this paper). Mining method selection depends on physical parameters, mechanical parameters, economical parameters and technical parameters. WPM and PROMETHEE techniques have the ability to consider the relationship between the parameters and mining methods. The proposed techniques give higher accuracy and faster computation capability when compared with other decision making techniques. The proposed techniques are presented to determine the effective mining method for bauxite mine. The results of these techniques are compared with methods used in the earlier research works. The results show, conventional cut and fill method is the most suitable mining method.

Evolutionary selection growth of two-dimensional materials on polycrystalline substrates

NASA Astrophysics Data System (ADS)

Vlassiouk, Ivan V.; Stehle, Yijing; Pudasaini, Pushpa Raj; Unocic, Raymond R.; Rack, Philip D.; Baddorf, Arthur P.; Ivanov, Ilia N.; Lavrik, Nickolay V.; List, Frederick; Gupta, Nitant; Bets, Ksenia V.; Yakobson, Boris I.; Smirnov, Sergei N.

2018-03-01

There is a demand for the manufacture of two-dimensional (2D) materials with high-quality single crystals of large size. Usually, epitaxial growth is considered the method of choice1 in preparing single-crystalline thin films, but it requires single-crystal substrates for deposition. Here we present a different approach and report the synthesis of single-crystal-like monolayer graphene films on polycrystalline substrates. The technological realization of the proposed method resembles the Czochralski process and is based on the evolutionary selection2 approach, which is now realized in 2D geometry. The method relies on `self-selection' of the fastest-growing domain orientation, which eventually overwhelms the slower-growing domains and yields a single-crystal continuous 2D film. Here we have used it to synthesize foot-long graphene films at rates up to 2.5 cm h-1 that possess the quality of a single crystal. We anticipate that the proposed approach could be readily adopted for the synthesis of other 2D materials and heterostructures.

Adaptive compressed sensing of remote-sensing imaging based on the sparsity prediction

NASA Astrophysics Data System (ADS)

Yang, Senlin; Li, Xilong; Chong, Xin

2017-10-01

The conventional compressive sensing works based on the non-adaptive linear projections, and the parameter of its measurement times is usually set empirically. As a result, the quality of image reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was given. Then an estimation method for the sparsity of image was proposed based on the two dimensional discrete cosine transform (2D DCT). With an energy threshold given beforehand, the DCT coefficients were processed with both energy normalization and sorting in descending order, and the sparsity of the image can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of image effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparse degree estimated with the energy threshold provided, the proposed method can ensure the quality of image reconstruction.

Adaptive compressed sensing of multi-view videos based on the sparsity estimation

NASA Astrophysics Data System (ADS)

Yang, Senlin; Li, Xilong; Chong, Xin

2017-11-01

The conventional compressive sensing for videos based on the non-adaptive linear projections, and the measurement times is usually set empirically. As a result, the quality of videos reconstruction is always affected. Firstly, the block-based compressed sensing (BCS) with conventional selection for compressive measurements was described. Then an estimation method for the sparsity of multi-view videos was proposed based on the two dimensional discrete wavelet transform (2D DWT). With an energy threshold given beforehand, the DWT coefficients were processed with both energy normalization and sorting by descending order, and the sparsity of the multi-view video can be achieved by the proportion of dominant coefficients. And finally, the simulation result shows that, the method can estimate the sparsity of video frame effectively, and provides an active basis for the selection of compressive observation times. The result also shows that, since the selection of observation times is based on the sparsity estimated with the energy threshold provided, the proposed method can ensure the reconstruction quality of multi-view videos.

Regulatory requirements for nuclear power plant site selection in Malaysia-a review.

PubMed

Basri, N A; Hashim, S; Ramli, A T; Bradley, D A; Hamzah, K

2016-12-01

Malaysia has initiated a range of pre-project activities in preparation for its planned nuclear power programme. Clearly one of the first steps is the selection of sites that are deemed suitable for the construction and operation of a nuclear power plant. Here we outline the Malaysian regulatory requirements for nuclear power plant site selection, emphasizing details of the selection procedures and site characteristics needed, with a clear focus on radiation safety and radiation protection in respect of the site surroundings. The Malaysia Atomic Energy Licensing Board (AELB) site selection guidelines are in accord with those provided in International Atomic Energy Agency (IAEA) and United Stated Nuclear Regulatory Commission (USNRC) documents. To enhance the suitability criteria during selection, as well as to assist in the final decision making process, possible assessments using the site selection characteristics and information are proposed.

Do the Contents of Visual Working Memory Automatically Influence Attentional Selection during Visual Search?

ERIC Educational Resources Information Center

Woodman, Geoffrey F.; Luck, Steven J.

2007-01-01

In many theories of cognition, researchers propose that working memory and perception operate interactively. For example, in previous studies researchers have suggested that sensory inputs matching the contents of working memory will have an automatic advantage in the competition for processing resources. The authors tested this hypothesis by…

34 CFR 606.21 - What are the selection criteria for planning grants?

Code of Federal Regulations, 2010 CFR

2010-07-01

... adequate. (c) Project Management. The Secretary reviews each application to determine the quality of the... which the proposed project costs are necessary and reasonable. (Approved by the Office of Management and... resources to help implement the project; and (4) The planning process is likely to achieve its intended...

32 CFR 37.1020 - What must I document in my award file?

Code of Federal Regulations, 2010 CFR

2010-07-01

... commercial benefits that should result from the project supported by the TIA. If the recipient is a... collaboration. (b) Describe the process that led to the award of the TIA, including how you and program officials solicited and evaluated proposals and selected the one supported through the TIA. (c) Explain how...

A Case Study of MOOCs Design and Administration at Seoul National University

ERIC Educational Resources Information Center

Lim, Cheolil; Kim, Sunyoung; Kim, Mihwa; Han, Songlee; Seo, Seungil

2014-01-01

This research, based on the case study of edX at Seoul National University, which is running Korea's first Massive Open Online Courses (MOOCs), discussed and proposed the roles of principal facilitators, the process, and the relationships among various facilitators in selecting, designing, opening and administrating MOOCs classes. Researches on…

78 FR 13721 - Self-Regulatory Organizations; The NASDAQ Stock Market LLC; Order Granting Approval of Proposed...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-28

... Adviser will combine a fundamental credit selection process with top down relative value analysis when.... corporate debt obligations, bank loans, and convertible bonds. For purposes of determining whether a... minimum principal amount outstanding of $100 million or more with respect to U.S. corporate issuers and...

Andragogy and Motivation: An Examination of the Principles of Andragogy through Two Motivation Theories

ERIC Educational Resources Information Center

Houde, Joseph

2006-01-01

Andragogy, originally proposed by Malcolm Knowles, has been criticized as an atheoretical model. Validation of andragogy has been advocated by scholars, and this paper explores one method for that process. Current motivation theory, specifically socioemotional selectivity and self-determination theory correspond with aspects of andragogy. In…

77 FR 49819 - Agency Information Collection Activities; Proposed Collection; Comment Request; Pretesting of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-17

.... Knowledge of these decisionmaking processes will be applied by FDA to help design effective communication... effectively design messages and select formats that have the greatest potential to influence the target... each new pretest will vary, depending on the nature of the material or message being tested and the...

Analyzing Problem's Difficulty Based on Neural Networks and Knowledge Map

ERIC Educational Resources Information Center

Kuo, Rita; Lien, Wei-Peng; Chang, Maiga; Heh, Jia-Sheng

2004-01-01

This paper proposes a methodology to calculate both the difficulty of the basic problems and the difficulty of solving a problem. The method to calculate the difficulty of problem is according to the process of constructing a problem, including Concept Selection, Unknown Designation, and Proposition Construction. Some necessary measures observed…

34 CFR 606.21 - What are the selection criteria for planning grants?

Code of Federal Regulations, 2011 CFR

2011-07-01

... adequate. (c) Project Management. The Secretary reviews each application to determine the quality of the... which the proposed project costs are necessary and reasonable. (Approved by the Office of Management and... resources to help implement the project; and (4) The planning process is likely to achieve its intended...

34 CFR 606.21 - What are the selection criteria for planning grants?

Code of Federal Regulations, 2013 CFR

2013-07-01

... adequate. (c) Project Management. The Secretary reviews each application to determine the quality of the... which the proposed project costs are necessary and reasonable. (Approved by the Office of Management and... resources to help implement the project; and (4) The planning process is likely to achieve its intended...

34 CFR 606.21 - What are the selection criteria for planning grants?

Code of Federal Regulations, 2014 CFR

2014-07-01

... adequate. (c) Project Management. The Secretary reviews each application to determine the quality of the... which the proposed project costs are necessary and reasonable. (Approved by the Office of Management and... resources to help implement the project; and (4) The planning process is likely to achieve its intended...

34 CFR 606.21 - What are the selection criteria for planning grants?

Code of Federal Regulations, 2012 CFR

2012-07-01

... adequate. (c) Project Management. The Secretary reviews each application to determine the quality of the... which the proposed project costs are necessary and reasonable. (Approved by the Office of Management and... resources to help implement the project; and (4) The planning process is likely to achieve its intended...

Teaching Freshmen To Understand Research as a Process of Inquiry.

ERIC Educational Resources Information Center

Tracey, Karen

Freshmen often approach research papers by selecting a "giant topic" and going to the library to confront swamps and mountains of resources. A different approach to teaching research is designed to help students begin to shift the often counter-productive paradigm under which they operate. The classroom strategy proposed is 3-fold.…

76 FR 51442 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing of Proposed Rule Change To List...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

...-Adviser has designed the following quantitative stock selection rules to make allocation decisions and to..., the Sub-Adviser's investment process is quantitative. Based on extensive historical research, the Sub... open-end fund's portfolio composition must be subject to procedures designed to prevent the use and...

The Ganymede Interior Structure, and Magnetosphere Observer (GISMO) Mission Concept

NASA Technical Reports Server (NTRS)

Lynch, K. L.; Smith, I. B.; Singer, K. N.; Vogt, M. F.; Blackburn, D. G.; Chaffin, M.; Choukroun, M.; Ehsan, N.; DiBraccio, G. A.; Gibbons, L. J.;

Nonparametric Bayesian models for a spatial covariance.

PubMed

Reich, Brian J; Fuentes, Montserrat

2012-01-01

A crucial step in the analysis of spatial data is to estimate the spatial correlation function that determines the relationship between a spatial process at two locations. The standard approach to selecting the appropriate correlation function is to use prior knowledge or exploratory analysis, such as a variogram analysis, to select the correct parametric correlation function. Rather that selecting a particular parametric correlation function, we treat the covariance function as an unknown function to be estimated from the data. We propose a flexible prior for the correlation function to provide robustness to the choice of correlation function. We specify the prior for the correlation function using spectral methods and the Dirichlet process prior, which is a common prior for an unknown distribution function. Our model does not require Gaussian data or spatial locations on a regular grid. The approach is demonstrated using a simulation study as well as an analysis of California air pollution data.

Specific and selective target detection of supra-genome 21 Mers Salmonella via silicon nanowires biosensor

NASA Astrophysics Data System (ADS)

Mustafa, Mohammad Razif Bin; Dhahi, Th S.; Ehfaed, Nuri. A. K. H.; Adam, Tijjani; Hashim, U.; Azizah, N.; Mohammed, Mohammed; Noriman, N. Z.

2017-09-01

The nano structure based on silicon can be surface modified to be used as label-free biosensors that allow real-time measurements. The silicon nanowire surface was functionalized using 3-aminopropyltrimethoxysilane (APTES), which functions as a facilitator to immobilize biomolecules on the silicon nanowire surface. The process is simple, economical; this will pave the way for point-of-care applications. However, the surface modification and subsequent detection mechanism still not clear. Thus, study proposed step by step process of silicon nano surface modification and its possible in specific and selective target detection of Supra-genome 21 Mers Salmonella. The device captured the molecule with precisely; the approach took the advantages of strong binding chemistry created between APTES and biomolecule. The results indicated how modifications of the nanowires provide sensing capability with strong surface chemistries that can lead to specific and selective target detection.

A Neurobehavioral Model of Flexible Spatial Language Behaviors

PubMed Central

Lipinski, John; Schneegans, Sebastian; Sandamirskaya, Yulia; Spencer, John P.; Schöner, Gregor

2012-01-01

We propose a neural dynamic model that specifies how low-level visual processes can be integrated with higher level cognition to achieve flexible spatial language behaviors. This model uses real-word visual input that is linked to relational spatial descriptions through a neural mechanism for reference frame transformations. We demonstrate that the system can extract spatial relations from visual scenes, select items based on relational spatial descriptions, and perform reference object selection in a single unified architecture. We further show that the performance of the system is consistent with behavioral data in humans by simulating results from 2 independent empirical studies, 1 spatial term rating task and 1 study of reference object selection behavior. The architecture we present thereby achieves a high degree of task flexibility under realistic stimulus conditions. At the same time, it also provides a detailed neural grounding for complex behavioral and cognitive processes. PMID:21517224

Progressive sample processing of band selection for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Liu, Keng-Hao; Chien, Hung-Chang; Chen, Shih-Yu

2017-10-01

Band selection (BS) is one of the most important topics in hyperspectral image (HSI) processing. The objective of BS is to find a set of representative bands that can represent the whole image with lower inter-band redundancy. Many types of BS algorithms were proposed in the past. However, most of them can be carried on in an off-line manner. It means that they can only be implemented on the pre-collected data. Those off-line based methods are sometime useless for those applications that are timeliness, particular in disaster prevention and target detection. To tackle this issue, a new concept, called progressive sample processing (PSP), was proposed recently. The PSP is an "on-line" framework where the specific type of algorithm can process the currently collected data during the data transmission under band-interleavedby-sample/pixel (BIS/BIP) protocol. This paper proposes an online BS method that integrates a sparse-based BS into PSP framework, called PSP-BS. In PSP-BS, the BS can be carried out by updating BS result recursively pixel by pixel in the same way that a Kalman filter does for updating data information in a recursive fashion. The sparse regression is solved by orthogonal matching pursuit (OMP) algorithm, and the recursive equations of PSP-BS are derived by using matrix decomposition. The experiments conducted on a real hyperspectral image show that the PSP-BS can progressively output the BS status with very low computing time. The convergence of BS results during the transmission can be quickly achieved by using a rearranged pixel transmission sequence. This significant advantage allows BS to be implemented in a real time manner when the HSI data is transmitted pixel by pixel.

Adaptive multifocus image fusion using block compressed sensing with smoothed projected Landweber integration in the wavelet domain.

PubMed

V S, Unni; Mishra, Deepak; Subrahmanyam, G R K S

2016-12-01

The need for image fusion in current image processing systems is increasing mainly due to the increased number and variety of image acquisition techniques. Image fusion is the process of combining substantial information from several sensors using mathematical techniques in order to create a single composite image that will be more comprehensive and thus more useful for a human operator or other computer vision tasks. This paper presents a new approach to multifocus image fusion based on sparse signal representation. Block-based compressive sensing integrated with a projection-driven compressive sensing (CS) recovery that encourages sparsity in the wavelet domain is used as a method to get the focused image from a set of out-of-focus images. Compression is achieved during the image acquisition process using a block compressive sensing method. An adaptive thresholding technique within the smoothed projected Landweber recovery process reconstructs high-resolution focused images from low-dimensional CS measurements of out-of-focus images. Discrete wavelet transform and dual-tree complex wavelet transform are used as the sparsifying basis for the proposed fusion. The main finding lies in the fact that sparsification enables a better selection of the fusion coefficients and hence better fusion. A Laplacian mixture model fit is done in the wavelet domain and estimation of the probability density function (pdf) parameters by expectation maximization leads us to the proper selection of the coefficients of the fused image. Using the proposed method compared with the fusion scheme without employing the projected Landweber (PL) scheme and the other existing CS-based fusion approaches, it is observed that with fewer samples itself, the proposed method outperforms other approaches.

Additive Manufacturing of Nickel Superalloys: Opportunities for Innovation and Challenges Related to Qualification

NASA Astrophysics Data System (ADS)

Babu, S. S.; Raghavan, N.; Raplee, J.; Foster, S. J.; Frederick, C.; Haines, M.; Dinwiddie, R.; Kirka, M. K.; Plotkowski, A.; Lee, Y.; Dehoff, R. R.

2018-06-01

Innovative designs for turbines can be achieved by advances in nickel-based superalloys and manufacturing methods, including the adoption of additive manufacturing. In this regard, selective electron beam melting (SEBM) and selective laser melting (SLM) of nickel-based superalloys do provide distinct advantages. Furthermore, the direct energy deposition (DED) processes can be used for repair and reclamation of nickel alloy components. The current paper explores opportunities for innovation and qualification challenges with respect to deployment of AM as a disruptive manufacturing technology. In the first part of the paper, fundamental correlations of processing parameters to defect tendency and microstructure evolution will be explored using DED process. In the second part of the paper, opportunities for innovation in terms of site-specific control of microstructure during processing will be discussed. In the third part of the paper, challenges in qualification of AM parts for service will be discussed and potential methods to alleviate these issues through in situ process monitoring, and big data analytics are proposed.

Cultural group selection plays an essential role in explaining human cooperation: A sketch of the evidence.

PubMed

Richerson, Peter; Baldini, Ryan; Bell, Adrian V; Demps, Kathryn; Frost, Karl; Hillis, Vicken; Mathew, Sarah; Newton, Emily K; Naar, Nicole; Newson, Lesley; Ross, Cody; Smaldino, Paul E; Waring, Timothy M; Zefferman, Matthew

2016-01-01

Human cooperation is highly unusual. We live in large groups composed mostly of non-relatives. Evolutionists have proposed a number of explanations for this pattern, including cultural group selection and extensions of more general processes such as reciprocity, kin selection, and multi-level selection acting on genes. Evolutionary processes are consilient; they affect several different empirical domains, such as patterns of behavior and the proximal drivers of that behavior. In this target article, we sketch the evidence from five domains that bear on the explanatory adequacy of cultural group selection and competing hypotheses to explain human cooperation. Does cultural transmission constitute an inheritance system that can evolve in a Darwinian fashion? Are the norms that underpin institutions among the cultural traits so transmitted? Do we observe sufficient variation at the level of groups of considerable size for group selection to be a plausible process? Do human groups compete, and do success and failure in competition depend upon cultural variation? Do we observe adaptations for cooperation in humans that most plausibly arose by cultural group selection? If the answer to one of these questions is "no," then we must look to other hypotheses. We present evidence, including quantitative evidence, that the answer to all of the questions is "yes" and argue that we must take the cultural group selection hypothesis seriously. If culturally transmitted systems of rules (institutions) that limit individual deviance organize cooperation in human societies, then it is not clear that any extant alternative to cultural group selection can be a complete explanation.

Content-based analysis of Ki-67 stained meningioma specimens for automatic hot-spot selection.

PubMed

Swiderska-Chadaj, Zaneta; Markiewicz, Tomasz; Grala, Bartlomiej; Lorent, Malgorzata

2016-10-07

Hot-spot based examination of immunohistochemically stained histological specimens is one of the most important procedures in pathomorphological practice. The development of image acquisition equipment and computational units allows for the automation of this process. Moreover, a lot of possible technical problems occur in everyday histological material, which increases the complexity of the problem. Thus, a full context-based analysis of histological specimens is also needed in the quantification of immunohistochemically stained specimens. One of the most important reactions is the Ki-67 proliferation marker in meningiomas, the most frequent intracranial tumour. The aim of our study is to propose a context-based analysis of Ki-67 stained specimens of meningiomas for automatic selection of hot-spots. The proposed solution is based on textural analysis, mathematical morphology, feature ranking and classification, as well as on the proposed hot-spot gradual extinction algorithm to allow for the proper detection of a set of hot-spot fields. The designed whole slide image processing scheme eliminates such artifacts as hemorrhages, folds or stained vessels from the region of interest. To validate automatic results, a set of 104 meningioma specimens were selected and twenty hot-spots inside them were identified independently by two experts. The Spearman rho correlation coefficient was used to compare the results which were also analyzed with the help of a Bland-Altman plot. The results show that most of the cases (84) were automatically examined properly with two fields of view with a technical problem at the very most. Next, 13 had three such fields, and only seven specimens did not meet the requirement for the automatic examination. Generally, the Automatic System identifies hot-spot areas, especially their maximum points, better. Analysis of the results confirms the very high concordance between an automatic Ki-67 examination and the expert's results, with a Spearman rho higher than 0.95. The proposed hot-spot selection algorithm with an extended context-based analysis of whole slide images and hot-spot gradual extinction algorithm provides an efficient tool for simulation of a manual examination. The presented results have confirmed that the automatic examination of Ki-67 in meningiomas could be introduced in the near future.

Using multi-attribute decision-making approaches in the selection of a hospital management system.

PubMed

Arasteh, Mohammad Ali; Shamshirband, Shahaboddin; Yee, Por Lip

2018-01-01

The most appropriate organizational software is always a real challenge for managers, especially, the IT directors. The illustration of the term "enterprise software selection", is to purchase, create, or order a software that; first, is best adapted to require of the organization; and second, has suitable price and technical support. Specifying selection criteria and ranking them, is the primary prerequisite for this action. This article provides a method to evaluate, rank, and compare the available enterprise software for choosing the apt one. The prior mentioned method is constituted of three-stage processes. First, the method identifies the organizational requires and assesses them. Second, it selects the best method throughout three possibilities; indoor-production, buying software, and ordering special software for the native use. Third, the method evaluates, compares and ranks the alternative software. The third process uses different methods of multi attribute decision making (MADM), and compares the consequent results. Based on different characteristics of the problem; several methods had been tested, namely, Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Elimination and Choice Expressing Reality (ELECTURE), and easy weight method. After all, we propose the most practical method for same problems.

Radiation sterilization of aseptically manufactured products.

PubMed

Fairand, Barry P; Fidopiastis, Niki

2010-01-01

This paper discusses an approach for establishing a sterilization dose for an aseptically processed product after the product is in its final packaged state, in other words, terminal sterilization. It applies to aseptic processes where the fill/finish operation is conducted in a closed system using isolator or restricted access barrier technology, that is, no human intervention. The example that is given in this paper uses gamma radiation as the sterilizing agent. Other forms of radiation such as high-energy electrons or X-rays also could serve as the sterilizing agent. The proposed approach involves irradiation of the aseptically processed product at very low doses of radiation, which is possible due to the extremely low levels of bioburden that may be present on the product following a fill/finish operation. Rather than sacrificing a large number of product units that may be required to obtain a statistically significant sampling of the product for bioburden analysis and other test purposes, the test unit is a surrogate consisting of actual pharmaceutical product that was inoculated with a highly radiation-resistant micro-organism. Selection of the microorganism was based on analysis of a library of environmental monitoring data taken from the aseptic area. Because of microbial diversity between different aseptic processing facilities, selection of the test microorganism would depend on the aseptic area under study. The approach that is discussed in this paper addresses selection and preparation of the surrogate, test of sterility of the surrogate following irradiation, determination of the radiation resistance of the test microorganism, and application of the approach to calculate a sterilization dose that is less than 10 kGy. At this low dose, it may be possible to terminally sterilize radiation-sensitive pharmaceutical products, for example, those in liquid form. Additional studies are warranted to determine the general applicability of the proposed approach.