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Development of a proton Computed Tomography (pCT) scanner at NIU  [PDF]
S. A. Uzunyan,G. Blazey,S. Boi,G. Coutrakon,A. Dyshkant,B. Erdelyi,A. Gearhart,D. Hedin,E. Johnson,J. Krider,V. Zutshi,R. Ford,T. Fitzpatrick,G. Sellberg,J. E. Rauch,M. Roman,P. Rubinov,P. Wilson,K. Lalwani,M. Naimuddin
Physics , 2013,
Abstract: We describe the development of a proton Computed Tomography (pCT) scanner at Northern Illinois University (NIU) in collaboration with Fermilab and Delhi University. This paper provides an overview of major components of the scanner and a detailed description of the data acquisition system (DAQ).
A Multi-Criteria Group Decision Support Approach for Requirements Elicitation Techniques Selection
Yirsaw Ayalew,Audrey Masizana-Katongo
Asian Journal of Information Technology , 2012,
Abstract: Requirements elicitation is concerned with the extraction of users’ requirements, which involves cognitive, social, communication and technical issues. To support and improve the elicitation process, there are many techniques each with its own strengths and weaknesses. However, some of them are misused, others are never used and only a few are applied again and again. The reason is that requirement engineers have difficulty in deciding what elicitation techniques to use in a particular software development project due to lack of information regarding the available elicitation techniques, their usefulness and how suitable they are to the project. This study proposes a multi-criteria group decision support approach for the selection of requirements elicitation techniques by incorporating the factors that affect the selection of elicitation techniques. This approach is based on the practice of decision making process which involves a group of requirements engineers selecting a particular technique or set of techniques which are suitable for the project at hand. The multi-criteria group decision support based on AHP (Analytic Hierarchy Process) provides a formal model of specifying the relative importance of the selection factors and the applicability of the techniques with respect to each of the factors. AHP can prevent subjective judgment errors and increase the likelihood that the results are reliable.
A multiple choice decision analysis: an integrated QFD – AHP model for the assessment of customer needs
F De Felice, A Petrillo
International Journal of Engineering, Science and Technology , 2010,
Abstract: The aim of this work is to propose a new methodological approach to define customer specifications through the employment of an integrated Quality Function Deployment (QFD) – Analytic Hierarchy Process (AHP) model. The model, which is loosely based on QFD, incorporates the AHP approach to delineate and rank the relative importance weight of expressed judgments for customer needs and functional characteristics. The Analytic Hierarchy Process is very useful for this aim because it is a mathematically rigorous, proven process for prioritization and decision-making. By reducing complex decisions to a series of pair-wise comparisons, then synthesizing the results, decision-makers arrive at the best decision with a clear rationale for that decision. The methodology adopted in this work is directed to evaluate as well as rank the definition of the customer’s needs and functional characteristics among several alternatives. The approach has been validated in a real case study concerning the filter in ceramic material production.
On Elicitation Complexity and Conditional Elicitation  [PDF]
Rafael Frongillo,Ian A. Kash
Mathematics , 2015,
Abstract: Elicitation is the study of statistics or properties which are computable via empirical risk minimization. While several recent papers have approached the general question of which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important question is how elicitable. Specifically, what is the minimum number of regression parameters needed to compute the property? Building on previous work, we introduce a new notion of elicitation complexity and lay the foundations for a calculus of elicitation. We establish several general results and techniques for proving upper and lower bounds on elicitation complexity. These results provide tight bounds for eliciting the Bayes risk of any loss, a large class of properties which includes spectral risk measures and several new properties of interest. Finally, we extend our calculus to conditionally elicitable properties, which are elicitable conditioned on knowing the value of another property, giving a necessary condition for the elicitability of both properties together.
The Application of AHP in Electric Resource Evaluation  [cached]
Chunlan Qiu,Yonglin Xiao
Computer and Information Science , 2009, DOI: 10.5539/cis.v1n2p135
Abstract: This article utilizes the analytic hierarchy process (AHP) method to study and establish the hierarchy model and its evaluation system for the electric resource evaluation.
Decision Making via AHP  [PDF]
M. Andrecut
Statistics , 2014,
Abstract: The Analytic Hierarchy Process (AHP) is a procedure for establishing priorities in multi-criteria decision making problems. Here we discuss the Logarithmic Least Squares (LLS) method for the AHP and group-AHP, which provides an exact and unique solution for the priority vector. Also, we show that for the group-AHP, the LLS method is equivalent with the minimization of the weighted sum of generalized Kullback-Leibler divergences, between the group-priority vector and the priority vector of each expert.
Complexity of Terminating Preference Elicitation  [PDF]
Toby Walsh
Computer Science , 2009,
Abstract: Complexity theory is a useful tool to study computational issues surrounding the elicitation of preferences, as well as the strategic manipulation of elections aggregating together preferences of multiple agents. We study here the complexity of determining when we can terminate eliciting preferences, and prove that the complexity depends on the elicitation strategy. We show, for instance, that it may be better from a computational perspective to elicit all preferences from one agent at a time than to elicit individual preferences from multiple agents. We also study the connection between the strategic manipulation of an election and preference elicitation. We show that what we can manipulate affects the computational complexity of manipulation. In particular, we prove that there are voting rules which are easy to manipulate if we can change all of an agent's vote, but computationally intractable if we can change only some of their preferences. This suggests that, as with preference elicitation, a fine-grained view of manipulation may be informative. Finally, we study the connection between predicting the winner of an election and preference elicitation. Based on this connection, we identify a voting rule where it is computationally difficult to decide the probability of a candidate winning given a probability distribution over the votes.
Analysis framework for the J-PET scanner  [PDF]
W. Krzemień,A. Gajos,A. Gruntowski,K. Stola,D. Trybek,T. Bednarski,P. Bia?as,E. Czerwiński,D. Kamińska,L. Kap?on,A. Kochanowski,G. Korcyl,J. Kowal,P. Kowalski,T. Kozik,E. Kubicz,P. Moskal,Sz. Nied?wiecki,M. Pa?ka,L. Raczyński,Z. Rudy,P. Salabura,N. G. Sharma,M. Silarski,A. S?omski,J. Smyrski,A. Strzelecki,A. Wieczorek,W. Wi?licki,M. Zieliński,N. Zoń
Physics , 2015, DOI: 10.12693/APhysPolA.127.1491
Abstract: J-PET analysis framework is a flexible, lightweight, ROOT-based software package which provides the tools to develop reconstruction and calibration procedures for PET tomography. In this article we present the implementation of the full data-processing chain in the J-PET framework which is used for the data analysis of the J-PET tomography scanner. The Framework incorporates automated handling of PET setup parameters' database as well as high level tools for building data reconstruction procedures. Each of these components is briefly discussed.
Comment: Expert Elicitation for Reliable System Design  [PDF]
Norman Fenton,Martin Neil
Statistics , 2007, DOI: 10.1214/088342306000000529
Abstract: Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]
Rejoinder: Expert Elicitation for Reliable System Design  [PDF]
Tim Bedford,John Quigley,Lesley Walls
Statistics , 2007, DOI: 10.1214/088342306000000556
Abstract: Rejoinder: Expert Elicitation for Reliable System Design [arXiv:0708.0279]
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