Research on Preference Polyhedron Model Based Evolutionary Multiobjective Optimization Method for Multilink Transmission Mechanism Conceptual Design

To make the optimal design of the multilink transmission mechanism applied in mechanical press, the intelligent optimization techniques are explored in this paper. A preference polyhedron model and new domination relationships evaluation methodology are proposed for the purpose of reaching balance among kinematic performance, dynamic performance, and other performances of the multilink transmission mechanism during the conceptual design phase. Based on the traditional evaluation index of single target of multicriteria design optimization, the robust metrics of the mechanism system and preference metrics of decision-maker are taken into consideration in this preference polyhedron model and reflected by geometrical characteristic of the model. At last, two optimized multilink transmission mechanisms are designed based on the proposed preference polyhedron model with different evolutionary algorithms, and the result verifies the validity of the proposed optimization method

Multipath/RFI/modulation study for DRSS-RFI problem: Voice coding and intelligibility testing for a satellite-based air traffic control system

Analog and digital voice coding techniques for application to an L-band satellite-basedair traffic control (ATC) system for over ocean deployment are examined. In addition to performance, the techniques are compared on the basis of cost, size, weight, power consumption, availability, reliability, and multiplexing features. Candidate systems are chosen on the bases of minimum required RF bandwidth and received carrier-to-noise density ratios. A detailed survey of automated and nonautomated intelligibility testing methods and devices is presented and comparisons given. Subjective evaluation of speech system by preference tests is considered. Conclusion and recommendations are developed regarding the selection of the voice system. Likewise, conclusions and recommendations are developed for the appropriate use of intelligibility tests, speech quality measurements, and preference tests with the framework of the proposed ATC system

Evaluation of Knowledge Management Levels Based on Multi Criteria Analysis

For thousands of years, humans have been discussing the meaning of knowledge, what it is to know something, and how people can generate and share new knowledge. It is interesting to consider, that despite the pervasiveness of epistemological discussions throughout history, the world of business has begun to recognize the importance of knowledge as a resource recently, and today, it is considered as a leading driving force behind any organization. Today, organizations are getting involved in more and more knowledge management (KM) activities, out of which performance of knowledge management has acquired prime importance. The performance evaluation of knowledge management is a scientific evaluation of the effectiveness of organizing knowledge management activity. In present research work, evaluation of knowledge management levels based on multi criteria analysis is proposed by the candidate. For this purpose, different Multi criteria analysis (MCA) techniques, Analytical Hierarchy Process (AHP), Simple Additive Weighting (SAW), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and Višekriterijumsko Kompromisno Rangiranje or Compromise Ranking (VIKOR), are used for evaluation of alternatives. For the purpose hidden variable identification for a set of KM evaluation criteria, a well known multivariate technique Principal Component Analysis (PCA) is also used

Multiattribute Decision-Making: Use of Three Scoring Methods to Compare the Performance of Imaging Techniques for Breast Cancer Detection

Multiple Attribute Decision Making (MADM) involves making preference decisions (such as evaluation, prioritization, selection) over the available alternatives that are characterized by multiple, usually conflicting, attributes . The problems of MADM are diverse, and can be found in virtually any topic. In this paper, we use three different scoring methods for evaluating the performance of different imaging techniques used to detect cancers in the female breast. The need for such a decision support system arises from the fact that each of the several techniques which helps diagnose breast cancer today, has its own specific characteristics, advantages and drawbacks. These characteristics or attributes are generally conflicting. The goal is to detect as many malignant lesions in the breast as is possible, while identifying the maximum number of benign lesions. The four imaging techniques that are compared here are Magnetic Resonance Imaging (MRI), Mammography, Ultrasonography, and Nuclear Medicine. The three different multiattribute scoring methods are the Simple Additive Weighting method (SAW), the Weighted Product Method (WPM), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The three methods are described in detail, and then used to rank the four imaging techniques. The results are analyzed and the validity and robustness of the methods are tested using post-evaluation analysis

Progressive Skyline Query Processing in Wireless Sensor Networks

With the further development of sensor techniques in wireless sensor networks (WSNs), it is becoming urgent that they should be able to support complicated queries like skyline query for multi-preference and decision making. In this paper, we consider skyline query evaluation in WSNs by devising evaluation algorithms for finding skyline points on a dataset progressively. The core techniques adopted are to partition the dataset into several disjoint subsets and output the skyline points by examining each subsequent subset progressively, using some of the skyline points obtained so far to filter out those unlikely skyline points in the current processing subset from transmission. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on synthetic and real datasets. The experimental results show that the proposed algorithms outperform existing algorithms significantly in network lifetime prolongation

Design and enhanced evaluation of a robust anaphor resolution algorithm

Syntactic coindexing restrictions are by now known to be of central importance to practical anaphor resolution approaches. Since, in particular due to structural ambiguity, the assumption of the availability of a unique syntactic reading proves to be unrealistic, robust anaphor resolution relies on techniques to overcome this deficiency. This paper describes the ROSANA approach, which generalizes the verification of coindexing restrictions in order to make it applicable to the deficient syntactic descriptions that are provided by a robust state-of-the-art parser. By a formal evaluation on two corpora that differ with respect to text genre and domain, it is shown that ROSANA achieves high-quality robust coreference resolution. Moreover, by an in-depth analysis, it is proven that the robust implementation of syntactic disjoint reference is nearly optimal. The study reveals that, compared with approaches that rely on shallow preprocessing, the largely nonheuristic disjoint reference algorithmization opens up the possibility/or a slight improvement. Furthermore, it is shown that more significant gains are to be expected elsewhere, particularly from a text-genre-specific choice of preference strategies. The performance study of the ROSANA system crucially rests on an enhanced evaluation methodology for coreference resolution systems, the development of which constitutes the second major contribution o/the paper. As a supplement to the model-theoretic scoring scheme that was developed for the Message Understanding Conference (MUC) evaluations, additional evaluation measures are defined that, on one hand, support the developer of anaphor resolution systems, and, on the other hand, shed light on application aspects of pronoun interpretation

Research Proposal: Preference Acquisition through Reconciliation of Inconsistencies

The quality of performance of a decision-support system (or an expert system) is determined to a large extent by its underlying preference model (or knowledge base). The difficulties in preference and knowledge acquisition make them a major focus of current research in decision-support and expert systems. Researchers have used various concepts to develop promising acquisition techniques. One of the concepts used is knowledge maintenence where the knowledge base is changed in response to incorrect or inadequate performance by the expert system. This dissertation investigates a preference acquisition technique based on the reconciliation of inconsistencies between the preference model and the decision maker by allowing the decision maker to modify the preference model interactively. The technique can be used in the class of decision-support systems which objectively evaluate competing plans and select the best plan. The technique will be implemented in the domain of evaluating three-dimensional (3-D) radiation treatment plans. Another major aim of the dissertation is to develop a clinically-relevant objective plan-evaluation model for 3-D radiation treatment plans, and to build a clinical decision-support system to assist in that task using the new preference acquisition method

Performance evaluation of preference queries techniques over a high multidimensional database

In recent years, there has been much focus on the design and development of database management systems that incorporate and provide more flexible query operators that return data items which are dominating other data items in all attributes (dimensions). This type of query operations is named preference queries as they prefer one data item over the other data item if and only if it is better in all dimensions and not worse in at least one dimension. Several preference evaluation techniques for preference queries have been proposed including top-k, skyline, top-k dominating, k-dominance, and k-frequency. All of these preference evaluation techniques aimed at finding the “best” answer that meet the user preferences. This paper evaluates these five preference evaluation techniques on real application when huge number of dimensions is the main concern. To achieve this, a recipe searching application with maximum number of 60 dimensions has been developed which assists users to identify the most desired recipes that meet their preferences. Two analyses have been conducted, where execution time is the measurement used

Sensory profiles and preference analysis in ornamental horticulture: The case of the rosebush

The context of ornamental horticulture is considered in order to extend the techniques of sensory and preference evaluation by taking the rosebush as a plant model. In a preliminary study (Boumaza, Demotes-Mainard, Huché-Thélier, & Guérin, 2009), a sensory evaluation was conducted in order to set up a list of attributes. Subsequently, this list was adapted to assess 10 rosebushes. After the control of the panel performance using a multivariate strategy of analysis, the average scores were used in product mapping. The evaluation of the preferences with regard to these rosebushes was undertaken: 253 subjects were asked to rank the products by decreasing order of liking. Thereafter, the preference data were subjected to an internal preference mapping and a cluster analysis. Six homogeneous segments of consumers were eventually retained. By way of performing an external preference mapping, the average ranks were regressed upon the sensory attributes using principal component regression: the preferences of 67% of the consumers were satisfactorily explained by the attributes