bluuebunny/crossref_metadata_2025 huggingface dataset:
Asset Structure and Solvency of Insurance Companies in China with Path Identification Model
Authors: Xu Bing ,   Research Institute of Econometrics and Statistics, Zhejiang Gongshang University, Pan Lingling , Yang Jingwen
Published: April 2016

Based on weighted kernel density estimation and the nonparametric path identification model, this paper explores the impact of asset structure on solvency and risk exposure by insurance companies. To test the differences in solvency and risk exposure of admitted assets under stock and time deposit investment paths, we compared distributions under different investment paths to benchmark distribution. Our results suggest that both stock and time deposit investment impact positively on solvency, meaning that, all other things being equal, solvency increases when investment assets are expanded but risk increases simultaneously. The impact of stock investment is also greater than that of time deposit investment. The extent of these differences increased gradually over the year under the stock investment path, but reduced under the time deposit investment. Under the stock investment path, the value of the high quantiles of the distribution are likely to shift from that of low quantiles. Expected value and risk exposure under time deposit investment are not necessarily reduced, so regulators should both emphasize solvency indicators in daily supervision and also take into account changes in the investment structure, improve how admissible assets are calculated in case the insurance company increases the solvency margin by expanding high-risk investment.
A Heuristic Algorithm Based on Leadership Strategy: Leader of Dolphin Herd Algorithm (LDHA)
Authors: Zhao Jianqiang ,   School of Mathematic and Physical Science, Xuzhou Institute of Technology, Ge Kao , Xu Kangyao
Published: April 2016

A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA is based on a leadership strategy. Using the leadership strategy as reference, we have designed the proposed algorithm by simulating the preying actions of dolphin herds. Several intelligent behaviors, such as “producing leaders,” “group gathering,” “information sharing,” and “rounding up prey,” are abstracted by LDHA. The proposed algorithm is tested on 15 typical complex function optimization problems. The testing results reveal that compared with the particle swarm optimization and the genetic algorithms, LDHA has relatively high optimization accuracy and capability for complex functions. Further, it is almost unaffected by the inimicality, multimodality, or dimensions of functions in the function optimization section, which implies better convergence. In addition, ultra-high-dimensional function optimization capabilities of this algorithm were tested using the IEEE CEC 2013 global optimization benchmark. Unfortunately, the proposed optimization algorithm has a limitation in that it is not suitable for ultra-high-dimensional functions.
Solving the Dynamic Stochastic General Equilibrium Model with Stochastic Volatility: An Application in China
Authors: Zhang Shangfeng ,   School of Economics, Zhejiang University, Wang Yuying , Xu Bing ,   Research Institute of Econometrics and Statistics, Zhejiang Gongshang University
Published: April 2016

A first-order approximation technique is not suited to handle issues such as welfare comparison, time-varying variance. Following Schmitt-Grohe and Uribe [1], in this paper, we derive a second-order approximation to estimate the dynamic stochastic equilibrium model with stochastic volatility, to capture the different impacts of the level shocks and the volatility shocks. Furthermore, the paper presents an application of standard quantitative New Keynesian business cycle model, and the results shows the negative effects of stochastic volatility shocks. Furthermore, the paper presents an application of standard quantitative New Keynesian business cycle model, and the empirical results find that the level shocks have positive effects on consumption, investment and output, while the volatility shocks have negative effects on consumption, investment and output.
Minimax Portfolio Optimization Under Interval Uncertainty
Authors: Yuan Meng ,   Graduate School of Information, Production, and Systems, Waseda University, Lin Xu , Watada Junzo , Kreinovich Vladik ,   Department of Computer Science, University of Texas at El Paso
Published: April 2016

In the 1950s, Markowitz proposed to combine different investment instruments to design a portfolio that either maximizes the expected return under constraints on volatility (risk) or minimizes the risk under given expected return. Markowitz’s formulas are still widely used in financial practice. However, these formulas assume that we know the exact values of expected return and variance for each instrument, and that we know the exact covariance of every two instruments. In practice, we only know these values with some uncertainty. Often, we only know the lower and upper bounds on these values – i.e., in other words, we only know the intervals that contain these values. In this paper, we show how to select an optimal portfolio under such interval uncertainty.
Optimal Outpatient Appointment System with Uncertain Parameters Using Adaptive-Penalty Genetic Algorithm
Authors: Harnpornchai Napat ,   Faculty of Economics, Chiang Mai University, Autchariyapanitkul Kittawit , Sirisrisakulchai Jirakom , Sriboonchitta Songsak ,   Faculty of Economics, Maejo University
Published: April 2016

The optimal number of doctors and appointment interval for an outpatient appointment system in a class of individual block/fixed interval are determined using an adaptive-penalty Genetic Algorithm. The length of service time for doctor consultation, the time required for the laboratory tests, and the time deviating from the appointment time are modelled by random variables. No-show patients are also included in the system. Using the adaptive penalty scheme, optimization constraints are automatically and numerically handled. The solution methodology is readily applicable to other appointment systems. The study has a significant implication from the viewpoint of economic and risk management of health care service.
Risk Evaluation of Strategic Emerging Industries’ Technical Standards Alliances: The Ecosystem Perspective
Authors: Hu Jing ,   College of Economics and Management, China Jiliang University, Zhang Yueyi
Published: April 2016

This paper conducts a risk assessment of strategic emerging industries’ technical standards alliances. First, we study the ecological properties and operational cycles of technical standards alliances based on the ecological system. Following the rule by which identification precedes assessment, we apply factor analysis to identify the key risk factors in the ecological system of such alliances. We then construct a fuzzy synthetic assessment model and conduct an empirical analysis on the energy saving and environmental protection industries in China. Taking the unique research perspective of the ecological system and using interdisciplinary research methods, this study establishes itself as a theoretical model to be used as a guideline in practice. The result is expected to provide a useful reference for furthering technological innovation in China’s strategic emerging industries.
On Objective-Based Rough Clustering with Fuzzy-Set Representation
Authors: Kinoshita Naohiko ,   Graduate School of Systems and Information Engineering, University of Tsukuba, Endo Yasunori , Onishi Ken ,   Faculty of Engineering, Information and Systems, University of Tsukuba,   Toyota Motor Corporation
Published: April 2016

The rough clustering algorithm we proposed based on the optimization of objective function (RCM) has a problem because conventional rough clustering algorithm results do not ensure that solutions are optimal. To solve this problem, we propose rough clustering algorithms based on optimization of an objective function with fuzzy-set representation. This yields more flexible results than RCM. We verify algorithm effectiveness through numerical examples.
On Estimation of Tangential Force in Railways Brake Systems by Fuzzy Inference
Authors: Sano Toru ,   Master's Program in Risk Engineering, University of Tsukuba, Endo Yasunori , Nakazawa Shin-ichi , Hijikata Daisuke ,   Faculty of Engineering, Information and Systems, University of Tsukuba,   Railway Technical Research Institute
Published: April 2016

The tangential force that occurs between railway wheels and rails and the force is very important because it greatly affects the brake performance. However,tangential force, which fluctuates due to factors such as weather and rail conditions, is difficult to actually measure. We consider how to estimate tangential force based on observed in railway operation. First, we try using a dynamical model to improve the input accuracy in estimating tangential force. Second, we propose a way to estimate tangential force by using fuzzy inference with modified input. Third, we show through numerical experiments that our proposed method works appropriately and effectively.
The Improvement of Optimality Test over Possible Reaction Set in Bilevel Linear Optimization with Ambiguous Objective Function of the Follower
Authors: Sariddichainunta Puchit ,   Graduate School of Engineering Science, Osaka University, Inuiguchi Masahiro
Published: April 2016

Verifying a rational response is the most crucial step in searching for an optimal solution in bilevel linear programming. Such verification is even difficult in a model with ambiguous objective function of the follower who reacts rationally to a leader’s decision. In our model, we assume that the ambiguous coefficient vector of follower lies in a convex polytope and we formulate bilevel linear programming with the ambiguous objective function of the follower as a special three-level programming problem. We use thek-th best method that sequentially enumerates a solution and examine whether it is the best of all possible reactions. The optimality test process over possible reactions in lower-level problems usually encounters degenerate bases that become obstacles to verifying the optimality of an enumerated solution efficiently. To accelerate optimality verification, we propose search strategies and the evaluation of basic possible reactions adjacent to a degenerate basic solution. We introduce these methods in both local and global optimality testing, confirming the effectiveness of our proposed methods in numerical experiments.
Visualizing State of Time-Series Data by Supervised Gaussian Process Dynamical Models
Authors: Yamaguchi Nobuhiko ,   Faculty of Science and Engineering, Saga University
Published: April 2016

Gaussian Process Dynamical Models (GPDMs) constitute a nonlinear dimensionality reduction technique that provides a probabilistic representation of time series data in terms of Gaussian process priors. In this paper, we report a method based on GPDMs to visualize the states of time-series data. Conventional GPDMs are unsupervised, and therefore, even when the labels of data are available, it is not possible to use this information. To overcome the problem, we propose a supervised GPDM (S-GPDM) that utilizes both the data and their corresponding labels. We demonstrate experimentally that the S-GPDM can locate related motion data closer together than conventional GPDMs.
Fuzzy Co-Clustering Induced by Multinomial Mixture Models
Authors: Honda Katsuhiro ,   Graduate School of Engineering, Osaka Prefecture University, Oshio Shunnya , Notsu Akira
Published: April 2016

A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be properly tuned. The advantages of the dual fuzzy partition are demonstrated through several experimental results including document clustering applications.
A Maximizing Model of Spherical Bezdek-Type Fuzzy Multi-Medoids Clustering
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology
Published: April 2016

This paper proposes three modifications for the maximizing model of spherical Bezdek-type fuzzyc-means clustering (msbFCM). First, we use multi-medoids instead of centroids (msbFMMdd), which is similar to modifying fuzzyc-means to fuzzy multi-medoids. Second, we kernelize msbFMMdd (K-msbFMMdd). msbFMMdd can only be applied to objects in the first quadrant of the unit hypersphere, whereas its kernelized form can be applied to a wider class of objects. The third modification is a spectral clustering approach to K-msbFMMdd using a certain assumption. This approach improves the local convergence problem in the original algorithm. Numerical examples demonstrate that the proposed methods can produce good results for clusters with nonlinear borders when an adequate parameter value is selected.
Improving Rough Set Rule-Based Classification by Supplementary Rules
Authors: Inuiguchi Masahiro ,   Graduate School of Engineering Science, Osaka University, Washimi Keisuke
Published: April 2016

In rough set approaches, decision rules are induced from a given data set consisting of attribute values and a decision value. Induced rules are used to classify new objects, but this classification is not perfect, perhaps because the given data set does not include all possible patterns. No induced decision rules are matched totally for objects having missing patterns, and partially matched decision rules are used to estimate their classes. The classification accuracy of such an object is usually lower than that of an object totally matching decision rules. To improve the classification accuracy, we propose adding supplementary rules to the induced rules, defining the supplementary rules to improve the classification accuracy of objects only partially matching decision rules. We propose an algorithm for inducing supplementary rules, considering four classifiers consisting of supplementary rules together with originally induced rules.We investigate their performance. We also compare their classification accuracies to that of conventional classifier with originally induced rules.
A Comparison of Ligament Tensions Between Intra- and Extra-Articular Measurement in Anterior Cruciate Ligament Reconstruction
Authors: Kawaguchi Shogo ,   Graduate School of Engineering, University of Fukui, Nagamune Kouki , Nishizawa Yuichiro , Oka Shinya , Araki Daisuke , Hoshino Yuichi , Matsushita Takehiko , Kuroda Ryosuke , Kurosaka Masahiro ,   Graduate School of Medicine, Kobe University
Published: April 2016

Anterior cruciate ligament (ACL) reconstruction is one of the treatments of ACL injuries. In the surgery, the reconstructed ligament should be properly tensioned to provide a normal ligament behavior. However, the ligament tension has been measured with an extra-articular technique in past studies, while the intra-articular ligament tension is still unknown. The purpose of this study is to compare the ligament tensions between intra- and extra-articular measurements in the ACL reconstruction. Intra-articular measurement employs a micro-force sensor designed with a width and thickness same as those of the reconstructed ligament. This study performed two experiments (i.e., sensor accuracy and cadaveric study). In the sensor accuracy experiment, the accuracy of the sensor was about 3% until an applied force of 100 N. In the cadaveric study, the results of the intra- and extra-articular measurement tensions were 13.6±3.9 N and 18.7±1.3 N (n= 6), respectively. The significant difference in student t-test (p-value was 0.026) between the intra- and extra- articular measurements was observed. The bending angle and friction between the graft and bone tunnel, and the shape of the intra-articular edge of tibial bone tunnel affected the intra-articular measurement in ACL reconstruction.
Distributed Mining of Closed Patterns from Multi-Relational Data
Authors: Kamiya Yohei ,   Department of Computer Science, Nagoya Institute of Technology, Seki Hirohisa
Published: April 2016

In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from distributed multi-relational databases (MRDBs). Since the computation of MRDM is costly compared with the conventional itemset mining, we propose some efficient methods for computing closed patterns using the techniques studied in Inductive Logic Programming (ILP) and Formal Concept Analysis (FCA). Given a set oflocaldatabases, we first compute sets of their closed patterns (concepts) using a closed pattern mining algorithm tailored to MRDM, and then generate the set of closed patterns in the global database by utilizing themergeoperator. We also present some experimental results, which shows the effectiveness of the proposed methods.
Partially Exclusive Item Partition in MMMs-Induced Fuzzy Co-Clustering and its Effects in Collaborative Filtering
Authors: Honda Katsuhiro ,   Graduate School of Engineering, Osaka Prefecture University, Nakano Takaya , Oh Chi-Hyon , Ubukata Seiki , Notsu Akira ,   Faculty of Liberal Arts and Sciences, Osaka University of Economics and Law
Published: April 2016

The interpretability of fuzzy co-cluster partitions were shown to be improved by introducing exclusive penalties on both object and item memberships although the conventional fuzzy co-clustering adopted exclusive natures only on object memberships. In real applications, however, fully exclusive constraints may bring inappropriate influences to some items, and partially exclusive penalties should be forced reflecting the characteristics of each item. For example, in customer-product analysis, the degree of popularity of each product may be a measure of compatibility in multiple customer groups, and exclusive penalties should be forced only to some specific products. In this paper, the conventional exclusive constraint model is further modified by forcing exclusive penalties only to some selected items, and the effects of partially exclusive partition are demonstrated from the view points of not only partition quality but also collaborative filtering applicability. In a document-keyword analysis experiment, word class is shown to be useful for exclusively selecting keywords so that the interpretability of document cluster is improved. In a collaborative filtering experiment, the recommendation capability is demonstrated to be improved by considering intrinsic differences of popularity of each product.
Bezdek-Type Fuzzified Co-Clustering Algorithm
Authors: Kanzawa Yuchi ,   Shibaura Institute of Technology
Published: April 2016

In this study, two co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are proposed for categorical multivariate data. The two proposed algorithms are motivated by the fact that there are only two fuzzy co-clustering methods currently available – entropy regularization and quadratic regularization – whereas there are three fuzzy clustering methods for vectorial data: entropy regularization, quadratic regularization, and Bezdek-type fuzzification. The first proposed algorithm forms the basis of the second algorithm. The first algorithm is a variant of a spherical clustering method, with the kernelization of a maximizing model of Bezdek-type fuzzy clustering with multi-medoids. By interpreting the first algorithm in this way, the second algorithm, a spectral clustering approach, is obtained. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.
Three Layers Framework Concept for Adjustable Artificial Intelligence
Authors: Vallade Benoît ,   Department of Computer Science and Intelligent Systems, Osaka Prefecture University, David Alexandre , Nakashima Tomoharu
Published: April 2016

This paper proposes a concept of layered framework for adjustable artificial intelligence. Artificial intelligences are used in various areas of computer science for decision making tasks. Traditionally artificial intelligences are developed in order to be used for a specific purpose within a particular software. However, this paper stands as the first step of a research in progress whose final objective is to design an artificial intelligence adjustable to every types of problems without any modification in its source code. The present work focuses on a framework of such an artificial intelligence and is conducted in the context of video games. This framework, composed of three layers, would be re-usable for all types of game.
Marginal Model Synthesization Algorithm for Data Envelopment Analysis and its Application
Authors: Kyo Koki ,   Department of Human Sciences, Obihiro University of Agriculture and Veterinary Medicine, Noda Hideo ,   School of Management, Tokyo University of Science
Published: April 2016

In this paper, we propose a new approach for determining the unknown quantities in Banker–Charnes–Cooper models for data envelopment analysis by developing the marginal model synthesization algorithm. In this algorithm, several marginal fractional programming models are first constructed based on a simple numeric optimization. Then, a set of synthetic Banker–Charnes–Cooper models is obtained by compounding the marginal fractional programming models. A comparison of the proposed and existing approaches in terms of computational cost and stability of results shows that the former approach has distinct advantages. We also present an application of the proposed approach for analyzing the efficiency of industries in Japanese prefectures.
Projector Camera System Presenting Color Information for the Color Vision Deficient
Authors: Kawakami Daiki ,   Mitsubishi Electric Corporation, Makino Kaito , Kobayashi Yuichi , Kaneko Toru , Yamashita Atsushi , Asama Hajime ,   Shiroki Corporation,   Shizuoka University,   Department of Precision Engineering, The University of Tokyo
Published: April 2016

There are people who cannot distinguish between specific colors easily. This paper presents an improvement to a system for the color vision deficient. The system consists of a camera that acquires an image of an object and a projector that projects light on that object. One of the features of the system is that it handles real-life objects. When objects have colors that cannot be distinguished, the system converts the color to a distinguishable one using the projector. The improvement in the proposed system is that it produces images with patterns and blinking light to handle conventional color conversion operations that cannot produce distinguishable color images because of excessive multiplicity of color combinations. We verify through experiments the effectiveness of the proposed color projection system with its patterns and blinking light.
Fuzzified Evaluation of Cardiotocography Data for Real Medical Data
Authors: Hatakeyama Yutaka ,   Center of Medical Information Science, Kochi University Medical School, Kataoka Hiromi , Nakajima Noriaki , Watabe Teruaki , Okuhara Yoshiyasu
Published: April 2016

Level evaluation system in cardiotocography (CTG) taken in the real medical practice is constructed for the use of e-learning materials. The system consists of 3 parts, preprocessing for FHR by particle filter, extraction process of deceleration information, and level evaluation. The extraction and evaluation processes are executed by fuzzy inference. To check the effectiveness of the proposed system, level evaluation experiments are executed for the real CTG data extracted from records at the Kochi Medical School hospital. The experimental results show that the extracted CTG features are suitable for the material, and that the calculated level of the patients with abnormal pH value is evaluated as abnormal situations. The proposed system can provide the possible data for the materials for evaluation learning of real CTG.
Improved Object Recognition with Decision Trees Using Subspace Clustering
Authors: Peralta Billy ,   Catholic University of Temuco, Caro Luis Alberto
Published: April 2016

Generic object recognition algorithms usually require complex classificationmodels because of intrinsic difficulties arising from problems such as changes in pose, lighting conditions, or partial occlusions. Decision trees present an inexpensive alternative for classification tasks and offer the advantage of being simple to understand. On the other hand, a common scheme for object recognition is given by the appearances of visual words, also known as the bag-of-words method. Although multiple co-occurrences of visual words are more informative regarding visual classes, a comprehensive evaluation of such combinations is unfeasible because it would result in a combinatorial explosion. In this paper, we propose to obtain the multiple co-occurrences of visual words using a variant of the CLIQUE subspace-clustering algorithm for improving the object recognition performance of simple decision trees. Experiments on standard object datasets show that our method improves the accuracy of the classification of generic objects in comparison to traditional decision tree techniques that are similar, in terms of accuracy, to ensemble techniques. In future we plan to evaluate other variants of decision trees, and apply other subspace-clustering algorithms.
Vision-Based Mowing Boundary Detection Algorithm for an Autonomous Lawn Mower
Authors: Fukukawa Tomoya ,   Department of Mechanical Science and Engineering, Nagoya University, Sekiyama Kosuke , Hasegawa Yasuhisa , Fukuda Toshio ,   Department of Micro-Nano Systems Engineering, Nagoya University,   Faculty of Science and Engineering, Meijo University
Published: April 2016

This study proposes a vision-based mowing boundary detection algorithm for an autonomous lawn mower. An autonomous lawn mower requires high moving accuracy for efficient mowing. This problem is solved by using a vision system to detect the boundary of two regions, i.e., before and after the lawn mowing process. The mowing boundary cannot be detected directly because it is ambiguous. Therefore, we utilize a texture classification method with a bank of filters for classifying the input image of the lawn field into two regions as mentioned above. The classification is performed by threshold processing based on a chi-squared statistic. Then, the boundary line is detected from the classified regions by using Random sample consensus (RANSAC). Finally, we apply the proposed method to 12 images of the lawn field and verified that the proposed method can detect a mowing boundary line with centimeter accuracy in a dense lawn field.
Topic Evolution Analysis Based on Cluster Topic Model
Authors: Xi Yaoyi ,   Zhengzhou Information Science and Technology Institute, Chen Gang , Li Bicheng , Tang Yongwang
Published: April 2016

Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two events is identified based on the aspect distributions of documents. Finally, by choosing the representative document of each event, topic evolution graph is constructed to display the development of the topic along the timeline. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.
Dynamic Aggregation Method for Target Enclosure Using Smoothed Particle Hydrodynamics Technique – An Implementation in Quadrotor Unmanned Aerial Vehicles (QUAV) Swarm –
Authors: Bandala Argel A. ,   De La Salle University, Manila, Dadios Elmer P.
Published: April 2016

This paper presents an aggregation behavior derived from fluid characteristics by adapting Smoothed Particle Hydrodynamics (SPH) Technique. The most basic behavior in a swarm-like system is aggregation. The essential requirement of a swarm is to aggregate or collect itself in proximity to a singular point in order to execute higher level swarm behaviors. The aggregation behavior is further put into use by initiating a near convergence status in a single target enclosing it by the swarm with a given specific distance by using different fluid containers. In this paper, there are three fluid containers each is introduced with different characteristics. These containers are plane, spherical and toroidal containers. Using computer simulations with different trials, the proponents were able to determine the accuracy of containing the swarm elements in a desirable area. Furthermore, the ability of the swarm to maintain collectiveness is tested. The experiment results showed that the plane fluid container yielded an accuracy of 84.88%. A spherical fluid container displayed an accuracy of 95.23%. And using toroidal particle container showed an accuracy of 92.44%.
A Hybrid Fuzzy Logic – PLC-Based Controller for Earthquake Simulator System
Authors: Baldovino Renann G. ,   De La Salle University, Dadios Elmer P.
Published: April 2016

This paper presents an intelligent motor speed controller for an earthquake simulator using fuzzy logic algorithm developed inside a programmable logic controller environment. The desired motor speed is obtained using two fuzzy inputs namely, the process error and the rate of process error. These fuzzy inputs are feedback data from the motor drive. Different earthquake intensities were used to test the controller’s performance in real time undergoing different load variations. Experiment results showed that the developed controller is accurate, reliable and robust.
Novel Complex-Valued Neural Network for Dynamic Complex-Valued Matrix Inversion
Authors: Liao Bolin ,   College of Information Science and Engineering, Jishou University, Xiao Lin , Jin Jie , Ding Lei , Liu Mei ,   College of Physics, Mechanical and Electrical Engineering, Jishou University
Published: April 2016

Static matrix inverse solving has been studied for many years. In this paper, we aim at solving a dynamic complex-valued matrix inverse. Specifically, based on the artful combination of a conventional gradient neural network and the recently-proposed Zhang neural network, a novel complex-valued neural network model is presented and investigated for computing the dynamic complex-valued matrix inverse in real time. A hardware implementation structure is also offered. Moreover, both theoretical analysis and simulation results substantiate the effectiveness and advantages of the proposed recurrent neural network model for dynamic complex-valued matrix inversion.
Interactive Document Clustering System Based on Coordinated Multiple Views
Authors: Takama Yasufumi ,   Graduate School of System Design, Tokyo Metropolitan University, Tonegawa Takuma
Published: April 2016

This paper proposes an interactive document clustering system, which is designed based on the concept of CMV (coordinated multiple views). An interactive document clustering is used by a user to obtain a set of document groups from a document collection in interactive manner. It is expected to be useful for various tasks such as text mining and document retrieval. As the result of document clustering consists of multiple objects such as clusters (document groups), documents, and words, each of those should be presented to users in different ways. Based on this consideration, the proposed system employs multiple views, each of which is designed for specific object such as document and keyword. A prototype system is implemented on TETDM (Total Environment for Text Data Mining), which is one of environments for developing text data mining tools. As it can provide the mechanism of coordination between modules, we decided to use it for developing the prototype system. The proposed system classifies information to be presented into 4 levels: clusters, document, bag of words, and word, each of which is displayed with different views. Experimental results with test participants show the effectiveness of the proposed system.
Multi-Channel Quantum Image Scrambling
Authors: Yan Fei ,   School of Computer Science and Technology, Changchun University of Science and Technology, Guo Yiming , Iliyasu Abdullah M. , Jiang Zhengang , Yang Huamin ,   College of Engineering, Prince Sattam Bin Abdulaziz University
Published: April 2016

Image scrambling is a technique used for confidential storage and transmission as well as for image information hiding. In this study, we propose a multi-channel quantum image scrambling method, which applies both color and geometric transformations of an image. This is a simple and reliable method for transforming a meaningful quantum image into a meaningless or disordered one. We performed two simulation experiments, which demonstrated the efficiency and flexibility of the proposed method. Previous studies mainly developed scrambling strategies for grayscale quantum images, whereas the proposed method is effective for the color image scrambling in the quantum computing domain.
Hybrid Modulation Strategy for Two-Stage Matrix Converter and its Application in Vector Control of Doubly Fed Induction Generator
Authors: Li Danyun ,   School of Information Science and Engineering, Central South University, Shen Quntai , Liu Zhentao , Liu Fang ,   School of Automation, China University of Geosciences
Published: April 2016

A hybrid modulation strategy (HMS) for a two-stage matrix converter (TSMC) is presented in this paper. According to the variation of voltage transfer ratio, different combinations of modulation modes for rectifier-side converter (RSC) and inverter-side converter (ISC) of TSMC are adopted. Two different current space vector modulation methods are used for RSC to obtain maximum and minor DC voltages. The power loss of TSMC is reduced based on the minor DC voltage. In addition to the linear space vector modulation for ISC, an overmodulation method is presented in order to increase the voltage transfer ratio of TSMC. HMS ensures smooth switching between different modulation modes and makes the best use of the advantage of these modes. Finally, HMS is applied in the case where TSMC is used as an AC-excitation converter for doubly fed induction generator (DFIG) to achieve maximum power point tracking (MPPT). The simulation results confirm the accuracy and feasibility of HMS and the good performance of the MPPT operation of DFIG excited by TSMC.
Optimistic Value Model of Uncertain Linear Quadratic Optimal Control with Jump
Authors: Deng Liubao ,   School of Finance, Anhui University of Finance and Economics, Chen Yuefen ,   College of Mathematics and Information Science, Xinyang Normal University
Published: April 2016

Based on the optimistic value model of uncertain optimal control model with jump, in this paper, an optimistic value model of uncertain linear quadratic (LQ) optimal control with jump is proposed. Then, the necessary and sufficient condition for the existence of optimal control is obtained. Finally, an enterprize's inventory problem is given to illustrate usefulness of the proposed model.
Active Structural Control Based on Integration ofΗ∞Control and Equivalent-Input-Disturbance Approach
Authors: Fang Mingxing ,   College of Physics and Electronic Information, Anhui Normal University, Wu Lijun , Cheng Jing , Du Youwu , She Jinhua ,   School of Automation, China University of Geosciences,   School of Engineering, Tokyo University of Technology
Published: April 2016

This paper describes an approach for suppressing earthquake-induced vibrations of building structures. The design of the control system is based on the equivalent-input-disturbance approach for improving the vibration rejection performance. A control system configuration with a vibration estimator is described, and a method of designing such a control system that employsΗ∞control is presented. The vibration rejection performance is guaranteed by the control structure, in which an equivalent vibration signal on the control input channel is estimated and directly incorporated into the control input. The validity of our method is demonstrated through simulations.
Objective Image Quality Assessment Based on Saliency Map
Authors: Wei Longsheng ,   School of Automation, China University of Geosciences, Liu Wei , Wang Xinmei , Liu Feng , Luo Dapeng ,   Computational Intelligence & Information Processing Laboratory, China University of Geosciences,   Faculty of Mechanical and Electronic Information, China University of Geosciences
Published: April 2016

The development of objective image quality assessment metrics aligned with human perception is of fundamental importance to numerous image processing applications. In this paper, an objective image quality assessment approach based on saliency map is proposed. By local shift estimation method, the retargeted image is resized to the same size as the reference image. A gradient magnitude similarity map is computed by comparing the retargeted and reference images. The more similarly, the brighter of pixels in the gradient magnitude similarity map. At the same time, a saliency map of reference image is achieved by visual attention. Finally, an overall image quality score is computed from the gradient magnitude similarity map via saliency pooling strategy. The most important step in our approach is to generate a gradient magnitude similarity map that indicates at each spatial location in the source image how the structural information is preserved in the retargeted image. There are two key contributions in this paper, one is that we add the texture feature in computing saliency map because image gradient is very sensitive to texture information, and the other is that we propose a new objective image quality metrics by introducing saliency map into image quality evaluation. Experimental results indicate that the evaluation indexes of our approach are better than existing methods in the literature.
Stability Analysis and Hopf Bifurcation Control for a Wireless Network Model
Authors: Liu Feng ,   School of Automation, China University of Geosciences, Yin Xiang , Wang Xinmei , Liu Wei , Wei Longsheng , Wang Hua O. ,   Department of Mechanical Engineering, Boston University
Published: April 2016

A wireless network model with the hybrid control is considered. The stability and Hopf bifurcation behavior of the wireless network model are investigated. The stability analysis and the parameter condition that the Hopf bifurcation occurs are deduced. The stability and direction of the bifurcating periodic solutions are analyzed by applying the normal form theory and the center manifold theorem. Numerical simulations demonstrate the complex behavior of the system and verify the theoretical analysis.
Asymmetric Prandtl-Ishlinskii Hysteresis Model for Giant Magnetostrictive Actuator
Authors: Nie Zhuoyun ,   School of Information Science and Engineering, National Huaqiao University, Fu Chanjun , Liu Ruijuan , Guo Dongsheng , Ma Yijing ,   School of Applied Mathematics, Xiamen University of Technology
Published: April 2016

An asymmetric Prandtl–Ishlinskii (API) hysteresis model for a giant magnetostrictive actuator (GMA) is proposed in this paper. The classical Prandtl–Ishlinskii (PI) model is analyzed and divided into two parts: linear function and operator summation. To enhance model asymmetry, a polynomial function is used in the API model as the center curve of the hysteresis instead of the linear function. The remaining curve of the hysteresis is modeled by a new operator that provides some basic asymmetric hysteresis. In this manner, the proposed API model requires relatively less operators and fewer parameters to describe the asymmetric hysteresis behavior of the GMA. All parameters of the API model are identified by a standard least square method. Simulation results show that the API model is very successful in formulating an asymmetric hysteresis of the GMA. In addition, it provides better identification results compared with the classical PI model.
Online Estimation of Image Jacobian Matrix with Time-Delay Compensation
Authors: Wang Xinmei ,   School of Automation, China University of Geosciences, Wei Wu , Liu Feng , Wei Longsheng , Liu Zhihui ,   College of Automation Science and Engineering, South China University of Technology,   School of Mathematics and Physics, China University of Geosciences
Published: April 2016

Time delay exists in image-based visual servoing system. To compensate for the impact of time delay, the feature point image and image Jacobian matrix with time-delay compensation is discussed in this paper. Firstly, the current position and velocity estimation of the feature point in the image space is based on Kalman filtering, but Markov chain model is applied in the description of the measurement noise, then the cross-correlation between the process noise and measurement noise is produced, the traditional Kalman filtering algorithm is restricted, by introducing a filtering revision matrix, the process equation and measurement equation are redefined, under the mathematical properties of the noise in Kalman filtering algorithm, the filtering revision matrix can be obtained for the elimination of the cross-correlation, a robust Kalman filtering model can be constructed. Secondly, for the measurement vectors which cannot be obtained during time delay in the robust Kalman filtering model, a polynomial fitting method is proposed in which the selection of the polynomial includes the position, the velocity and the acceleration of the feature point which impact the feature point trajectory. Finally, from the current predicted position and velocity of the feature point in the image space, the current accurate image Jacobian matrix with time-delay compensation can be obtained. Simulation and experimental results verify the feasibility and superiority of this method.
A Self-Tuning PID Control System Based on Control Performance Assessment
Authors: Cao Weihua ,   School of Automation, China University of Geosciences, Hu Xuemin , Wu Min , Yin Wei ,   Department of Information Science and Engineering, Central South University
Published: April 2016

A Quasi-Newton iterative method is developed for the calculation of the best achievable PID control performance and the corresponding optimal PID setting based on the control parameters and input-output data. At the basis of the proposed method, a self-tuning PID control system is proposed for the time-variant dynamic process. When controllers performance deteriorates below the general performance, controller parameters are directly adjusted with the Quasi-Newton iterative method. When below the poor performance, it can be indirectly adjusted with the identification of the closed-loop impulse response matrix. A data-driven solution is developed for calculation of the closed-loop impulse response matrix. Based on the acquired state information, system is assessed and adjusted cyclically so that a self-tuning PID control system is finally realized. Simulation results show the practicality and utility of this method.
Subspace Modeling Method for Burn-Through Point
Authors: Zhang Yongyue ,   School of Information Science and Engineering, Central South University, Cao Weihua , Wu Min ,   School of Automation, China University of Geosciences
Published: April 2016

In the study, for the iron sintering process which is strongly time-delayed and confounding, a data-driven modeling method called subspace modeling method is proposed to predict the burn-through point (BTP) in this paper. First, by analyzing the mechanism of the sintering process and processing the industrial data, the relationship between the exhaust gas temperature and bellows is confirmed. Then, based on the position of the BTP, a subspace modeling method is used to establish a temperature identification model. This model outputs, the temperature of the BTP and its inputs include the exhaust gas temperature of the front three bellows and the operating parameters. Finally, we compare the obtained result with those of other algorithms. The simulation experiment shows the effectiveness and veracity of the subspace model for BTP, which will provide a precise model for controller design.
Proposing a Transit Oriented Development Model Through Sensitivity Analysis of the Resident’s Utility Function
Authors: Yano Kojin ,   Research & Development Group, Hitachi Ltd., Hashimoto Hirobumi , Terano Takao ,   Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology
Published: April 2016

Transit oriented development (TOD) is a concept related to urban and transportation planning that aims to eliminate traffic jams and decrease CO2emissions. In this paper, we consider the viewpoints of residents when discussing the desirable conditions for developing a TOD system. First, we design a basic model for a utility function of residents and propose new criteria for evaluating TOD systems. Then, we conduct a simulation to confirm the effectiveness of the proposed utility function and criteria. For this confirmation, we apply a sensitivity analysis to the simulation. The simulation results suggest that the proposed criteria can be applied to compare the feasibility of strategies for TOD.
Research on Fuzzy PID Pitch-Controlled System Based on SVM
Authors: Yu Huijun ,   School of Information Science and Engineering, Central South University, He Yong , Zhao Zhengli , Wu Min ,   School of Electrical and Information Engineering, Hunan University of Technology,   School of Automation, China University of Geosciences
Published: April 2016

In a wind turbine generator, it is difficult to achieve a good control performance using the conventional PID variable pitch controller because of the multi-variables and nonlinearities of the controller. Based on the principles of variable pitch control of a wind generating set, this study introduces a mathematical model for wind turbine generators and proposes a variable pitch fuzzy PID control method by combining the fuzzy control and PID control methods to effectively overcome the deficiencies of the conventional PID controller. In order to enhance self-learning and predictability of the system, an improved method for the variable pitch control of the fuzzy PID is proposed based on an online support vector machine (SVM). Finally, the simulation comparisons show that the fuzzy PID variable pitch control based on the SVM further improves the control performance of a variable pitch wind turbine generator. In addition, it is robust at random wind speeds and can adjust the pitch angle more smoothly. It ensures a stable output power of wind turbine generator, thus effectively improving the control performance of the variable pitch system of the wind generating set.