bluuebunny/crossref_metadata_2025 huggingface dataset:
Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification
Authors: Hilado Samantha Denise F. ,   Mechanical Engineering Department, De La Salle University, Manila, 2401 Taft Ave., Manila 1004, Philippines, Lim Laurence A. Gan , Naguib Raouf N. G. , Dadios Elmer P. , Avila Jose Maria C. ,   BIOCORE Research and Consultancy International (BIOCORE, Coventry University), Liverpool, United Kingdom,   Manufacturing Engineering and Management Department, De La Salle University, Manila, 2401 Taft Ave., Manila 1004, Philippines,   Department of Pathology, University of the Philippines, Manila, Philippines
Published: April 2016

Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of accuracy.
Weather Forecasting Using Artificial Neural Network and Bayesian Network
Authors: Abistado Klent Gomez ,   Advanced World Systems Inc., Cebu City, Philippines, Arellano Catherine N. , Maravillas Elmer A. ,   Department of Computer Science, Cebu Institute of Technology University, Cebu City, Philippines
Published: April 2016

This paper presents a scheme of weather forecasting using artificial neural network (ANN) and Bayesian network. The study focuses on the data representing central Cebu weather conditions. The parameters used in this study are as follows: mean dew point, minimum temperature, maximum temperature, mean temperature, mean relative humidity, rainfall, average wind speed, prevailing wind direction, and mean cloudiness. The weather data were collected from the PAG-ASA Mactan-Cebu Station located at latitude: 10°19´, longitude: 123°59´ starting from January 2011 to December 2011 and the values available represent daily averages. These data were used for training the multi-layered backpropagation ANN in predicting the weather conditions of the succeeding days. Some outputs from the ANN, such as the humidity, temperature, and amount of rainfall, are fed to the Bayesian network for statistical analysis to forecast the probability of rain. Experiments show that the system achieved 93%–100% accuracy in forecasting weather conditions.
A Study on Guiding an Attention Direction of a Driver by an Ambient Visual Mark
Authors: Takahashi Hiroshi ,   Department of Applied Computer Sciences, Shonan Institute of Technology, 1-1-25, Tsujido-Nishikaigan, Fujisawa, Kanagawa, 251-8511, Japan
Published: April 2016

This paper examines the possibility of a new warning method that would increase drivers’ sensitivity to hazardous factors in the driving environment. The method is based on a visual warning mark in the peripheral vision, called an ambient warning. In this study, the use of ambient visual marks is investigated. These marks are soft visual warnings and lack officious interference with the task performed in the central vision field. Experimentation with a 27-inch monitor display resulted in decreased response times for detecting a flashing mark when an ambient mark was shown in advance. The results suggest that information observed in the peripheral vision field could help people drive more safely.
Analysis of Consistent Equilibria in a Mixed Duopoly
Authors: Kalashnikov Vyacheslav V. ,   Tecnológico de Monterrey (ITESM), Campus Monterrey, 64849, Mexico, Bulavsky Vladimir A. , Kalashnykova Nataliya I. , Watada Junzo , Hernández-Rodríguez Diego de Jesús ,   Central Economics & Mathematics Institute (CEMI), Russian Academy of Sciences (RAS), Moscow 117418, Russia,   Sumy State University, Sumy, 40007, Ukraine,   Department of Physics & Maths (FCFM), Universidad Autónoma de Nuevo León (UANL), San Nicolás de los Garza 66450, Mexico,   Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsuku, Kitakyushu, Fukuoka 808-0135, Japan
Published: April 2016

This paper examines a model of a mixed duopoly with conjectural variations equilibrium (CVE), in which one of the agents maximizes a convex combination of his/her net profit and domestic social surplus. The agents’ conjectures concern the price variations, which depend on their production output variations. Based on the already established existence and uniqueness results for the CVE (called theexterior equilibrium) for any set of feasible conjectures, the notion of interior equilibrium is introduced by developing a consistency criterion for the conjectures (referred to as influence coefficients), and the existence theorem for the interior equilibrium (understood as a CVE state withconsistent conjectures) is proven. When the convex combination coefficient tends to 1, thus transforming the model into the mixed duopoly in its extreme form, two trends are apparent. First, for the private company, the equilibrium with consistent conjectures becomes more proficient than the Cournot-Nash equilibrium. Second, there exists a (unique) value of the combination coefficient such that the private agent’s profit is the same in both of the above-mentioned equilibria, which makes subsidies to the producer or to consumers unnecessary.
Interest Rate Liberalization and Fiscal Policy in China: A New Keynesian DSGE Model
Authors: Xu Bing ,   Research Institute of Econometrics and Statistics, Zhejiang Gongshang University, 18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China, He Qiuqin , Hu Xiaowen , Zhang Shangfeng ,   School of Economics and Management, Anhui Normal University, 1 Beijing East Road, Wuhu, Anhui 241000, China,   College of Economics, Zhejiang University, 38 Zheda Road, Xihu District, Hangzhou, Zhejiang 310027, China
Published: April 2016

By building a new Keynesian dynamic stochastic general equilibrium (DSGE) model, we analyze the effect of interest rate liberalization on fiscal policy. First, we find that when the interest rate increases, technology shocks, monetary policy shocks, and fiscal policy shocks can effectively stabilize economic fluctuations. Second, when the interest rate rises, fiscal policy enhances the positive effect on output first, with decreasing the negative effect on output later. Third, fiscal policy increases the original crowding-out effect on consumption and investment. However, this increase in the crowding-out effect does not restrain the positive effect of fiscal policy on output, which benefits from interest rate liberalization.
Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes
Authors: Sulaiman Junaida Binti ,   Department of Systems Design and Informatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, 820-8502 Fukuoka, Japan, Darwis Herdianti , Hirose Hideo
Published: April 2016

Successive days of precipitation are known to cause flooding in monsoon-susceptible countries. Forecasting of daily precipitation facilitates the prediction of the occurrences of rainfall and number of wet days. Using the maximum five-day accumulated precipitation (MX5d), we can predict the magnitude of precipitation in a specific period as it may indicate the extreme precipitation. In this study, a method to forecast monthly extreme precipitation using artificial neural networks (ANNs) is assessed using past MX5d data and global climate indices such as Southern Oscillation Index (SOI), Madden Julian Oscillation (MJO), and Dipole Mode Index (DMI) in Kuantan and Kota Bharu, Malaysia. The use of combined inputs (MX5d with SOI, MJO, and DMI) is proposed to investigate the concurrent effect of lagged values of local precipitation data and global climate indices on seasonal extreme precipitation. Four cases of data are sampled representing two major seasonal variations in Malaysia. The analysis of extreme precipitation trends is important for the prediction of high precipitation events. ANNs are widely applied in the hydrology field because of their nonlinear ability in predicting nonstationary and seasonal data. In this paper, we have compared ANNs with seasonal autoregressive integrated moving average (ARIMA) and regression analysis using out-of-sample test data. The results for Kuantan indicate that seasonal ARIMA is the best method to forecast extreme precipitation when MX5d lags are used as input. For Kota Bharu, ANN exhibits better generalization ability than ARIMA and regression analysis when dual inputs (lagged MX5d and lagged global climate indices) are utilized in the model.
On Bayesian Clustering with a Structured Gaussian Mixture
Authors: Yamazaki Keisuke ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G5-19, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
Published: April 2016

Cluster analysis is commonly used in the fields of computational intelligence and pattern recognition. The task is to detect the unobservable labels that show to which clusters the observable data belong. A Gaussian mixture is a representative hierarchical model that is often used when taking a probabilistic approach to this task. Although it is widely used, the statistical properties of cluster analysis have not yet been clarified. The present paper analyzes the theory of Bayesian clustering for the case when the number of clusters is unknown and the variance-covariance matrix of the Gaussian distribution has a constraint. We refer to this constraint as the structure of the components. The result of this analysis shows that, even if the estimation method does not take account of the structure, the Bayes method provides an effective, tractable, and efficient algorithm. Based on an experiment with simulated data, we confirmed the advantages of the Bayes method over the expectationmaximization (EM) method.
Communication Support System Between Japanese Patients and Foreign Doctors Using Onomatopoeia to Express Pain Symptoms
Authors: Sakamoto Maki ,   Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofushi, Tokyo 182-8585, Japan, Ueda Yuya , Doizaki Ryuichi , Shimizu Yuichiro
Published: April 2016

In medical interviews, Japanese patients often use onomatopoeia, such as ‘zuki-zuki’ and ‘chiku-chiku,’ to express pain symptoms and medical conditions. However, onomatopoeia shows cross-linguistic variation, and thus Japanese onomatopoeia cannot be used effectively to express pain symptoms in medical interviews with foreign doctors who do not speak Japanese. In this study, we developed a system that supports communication between Japanese patients and foreign doctors by putting an onomatopoeia evaluation system to medical use. Our system estimates the quality of pain and other medical conditions based on the sound symbolic meanings expressed by certain onomatopoetic expressions. The relationships between the sound symbolic properties and rating scales were obtained through psychological experiments in which 120 participants evaluated the mental images of 354 Japanese onomatopoeia terms used to express pain symptoms and medical conditions against 35 semantic differential (SD) scales such as “sharp–dull,” “strong–weak,” and “momentary–continuous.” Our system accepts any Japanese onomatopoetic expression input by users and can also respond to any novel onomatopoetic expression. If the rating scales were translated into various languages, foreign doctors all over the world would be able to understand the meaning of Japanese onomatopoeia.
Effective Method for Wind and Solar Power Grid Systems Based on Recurrent Neural Networks
Authors: Kimura Keisuke ,   Nippon Institute of Technology, 4-1-1 Gakuendai, Miyashiro, Minami-Saitama, Saitama 345-8501, Japan, Kimura Takayuki , Hiraguri Takefumi , Jin’no Kenya
Published: April 2016

In this paper, the control method based on recurrent neural networks is proposed for optimizing large-scale wind and solar power generation systems. Recently, an optimal control method based on recurrent neural networks was proposed for wind and solar power generation systems. In this method, optimization problems are regarded as linear programming problems, which are solved by recurrent neural networks. Results suggest that this control method based on recurrent neural networks could be implemented in realworld systems. However, only small power generation systems were used to evaluate this control method in previous studies. Then, the method for power generation systems is evaluated by more realistic conditions. The results of our numerical experiments show that this control method delivers high performance with large-scale power generation systems. Furthermore, if the power generation systems has specific topologies, almost 20%of the supplying capacity is improved.
Run-Length Encoding Graphic Rules Applied to DNA-Coded Images and Animation Editable by Polymerase Chain Reactions
Authors: Hara Yuki ,   The University of Kitakyushu, 1-1 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan, Kawano Tomonori
Published: April 2016

We previously proposed novel designs for artificial genes as media for storing digitally compressed image data, specifically for biocomputing by analogy to natural genes mainly used to encode proteins. A run-length encoding (RLE) rule had been applied in DNA-based image data processing, to form coding regions, and noncoding regions were created as space for designing biochemical editing. In the present study, we apply the RLE-based image-coding rule to creation of DNAbased animation. This article consisted of three parts: (i) a theoretical review of RLE-based image coding by DNA, (ii) a technical proposal for biochemical editing of DNA-coded images using the polymerase chain reaction, and (iii) a minimal demonstration of DNAbased animation using simple model images encoded on short DNA molecules.
A Robust Visual-Feature-Extraction Method for Simultaneous Localization and Mapping in Public Outdoor Environment
Authors: Hua Gangchen ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, J3-13, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan, Hasegawa Osamu ,   Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, J3-13, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan
Published: April 2016

We describe a new feature extraction method based on the geometric structure of matched local feature points that extracts robust features from an image sequence and performs satisfactorily in highly dynamic environments. Our proposed method is more accurate than other such methods in appearance-only simultaneous localization and mapping (SLAM). Compared to position-invariant robust features [1], it is also more suitable for low-cost, single lens cameras with narrow fields of view. Testing our method in an outdoor environment at Shibuya Station. We captured images using a conventional hand-held single-lens video camera. These environments of experiments are public environments without any planned landmarks. Results have shown that the proposed method accurately obtains matches for two visual-feature sets and that stable, accurate SLAM is achieved in dynamic public environments.
On Cluster Extraction from Relational Data UsingL1-Regularized Possibilistic Assignment Prototype Algorithm
Authors: Hamasuna Yukihiro ,   Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-osaka, Osaka 577-8502, Japan, Endo Yasunori ,   Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

This paper proposes entropy-basedL1-regularized possibilistic clustering and a method of sequential cluster extraction from relational data.Sequential cluster extractionmeans that the algorithm extracts cluster one by one. The assignment prototype algorithmis a typical clustering method for relational data. The membership degree of each object to each cluster is calculated directly from dissimilarities between objects. An entropy-basedL1-regularized possibilistic assignment prototype algorithm is proposed first to induce belongingness for a membership grade. An algorithm of sequential cluster extraction based on the proposed method is constructed and the effectiveness of the proposed methods is shown through numerical examples.
On Objective-Based Rough Hard and Fuzzyc-Means Clustering
Authors: Kinoshita Naohiko ,   Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan, Endo Yasunori ,   Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
Published: April 2016

Clustering is one of the most popular unsupervised classification methods. In this paper, we focus on rough clustering methods based on rough-set representation. Rough k-Means (RKM) is one of the rough clustering method proposed by Lingras et al. Outputs of many clustering algorithms, including RKM depend strongly on initial values, so we must evaluate the validity of outputs. In the case of objectivebased clustering algorithms, the objective function is handled as the measure. It is difficult, however to evaluate the output in RKM, which is not objective-based. To solve this problem, we propose new objective-based rough clustering algorithms and verify theirs usefulness through numerical examples.
Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface –
Authors: Takase Haruhiko ,   Graduate School of Engineering, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan, Kawanaka Hiroharu , Tsuruoka Shinji ,   Graduate School of Regional Innovation Studies, Mie University, 1577 Kurima-Machiya, Tsu, Mie 514-8507, Japan
Published: April 2016

We focus on developing an e-learning system that supports the grasping of misunderstanding from descriptive answers. We propose real-time keyword extraction and an interface for grasping misunderstanding based on extracted keywords. The system extracts keywords without extra information. Teachers find majormisunderstandings by using the proposed interface, which consists of two views – keyword and description. Using these views, teachers browse answers in three steps – finding keywords, reading around keywords, and reading full answers. We use experiments to demonstrate the effectiveness of our system, this proposed keyword extraction extracts expected words. Subjects evaluate the proposed interface for its effectiveness in grasping misunderstandings. Using our proposed, teachers found major misunderstandings quickly and easily.
App Controllable Intelligent Desk Lamp System
Authors: Liao Yu Ping ,   Department of Electronic Engineering, Chien Hsin University of Science and Technology, Lu Ruei Chang , Liu Pang Tzu , Cao Jing Xiang , Wei Ko Chi ,   Department of Electronic Engineering, National Ilan University
Published: April 2016

In this paper, we propose a FPGA based intelligent desk lamp system with a camera, two servo motors and LEDs. The proposed lamp system can automatically trace a moving book with a red bookmark by pan and tilt control to provide sufficient illumination. Thus, the user can read book with enough light for eye caring. The architecture of the system includes a vision system and a servo-motor control system. The image processing module inside the system can obtain the book’s position data. According to the book’s position data, two servo motors automatically adjust the pan and tilt angles of the desk lamp. The adjustable ranges for pan and tilt angles are 40° and 30°, respectively. Additionally, an App is designed and allows the user to remotely control the lamp by using a smartphone. All the processing modules are implemented on Altera DE0-Nano kit with Cyclonetex® IV EP4CE22F17C6N FPGA with Verilog HDL.
Neural Network Structure Analysis Based on Hierarchical Force-Directed Graph Drawing for Multi-Task Learning
Authors: Shibata Atsushi ,   Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology, Dong Fangyan , Hirota Kaoru ,   Education Academy of Computational Life Sciences, Tokyo Institute of Technology
Published: April 2016

A hierarchical force-directed graph drawing is proposed for the analysis of a neural network structure that expresses the relationship between multitask and processes in neural networks represented as neuron clusters. The process revealed by our proposal indicates the neurons that are related to each task and the number of neurons or learning epochs that are sufficient. Our proposal is evaluated by visualizing neural networks learned on the Mixed National Institute of Standards and Technology (MNIST) database of handwritten digits, and the results show that inactive neurons, namely those that do not have a close relationship with any tasks, are located on the periphery part of the visualized network, and that cutting half of the training data on one specific task (out of ten) causes a 15% increase in the variance of neurons in clusters that react to the specific task compared to the reaction to all tasks. The proposal aims to be developed in order to support the design process of neural networks that consider multitasking of different categories, for example, one neural network for both the vision and motion system of a robot.
RobustH∞Damping Control of Multi-FACTS Devices for Stability Enhancement of Power Systems with Signal’s Time Delay
Authors: Liu Fang ,   School of Information Science and Engineering, Central South University, Wu Min , Li Danyun , He Yong , Yokoyama Ryuichi ,   School of Automation, China University of Geosciences,   Graduate School of Environmental and Energy Engineering, Waseda University
Published: April 2016

In this paper, a robustH∞damping controller of multi-FACTS device for a power system is developed with considering the time delay of the remote feedback signals transmitted by wide-area measurement systems (WAMS). A free-weighting matrices method based on linear control design approach is presented to design the robustH∞damping controller to improve the dynamical performance of power systems. Firstly, the linearized reduced-order plant model is established, which efficiently considers the signal’s time delay and disturbance. Then, the design of multi-FACTS robustH∞damping controller is formulated as the standard control problem on delay-dependent state-feedback robust control based on the robust control theory. Finally, the simulation tests are carried out on the 2-area 4-machine power systems. Satisfactory test results verify the correctness of the proposed damping controller.
Ground Target Tracking and Collision Avoidance for UAV Based Guidance Vector Field
Authors: Peng Zhihong ,   School of Automation, Beijing Institute of Technology, Chen Zhimin
Published: April 2016

This paper focuses on ground-moving target tracking of an unmanned aerial vehicle (UAV) in the presence of static obstacles and moving threat sources. Due to a UAV is restricted by airspace restrictions and measurement limitations during flight, we derive a dynamic path planning strategy by generating guidance vector filed combined Lyapunov vector field with collision avoidance potential function to track target in standoff distance loitering pattern, and resolved collision avoidance, simultaneously. This method relies only on the current information of the UAV and target, and generates a single-step route plan in realtime. Its performance is simple, efficient, and fast and have low computational complexity. The results of numerical simulation verify the effectiveness of the tracking and collision avoidance process of the UAV.
PSO Based Deterministic ESN Models for Stock Price Forecasting
Authors: Dan Jingpei ,   College of Computer Science, Chongqing University, Guo Wenbo , Shi Weiren , Fang Bin , Zhang Tingping ,   College of Computing and Software Systems, University of Washington Bothell,   School of Automation, Chongqing University,   College of Information Science and Engineering, Chongqing Jiaotong University
Published: April 2016

Deterministic echo state network (ESN) models integrated with particle swarm optimization (PSO) are proposed to improve the accuracy and efficiency of stock price forecasting. ESNs have been successfully applied to financial time series forecasting because of their efficient and powerful computational ability in approximating nonlinear dynamical systems. However, reservoir construction in standard ESNs is primarily driven by a series of randomized model-building stages, because of which both researchers and practitioners have to rely on a series of trials and errors to determine parameters. An ESN with a deterministically constructed reservoir is comparable in performance to a standard ESN and has minimal complexity as well as potential for optimizations with regard to ESN parameters. In this paper, forecasting performances of the proposed PSO-DESN models are compared with those of standard ESNs for stock price prediction on the benchmark dataset of S&P 500. The comparison results demonstrate that the proposed PSO-DESNs exhibit better performance in stock price forecasting in terms of both accuracy and efficiency, thereby verifying the potential of PSO-DESNs for financial predictions.
Fully Automated Determination of Femoral Coordinate System in CT Image Based on Epicondyles
Authors: Uozumi Yosuke ,   Graduate School of Engineering, University of Fukui, Nagamune Kouki , Nakano Naoki , Nagai Kanto , Araki Daisuke , Hoshino Yuichi , Matsushita Takehiko , Kuroda Ryosuke , Kurosaka Masahiro ,   Graduate School of Medicine, Kobe University,   Kaisei Hospital
Published: April 2016

We propose a fully automated determination of the femoral coordinates in computerized tomography (CT) imaging based on epicondyles. The challenge point of this paper is that we take up how to calculate the femoral coordinate system (FCS), which is difficult to determine automatically. Our proposed method automatically determines the FCS based on anatomical reference points. We evaluated 10 subjects (six men and four women 28.9 ± 9.3 years old, three left-handed and seven right-handed) who had no history of joint injury. We examined the proposed method by comparing the expert and algorithm. The medial epicondyle was 1.41 ± 0.75 mmp= 0.42 > 0.05, student’sttest) in positioning accuracy. The lateral epicondyle was 1.36 ± 0.70 mmp= 0.42) in positioning accuracy. The origin was 0.87 ± 0.40 mmp= 0.71). in positioning accuracy. The lateral axis angle accuracy was 0.53 ± 0.84°p= 0.44). In short, the proposed method constructed patient-specific coordinate systems more accurately than expert manual.
Financial Conditions Index Construction Through WeightedLp-Norm Support Vector Regression
Authors: Ye Ya-Fen ,   College of Economics, Zhejiang University, Jiang Yue-Xiang , Shao Yuan-Hai , Li Chun-Na ,   Zhijiang College, Zhejiang University of Technology
Published: April 2016

This study proposes weightedLp-norm support vector regression (WLp-SVR) robust against both noise and outliers. UsingLp-norm enables WLp-SVR to select financial variables for creating the financial conditions index (FCI) reliably. We use a weighted sum method to construct a Chinese FCI. We then evaluate our FCI’s ability to forecast real output based on the Granger-causality and Engle-Granger cointegration tests. Regression results show that our FCI has strong predictive power in forecasting real output, indicating that our FCI is a potential leading indicator of the future state of the economy.
A Hybrid Particle Swarm Optimization and Neural Network with Fuzzy Membership Function Technique for Epileptic Seizure Classification
Authors: Abuhasel Khaled A. ,   College of Engineering, Salman Bin Abdulaziz University, Iliyasu Abdullah M. , Fatichah Chastine ,   Department of Computational Intelligence & Systems Science, Tokyo Institute of Technology,   Informatics Department, Institut Teknologi Sepuluh Nopember
Published: April 2016

A hybrid particle swarm optimization (PSO) integrating neural network with fuzzy membership function (NEWFM) technique is proposed for epileptic seizure classification tasks based on brain electroencephalography (EEG) signals. By combining PSO and NEWFM, the proposed method obtains the optimal parameters from the EEG data training required to achieve the best accuracy in disease diagnosis. NEWFM, a model of neural networks, is expected to improve the accuracy by updating weights of fuzzy membership functions. The PSO, a swarm-inspired optimization algorithm, is used to obtain the optimal parameters from the NEWFM. A standard dataset comprising of 5 sets of epileptic seizure detection data, each consisting 100 single EEGs segments is employed to evaluate the proposed technique’s performance. Based on the experiments, the classification results show that the best accuracy of Z–S classification task is 99.5% with the optimal parameters of α = 0.1 and β=0.1. For the ZNF–S classification task, the best accuracy is 97.73% with the optimal parameters of α=0.1 or 0.2 and β =0.2. Similar results for the ZNFO–S classification task is 97.64% with the optimal parameters set at α =0.1 or 0.2 and β = 0.1.
Coordination of Monetary and Exchange Rate Policy in China: Market Interest Rate Approach
Authors: Xu Bing ,   Research Institute of Econometrics and Statistics, Zhejiang Gongshang University, He Qiuqin , Hu Xiaowen ,   School of Economics and Management, Anhui Normal University
Published: April 2016

We propose a unique time-varying identification approach to the market interest rate based on Taylor Rule for coordinating the monetary and exchange rate policies. The significant differences exist between real and market interest rates — 2001 and 2009 are high real interest rates, and 2004-2005 and 2010-2012 low real interest rates — that identify monetary and exchange rate policy conflicts in China. These conflicts derive from the indirect effect of monetary factor through interest rate inertia and expected output gap in 2001; the indirect effect of exchange rate factor through interest rates and inflation inertia in 2004-2005; the direct effects of monetary and the exchange rate factors and the indirect effects through interest rate and inflation inertia, and the expected inflation and output gap since 2009. Our empirical results provide decision support for the monetary and exchange rate policy for reforming Chinese market interest rates.
Complex Multi-Issue Negotiation Using Utility Hyper-Graphs
Authors: Hadfi Rafik ,   Department of Computer Science and Engineering, Nagoya Institute of Technology, Ito Takayuki
Published: April 2016

We propose to handle the complexity of utility spaces used in multi-issue negotiation by adopting a new representation that allows a modular decomposition of the issues and the constraints. This is based on the idea that a constraint-based utility space is nonlinear with respect to issues, but linear with respect to the constraints. This allows us to rigorously map the utility space into an issue-constraint hyper-graph. Exploring the utility space reduces then to a message passing mechanism along the hyper-edges of the hyper-graph by means of utility propagation. Optimal contracts are found efficiently using a variation of the Max-Sum algorithm. We evaluate the model experimentally using parameterized nonlinear utility spaces, showing that it can handle a large family of complex utility spaces by finding optimal contracts, outperforming previous sampling-based approaches. We also evaluate the model in a negotiation setting. We show that under high complexity, social welfare could be greater than the sum of the individual agents’ best utilities.
A Class Association Rule Based Classifier Using Probability Density Functions for Intrusion Detection Systems
Authors: Mabu Shingo ,   Graduate School of Science and Engineering, Yamaguchi University, Li Wenjing , Hirasawa Kotaro ,   Graduate School of Information, Production and Systems, Waseda University
Published: April 2016

As the number of computer systems connected to the Internet is increasing exponentially, the computer security has become a crucial problem, and many techniques for Intrusion detection have been proposed to detect network attacks efficiently. On the other hand, data mining algorithms based on Genetic Network Programming (GNP) have been proposed and applied to Intrusion detection recently. GNP is a graph-based evolutionary algorithm and can extract many important class association rules by making use of the distinguished representation ability of the graph structure. In this paper, probabilistic classification algorithms based on multi-dimensional probability distribution are proposed and combined with conventional class association rule mining of GNP, and applied to network intrusion detection for the performance evaluation. The proposed classification algorithms are based on 1) one-dimensional probability density functions and 2) a two-dimensional joint probability density function. These functions represent the distribution of normal and intrusion accesses and efficiently classify a new access data into normal, known intrusion or even unknown intrusion. The simulations using KDD99Cup database from MIT Lincoln Laboratory show some advantages of the proposed algorithms over the conventional mean and standard deviation-based method.
How Design Quality Improves with Increasing Computational Abilities: General Formulas and Case Study of Aircraft Fuel Efficiency
Authors: Lorkowski Joe ,   University of Texas at El Paso, Kosheleva Olga , Kreinovich Vladik , Soloviev Sergei ,   Institut de Recherche en Informatique de Toulouse (IRIT),   St. Petersburg State University of Information Technologies, Mechanics, and Optics (ITMO)
Published: April 2016

It is known that the problems of optimal design are NP-hard – meaning that, in general, a feasible algorithm can only produce close-to-optimal designs. The more computations we perform, the better design we can produce. In this paper, we theoretically derive quantitative formulas describing how the design qualities improves with the increasing computational abilities. We then empirically confirm the resulting theoretical formula by applying it to the problem of aircraft fuel efficiency.
Using Brainwaves and Eye Tracking to Determine Attention Levels for Auto-Lighting Systems
Authors: Watada Junzo ,   Graduate School of Information, Production, and Systems, Waseda University, Hsiao Yung-Chin , Kitagawa Hanayuki
Published: April 2016

To prevent car accidents, it should be possible for pedestrians and other drivers to detect oncoming vehicles. Many car accidents are caused because persons are not aware of approaching traffic, and this applies especially to visual awareness. The daytime running light (DRL) and the third braking light (TBL) were developed to significantly increase the visibility of vehicles, and their effectiveness has been verified through numerous studies. Usage of light-emitting diode (LED) lighting technology has also become popular in auto-lighting systems because of its advantages of energy efficiency, long life, and stylish appearance. However, LED lighting technology is very different from conventional incandescent or high-intensity discharge (HID) lighting technology. In this paper, we determine the effectiveness of LEDs as DRLs and TBLs. We measure human attention levels by observing brainwaves and performing eye-tracking experiments that shows the relationship between the theory of attention, brainwaves, and eye tracking. The results obtained show that it is feasible to evaluate automotive exterior lighting using the attention levels of subjects.
Fuzzyc-Means with Quadratic Penalty-Vector Regularization Using Kullback-Leibler Information for Uncertain Data
Authors: Kinoshita Naohiko ,   Graduate School of Systems and Information Engineering, University of Tsukuba, Endo Yasunori , Hamasuna Yukihiro ,   Faculty of Engineering, Information and Systems, University of Tsukuba,   Department of Informatics, Kindai University
Published: April 2016

Clustering, a highly useful unsupervised classification, has been applied in many fields. When, for example, we use clustering to classify a set of objects, it generally ignores any uncertainty included in objects. This is because uncertainty is difficult to deal with and model. It is desirable, however, to handle individual objects as is so that we may classify objects more precisely. In this paper, we propose new clustering algorithms that handle objects having uncertainty by introducing penalty vectors. We show the theoretical relationship between our proposal and conventional algorithms verifying the effectiveness of our proposed algorithms through numerical examples.
On Sequential Cluster Extraction Based onL1-Regularized Possibilisticc-Means
Authors: Hamasuna Yukihiro ,   Department of Informatics, School of Science and Engineering, Kindai University, Endo Yasunori ,   Faculty of Engineering, Information and Systems, University of Tsukuba
Published: April 2016

Sequential cluster extraction algorithms are useful clustering methods that extract clusters one by one without the number of clusters having to be determined in advance. Typical examples of these algorithms are sequential hardc-means (SHCM) and possibilistic clustering (PCM) based algorithms. Two types ofL1-regularized possibilistic clustering are proposed to induce crisp and possibilistic allocation rules and to construct a novel sequential cluster extraction algorithm. The relationship between the proposed method and SHCM is also discussed. The effectiveness of the proposed method is verified through numerical examples. Results show that the entropy-based method yields better results for the Rand Index and the number of extracted clusters.
Analysis of Pareto Solutions Based on Non-Correspondence in Spread Between Objective Space and Design Variable Space
Authors: Yoshida Toru ,   Department of Computational Science and Engineering, Nagoya University, Yoshikawa Tomohiro
Published: April 2016

Recently, many studies have been conducted on Multi-Objective Genetic Algorithm (MOGA), in which Genetic Algorithms are applied to Multi-objective Optimization Problems (MOPs). Among various applications, MOGA is also applied to engineering design problems, which require not only high-performance Pareto solutions to be obtained, but also an analysis of the obtained Pareto solutions and extraction of design knowledge about the problem itself. In order to analyze the Pareto solutions obtained by MOGA, it is necessary to consider the objective space and the design variable space. The aim of this study is to extract and analyze solutions of relevant interest to designers. In this paper, we propose three solutions to analyze and extract design knowledge from MOGA. (1) We define “Non-Correspondence in Spread” between the objective space and the design variable space. (2) We try to extract the Non-Correspondence area in Spread using the index defined in this paper. (3) We apply the defined index to genetic search to obtain Pareto solutions that have different design variables and similar fitness values. This paper applies the above index to the trajectory design optimization problem and extracts Non-Correspondence area in Spread from the obtained Pareto solutions. This paper also shows that robust Pareto solutions can be obtained using genetic search using the defined index.
Design and Evaluation of Consumer-Oriented Reviewing Platform with Receipt Log
Authors: Tokunaga Seiki ,   Kobe University, Okushi Akihiro , Saiki Sachio , Matsumoto Shinsuke , Nakamura Masahide ,   NS Solutions Kansai Corporation
Published: April 2016

This paper developsReceiptLogService Platform, which enables consumers to using their personal purchase receipts, store their receipt logs, and to use the data for various consumer services. The proposed platform consists of three components:receipt scanner,ReceiptLog DB, andReceiptLog API. The receipt scanner digitizes daily receipts, and the ReceiptLog DB manages the scanned data. The ReceiptLog API provides the receipt log as a service. The API consists of the BasicAPI, which provides fundamental access for the receipt log, whereas the MiningAPI performs a statistical analysis of the receipt log. These APIs are published as Web services, and can used by multiple applications and services for various purposes. We also conduct an experimental evaluation with actual subjects, to confirm the usefulness of services with receipt log.
On the Optimal Hyperparameter Behavior in Bayesian Clustering
Authors: Yamazaki Keisuke ,   Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
Published: April 2016

In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy of the label estimation has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. The prior distribution has the parameter, which is the hyperparameter. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, and we propose an evaluation method for the clustering result, based on the prior optimization.
An Algebraic Aspect of Correspondences Between Implicational Fragment Logics and Fuzzy Logics
Authors: Kawaguchi Mayuka F. ,   Division of Computer Science and Information Technology, Hokkaido University, Kondo Michiro ,   Tokyo Denki University
Published: April 2016

This research report treats a correspondence between implicational fragment logics and fuzzy logics from the viewpoint of their algebraic semantics. The authors introduce monotone BI-algebras by loosening the axiomatic system of BCK-algebras. We also extend the algebras of fuzzy logics with weakly-associative conjunction from the case of the real unit interval to the case of a partially ordered set. As the main result of this report, it is proved that the class of monotone BI-algebras with condition (S) coincides with the class of weakly-associative conjunctive algebras.
Priority Rule-Based Construction Procedure Combined with Genetic Algorithm for Flexible Job-Shop Scheduling Problem
Authors: Yokoyama Soichiro ,   Graduate School of Information Science and Technology, Hokkaido University, Iizuka Hiroyuki , Yamamoto Masahito
Published: April 2016

The heuristic method we propose solves the flexible job-shop scheduling problem (FJSP) using a solution construction procedure with priority rules. FJSP is more complex than classical scheduling problems in that operations are processed on one of multiple candidate machines, one of which must be selected to get a feasible solution. The solution construction procedure with priority rules is implemented on top of the efficient existing method for solving the FJSP which consists of a genetic algorithm and a local search method. The performance of the proposed method is analyzed using various benchmark problems and it is confirmed that our proposed method outperforms the existing method on problems with particular conditions. The conditions are further investigated by applying the proposed method on newly created benchmark.
On Hierarchical Linguistic-Based Clustering
Authors: Kinoshita Naohiko ,   Graduate School of Systems and Information Engineering, University of Tsukuba, Endo Yasunori , Sugawara Akira ,   Faculty of Engineering, Information and Systems, University of Tsukuba,   Canon Inc.
Published: April 2016

Clustering is representative unsupervised classification. Many researchers have proposed clustering algorithms based on mathematical models – methods we call model-based clustering. Clustering techniques are very useful for determining data structures, but model-based clustering is difficult to use for analyzing data correctly because we cannot select a suitable method unless we know the data structure at least partially. The new clustering algorithm we propose introduces soft computing techniques such as fuzzy reasoning in what we call linguistic-based clustering, whose features are not incident to the data structure. We verify the method’s effectiveness through numerical examples.
Motion-Based Depth Estimation for 2D to 3D Video Conversion
Authors: Guo Fan ,   School of Information Science and Engineering, Central South University, Tang Jin , Zou Beiji ,   Mobile Health Ministry of Education, China Mobile Joint Laboratory
Published: April 2016

Recent advances in 3D have increased the importance of stereoscopic content creation and processing. Therefore, converting existing 2D videos into 3D videos is very important for growing 3D market. The most difficult task in 2D-to-3D video conversion is estimating depth map from single-view frame images. Thus, in this paper, we propose a novel motion-based 2D to 3D video conversion method. The method first determines the motion type using the optical flow estimation. Then, different depth estimation processes are performed based on the motion type. For global motion, the depth from motion parallax provides the final depth map. For local motion, the depth from template together with the bilateral filter is used to produce the depth map. Finally, the left- and right-view images are synthesized to generate realistic stereoscopic results for viewers. During the process, the visual artifacts of the synthesized virtual views are effectively eliminated by recovering the separation and loss of foreground objects. A comparative study and quantitative evaluation with other conversion methods are carried out, which demonstrate that better overall quality results may be obtained using the proposed method.
Mining Visual Phrases for Visual Robot Localization
Authors: Tanaka Kanji ,   University of Fukui, Chokushi Yuuto , Ando Masatoshi
Published: April 2016

We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic, and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available visual experience, images shared by other colleague robots, and publicly available image data on the Web) and directly mine it to find visual phrases (VPs) that discriminatively and compactly explain an input query/database image. Our mining approach is motivated by recent success achieved in the field of common pattern discovery – specifically mining of common visual patterns among scenes – and requires only a single library of raw images that can be acquired at different times or on different days. Experimental results show that, although our scene descriptor is significantly more compact than conventional descriptors, its recognition performance is relatively high.