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STLF using Neural Networks and Fuzzy for anomalous load scenarios - A case study for Hajj

STLF using Neural Networks and Fuzzy for anomalous load scen...

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International Conference on Electrical and computer Engineering (ICECE)

作者： Suman Ahmmed Mohammad Asif Ashraf Khan Md. Khairul Hasan Ahmed Yousuf Saber Mohammad Nurul Huda Mohammad Zahidur Rahman Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh Real-Time Power and Intelligent Systems Laboratory Missouri University of Science and Technology USA Department of Computer Science and Engineering Jahangir Nagar University Dhaka Bangladesh

Load Forecasting has an important role in load generation, scheduling, planning etc. in power system. Different computational intelligent techniques are used in Short Term Load Forecasting (STLF) to make it more effective. Neural Networks (NN) is an effective mapping algorithm that can map complex input-output relationships, which is an important technique to do STLF having existing dataset. Usually a proper NN is sufficient to achieve accepted level of performance. But different load dataset may bear some irregular nature of load demand scenario due to having special events, where accuracy of NN suffers significantly. To enhance the performance for those situations, the authors propose a hybrid STLF approach-Neural Networks and Fuzzy (NNF) method. The authors first try to select the best possible trained NN and do STLF. Considering historical data trend and of existing errors of NN solution, for those special days, NNF determine the Load Change trend. Fuzzy Inference Rules (FIR) have been developed to further improvement by fuzzy method. In fuzzy part the NNF apply FIR on two inputs: STLF of NN and Load Change trend, to enhance the performance of STLF for special events. To evaluate the proposed method it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Since the authors deal with the daily load dataset of Saudia Arabia of Hijri years (Arabian years), Hajj has been chosen as one of the anomalous load scenario. Empirical results show that for Hajj event of Hijri 1428 year, the accuracy of STLF by NN is approximately 6.37%, whereas proposed NNF can decline the error at only 1.92%.

关键词： Artificial neural networks Data structures Boolean functions Load forecasting Finite impulse response filter Forecasting Load modeling

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A detection model for driver's unsafe states based on real-time face-vision

A detection model for driver's unsafe states based on real-t...

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International Conference on Image Analysis and Signal Processing, IASP

作者： Fangwen Zhai Zehong Yang Yixu Song Hongbiao Ma State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

The auto industry has developed fast in the last 100 years. Although it brings us convenience, more and more traffic accidents are happening every day. There are many factors which can cause a car accident. Based on the record of a large number of accidents, fatigue is one of the most important factors. Additionally, driver's distraction and conversations with passengers during driving can lead to serious results. In this paper, a real-time vision-based model is proposed for monitoring driver's unsafe states, including fatigue state, distraction state and talking state, etc. By analyzing driver's real-time face vision, a method for detecting driver's fatigue, distraction and talking states is given. Also, the model is its extreme high speed and very simple equipment. It can run at about 20 frames per second in video with 640*480 resolutions on a normal computer platform.

关键词： Face detection Fatigue Mouth Vehicle driving US department of Transportation Road accidents Vehicle safety Object detection Real time systems Laboratories

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Vision-based detection for learning articulation models of cabinet doors and drawers in household environments

Vision-based detection for learning articulation models of c...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jürgen Sturm Kurt Konolige Cyrill Stachniss Wolfram Burgard Autonomous Intelligent Systems Lab Computer Science Department University of Freiburg Germany Willow Garage Inc. Menlo Park CA USA

ISBN: (纸本)9781424450381;9781424450404

Service robots deployed in domestic environments generally need the capability to deal with articulated objects such as doors and drawers in order to fulfill certain mobile manipulation tasks. This however, requires, that the robots are able to perceive the articulation models of such objects. In this paper, we present an approach for detecting, tracking, and learning articulation models for cabinet doors and drawers without using artificial markers. Our approach uses a highly efficient and sampling-based approach to rectangle detection in depth images obtained from a self-developed active stereo system. The robot can use the generative models learned for the articulated objects to estimate their articulation type, their current configuration, and to make predictions about possible configurations not observed before. We present experiments carried out on real data obtained from our active stereo system. The results demonstrate that our technique is able to learn accurate articulation models. We furthermore provide a detailed error analysis based on ground truth data obtained in a motion capturing studio.

关键词： Object detection Cameras Service robots Predictive models Robot vision systems Image segmentation Iterative algorithms Robotics and automation USA Councils Error analysis

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACG University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide sub-optimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems. © 2010 IEEE.

关键词： Global optimization

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Semi unsupervised M-FISH chromosome image classification

Semi unsupervised M-FISH chromosome image classification

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Petros Karvelis Aristidis Likas Dimitrios I. Fotiadis Department of Computer Science University of Ioannina (UoI) Ioannina Greece Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina (UoI) Ioannina Greece

M-FISH (Multiplex Fluorescent In Situ Hybridization) is a multichannel chromosome imaging technique that allows the color discrimination of human chromosomes. Although M-FISH facilitates the visual detection of chromosome rearrangements, the success of this technique largely depends on the accuracy of the pixel-by-pixel classification. In this study, we present a semi unsupervised method for M-FISH chromosome image classification. First, we segment the chromosome pixels using an automated thresholding procedure. Five features for each pixel are extracted, describing the intensity of the five channels. These features are then normalized. Second, we employed the K-means algorithm in order to cluster the chromosome pixels into the 24 chromosome classes. We have used the emission information for each chromosome class in order to initialize the cluster centers. Our method has been tested on the ADIR M-FISH database and an overall accuracy of 72.48% is achieved. This methods that use training set to build the classifiers, while our method does not use a training set.

关键词： Biological cells Pixel

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A 3D Simulator for Optimizing Production Line System

A 3D Simulator for Optimizing Production Line System

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International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Hitoshi Kitani Hideki Kudo Hiroaki Nishino Tsuneo Kagawa Kouichi Utsumiya Graduate School of Engineering Oita University Dannoharu Oita Japan Department of Computer Science and Intelligent Systems Oita University Dannoharu Oita Japan

Many companies employ line production system (LPS) for producing their products in large volume. The design and installation of LPS takes enormous amount of time and costs. If any fatal troubles become obvious after the line installation, they cause heavy monetary and temporal damages to the companies. Whereas discovering any trouble spots during design phase is a crucial challenge, the advanced detection of the troubles is a very difficult issue. We propose an LPS simulator allowing user to prototype a planned production line in a 3D virtual environment and put it in action for detecting potential trouble spots and bottlenecks before the installation. In this paper, we describe principal functions of the proposed system including virtual prototyping for designing and verifying a production line in a 3D virtual space and real-time and batch simulation functions based on 3D computer graphics.

关键词： Three dimensional displays Solid modeling Belts Generators Converters

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A Three-Stage Method to Select Informative Genes from Gene Expression Data in Classifying Cancer Classes

A Three-Stage Method to Select Informative Genes from Gene E...

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International Conference on intelligent systems, Modelling and Simulation (ISMS)

作者： Mohd Saberi Mohamad Sigeru Omatu Safaai Deris Michifumi Yoshioka Department of Software Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Department of Computer Science and Intelligent Systems Osaka Prefecture University Sakai Osaka Japan

ISBN: (纸本)9781424459841;9780769539737

The process of gene selection for the cancer classification faces with a major problem due to the properties of the data such as the small number of samples compared to the huge number of genes, irrelevant genes, and noisy data. Hence, this paper aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve the aim, a three-stage method has been proposed. It has three stages: (1) pre-selecting genes using a filter method; (2) optimizing the gene subset using a multi-objective hybrid method; (3) analyzing the frequency of appearance of each gene. By performing experiments on three public gene expression data sets, classification accuracies and the number of selected genes of the proposed method are better than those of other experimented methods and previous works. A list of informative genes in the final gene subsets is also presented for biological usage.

关键词： Gene expression Cancer Filters Genetic algorithms Support vector machines Support vector machine classification Frequency intelligent systems Computational modeling computer simulation

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An Interactive CG Learning System Through 3D Authoring and programming

An Interactive CG Learning System Through 3D Authoring and P...

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International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Shogo Sueyasu Tatsuro Nagano Hiroaki Nishino Tsuneo Kagawa Kouichi Utsumiya Graduate School of Engineering Oita University Dannoharu Oita Japan Department of Computer Science and Intelligent Systems Oita University Dannoharu Oita Japan

3 dimensional computer graphics (3DCG) becomes an easily accessible technology for general public. Average PC users can design and embed high quality 3DCG images and animations in their work. They must, however, study text books and related web pages with captured 2D images for understanding 3DCG theories and graphics programming to become a good designer. They must also master complex 3D operations specific to a dedicated software tool for acquiring appropriate authoring skills. These efforts require enormous amount of time and impose a heavy burden on the users. To solve the problem, we have been developing a 3DCG learning support system enabling the users to intuitively acquire knowledge and skills necessary for 3DCG contents authoring and graphics programming. In this paper, we describe the concept, implementation method, and evaluation of the proposed system.

关键词： Three dimensional displays programming Rendering (computer graphics) Animation Visualization Light sources Solid modeling

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TCP-FIT — A novel TCP congestion control algorithm for wireless networks

TCP-FIT — A novel TCP congestion control algorithm for wire...

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IEEE Globecom Workshops

作者： Jingyuan Wang Jiangtao Wen Jun Zhang Yuxing Han Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

The Transport Control Protocol (TCP) has been widely used in wired and wireless Internet applications such as FTP, email and HTTP. However, performances of traditional TCP congestion algorithms degrade significantly when deployed over wireless networks. In this paper, we proposed an improved TCP congestion control algorithm for wireless networks, named TCP-FIT, and compared its performance with existing state-of-the-art congestion control algorithms as well as an application layer Parallel TCP scheme. Experiment results show significant performance improvement, good fairness and lower end-to-end latency for TCP-FIT.

关键词： Bandwidth Throughput Propagation delay Delay Wireless networks Linux

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All for one or one for all? Combining heterogeneous features for activity spotting

All for one or one for all? Combining heterogeneous features...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Ulf Blanke Bernt Schiele Matthias Kreil Paul Lukowicz Bernhard Sick Thiemo Gruber Computer Science Department Technical University of Darmstadt Germany Embedded Systems Laboratory (ESL) University of Passau Germany Computationally Intelligent Systems Lab (CIS) University of Passau Germany

Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work are used. While feature selection algorithms allow automatic decision, definition of features remains a manual task. We conduct a comparative study of features with very different origin. To this end, we propose a new type of features based on polynomial approximation of signals. The new feature type is compared to features used routinely for motion based activity recognition as well as to recently proposed body-model based features. Experiments were performed on three different, large datasets allowing a thorough, in-depth analysis. They not only show the respective strengths of the different feature types but also their complementarity resulting in improved performance through combination. It shows that each feature type with its individual and complementary strengths and weaknesses can improve results by combination.

关键词： Wearable sensors Polynomials Performance analysis Frequency Boosting computer science Embedded system Embedded computing intelligent systems Computational intelligence

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