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Result representation of Rao-Blackwellized particle filtering for SLAM

Result representation of Rao-Blackwellized particle filterin...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Nosan Kwak Beom-Hee Lee Kazuhito Yokoi Joint Japanese-French Robotics Laboratory Intelligent Systems Research Institute National Institute for Advanced Industrial Science and Technology Tsukuba Japan School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea

Recently, particle filters have been applying to many robotic problems including the simultaneous localization and mapping (SLAM). Specifically, SLAM approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze representation of the results of particle filtering. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. In most cases, the result of the particle that has the highest importance weight is represented as the result. However, this approach does not give the best result all the time. Thus, We provide the analysis of final representation of particle filtering. In this paper, we compares several methods to derive the final representation of the result after finishing RBPF-SLAM. According to the result, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

关键词： Filtering Simultaneous localization and mapping Particle filters intelligent robots robotics and automation Service robots Electronic mail Finishing Diversity reception Automatic control

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Optimization of HMM with interactive evolutionary computation for composing system

Optimization of HMM with interactive evolutionary computatio...

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IEEE Workshop on Soft Computing in Industrial Applications (SMCia)

作者： Hiroshi Hasui Hisakazu Ogura Department of Computer Science and Systems Engineering Muroran Institute of Technology Muroran Japan Graduate School of Human and Artificial Intelligent Systems Fukui University of Technology Fukui Japan

ISBN: (纸本)9781424437825

This paper presents the interactive composing system, i-Sonneteer. This system optimized HMM into the model of favorite with interactive evolutionary computation. The user rates and improves a melody with GUI. HMM learns 7 melodies which are improved before by the user, and generates the melody based on the learned data. If HMM learns the melodies that can be selected well, the HMM generates the pleasant melody. In interactive evolutionary computation, an individual is HMM, and a gene is an improved melody. When HMM is not mutated, the HMM generates the melody that has the highest probabilities in oneself. When HMM is mutated, the HMM generates the melody with the randomwalk or the older melodies learning method. This effects that the user's favorite is improved more clear. I-Sonneteer does not only optimize the HMM but also makes the favorite more clear. If the favorite is defined, the user can compose the pleasant melody. So we guess the definition of the favorite is more important than the optimization of the HMM. We experiment in comparing the randomwalk with the older melodies learning method in the mutation.

关键词： Hidden Markov models Evolutionary computation Learning systems Genetic mutations Random number generation computer applications computer industry Application software computer science systems engineering and theory

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Within-problem learning for efficient lower bound computation in max-SAT solving

Within-problem learning for efficient lower bound computatio...

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23rd AAAI Conference on Artificial Intelligence and the 20th Innovative Applications of Artificial Intelligence Conference, AAAI-08/IAAI-08

作者： Lin, Han Su, Kaile Li, Chu-Min Department of Computer Science Sun Yat-sen University Guangzhou 510275 China Ministry of Education Beijing China Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia MIS Université de Picardie Jules Verne 33 Rue St. Leu 80039 Amiens France

ISBN: (纸本)9781577353683

This paper focuses on improving branch-and-bound Max-SAT solvers by speeding up the lower bound computation. We notice that the existing propagation-based computing methods and the resolution-based computing methods, which have been studied intensively, both suffer from several drawbacks. In order to overcome these drawbacks, we propose a new method with a nice property that guarantees the increment of lower bounds. The new method exploits within-problem learning techniques. More specifically, at each branch point in the search-tree, the current node is enabled to inherit inconsistencies from its parent and leam information about effectiveness of the lower bound computing procedure from previous nodes. Furthermore, after branching on a new variable, the inconsistencies may shrink by applying unit propagation to them, and such process increases the probability of getting better lower bounds. We graft the new techniques into maxsatz and the experimental results demonstrate that the new solver outperforms the best state-of-the-art solvers on a wide range of instances including random and structured ones. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Problem solving

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A sparse variational Bayesian approach for fMRI data analysis

A sparse variational Bayesian approach for fMRI data analysi...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Vangelis P. Oikonomou Evanthia E. Tripoliti Dimitrios I. Fotiadis Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece Biomedical Research Institute-FORTH Ioannina Greece

The aim of this work is to propose a new approach for the determination of the design matrix in fMRI experiments. The design matrix embodies all available knowledge about experimentally controlled factors and potential confounds. This knowledge is expressed through the regressors of the design matrix. However, in a particular fMRI time series some of those regressors may not be present. In order to take into account this prior information a Bayesian approach based on hierarchical prior, which expresses the sparsity of the design matrix, is used over the parameters of the generalized linear model. The proposed method automatically prunes the columns of the design matrix which are irrelevant to the generation of data. The evaluation of the proposed approach on simulated and real experiments have shown higher performance compared to the conventional t-test approach.

关键词： Bayesian methods Data analysis Statistical analysis Parameter estimation Magnetic resonance imaging Sparse matrices Neurons Smoothing methods Statistics Maximum likelihood estimation

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Fractional-order integral and derivative controller design for temperature profile control

Fractional-order integral and derivative controller design f...

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Chinese Control and Decision Conference, CCDC

作者： Hyo-Sung Ahn Varsha Bhambhani YangQuan Chen Department of Mechatronics Gwangju Institute of Science and Technology Gwangju South Korea Center of Self-Organizing and Intelligent Systems Department of Electrical and Computer Engineering Utah State University Logan UT USA Center of Self-Organizing and Intelligent Systems Department of Electrical and Computer Engineering Utah State University Logan USA

This paper presents a strategy to tune a fractional order integral and derivative controller satisfying gain and phase margins. The closed-loop system designed has a feature of robustness to gain variations with step responses exhibiting a nearly iso-damping property. This paper aims to apply the tuning procedure proposed to temperature control at selected points in Quanserpsilas heat flow experimental platform. From the comparison with the traditional PI/PID controller based on Ziegler Nicholpsilas tuning method, the effectiveness and validity of the proposed methodologies are illustrated.

关键词： Temperature control Temperature sensors Gain Tuning Calculus Heating Control systems

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Genetic Algorithm Implementation for Distributed Security systems Optimization

Genetic Algorithm Implementation for Distributed Security Sy...

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6th IEEE International Conference on Computational Intelligence for Measurement systems and Applications (CIMSA 2008)

作者： P. Bykovyy Y. Pigovsky V. Kochan A. Sachenko G. Markowsky S. Aksoy Research Institute of Intelligent Computer Systems Ternopil National Economic University Ternopil Ukraine Professor Department of Computer Science Silesian University of Technology Zabrze Poland Professor Department of Computer Science University of Maine Orono ME USA Electronics Engineering Department Gebze Institute of Technology (GYTE) Gebze Kocaeli Turkey

This paper describes two algorithms for optimizing the design of distributed perimeter security systems. The first algorithm is a straightforward algorithm whose primary purpose is to help us formulate the problem formally. The second algorithm, is a genetic algorithm. Both algorithms were incorporated into a new module for our distributed security systems CAD software. Tests of the module show that it produced better solutions more quickly than any other algorithms that are known to us.

关键词： Distributed security systems Genetic algorithms Security system optimization

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A supervised method to assist the diagnosis and classification of the status of Alzheimer's disease using data from an fMRI experiment

A supervised method to assist the diagnosis and classificati...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Evanthia E. Tripoliti Dimitrios I. Fotiadis Maria Argyropoulou Department of Computer Science Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute Loannina Greece Department of Radiology Medical School University of Ioannina (UoI) Loannina Greece

The aim of this work is the development of a method to assist the diagnosis and classification of the status of Alzheimer's Disease (AD) using information that can be extracted from fMRI. The method consists of five stages: a) preprocessing of fMRI data to remove non-task related variability, b) modeling BOLD response depending on stimulus, c) feature extraction from fMRI data, d) feature selection and e) classification using the Random Forests (RF) algorithm. The proposed method is evaluated using data from 41 subjects (14 young adults, 14 non demented older adults and 13 demented older adults.

关键词： Feature extraction Functional magnetic resonance imaging Alzheimer's disease Radio frequency Classification algorithms

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Systematic elicitation of sequence patterns associated with non-proline cis peptide bonds

Systematic elicitation of sequence patterns associated with ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantinos P. Exarchos Themis P. Exarchos Costas Papaloukas Anastassios N. Troganis Dimitrios I. Fotiadis Unit of Medical Technology and Intelligent Information Systems Department of Computer Science Department of Medical Physics Medical School University of Ioannina (UoI) Ioannina Greece Institute of Biomedical Technology CERETETH Larissa Greece Department of Biological Applications and Technology University of Ioannina (UoI) Ioannina Greece Unit of Medical Technology and Intelligent Information Systems Department of Computer Science University of Ioannina (UoI) Ioannina Greece CERETETH Larissa Greece

Non-proline cis peptide bonds have been quite underrated for many years, due to the limited amount of structural information available. There is now significant evidence that non-proline cis peptide bonds occur more frequently than previously thought, and that they are often located at or near important sites of the protein molecule. In this work, we employ a combinatorial pattern discovery algorithm in order to identify simple and specific amino acid patterns, associated with the occurrence of non-proline cis peptide bonds in proteins. The derived patterns after careful validation help in gaining insight into the factors that influence the formation of non-proline cis peptide bonds.

关键词： Sequences Computational Intelligence Society Peptides Proteins Amino acids intelligent systems Information systems computer science Magnetic field induced strain Nuclear magnetic resonance

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A low-cost pedestrian-detection system with a single optical camera

A low-cost pedestrian-detection system with a single optical...

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作者： Cao, Xian-Bin Qiao, Hong Keane, John IEEE Department of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230026 China Laboratory of Complex Systems and Intelligent Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China School of Informatics University of Manchester M60 1QD Manchester United Kingdom University of Manchester M13 9PL Manchester United Kingdom

The ultimate purpose of a pedestrian-detection system (PDS) is to reduce pedestrian-vehicle-related injury. Most such systems tend to adopt expensive sensors, such as infrared devices, in expectation of better performance. In comparison, a low-cost optical-camera-based system has much potential practical value, including a greater detection range, and can easily be trained to detect other objects. However, such low-cost systems are difficult to design (e.g., little original information can be collected, and the scene is very complex). To address these problems, an effective and reliable classifier is needed. The classifier should have a proper structure, its features need to be well selected, and a large number of high-quality samples are necessary for training. In this paper, we present a low-cost PDS which only uses a single optical camera. We design a cascade classifier to achieve an effective and reliable detection. First, our system scans two sequential frames at each zoom scale with a sliding window. Second, with each window, both appearance and motion features are extracted. A well-trained cascade classifier, combining statistical learning with a decomposed support-vector-machine classifier, then determines whether the window contains a human body. At the same time, to provide as much information as possible about the pedestrian, a small-scale weighted template tree trained by a coevolutionary algorithm is adopted to identify each pedestrian's direction, and the distance of each from the vehicle is also provided using an estimation algorithm. During the training procedure, we select key features by using the AdaBoost algorithm and a large number of high-quality samples. Experimental results demonstrate that the system is suitable for pedestrian detection in city traffic: The detection speed is more than 10 ft/s, the detection rate reaches 80%, and the false positive rate is no more than 0.3‰. © 2008 IEEE.

关键词： Pedestrian safety

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Multi-granular representation-the key to machine intelligence

Multi-granular representation-the key to machine intelligenc...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Bo Zhang Ling Zhang Department of Computer Science and Technology Tsinghua National Laboratory for Information Science & Technology State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing China Institute of Artificial Intelligence Anhui University Hefei Anhui China

One of the basic characteristics in human problem solving is the ability to conceptualize the world at different granularities and translate from one abstraction level to the others easily. But so far computers can only deal with one abstraction level in problem solving generally. It seems important to develop new techniques which will in some way enable the computers to represent the world at different granularities. So the multi-granular representation is the key to machine intelligence. In the talk, we first introduce the quotient space based problem solving theory. In the theory, a problem is represented by a triplet (X,F,T), where X - the universe with the finest grain-size, F -the attribute of X, and T- the structure of X. When we view the same problem at a coarser grain size, we have a coarse-grained universe denoted by [X]. Then we have a new representation ([X],[F],[T]) of the problem. The coarse universe [X] is defined by an equivalence relation R on X. Then, representation ([X],[F],[T]) is called a quotient space of(X,F,T), where [X] -the quotient set of X, [F] -the quotient attribute of F, and [T] -the quotient structure of T. Obviously, the set of representations of a problem at different granularities composes a complete semi-order lattice. That is, in the theory the concept, quotient space, in algebra is used as a mathematical model to represent the relationship between representations with different grain-sizes. Multi-granular representation methodology can be used both in problem solving and machine learning. In multi-granular problem solving, a problem is solved from the coarse grain-size to the fine one hierarchically. The aim of hierarchical problem solving is intended to reduce the computational complexity. Multi-granular machine learning is intended to learn the knowledge from representations with different grain-size, i.e., the so-called multi-information fusion. Generally speaking, the fine representation has more details but less robustness.

关键词： Machine intelligence Problem-solving Machine learning Robustness Humans Grain size Lattices Algebra Mathematical model Computational complexity

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