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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Eklund, Anders Andersson, Mats Ohlsson, Henrik Ynnerman, Anders Knutsson, Hans Division of Medical Informatics Department of Biomedical Engineering Linköping University Sweden Division of Automatic Control Department of Electrical Engineering Linköping University Sweden Division for Visual Information Technology and Applications Department of Science and Technology Linköping University Sweden Linköping University Sweden

ISBN: (纸本)9780769541099

We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating the left toes and up by activating the right toes. To select a letter, the subject simply rests for a number of seconds. We can thus communicate with the subject in the scanner by for example showing questions that the subject can answer. Similar BCI for communication have been made with electroencephalography (EEG). In these implementations the subject for example focuses on a letter while different rows and columns of the virtual keyboard are flashing. The system then tries to detect if the correct letter is flashing or not. In our setup we instead classify the brain activity. Our system is not limited to a communication interface, but can be used for any interface where five degrees of freedom is necessary. © 2010 IEEE.

关键词： Brain computer interface

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Recognition of Fusarium diseased corn kernels using NIR spectroscopy and Neuro – Fuzzy classifier

Recognition of Fusarium diseased corn kernels using NIR spec...

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作者： Draganova, Tsvetelina Daskalov, Plamen Tsenkova, Roumiana Atanassova, Stefka Veleva-Doneva, Petya Department of Automatics Information and Control Engineering University of Rousse Rousse 7017 Bulgaria Biomeasurement Technology Laboratory Faculty of Agriculture Kobe University Kobe 657-8501 Japan Department of Physics Trakia University Stara Tagora 6000 Bulgaria Department of Informatics and Mathematics Trakia University Stara Tagora 6000 Bulgaria

ISBN: (纸本)9783902661906

In this paper we present the results obtained recently by recognizing corn kernels (healthy and Fusarium diseased) using spectral characteristics and Neuro-Fuzzy classifier. The proposed approach to corn kernel type diagnostic is based on three steps: spectral data acquisition, preliminarily processing and feature extraction, and classification. In the first step, the near infrared reflectance spectral characteristics are obtained in the range 400 - 2500 nm with step 2 nm. In the second step informative features are extracted using principal component analysis. In order to make a compact spectral data representation, the number of features is reduced. Adaptive Neuro - Fuzzy Inference System (ANFIS) is implemented in order to establish decision boundaries in the space of the selected features which separate samples belonging to two different classes (healthy and Fusarium diseased corn kernels). The inputs to ANFIS are several principal components. ANFIS has only one output node, the corn kernel type. The system is trained by data of train sample. We then evaluate the deviation of our estimate compared to the expert assessment. The procedure is applied to the 1800 corn kernels spectral data and the average accuracy is given. The average testing error for class healthy is 0,023 % and for class diseased is 0,329 %. As a result of the presented experiments the NIR spectroscopy is applicable as a nondestructive tool for Fusarium diseased corn kernels recognition and may become a powerful tool for monitoring the safety of our food supply. For practical implementation in the grain industry, a ANFIS classifier can be used effectively.

关键词： Food supply

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Detection of bacterial contamination in milk using NIR spectroscopy and two classification methods - SIMCA and Neuro – Fuzzy classifier

Detection of bacterial contamination in milk using NIR spect...

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作者： Veleva-Doneva, Petya Draganova, Tsvetelina Atanassova, Stefka Tsenkova, Roumiana Department of Informatics and Mathematics Trakia University Stara Zagora 6000 Bulgaria Department of Automatics Information and Control Engineering University of Rousse Rousse 7000 Bulgaria Department of Physics Trakia University Stara Zagora 6000 Bulgaria Biomeasurement Technology Laboratory Faculty of Agriculture Kobe University Kobe 657-8501 Japan

ISBN: (纸本)9783902661906

Potential of near-infrared spectroscopy combined with multivariate classification methods for detection of bacteria in cow milk was investigated. Spectra of milk samples were obtained in a region from 600 to 1880nm. Presence of Staphylococcus aureus, Streptococcus agalactiae and other bacteria from genus Streptococcus, one of bacteria, causing mastitis in cows, was obtained in some of the milk samples by classical microbiological methods. One hundred milk samples negative for bacteria (class healthy) and one hundred milk samples with presence of bacteria (class contaminated) were used in the investigation. Two classification methods - Soft Independent Modeling of Class Analogy (SIMCA) and adaptive Neuro - Fuzzy Inference System (ANFIS) were implemented. SIMCA develops models for each class based on principal components (PC) that describe the variations of the spectral data. Once each class has its own model, new samples could be classified to one or another classes according to their spectra. The inputs to ANFIS were several principal components. ANFIS had only one output node, the class type. One-half of samples from each class were used as a training set for creation of SIMCA models or trained the ANFIS. The rest of the samples were used as test set for verification the obtained classifiers. SIMCA models, based on 7 PC correct classified 90% of samples from class contaminated and 88% of samples from class healthy for training data set. The results for testing the models with samples from test set were as follow: 90% of samples from class contaminated and 86% of samples from class healthy were correct classified. The average testing error for ANFIS classifier was 0.058% for class healthy was 0,032 % for class contaminated. Results of the presented experiments showed possibility to establish classifiers for identification of raw milk samples, infected with bacteria. Near infrared spectroscopy in combination with multivariate classification techniques offers an alter

关键词： Bacteria

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Recognition of Fusarium diseased corn kernels using MIR spectroscopy and Neuro – Fuzzy classifier

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IFAC Proceedings Volumes 2010年第26期43卷 219-224页

作者： Tsvetelina Draganova Plamen Daskalov Roumiana Tsenkova Stefka Atanassova Petya Veleva-Doneva Principal Assistant Professor Department of Automatics Information and Control Engineering University of Rousse Rousse 7017 Bulgaria Associate Professor Department of Automatics Information and Control Engineering University of Rousse Rousse Bulgaria Professor Biomeasurement Technology Laboratory Faculty of Agriculture Kobe University Kobe 657-8501 Japan Associate Professor Department of Physics Trakia University Stara Zagora 6000 Bulgaria PhD student Department of Informatics and Mathematics Trakia University Stara Zagora 6000 Bulgaria

In this paper we present the results obtained recently by recognizing corn kernels (healthy and Fusarium diseased) using spectral characteristics and Neuro-Fuzzy classifier. The proposed approach to corn kernel type diagnostic is based on three steps: spectral data acquisition, preliminarily processing and feature extraction, and classification. In the first step, the near infrared reflectance spectral characteristics are obtained in the range 400 – 2500 nm with step 2 nm. In the second step informative features are extracted using principal component analysis. In order to make a compact spectral data representation, the number of features is reduced. Adaptive Neuro – Fuzzy Inference System (ANFIS) is implemented in order to establish decision boundaries in the space of the selected features which separate samples belonging to two different classes (healthy and Fusarium diseased corn kernels). The inputs to ANFIS are several principal components. ANFIS has only one output node, the corn kernel type. The system is trained by data of train sample. We then evaluate the deviation of our estimate compared to the expert assessment. The procedure is applied to the 1800 corn kernels spectral data and the average accuracy is given. The average testing error for class healthy is 0,023 % and for class diseased is 0,329 %. As a result of the presented experiments the NIR spectroscopy is applicable as a nondestructive tool for Fusarium diseased corn kernels recognition and may become a powerful tool for monitoring the safety of our food supply. For practical implementation in the grain industry, a ANFIS classifier can be used effectively.

关键词： corn kernels Fusarium disease NIR spectroscopy Neuro - Fuzzy PCA

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Detection of bacterial contamination in milk using NIR spectroscopy and two classification methods - SIMCA and Neuro – Fuzzy classifier

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IFAC Proceedings Volumes 2010年第26期43卷 225-229页

作者： Petya Veleva-Doneva Tsvetelina Draganova Stefka Atanassova Roumiana Tsenkova PhD student Department of Informatics and Mathematics Trakia University Stara Zagora 6000 Bulgaria (Tel:+***) Principal Assistant Professor Department of Automatics Information and Control Engineering University of Rousse Rousse 7000 Bulgaria Associate Professor Department of Physics Trakia University Stara Zagora 6000 Bulgaria Professor Biomeasurement Technology Laboratory Faculty of Agriculture Kobe University Kobe 657-8501 Japan

Potential of near-infrared spectroscopy combined with multivariate classification methods for detection of bacteria in cow milk was investigated. Spectra of milk samples were obtained in a region from 600 to 1880nm. Presence of Staphylococcus aureus, Streptococcus agalactiae and other bacteria from genus Streptococcus , one of bacteria, causing mastitis in cows, was obtained in some of the milk samples by classical microbiological methods. One hundred milk samples negative for bacteria (class healthy) and one hundred milk samples with presence of bacteria (class contaminated) were used in the investigation. Two classification methods - Soft Independent Modeling of Class Analogy (SIMCA) and adaptive Neuro – Fuzzy Inference System (ANFIS) were implemented. SIMCA develops models for each class based on principal components (PC) that describe the variations of the spectral data. Once each class has its own model, new samples could be classified to one or another classes according to their spectra. The inputs to ANFIS were several principal components. ANFIS had only one output node, the class type. One-half of samples from each class were used as a training set for creation of SIMCA models or trained the ANFIS. The rest of the samples were used as test set for verification the obtained classifiers. SIMCA models, based on 7 PC correct classified 90% of samples from class contaminated and 88% of samples from class healthy for training data set. The results for testing the models with samples from test set were as follow: 90% of samples from class contaminated and 86% of samples from class healthy were correct classified. The average testing error for ANFIS classifier was 0.058% for class healthy was 0,032 % for class contaminated. Results of the presented experiments showed possibility to establish classifiers for identification of raw milk samples, infected with bacteria. Near infrared spectroscopy in combination with multivariate classification techniques offers an alte

关键词： milk mastitis NIR spectroscopy SIMCA Neuro – Fuzzy

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Common and distinct neural substrates of forward-chaining and backward-chaining syllogistic reasoning

Common and distinct neural substrates of forward-chaining an...

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IEEE/ICME International Conference on Complex medical engineering, CME

作者： Xiuqin Jia Shengfu Lu Ning Zhong Yiyu Yao Kuncheng Li Yanhui Yang International WIC Institute Beijing University of Technology Beijing China International WIC Institute School of Electronics Information and Control Engineering Beijing University of Technology Beijing China Department of Life Science and Informatics Maebashi Institute of Technology Maebashi Japan Department of Computer Science University of Regina Regina Canada Xuanwu Hospital Capital Medical University Beijing China

Syllogistic reasoning is a form of deductive reasoning in which a logical conclusion is drawn from premises. Forward-chaining syllogistic reasoning refers to response pattern of figure 1 syllogism (M-P, S-M, S-P with response chain of S-M-P) and backward-chaining syllogistic reasoning refers to response pattern of figure 4 syllogism (P-M, S-M, S-P with response chain of P-M-S). Previous psychological studies show that forward-chaining is significantly higher accuracy and less response time, and backward-chaining is significantly lower accuracy and more response time. However, little is known about the underlying neural mechanisms. We carried out a study using event-related fMRI to clarify their neural substrates. Conjunction analysis showed common areas of activations were the bilateral caudate, the left ventral lateral prefrontal cortex (VLPFC), the left superior parietal lobule, and the right lingual gyrus. Direct comparisons showed distinct area of activation for forward-chaining in contrast to backward-chaining involved more activation in the left inferior parietal lobule, whereas backward-chaining in contrast to forward-chaining recruited the left dorsal lateral prefrontal cortex (DLPFC), the left medial frontal gyrus, and the right cerebellum. The results suggested that forward-chaining and backward-chaining syllogistic reasoning engaged distinct neural substrates based on common brain areas.

关键词： Delay Educational technology Hospitals Statistics Psychology Recruitment Brain Laboratories control engineering informatics

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Particle Swarm Optimization in Solving Vehicle Routing Problem

Particle Swarm Optimization in Solving Vehicle Routing Probl...

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International Conference on Intelligent Computation technology and Automation, ICICTA

作者： Hai Shen Yunlong Zhu Ting Liu Li Jin College of Physics Science and Technology Shenyang Normal University Shenyang China Key Laboratory of Industrial Informatics Shenyang Institute of Automation Chinese Academy and Sciences Shenyang China Key Laboratory of Industrial Informatics Shenyang Institute of Automation Chinese Academy and Sciences Beijing China Department of Automatic Control School of Information Science and Engineering Northeastern University Shenyang China Chinese Academy of Sciences Beijing Beijing CN

The vehicle routing problem (VRP) is a NP complete problem and is also a hot topic in the operational research. But traditional methods might suffer from slow convergence and the curse of large sizes, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO) is known to effectively solve engineering optimization problems. In this paper, the PSO in solving VRP is comprehensive surveyed from two basic aspects: the improved PSO algorithms and the particle encoding method. For each application, technical details that are required are all discussed. Finally, a summary is given together with suggestions for future research.

关键词： Particle swarm optimization Vehicles Routing Automation Encoding Laboratories Computer industry Industrial control informatics Educational institutions

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Scaling reduction of port-Hamiltonian systems for numerical calculation

Scaling reduction of port-Hamiltonian systems for numerical ...

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SICE Annual Conference

作者： Gou Nishida Daiji Ichishima Kenji Kashima Kenji Fujimoto Masaki Yamakita Bernhard Maschke Ryojun Ikeura Robotic Motion Laboratory RTC Advanced Science Institute RIKEN Japan Research and Development Center Sumitomo Heany Industries Limited Japan Department of Mechanical and Environmental Informatics Graduate School of Information Science and Engineering Tokyo Institute of Technology Japan Department of Mechanical Science and Engineering Graduate School of Engineering University of Nagoya Japan Department of Mechanical and Control Systems Engineering Tokyo Institute of Technology Japan Laboratoire dAutomatique et de Génie des Procédés UMR CNRS 5007 Université Claude Bemard Lyon 1 France Department of Mechanical Engineering Mie University Japan

We present a novel reduction method of port-Hamiltonian systems by a renormalization group. The reduced system is suitable for a fast numerical calculation in a mesoscopic scale. Especially, we aim at the development of renormalized molecular dynamics with controls. First, we define a renormalized Hamiltonian with coarse graining. Next, we introduce a renormalized canonical Hamiltonian system. Moreover, we clarify the relationship between the renormalized Hamiltonian system and a Dirac structure. Finally, we obtain necessary conditions of boundary connections for each renormalized Hamiltonian system with different scales.

关键词： Microscopy Solids control systems Robot motion Research and development Electrical equipment industry informatics information science Systems engineering and theory Mechanical engineering

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Future trends in robotic neurosurgery

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作者： Haidegger, Tamas Kovacs, L. Fordos, G. Benyo, Z. Kazanzides, P. Budapest University of Technology and Economics Department of Control Engineering and Information Technology Laboratory of Medical Informatics Budapest Hungary Johns Hopkins University Center for Computer Integrated Surgical Systems and Technology Baltimore MD United States

ISBN: (纸本)9783540693666

Computer Integrated Surgery has only existed for two decades, however it has already spread out world wide, with well over 100.000 operations performed. In the near future, newly developed robotic systems may conquer even the most challenging fields-such as neurosurgery-to provide better patient care and medical outcome. This paper presents the major systems and different strategies applied in robotic neurosurgery. Besides appropriate design, adequate control strategies are required to ensure maximal safety. This makes automated neurosurgery a technologically challenging area for researchers. It is also important during the design phase to consider market aspects. We anticipate that the future trends of clinical applications are outlined by the current leading research directions. The conclusions of the past years of innovation will lead forward on the path of further improvement. © 2008 Springer-Verlag.

关键词： Neurosurgery

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Accuracy improvement of a neurosurgical robot system

Accuracy improvement of a neurosurgical robot system

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Tamas Haidegger Tian Xia Peter Kazanzides Department of Control Engineering and Information Technology-Laboratory of Medical Informatics Budapest University슠of슠Technology슠and슠Economics Budapest Hungary Center for Computer Integrated Surgical Systems and Technology Johns Hopkins University Baltimore MD USA

Neurosurgery was the first field of application for robots in interventional medicine 20 years ago, and since then dozens of research projects have been focusing on brain and spine surgery, differently addressing the challenges of accuracy and effectiveness. We have developed a cooperatively-controlled system to assist with skull base drilling, to improve the safety and quality of neurosurgery while reducing the operating time. This paper presents the entire system, the preliminary results of phantom and cadaver tests and our efforts to improve the accuracy of the components. The residual error of NeuroMate robot calibration was reduced by 24%, while decreasing the noise of the integrated StealthStation navigation system by 53%. An effective optical tracking based patient motion compensation method has been implemented and tested. The results verify the effectiveness of the system and allow for further research. We hope that by improving the accuracy and safety, we can one day introduce this system into clinical use.

关键词： Neurosurgery Robots medical robotics Safety Optical noise Surgery Skull Drilling Imaging phantoms Cadaver

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