Computer Industry Laboratory

Shigeru Kanemoto Professor	Incheon Paik Assistant Professor	Kenta Ofuji Assistant Professor	Igor Lubashevskiy Visiting Researcher

Research by faculties and students in the Computer Industry Laboratory has been carried out in the fields of both basic computer science and industrial applications, such as Artificial Intelligence, Semantic Web Service, Web Data Mining, e-Business, Signal processing, Simulation engineering, and Functional Safety, and environmental impact analysis of energy industry. Several articles were published as book chapters, journal-contributions, proceedingscontributions in conferences, and technical reports. The research activities of the Computer Industry Laboratory include the following topics: Signal processing for plant monitoring and control Dynamical system identification and analysis Inverse problem solving for Non-destructive evaluation Multi-Agent Simulation Traffic simulation for emergency response management Functional safety of embedded system [Incheon PAIK] Web Service Semantic Web Workflow and Business Process Management Web Data Mining Semantic e-Business Application [Kenta OFUJI] Environmental impact of energy industry Environmental economics and computer simulation Assessment of new technologies in electric power generation and consumption

Refereed Journal Papers

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[kanemoto-01:2009]	S.Kanemoto. Acoustic monitoring of rotating machine by advanced signal processing technology. Nuclear Safety and Simulation, 1(1):22 E9, 2010.
	The acoustic data remotely measured by handy type microphones are investigated for monitoring and diagnosing the rotational machine integrity in nuclear power plants. The plant operator ’s patrol monitoring is one of important activities for condition monitoring. However, remotely measured sound has some difficulties for precise diagnosis or quantitative judgment of rotating machine anomaly, since the measurement sensitivity is different in each measurement, and also, the sensitivity deteriorates comparing with an attached type sensor. Hence, in the present study, several advanced signal processing methods are examined and compared in order to find optimum anomaly monitoring technology from the viewpoints of both sensitivity and robustness of performance. The dimension of pre-processed signal feature patterns are reduced into two-dimensional space for the visualization by using the standard principal component analysis (PCA) or the kernel based PCA. Then, the normal state is classified by using probabilistic neural network (PNN) or support vector data description (SVDD). By using the mockup test facility of rotating machine, it is shown that the appropriate combination of the above algorithms gives sensitive and robust anomaly monitoring performance.
[paikic-01:2009]	Incheon Paik, and Haruhiko Takada. Design of General User Interface for Automatic Service Composition. International Journal of Communication and Computer, 6(9):5 E, 2009.
	Automatic Web Service Composition provides new value-added services dynamically to compose existing services. Paik proposed logical and physical web service composition and ontologies for this. A useful composer needs well-defined Web interface for general users. A general user interface is designed and implemented in this paper. The user interface generates some HTML form based on the ontology and additional HTN ontology.

Refereed Proceeding Papers

[kanemoto-02:2009]	S. Kanemoto. Sensitivity Enhancement of Acoustic Monitoring Using Kernel PCA and Probabilistic Neural Network. In R. Wood, editor, Sixth American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies, NPIC&HMIT 2009, Knoxville, Tennessee, USA, April 2009.
	The acoustic data remotely measured by handy type microphones are investigated for monitoring and diagnosing the ball bearing type rotational machine integrity in nuclear power plants. The present study proposes the new signal pre-processing method which normalizes the fundamental oscillation period into the same length and timing by using zero-crossing interval of filtered acoustic signal. The pre-processed signal patterns are classified by kernel-based principal component analysis (KPCA) and probabilistic neural network (PNN). It is shown that the monitoring index defined by KPCA and PNN is useful for sensitive and robust classification of the normal and abnormal machine states operated in different rotating conditions.
[kanemoto-03:2009]	S. Kanemoto. Acoustic Monitoring Using Kernel PCA and Probabilistic Neural Network. In K. Miya, editor, 7th International Conference on NDE, Yokohama, Japan, May 2009.
	The acoustic data remotely measured by handy type microphones are investigated for monitoring and diagnosing the rotational machine integrity in nuclear power plants. The plant operator ’s patrol monitoring is one of important activities for condition monitoring. However, remotely measured sound has some difficulties for precise diagnosis or quantitative judgment of rotating machine anomaly, since the measurement sensitivity is different in each measurement, and also, the sensitivity deteriorates comparing with an attached type sensor. Hence, in the present study, several signal feature extraction methods are compared in order to compensate the sensitivity variation and deterioration and to find both sensitive and robust monitoring algorithms. These preprocessed signal feature patterns are classified by kernel based principal component analysis (KPCA) and probabilistic neural network (PNN). KPCA is useful to increase the sensitivity and robustness for classification of normal and abnormal states. Also, PNN is useful to classify the known states and unknown states. The developed algorithms are applied to the acoustic data measured by mockup test facility of rolling bearing type rotating machine which can simulate normal and abnormal states. Here, several kinds of abnormal state data were measured, which consist of inner and outer race defects (Large, Middle and Small defects), and, bearing ball defect as well as normal operation data. It is shown that the developed algorithms can effectively classify the above several kinds of normal and abnormal data.
[paikic-02:2009]	Haruhiko Takada and Incheon Paik. Transformation of Non-Functional Properties for Automatic Service Composition. In NFPSLAM Orgainizing Committee, editor, Proceedings of The 3rd Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing NFPSLAMSOC E09, Stockholem, Sweden, November 2009. NFPSLAM Orgainizing Committee, Springer.
	Service-oriented computing provides an evolving paradigm for flexible and scalable applications of open systems. Web services and their automatic composition are in the mainstream of the evolution of new value-added services. Functional and nonfunctional aspects are considered together for automatic service composition (ASC). After locating suitable functionality for the required composition, non-functionalities are considered to select the final set of services. Non-functional properties (NFPs) obtained from users or identified during planning or discovery usually have abstract concepts that cannot be identified at the selection stage. In this paper, we propose a transformation technique for automatic composition that identifies binding information (service I/O) in the selection stage from intermediate-abstraction NFPs. The classification of abstraction level in NFPs, a model to define abstract and concrete NFPs, and an algorithm for transformation from intermediate to concrete level are presented. The identification of the binding information is based on domain ontologies for services. Evaluation in our algorithm according to characteristics of NFPs is shown. Our work will contribute to modeling and identification of NFPs for ASC.
[paikic-03:2009]	Incheon Paik, and Haruhiko Takada. Modeling and Transforming Abstract Constraints for Automatic Service Composition. In CIT 2009 Commitee, editor, Proceedings of the IEEE International Conference on Computer Information Technology (CIT E009), pages 136 E41, Xiamen, China, October 2009. CIT 2009 Committee, IEEE Publishing Service.
	Automatic service composition typically consists of logical composition to produce an abstract workflow, followed by physical composition to transform it into a concrete workflow satisfying composition properties, such as Quality of service (QoS), preferences, and logic constraints. The composition properties have not been considered together up to now and, because they are abstract during the logical composition phase, they cannot be understood by the physical composer that reads concrete composition properties. This is an impediment to automatic service composition. Therefore, abstract properties in the logical composer have to be transformed to concrete properties for automated physical composition. This research investigates a stack of composition properties for QoS, preferences, and logic constraints considered together and an architecture for automatic service composition, along with semi-automatic transformation of intermediate constraints in the architecture. The architecture together with an ontology for transforming constraints, and evaluation of our prototype are described.

Unrefereed Papers

[o-fu-01:2009]	K. Ofuji, N. Yamaguchi, and M. Goto. Next-day hydropower output forecast with Kalman filter tank model. In IAEE European Conference 2009, pages 3–VI, Wien, September 2009. International Society for Energy Economics, International Society for Energy Economics.
[o-fu-02:2009]	K. Ofuji. Tradable commodity line-up and profit-earning structure in European power exchanges. In Tohoku Branch conference of IEEJ 2009, page 224, Sendai, August 2009. Tohoku Branch of the Institute of Electrical Engineers of Japan, Tohoku Branch of the Institute of Electrical Engineers of Japan.
[o-fu-03:2009]	K. Ofuji. Fundamental Analysis on Dividend Policies in Japan and US Electric Utilities. In Japan Society of Public Utility Economics, Tokyo, June 2009. Japan Society of Public Utility Economics, Japan Society of Public Utility Economics.
[o-fu-04:2009]	K. Ofuji. On fuel adjustment clauses and cash flow of electric power companies. In 26th Energy and Environment Conference of Japan Society of Energy and Resources, pages 26 E, Osaka, January 2010. Japan Society of Energy and Resources, Japan Society of Energy and Resources.
[o-fu-05:2009]	T. Hattori, K. Ofuji, M. Endo, and K. Furusawa. A questionnaire survey on vitalization of Japan Electric Power Exchange(JEPX). In IABPAD Conferece 2009, page 169, Memphis, October 2009. The international academy of business and public administration disciplines, The international academy of business and public administration disciplines.
[o-fu-06:2009]	T. Hattori, K. Ofuji, M. Endo, and K. Furusawa. A questionaire survey on vitalization of Japan electric power exchange. In 28th Energy and Environment Workshop of Japan Society of Energy and Resources, pages 11 E, Osaka, June 2009. Japan Society of Energy and Resources, Japan Society of Energy and Resources.
[o-fu-07:2009]	K. Ofuji, M. Tsutsui, H. Goto, and K. Furusawa. A survey on current asset management practices of transmission equipment in Japanese power companies. In Power and Energy Conferece 2009, page 331, Tokyo, August 2009. Institute of Electrical Engineers of Japan, Institute of Electrical Engineers of Japan.

Chapters in Book

[paikic-04:2009]

Incheon Paik. User Interface for Automatic Service Composition, pages 1  E20. User Interface. IN-TECH Publishing Co., Vienna, Austria, 2009.

Academic Activities

[o-fu-08:2009]	Incheon Paik, Sep. 2008. Technical Program Committee Member
[paikic-07:2008]	K. Ofuji, April 2009. Member, Asset Management Survey Committee, Institute of Electrical Engineers of Japan
[paikic-05:2009]	Incheon Paik, Oct. 2009. Steering Committee Member, IEEE International Conference on Computer and Information Technology (CIT09)
[paikic-06:2009]	Incheon Paik, Apr. 2009. Reviewer, IEEE Transactions on Service Computing
[paikic-07:2009]	Incheon Paik, May 2009. Editorial Borad, Journal of Information Processing Systems of Korea Information Processing Society
[paikic-08:2009]	Incheon Paik, Apr. 2009. Technical Program Committee Member, International Workshop on Benchmarking of XML and Semantic Web Applications (Xbench 2009)

Ph.D, Master and Graduation Theses

[kanemoto-04:2009]	Yuho Sasaki. Graduation Thesis: Tracking system using particle filter in plant control room, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-05:2009]	Maya Inasaka. Graduation Thesis: Development of FAQ-based functional safety learning system, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-06:2009]	Shota Komori. Master Thesis: Research on functional safety of embedded system - HAZOP and Fault injection test for safety design -, University of Aizu, GS, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-07:2009]	Tatsuya Takahashi. Graduation Thesis: Structure defect detection by feature extraction from acoustic aignals, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-08:2009]	Masato Ootake. Graduation Thesis: Acoustic feature extraction for loose parts monitoring, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-09:2009]	Kohei Miura. Graduation Thesis: Defect size estimation by RFECT and inverse problem solving, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-10:2009]	Naoki Asami. Graduation Thesis: Image feature extraction for human tracking in a plant control room, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[kanemoto-11:2009]	Sho Inomata. Graduation Thesis: Hazard prediction of embedded systems based on HAZOP and UML diagram, University of Aizu, 2009. Thesis Advisor: S. Kanemoto, K. Ofuji
[o-fu-09:2009]	Yuuki Yanagisawa. Graduation Thesis: Evaluation of cost and CO2 abatement capability of photovoltaic power using Generation Unit Commitment Planning, University of Aizu, 2009. Thesis Advisor: K. Ofuji
[paikic-09:2009]	Haruhiko Takada. Master Thesis: Translation of Abstract NonFunctional Properties for Automatic Service Composition, Graduate School, University of Aizu, 2009. Thesis Advisor: I. Paikk
[paikic-10:2009]	Ryohei Komiya. Graduation Thesis: Classification of Malicious Code by Machine Learning, University of Aizu, 2009. Thesis Advisor: I. Paikk
[paikic-11:2009]	Seiji Ota. Graduation Thesis: Ontological Identification of Patterns for Choreographing Business Workflow, University of Aizu, 2009. Thesis Advisor: I. Paikk
[paikic-12:2009]	Hiromu Sato. Graduation Thesis: User Interface for Automatic Service Composition, University of Aizu, 2009. Thesis Advisor: I. Paikk
[paikic-13:2009]	Eigo Fujikawa. Graduation Thesis: AggregatingWeb Service Matchmaking Variants Using Web Search Engine and Machine Learning, University of Aizu, 2009. Thesis Advisor: I. Paikk
[paikic-14:2009]	Hiroshi Mizugai. Master Thesis: Scalable Orchestration Strategy for Automatic Service Composition, Graduate School, University of Aizu, 2009. Thesis Advisor: I. Paikk

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