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Recent years have witnessed the ever increasing renewable penetration in power generation systems, which entails modern unit commitment problems with modelling and computation burdens. This study aims to simulate the impacts of manifold uncertainties on system operation with emission concerns. First, probability theory and fuzzy set theory are applied to jointly represent the uncertainties such as wind generation, load fluctuation and unit outage that interleaved in unit commitment problems. Second, a Value-at-Risk-based multi-objective approach is developed as a bridge of existing stochastic and robust unit commitment optimizations, which not only captures the inherent conflict between operation cost and supply reliability, but also provides easy-to-adjust robustness against worst-case scenarios. Third, a multi-objective algorithm that integrates fuzzy simulation and particle swarm optimization is developed to achieve approximate Pareto-optimal solutions. The research effectiveness is exemplified by two case studies: The comparison between test systems with and without generation uncertainty demonstrates that this study is practicable and can suggest operational insights of generation mix systems. The sensitivity analysis on Value-at-Risk proves that our method can achieve adequate tradeoff between performance optimality and robustness, thus help system operators in making informed decisions. Finally, the model and algorithm comparisons also justify the superiority of this research.

Unit commitment, as one of the most important control processes in power systems, has been studied extensively in the past decades. Usually, the goal of unit commitment is to reduce as much production cost as possible while guaranteeing the power supply operated with a high reliability. However, system operators encounter increasing difficulties to achieve an optimal scheduling due to the challenges in coping with uncertainties that exist in both supply and demand sides. This study develops a day-ahead two-stage multi-objective unit commitment model which optimizes both the supply reliability and the total cost with environmental concerns of thermal generation systems. To tackle the manifold uncertainties of unit commitment in a more comprehensive and realistic manner, stochastic and fuzzy set theories are utilized simultaneously, and a unified reliability measurement is then introduced to evaluate the system reliability under the uncertainties of both sudden unit outage and unforeseen load fluctuation. In addition, a cumulative probabilistic method is proposed to address the spinning reserve optimization during the scheduling. To solve this complicated model, a multi-objective particle swarm optimization algorithm is developed. Finally, a series of experiments were performed to demonstrate the effectiveness of this research; we also justify its feasibility on test systems with generation uncertainty.

In this paper, an effective decision process method is proposed to address the challenge in a multiple criteria decision-making (MCDM) problem because of large number of criteria. This method is based on the criteria reduction, tolerance relation, and prospect theory (PT). By building a discernibility matrix for tolerance relation (DMTR) in an MCDM problem with numerical values or interval numbers, this method first allows us to recognize a set of critical criteria from a large criteria pool, and ignore the other criteria. Next, it establishes the criteria weights through the DMTR as they are usually not indicated in the data. Then, the method ranks all the choices and selects the most desirable choice according to the weighted majority advantage value (WMAV). Here two risk-preference assumptions are proposed based on the PT and tolerance degree to select the WMAVs of different interval numbers with the same expectation. Using different risk-preference assumptions, we separately build WMAVs for different types of DMs. Finally, we presented two voting examples to demonstrate the applicability and effectiveness of the proposed method.

In this paper, we propose a new model for decision support to address the ‘large decision table’ (eg, many criteria) challenge in intuitionistic fuzzy sets (IFSs) multi-criteria decision-making (MCDM) problems. This new model involves risk preferences of decision makers (DMs) based on the prospect theory and criteria reduction. First, we build three relationship models based on different types of DMs’ risk preferences. By building different discernibility matrices according to relationship models, we find useful criteria for IFS MCDM problems. Second, we propose a technique to obtain weights through discernibility matrix. Third, we also propose a new method to rank and select the most desirable choice(s) according to weighted combinatorial advantage values of alternatives. Finally, we use a realistic voting example to demonstrate the practicality and effectiveness of the proposed method and construct a new decision support model for IFS MCDM problems.

QoS model of composite services and Web services selection based on QoS are currently the hot issues in the web service composition area. Services selection based on QoS, which is a global optimal selection issue, has been proved a NP-HARD problem. Takes engine into account, this paper builds the QoS model of service selection in the Web composite services, uses the estimation of distribution algorithm to solve the NP-HARD problem of services selection, and presents a Web services selection method based on the UMDA. Example analysis and experimental analysis based on the UMDA method are performed; it's proved that the method is effective in solving the NP-HARD problem of Web services selection.

Knowledge reduction is one of the most important tasks in rough set theory, and most types of reductions in this area are based on complete information systems. Though one of the extended relations, similarity relation, has been presented in incomplete information systems, which do exist in real world, its reduction approach has not been examined. In this paper, based on similarity relation, the upper and lower approximation reduction are defined in incomplete information systems. The judgment theorems with respect to the consistent sets of the upper and lower approximation reduction are studied, their discernibility matrices are obtained and the approaches of the upper and lower approximation reduction based on discernibility matrices are presented. To overcome its drawback of NP-hard time complexity, two heuristic algorithms based on significance of attributes are proposed.

The information systems with incomplete attribute values and fuzzy decisions commonly exist in practical problems. On the base of the notion of variable precision rough set model for incomplete information system and the rough set model for incomplete and fuzzy decision information system, the variable rough set model for incomplete and fuzzy decision information system is constructed, which is the generalization of the variable precision rough set model for incomplete information system and that of rough set model for incomplete and fuzzy decision information system. The knowledge reduction and heuristic algorithm, built on the method and theory of precision reduction, are proposed., {"references":["M. Kryszkiewicz. \"Rough set approach to incomplete information\nsystems,\" Information Sciences, vol. 112, pp. 39-49, 1998.","M. Kryszkiewicz. \"Rules in incomplete information systems,\"\nInformation Sciences, vol. 113, pp. 271-292, 1999.","R. Slowinski and D. Vanderpooten. \"A Generalized Definition of Rough\nApproximation Based on Similarity,\" IEEE TRANSACTIONS ON\nKNOWLEDGE AND DATA ENGIERING, vol. 12. pp. 331-336,\nMarch/April 2000.","G. Y. Wang. Rough Set Theory and Knowledge Acquisition. Xi'an: Xi'an\nJiaoTong University Press, 2001.","G. Y. Wang. \"Extension of Rough Set under Incomplete Information\nsystems,\" Journal of Computer Research and Development, vol.39, no.\n10, pp. 1238-1243, Oct. 2002.","W. Z. Wu, J. S. Mi and W. X. Zhang. \"A New Rough Set Approach to\nKnowledge Discovery in Incomplete Information System,\" Proceedings\nof the Second International Conference on Machine Learning and\nCybernetics, Xi-an, pp. 1713-1718, 2-5 November 2003.","B. Huang, D. K. Wei and X. Z. Zhou. \"An algorithm for Maximum\nDistribution Reductions and Maximum Distribution Rules in Information\nsystem,\" Computer science, vol. 31, no. 10A, pp. 80-83, 2004.","B. Huang and X. Z. Zhou. \"Extension of Rough Set Model Based on\nConnection Degree under Incomplete Information System,\" Systems\nEngineering --Theory and Practice, no. 1, pp. 88-92, 2004.","D. K. Wei, X. Z. Zhou and Y. G. Zhu. \"Knowledge Reduction in\nIncomplete Information System Based on Improved-Tolerance Relation,\"\nComputer Science, vol. 32, no. 8.A, pp. 53-56, 2005.\n[10] D. K. We and X. Z. Zhou. \"Rough Set Model in Incomplete and Fuzzy\nDecision Information System Based on Improved-Tolerance Relation,\"\n2005 IEEE INTERNATIONAL CONFERENCE ON GRANULAR\nCOMPUTERING, Tsinghua University, China, pp. 278~283, July 2005.\n[11] D. K. Wei, B. Huang and X. Z Zhou. \"Rough Set Model and Knowledge\nReduction in Incomplete and Fuzzy Objective Information system,\"\nComputer Engineering, no.7, 2006. (to be published)\n[12] D. K. Wei, and X. Z Zhou. \"A Rough Set Approach to Incomplete and\nfuzzy decision information system,\" 2006 IEEE the 6th World Congress\non Intelligent Control Automation, Dalian, China, July 21-23, 2005. (to\nbe published)\n[13] H.Y. Zhang, and J.Y. Liang. \"Variable Precision Rough Set Model and a\nKnowledge Reduction Algorithm for Incomplete Information System,\"\nComputer Science, vol.30, no. 4, pp: 153-155, 2003."]}

Knowledge reduction is one of important issues in rough sets theory. Based on rough set models and knowledge reduction definitions, researching on the corresponding reduction methods is primary approach in knowledge reductions. In symbolic information systems, knowledge reduction definitions and algorithms are in depth examined, in which researches are concentrated on discernibility matrix and functions, heuristic algorithms, incremental algorithms, etc. Information systems are named as fuzzy information systems in which all values are fuzzy. In fuzzy information systems, some basic rough set models are presented, which are called fuzzy-rough set methods. In this paper, definitions of knowledge reductions in fuzzy information systems are improved, that is, some new knowledge reductions are proposed.

An algorithm on location and extraction of broadcast in news video is proposed in this paper. Firstly, input audio stream is divided into speech and non-speech segments by VQ (vector quantification) after a set of new features representing audio segments' time-variant characteristics are extracted, including HZCRR (high zero-crossing rate ratio), LSTER (low short-time energy ratio) and HBFERR (high basic-frequency-energy rate ratio), etc. Then a QGMM (quasi Gaussian mixture model) is presented to describe the speaker's identity and BIC (Bayesian information criterion) is used to detect speaker change. Finally speaker clustering is carried out with BIC, and location and extraction of broadcast is realized based on rules. Satisfactory results from experiments prove the effectiveness of this algorithm.

The topical rough set theory is a forceful tool to handle the complete information system, and its effect to process incomplete information system is poor, particularly, its function to combine the incomplete information system with fuzzy decision information system is poorer. On the base of improving on the tolerance relation made by M. Kryszkiewez, this paper proposes the improved-tolerance relation and improved-tolerance classes; we simultaneously present the definition of the incomplete and fuzzy decision information system based on the improved-tolerance relation, and give its rough set model, i.e. the rough set model in incomplete and fuzzy decision information system. Finally, the conception of precision reduction is defined, and its algorithm is also supplied.

Knowledge reduction is one of the most important tasks in rough set theory, and most types of reductions in this area are based on complete information systems. However, many information systems are not complete in real world. Though several extended relations have been presented under incomplete information systems, not all reduction approaches to these extended models have been examined. Based on similarity relation, the similarity matrix and the upper/lower approximation reduction are defined under incomplete information systems. To present similarity relation with similarity matrix, the rough computational methods based on similarity relation are studied. The heuristic algorithms for non-decision and decision incomplete information systems based on similarity matrix are proposed, and the time complexity of algorithms is analyzed. Finally, an example is given to illustrate the validity of these algorithms presented.

To handle the multi-label multiclass text categorization, a weak learning algorithm (WLA) is presented. The main idea of WLA is to find a highly accurate classification rule by combining many weak hypotheses, each of which may be only moderately accurate. We used a separate procedure, called the weak learner, to compute the weak hypotheses, and found a set of weak hypotheses by calling the weak learner repeatedly in a series of rounds. These weak hypotheses were then combined into a single rule called the final hypothesis, and the final hypothesis ranked the possible labels for a given document with the hope that the appropriate labels would appear at the top of the ranking. Using the three designed evaluation measures - ordinary-error, average-coverage and average-precision - our experiments show that the performance of WLA is generally better than the other algorithms on the same dataset.