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Han-Chieh Chao
Department of Electrical Engineering, National Dong Hwa University, 63373 Hualien, Taiwan

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Short Biography

Han-Chieh Chao received his M.S. and Ph.D. degrees in Electrical Engineering from Purdue University, West Lafayette, Indiana, in 1989 and 1993, respectively. He is currently a professor with the Department of Electrical Engineering, National Dong Hwa University, where he also serves as president. He is also with the Department of Computer Science and Information Engineering, National Ilan University, Taiwan. He was the Director of the Computer Center for Ministry of Education Taiwan from September 2008 to July 2010. His research interests include IPv6, Cross-Layer Design, Cloud Computing, IoT, and 5G Mobile Networks. He has authored or co-authored 4 books and has published about 400 refereed professional research papers. He has completed more than 150 MSEE thesis students and 11 Ph.D. students. He serves as the Editor-in-Chief for the Institution of Engineering and Technology Networks, the Journal of Internet Technology, the International Journal of Internet Protocol Technology, and the International Journal of Ad Hoc and Ubiquitous Computing. He is a Fellow of IET (IEE) and a Chartered Fellow of the British Computer Society. Dr. Chao has been ranked as the top 10 Computer Scientists in Taiwan for 2020 by Guide2Research. Due to Dr. Chao’s contribution of suburban ICT education, he has been awarded the US President's Lifetime Achievement Award and International Albert Schweitzer Foundation Human Contribution Award in 2016.

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Journal article
Published: 24 June 2021 in Symmetry
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To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

ACS Style

Lingxia Liao; Victor Leung; Zhi Li; Han-Chieh Chao. Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks. Symmetry 2021, 13, 1133 .

AMA Style

Lingxia Liao, Victor Leung, Zhi Li, Han-Chieh Chao. Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks. Symmetry. 2021; 13 (7):1133.

Chicago/Turabian Style

Lingxia Liao; Victor Leung; Zhi Li; Han-Chieh Chao. 2021. "Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks." Symmetry 13, no. 7: 1133.

Journal article
Published: 15 June 2021 in IEEE Transactions on Fuzzy Systems
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Fuzzy systems have well modelling capabilities in many data science scenarios, and can obtain human-explainable intelligence models that having explainability and interpretability. Different from the transaction data which has been extensive studied before, sequence data is more commonly seen in real-life applications. To obtain human-explainable data intelligence model for decision making, in this paper, we study the problem of explainable fuzzy-theoretic utility mining on multi-sequences. By exploring the fuzzy set theory to utility mining, we propose a novel method namely Pattern Growth Fuzzy Utility Mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. When dealing with sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, several data structures and pruning strategies are designed in detail. Finally, the proposed PGFUM algorithm is compared with a representative method PFUS, which is the first as well as the only method for the same task, through extensive experimental evaluation. It is shown that PGFUM not only towards human-explainable mining results that contain the original nature of revealable intelligibility, but also achieves a high efficiency in terms of runtime and memory cost.

ACS Style

Wensheng Gan; Zilin Du; Weiping Ding; Chunkai Zhang; Han-Chieh Chao. Explainable Fuzzy Utility Mining on Sequences. IEEE Transactions on Fuzzy Systems 2021, PP, 1 -1.

AMA Style

Wensheng Gan, Zilin Du, Weiping Ding, Chunkai Zhang, Han-Chieh Chao. Explainable Fuzzy Utility Mining on Sequences. IEEE Transactions on Fuzzy Systems. 2021; PP (99):1-1.

Chicago/Turabian Style

Wensheng Gan; Zilin Du; Weiping Ding; Chunkai Zhang; Han-Chieh Chao. 2021. "Explainable Fuzzy Utility Mining on Sequences." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.

Journal article
Published: 04 November 2019 in Information Sciences
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Utility is an important concept in Economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability.

ACS Style

Wensheng Gan; Jerry Chun-Wei Lin; Jiexiong Zhang; Han-Chieh Chao; Hamido Fujita; Philip S. Yu. ProUM: Projection-based utility mining on sequence data. Information Sciences 2019, 513, 222 -240.

AMA Style

Wensheng Gan, Jerry Chun-Wei Lin, Jiexiong Zhang, Han-Chieh Chao, Hamido Fujita, Philip S. Yu. ProUM: Projection-based utility mining on sequence data. Information Sciences. 2019; 513 ():222-240.

Chicago/Turabian Style

Wensheng Gan; Jerry Chun-Wei Lin; Jiexiong Zhang; Han-Chieh Chao; Hamido Fujita; Philip S. Yu. 2019. "ProUM: Projection-based utility mining on sequence data." Information Sciences 513, no. : 222-240.

Journal article
Published: 15 July 2019 in Information Sciences
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Recently, a new research field called utility-oriented mining has attracted great attention. However, previous studies have shown a limitation in that they rarely consider the inherent correlation of items among patterns. For example, considering the purchase behaviors of consumers, a high-utility group of products (w.r.t. multi-products) may contain several very high-utility products with some low-utility products. However, it is considered to be a valuable pattern even if this behavior/pattern may not be highly correlated, or even if it happens by chance. In light of these challenges, we propose an efficient utility-mining approach, called non-redundant Correlated high-Utility Pattern Miner (CoUPM) by considering the positive correlation and profitable value. The derived patterns with high utility and strong positive correlation can lead to more insightful availability than those patterns that only have high profitable values. The utility-list structure is revised and applied to store the necessary information of both correlation and utility. Several pruning strategies are further developed to improve the efficiency for discovering the desired patterns. Experimental results showed that the non-redundant correlated high-utility patterns have more effectiveness than some other kinds of interesting patterns. Moreover, the efficiency of the proposed CoUPM algorithm significantly outperformed the state-of-the-art algorithm.

ACS Style

Wensheng Gan; Jerry Chun-Wei Lin; Han-Chieh Chao; Hamido Fujita; Philip S. Yu. Correlated utility-based pattern mining. Information Sciences 2019, 504, 470 -486.

AMA Style

Wensheng Gan, Jerry Chun-Wei Lin, Han-Chieh Chao, Hamido Fujita, Philip S. Yu. Correlated utility-based pattern mining. Information Sciences. 2019; 504 ():470-486.

Chicago/Turabian Style

Wensheng Gan; Jerry Chun-Wei Lin; Han-Chieh Chao; Hamido Fujita; Philip S. Yu. 2019. "Correlated utility-based pattern mining." Information Sciences 504, no. : 470-486.