黄 潤和 
ファン ルンヘ 
HUANG Runhe 


Assistant Lecturer of National Defense University of Technology, China  1982/07/16-1988/12/31 
Ph.D Research Fellow of University of the West of England, U.K.  1989/01/01-1993/02/14 
Assistant Professor of University of Aizu, Japan  1993/04/05-2000/03/31 
Assistant Professor of University of Hosei, Japan  2000/04/01-2003/03/01 
Professor of University of Hosei, Japan  2003/04/01-現在 

Changsha Institute of Technology, China National Defense University of Technology, China  Department of Electronic Engineering  1982/07  卒業 

Ph.D(Computer Science)  西イングランド大学コンピュータ科学及び数学専攻(博) 



Active Media Technology  Runhe Huang, Ali A. Ghorbani, Gabriella Pasi, Takahira Yamaguchi, Neil Y. Yen, Beijing Jin  1-671  Springer  2012/12/04  978-3-642-35235-5  This volume contains the papers selected for presentation at The 2012 Inter- national Conference on Active Media Technology (AMT 2012), held as part of the 2012 World Intelligence Congress, a special event of Turing Centennial Cel- ebration jointly with other international conferences (BI12, WI12, IAT12, and ISMIS12) at Fisherman’s Wharf, Macau, China, on December 4th-7th, 2012. In this great digital era, we are witnessing many rapid scientific and techno- logical developments in human-centered and seamless computing environments, interfaces, devices, and systems with applications ranging from business and communication to entertainment and learning. These developments are collec- tively best characterized as Active Media Technology, a new area of intelligent information technology and computer science that emphasizes the proactive and seamless roles of interfaces and systems as well as new media in all aspects of digital life. An AMT based system offers services to enable the rapid design, implementation and support of customized solutions. 

研究論文(学術雑誌)  共著  Associative memory and recall model with KID model for human activity recognition  Runhe Huang, Peter Kimani Mungai, Jianhua Ma, Kevin I.-K. Wang  Future Generation Computer Systems  ELSEVIER  1-12  2018/09/25  10.1016/j.future.2018.09.007  By mimicking the human brain, this study proposes an associative memory and recall (AMR) model that stores associative knowledge from sensor data. Using chunking mechanisms, AMR can organize human activity knowledge in the manner that is efficient and effective to store and recall. The knowledge–information–data (KID) model is used for learning associative knowledge while the AMR continuously looks for associations among knowledge units and merges related units using merging mechanisms. The chunking mechanisms used in this study are inspired by the chunking mechanisms of the brain i.e. goal oriented chunking and automatic chunking. 
研究論文(学術雑誌)  共著  Academic Influence Aware and Multidimensional Network Analysis for Research Collaboration Navigation Based on Scholarly Big Data  Xiaokang Zhou, Wei Liang, Kevin I-Kai Wang, Runhe Huang, and Qun Jin  IEEE Transactions on Emerging Topics in Computing  1-12  2018/07/26  10.1109/TETC.2018.2860051  In this study, we focus on the academic influence aware and multidimensional network analysis based on the integration of multi-source scholarly big data. Following three basic relations: Researcher-Researcher, Researcher-Article, and Article-Article, a set of measures is introduced and defined to quantify correlations in terms of activity-based collaboration relationship, specialty-aware connection, and topic-aware citation fitness among a series of academic entities (e.g., researchers and articles) within a constructed multidimensional network model. An improved Random Walk with Restart (RWR) based algorithm is developed, in which the time-varying academic influence is newly defined and measured in a certain social context, to provide researchers with research collaboration navigation for their future works. 
研究論文(国際会議プロシーディングス)  共著  Recognition of Daily Activity Patterns with Associative Memory and Recall Model  Peter K. Mungai and Runhe Huang  IEEE ICCI*CC’18 at University of California, Berkeley, USA  471-477  2018/07/16  10.1109/ICCI-CC.2018.8482082  In this study, we propose a cognitive controller which encompasses knowledge acquisition, learning and recalling cognitive functions just as in the human brain by employing an associative memory and recall model (AMR) in conjunction with the KID model. Having a cognitive controller, a smart home system can work efficiently and effectively in recognition of human daily living activity. In this study, the Activities of Daily Living (ADL) dataset is used, the KID and the AMR based smart system is implemented for learning and identifying some cases of human daily living activities. 
研究論文(学術雑誌)  共著  Perception-enhancement based task learning and action scheduling for robotic limb in CPS environment  Shijian Li, Minhao Shi, Runhe Huang, Xinwei Chen, Gang Pan  Future Generation Computer Systems  Elseevier  1-41  2018/04/21  10.1016/j.future.2018.04.001  In this paper, a perception-enhanced smart robotic limb is presented to realize deeper incorporation of information in physical layers by deploying a three-channel perception and a semantic reasoning model to allow comprehensive perception of related objects, human actions and commands, for better adaption and robustness to environment changes. Based on the perception system, the robotic limb is capable of self-learning skill of new tasks from human demonstrations, The learning times and robustness to environment change were evaluated by a 50-task experiment. 
研究論文(学術雑誌)  共著  KID Model-Driven Things-Edge-Cloud Computing Paradigm for Traffic Data as a Service  Bowen Du, Runhe Huang, Zhipu Xie, Jianhua Ma, Weifeng Lv  IEEE Network  32/ 1, 34-41  2018/01/26  10.1109/MNET.2018.1700169  This article proposes a novel three-level transparency-of-traffic-data service framework, that is, a KID-driven TEC computing paradigm. Its aim is to enable edge servers to cooperatively work with a cloud server. A case study is presented to demonstrate the feasibility of the proposed new computing paradigm with associated mechanisms. The performance of the proposed system is also compared to other methods. 

口頭発表(一般)  Who Knows What You Want? - Smart environments based on the convergence of IT and AI  the 2012 FTRA International Conference on Advanced IT, engineering Management (AIM Summer 2012)  2012/07/11  When you are sitting in front of a computer and working for 3 or 4 hours, someone makes a cup of hot milk tea for you, you must be very happy. Why? Someone knows you well, knows what you want right now. This is so called “Smart”. There are many such cases happening in our daily life. It would make one happy if there would be a smart environment provides what he wants in the right place, at the right time. The convergence of state of the art information technology and dynamic intelligence computing makes it possible and makes this happen. This talk will present three key stages in the data cycle system: acquiring and collecting data with ubiquitous active IT technology;human centric data structuring and mining for knowledge discovery; and dynamic intelligence for the fusion of discovered knowledge and existing knowledge. Finally, this talk will show three projects on human centric awareness based active services in a smart eco-home system, in classroom effective teaching and learning system, and in a smart profitable cyber system. 

Received Sino-British Frendship Scholarship from Chinese Government and British Government  1988/10