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Nobuhara, Hajime,Takama, Yasufumi,Hirota, Kaoru Korean Institute of Intelligent Systems 2002 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.2 No.1
A fast iterative solving method of fuzzy relational equation is proposed. It is derived by eliminating a redundant comparison process in the conventional iterative solving method (Pedrycz, 1983). The proposed method is applied to image reconstruction, and confirmed that the computation time is decreased to 1 / 40 with the compression rate of 0.0625. Furthermore, in order to make any initial solution converge on a reconstructed image with a good quality, a new cost function is proposed. Under the condition that the compression rate is 0.0625, it is confirmed that the root mean square error of the proposed method decreases to 27.34% and 86.27% compared with those of the conventional iterative method and a non iterative image reconstruction method, respectively.
Zolkepli, Maslina,Dong, Fangyan,Hirota, Kaoru Korean Institute of Intelligent Systems 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.4
An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.
Maslina Zolkepli,Fangyan Dong,Kaoru Hirota 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.4
An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O(n³) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases. The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.
Deep Level Situation Understanding for Casual Communication in Humans-Robots Interaction
Yongkang Tang,Fangyan Dong,Yoichi Yamazaki,Takanori Shibata,Kaoru Hirota 한국지능시스템학회 2015 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.15 No.1
A concept of Deep Level Situation Understanding is proposed to realize human-like natural communication (called casual communication) among multi-agent (e.g., humans and robots/machines), where the deep level situation understanding consists of surface level understanding (such as gesture/posture understanding, facial expression understanding, speech/voice understanding), emotion understanding, intention understanding, and atmosphere understanding by applying customized knowledge of each agent and by taking considerations of thoughtfulness. The proposal aims to reduce burden of humans in humans-robots interaction, so as to realize harmonious communication by excluding unnecessary troubles or misunderstandings among agents, and finally helps to create a peaceful, happy, and prosperous humans-robots society. A simulated experiment is carried out to validate the deep level situation understanding system on a scenario where meeting-room reservation is done between a human employee and a secretary-robot. The proposed deep level situation understanding system aims to be applied in service robot systems for smoothing the communication and avoiding misunderstanding among agents.
Liang Wen,Yan Fei,Iliyasu Abdullah M.,Salama Ahmed S.,Hirota Kaoru 한국컴퓨터산업협회 2023 Human-centric Computing and Information Sciences Vol.13 No.-
During a quantum walk on a complex network, the observed results contain extensive redundant information generated by interference effects, which makes it difficult to determine a suitable walk step and find the structural characteristics of the network. A Grover coin driven quantum walk model (GWM) is proposed to identify significant nodes in undirected complex networks by simulating the particle moving on the network. To circumvent the negative effects of the associated redundant information, the proposed GWM adds a self-loop to each node and determines a three-step walk by exploiting the three degrees of influence rule. Experiments on correlation, Kendall coefficient, and robustness were reported to validate the effectiveness of the proposed GWM in identifying significant nodes. Outcomes show strong correlation between results from the susceptible-infected-recovered (SIR) model and our GWM, which signify accurate identification of the significant nodes of complex networks by our model. Furthermore, outcomes in terms of Kendall coefficient between different algorithms (comprising of conventional and quantum algorithms) alongside the proposed GWM further attest that the GWM can capture the structural characteristics of networks, e.g., triadic closure and degree. Additionally, based on robustness index, the practicality of the proposed GWM in terms of identifying significant nodes was demonstrated.
Fuzzy Feature Representation for White Blood Cell Differential Counting in Acute Leukemia Diagnosis
Chastine Fatichah,Martin L. Tangel,Janet P. Betancourt,M. Rahmat Widyanto,Fangyan Dong,Kaoru Hirota 제어·로봇·시스템학회 2015 International Journal of Control, Automation, and Vol.13 No.3
A fuzzy feature representation for white blood cell differential counting is proposed to diagnose types of acute leukemia. The accuracy of diagnosis is higher than that by numerical features by dealing with uncertainty of white blood cell features and inflexibility of diagnosing. Experiments on acute leukemia diagnosis use 120 acute leukemia images and fuzzy decision tree method with the accuracy rate of diagnosis is 84% using fuzzy features and 76.6% using numerical features. Given the importance of accurate diagnosis of acute leukemia in patients, this proposal is essential and planned to be introduced in an Indonesian hospital.