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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Peiyuan Zhou Andrew K.C. Wong George Michalopoulos Robert R. Quinn Matthew J. Oliver Yang Yang Zahid Butt Helen H. Chen System Design Engineering University of Waterloo Waterloo Canada Computer Science University of Waterloo Waterloo Canada Medicine & Community Health Sciences University of Calgary Calgary Canada Sunnybrook Health Sciences Centre Toronto Canada School of Public Health and Health Systems University of Waterloo Waterloo Canada

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Peritoneal dialysis (PD) removes waste products from blood when the kidney is malfunctioned. Since there is no clear criterion for PD recommendation for patients with kidney disease, existing machine learning models (ML), which rely on credible decision criterion, are ineffective in making PD eligibility decisions, especially when the correlated traits or indicators (patterns) inherent in the PD data are diverse and subtle. Furthermore, the lack of interpretable transparency in traditional ML also weakens the credibility of the decision they produce. Hence, an in-depth knowledge of the patients' characteristics is needed to render a clearer picture of the decision-making process and model to detect the rare PD eligibility cases. In this paper, we extend our previous work (Attribute-Value-Association Discovery and Disentanglement (ADD)), to an extended ADD for PD data analysis (PD-ADD) to overcome these problems. We show that PD-ADD is able to discover association patterns of patient profiles and symptoms to reveal PD characteristics and detect eligible rare cases. Experimental results show that PDADD is much superior to existing unsupervised clustering (with accuracy of 89.87% vs 73.37% of K-Means). It also enables straightforward interpretation of the underlying relations of patient characteristics in an unsupervised setting.

关键词： Kidney Diseases Clustering algorithms Decision making Abdomen Public healthcare Indexes

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Intelligent spectrum learning for wireless networks with reconfigurable intelligent surfaces

arXiv

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arXiv 2021年

作者： Yang, Bo Cao, Xuelin Huang, Chongwen Yuen, Chau Qian, Lijun Renzo, Marco Di Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore Zhejiang Provincial Key Lab of information processing communication and networking Zhejiang University No.38 Zheda Road Hangzhou310007 China Department of Electrical and Computer Engineering CREDIT Center Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) has become a promising technology for enhancing the reliability of wireless communications, which is capable of reflecting the desired signals through appropriate phase shifts. However, the intended signals that impinge upon an RIS are often mixed with interfering signals, which are usually dynamic and unknown. In particular, the received signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from non-intended users. To tackle this issue, we introduce the concept of intelligent spectrum learning (ISL), which uses an appropriately trained convolutional neural network (CNN) at the RIS controller to help the RISs infer the interfering signals directly from the incident signals. By capitalizing on the ISL, a distributed control algorithm is proposed to maximize the received SINR by dynamically configuring the active/inactive binary status of the RIS elements. Simulation results validate the performance improvement offered by deep learning and demonstrate the superiority of the proposed ISL-aided approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Human-Friendly Communication Robot for Public Service Based on iBeacon Technology

A Human-Friendly Communication Robot for Public Service Base...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Lianchao Li Shuai Shao Naoyuki Kubota Chang-Shing Lee Graduate School of System Design Tokyo Metropolitan University Tokyo Japan Computer Science and Information Engineering National University of Tainan Tainan Taiwan

This paper described a view about using human-friendly communication robots in service-centric systems. How-ever, the complete interaction between robots and human is extremely complicated. In this study, we focus on how to use human-friendly communication robots to improve human interaction, when people communicate with robots for the first time. Two people who speak different languages are often inefficient and misunderstood when communicating. Especially when traveling abroad or exhibiting, language barriers will reduce people's enthusiasm. We suggest to using human-friendly communication robot to automatically communicate with people in the language that the people are familiar with in order to bring more motion and satisfaction to people. In this paper, we proposed a method for human-friendly communication robot to identify the user information with utilizing iBeacon device. We introduced our communication robot, and then we made a preliminary experiment in our laboratory. We summarized the experimental setup and discussed the experimental results, then described our future work on this project.

关键词： Service robots Quality of service Robot sensing systems Base stations Games User experience

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Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

arXiv

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arXiv 2019年

作者： Palmieri, Nunzia Yang, Xin-She de Rango, Floriano Santamaria, Amilcare Francesco Department of Computer Engineering Modeling Electronics and System Science University of Calabria Italy Design Engineering and Mathematics School of Science and Technology Middlesex University London United Kingdom

This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental *** Codes 65D19, 68T40, 78M32, 90C26 Copyright © 2019, The Authors. All rights reserved.

关键词： Industrial robots

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Noise bias compensation for reconstructed HDR images in two layer coding

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The Institute of Electrical Engineers of Japan.

关键词： Image denoising

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A novel method to increase energy density of permanent magnet machine based on multi-physical field analysis

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UPB Scientific Bulletin, Series D: Mechanical engineering 2020年第4期82卷 79-92页

作者： Zheng, Qingchun Zhao, Yangyang Zhu, Peihao Wang, Sen Liu, Jingna Ma, Wenpeng Li, Xiang Xiao, Yinhong Wang, Naixin Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China School of Computer Science Tianjin University of Technology Tianjin300384 China School of Automation Shenyang Institute of Engineering Shenyang110136 China Baoding Power Supply Company Hebei Electric Power Company State Grid Corporation of China Baoding071000 China

As an important component of all-electric aircraft, the main propulsion machine is developing towards high energy density, high efficiency and high fault tolerance. In order to improve the energy density of main propulsion machine for all-electric aircraft, a novel approach, which can be used for excellent performance improvement of electric machine of all-electric aircraft, is reported in this paper. The analytical formula of energy density is put forward and the design method of high energy density permanent magnet machine is presented. A high energy density machine with rated power of 30kW is designed, and the electromagnetic field simulation analysis, strength and stress structure verification, temperature field simulation analysis and multi-physical field coupling analysis are obtained. A prototype is developed, the experiment platform is established, and experiments are performed. The energy density of main propulsion machine designed is up to 21kW·N·m/kg2, and the index is increased by 3.2 times. The optimal method based on the multi-physical field analysis can be also applied to other high energy density machines. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Electromagnetic fields

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Analyzing Customer Churn: A Comparative Study of Machine Learning Models on Pay-TV Subscribers in Turkey

Analyzing Customer Churn: A Comparative Study of Machine Lea...

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2023 4th International Informatics and Software engineering Conference (IISEC)

作者： Tuğçe Aydın Hataş Emir Obalı Ayşenur Yıldız Sibel Kırmızıgül Çalışkan Veysel Karani Yılmaz Erkan Kara Yasemin Kurtçu Meşe Ebru Öz Tuna Çakar Information Technologies MEF University Istanbul Turkey Subscriber Retention Manager Andromeda TV Digital Plat. Op. Inc. Istanbul Turkey R&D/Design Center Specialist Andromeda TV Digital Plat. Op. Inc. Istanbul Turkey Business Intelligence Manager Andromeda TV Digital Plat. Op. Inc. Istanbul Turkey Inf. Tech. and System Network CTO Andromeda TV Digital Plat. Op. Inc. Istanbul Turkey R&D Center Manager Andromeda TV Digital Plat. Op. Inc. Istanbul Turkey CVM and Loyalty Director Andromeda TV Digital Plat. Op. Inc. Istanbul Turkey Computer Engineering MEF University Istanbul Turkey

Understanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.

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Integration of visible light communication and internet of things for future communication

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AIP Conference Proceedings 2022年第1期2519卷

作者： B. Anitha Vijayalakshmi J. Seetha P. Gandhimathi R. M. Bommi 1Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India 2Department of Computer Science SRM Institute of Science and Technology SRMIST Ramapuram Campus Chennai Tamil Nadu India 3Kings Engineering College Department of Physics Chennai Tamil Nadu India 4Centre for System Design Chennai Institute of Technology Chennai Tamil Nadu India

The paper focuses on the need for optical communication in future eras. Spatial reprocess cannot meet the present conditions with increasing traffic demand in Radio Frequency (RF) communication. The frequency spectrum in traditional methods of wireless communication has been saturated. Visible Light Communication (VLC) and Internet-of-Things (IoT) integration will open up many indoor applications in the near future. Light Emitting Diodes (LEDs) and Photodetectors (PDs) in VLC support reliable uplink and downlink for Device-to-Device communication indoors. There is emergence of the energy-efficient variant of the VLC due to the shortage of energy budget in the upcoming era. The VLC works well in low illuminance. Energy conservation is supportedby consuming much lower energy through the usage of LEDs in *** can be achieved through the hybrid of single and multicarrier modulation. The work is about to discuss dimming techniques that support VLC in low illuminance and multicarrier modulation, which supports high data rate transmission.

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Synthesizing NPR Styled Street View Animation Based on Deep Learning

Synthesizing NPR Styled Street View Animation Based on Deep ...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Lieu-Hen Chen Cheng-Yu Sun Yasufumi Takama Department of Computer Science and Information Engineering National Chi Nan University Puli Taiwan Graduate school of System Design Tokyo Metropolitan University Tokyo Japan

More and more serious natural disasters happen nowadays. The inheritance of memories across generations consequently faces bigger challenges, since the traditional devices of saving memories such as diary, album, video tape, and even computer may be permanently damaged or lost during the disasters. Meanwhile, both of the software and hardware technologies keep advancing day by day. Thousands of hometown images have been saved on the Cloud by many internet services, such as Google Map and Google Street View. This gave us a new way to share our precious memories with our families and friends through the internet. However, these web services cannot do everything as we expected. For example, Google Street View images cannot show people under various weather conditions like rainy day or starry night. Therefore, we developed a street navigation system based on the image resources of Google Street View in this paper. By applying different visual effects on different regions, which are image segmented, based on deep learning technologies, this system creates vivid NPR (Non-Photorealistic Rendering) animations. In this way, we can create interesting weather effects and retouch the street images with more richness and variety.

关键词： Image color analysis Deep learning Google Image segmentation Navigation Rendering (computer graphics) Object detection

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FML-based Machine Learning Tool for Human Emotional Agent with BCI on Music Application

FML-based Machine Learning Tool for Human Emotional Agent wi...

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International Conference on Technologies and Applications of Artificial Intelligence (TAAI)

作者： Chang-Shing Lee Yi-Lin Tsai Mei-Hui Wang Haruka Sekino Tzong-Xiang Huang Wei-Fen Hsieh Eri Sato-Shimokawara Toru Yamaguchi Dept. of Computer Science and Information Engineering National University of Tainan Tainan Tawain Dept. of System Design Tokyo Metropolitan University Tokyo Japan

This paper presents a human emotional agent based on a fuzzy markup language (FML) tool and machine learning mechanism for music application. The human emotional agent contains a brain-compute-interface (BCI) preprocessing mechanism, a knowledge base, a rule base, a machine learning mechanism, and robots, for music application. We first select some famous songs with various languages, including English, Japanese, Chinese, and Taiwanese, and invite some learners to wear a wireless BCI device to listen to the chosen songs under the designed experimental modes and models. After listening, the learners help label their real-time feelings of each song. We analyze the learners' physiological indices transformed from the collected brainwaves to observe whether the learners could have mind communication with the robot. The experimental results show that the FML-based machine learning tool can help the proposed human emotional agent effectively on the music application. They also show that there is a relationship between the physiological indices and the learners' culture, familiarity, and preference for the listened songs.

关键词： Tools Knowledge based systems Fuzzy logic Reinforcement learning Electroencephalography Markup languages

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