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The Optimal "Speed-Torque" Control of Asynchronous Motors in the Field-Weakening Region Based on ADRC and ELM

The Optimal "Speed-Torque" Control of Asynchronous Motors in...

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international conference on Electrical Machines and Systems (ICEMS)

作者： Chenming Qiu Fang Xie Qunjing Wang Kangkang Liang Wenjie Hong Hong Jiang School of Electrical Engineering and Automation Anhui University Hefei China Engineering Research Center of Power Quality Ministry of Education Anhui University Hefei China Anhui Province Laboratory of Industrial Energy-Saving and Safety Anhui University Hefei China Anhui Zhongci Hi-tech Co. Ltd. Hefei China

ISBN: (纸本)9781728133997

In this paper, the Active Disturbance Rejection Control (ADRC) is introduced to improve the speed loop of the control system to speed up the response speed of "speed-torque" and reduce overshoot. The Extreme learning Machine (ELM) is introduced to improve the current loop to solve the problem of poor accuracy of the d-q axis stator current (i sd , i sq ) caused by the influence of temperature on the motor time constant (T r ). Thus, the optimal "speed-torque" control of asynchronous motor for electric vehicle in fieldweakening region is realized. First, the analytical model of the maximum electromagnetic torque is established, and the effects of i sd and i sq on the torque are analyzed. Second, to improve the response speed of "speed-torque" and reduce its overshoot, ADRC-PI is designed. Third, the maximum torque vector control system based on ADRC-PI is built to verify the effectiveness of the system by simulation. Then, an AVL (AVL List GmbH) experimental platform based on ADRC-PI control system is built, and the data samples under the optimal "speed-torque" are obtained by constant temperature experiment at 85 °C. ELM prediction model with slip, i sd , i sq , stator flux as input and d-q axis stator voltage as output is established. Finally, the ELM predictive model is embedded in the vector control system to output the optimal "speed-torque". Experiments verify the effectiveness of the proposed method.

关键词： Torque Stators Mathematical model Electromagnetics Analytical models Torque control

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Prediction of Broadband Spectrum Utilization data for Spectrum Sharing 22

Prediction of Broadband Spectrum Utilization Data for Spectr...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Suzuki, Nobuo Yoshioka, Tatsuya Adv Telecommun Res Inst Int 2-2-2 Hikaridai Seika Kyoto 6190288 Japan

The 5th generation mobile network system (5G) comprises the infrastructure of next generation communication services such as large-capacity video delivery services and real time entertainment services. It is extremely important to secure more spectrum resources to satisfy the 5G performance requirements. Solutions include spectrum sharing technology. This paper shows that the sequences consisting of the channel vacancy durations follow the exponential distribution by using the spectrum utilization data in an urban area where the spectrum is used at high density. The spectrum usage state could be described by using the channel vacancy periods. Furthermore, a prediction method is proposed of the future channel vacancy period with the frequent sequence mining technology by using short time period observation data. The result of the evaluation experiment is also reported. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Separated by semicolons Type your keywords here

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Bangla Intelligence Question Answering System Based on Mathematics and Statistics

Bangla Intelligence Question Answering System Based on Mathe...

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international conference on Computer and Information Technology (ICCIT)

作者： Md. Kowsher M M Mahabubur Rahman Sk Shohorab Ahmed Nusrat Jahan Prottasha Dept. of Applied Mathematics Noakhali Science and Technology University Noakhali Bangladesh Dept. of CSTE Noakhali Science and Technology University Noakhali Bangladesh Dept. of Information and Communication Engineering University of Rajshai Rajshai Bangladesh Dept of CSE Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

The Bangla Informative Question Answering System (BIQAS) is a significant Machine learning (ML) technique that helps a user to trace relevant information by Bengali Natural Language Processing (BNLP). In this research paper, we have applied three mathematical and statistical procedures for BIQAS based on question answering data. These procedures are cosine similarity, Jaccard similarity, and Naive Bayes algorithm. The cosine similarity has interacted with dimension reduction technique SVD on user questions and questions answering data in order to reduce the space and time complexity. These procedures of this research are separated into two parts: pre-processing data and establishment of a relationship between user's questions and contained informative questions. We have got 93.22% accurate answer by using cosine similarity, 84.64% by Jaccard similarity and 91.31% by Naïve Bayes algorithm.

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An Improved Support Vector Machine for Disease Diagnosis

An Improved Support Vector Machine for Disease Diagnosis

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international conference on Computer and Information Technology (ICCIT)

作者： Farjana Akter Tania Pintu Chandra Shill Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

Support Vector Machine is one of the most popular, promising and well-known machine learning tools for classification, regression and novelty detection. The performance of SVM totally depends on its core component of kernel functions like linear, polynomial and radial basis function (RBF). Conventional kernel functions have advantages and drawbacks, so it is very difficult to give the excellent performance while using only one kernel function for the large nonlinear dataset to nonlinear mapping from original input space to a high dimensional space. For this reason, in this paper, we proposed a hybrid kernel function combining linear and RBF kernel function and data preprocessing algorithms to reduce the noisy data for increasing learning ability and generalization performance. These two factors jointly improve the performance of SVM. In order to validate our proposed model, we used two different nonlinear breast cancer datasets and heart disease dataset with different characteristics. The proposed methods achieved an accuracy of 98.57%, 99.46% and 98.36% along with the sensitivity of 100% in the testing phase of these three datasets respectively. By comparing the simulation results, the results obtained from the proposed model are better than other SVMs constructed by ordinary kernel function and existing methods.

关键词： Kernel Support vector machines Classification algorithms data preprocessing Breast cancer

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Incentive Mechanism of Medical data Provider of Health data Bank 22

Incentive Mechanism of Medical Data Provider of Health Data ...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Zhou, Lijun Gong, Jianxia Zhao, Lindu Southeast Univ Sch Econ & Management 2 SIPAILOU Rd Nanjing 210096 Peoples R China

In a two-stage medical service supply chain system composed of health data bank and medical knowledge provider, the willingness of medical knowledge providers to share medical knowledge is of great importance. Based on the principal-agent theory, this paper designs an incentive mechanism to promote medical knowledge providers' efforts to improve the storage of high-quality medical knowledge based on the double incentive dimension of "remuneration + value", and comprehensively analyzes the relationship between the optimal effort level and the incentive intensities. The study finds that the level of effort of medical knowledge providers is not only affected by the incentive intensities, but also affected by their own conditions such as professional proficiency. Remuneration incentives can always play a positive role, and there is a mutual substitution relationship between the two remuneration incentive intensities of health data bank and medical institution. Value incentive may play a counter role, and the setting of value incentive intensity needs to be adjusted according to the status of the medical knowledge providers themselves. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Health data bank medical knowledge provider principal-agent theory incentive mechanism big data in health care

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A Machine learning Approach to Predict Vulnerability to Drug Addiction

A Machine Learning Approach to Predict Vulnerability to Drug...

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international conference on Computer and Information Technology (ICCIT)

作者： Arif Shahriar Fahim Faisal Sohan Uddin Mahmud Amitabha Chakrabarti Md Golam Rabiul Alam Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

There are significant amount of differences between an addicted and non-addicted person on their social and familial behavior. In this paper we tried to find out the characteristics of a person related to his social and familial life and also health issues that can prove his vulnerability to drug addiction. The research was held on the context of the people of Dhaka, Bangladesh and on an age group of 15 to 40 years. We have collected and analyzed data associated with addicted and non-addicted people from different areas of Dhaka. For addicted person's data we reached some rehabilitation center of Dhaka and for non-addicted person's data we communicated different aged group people of different colleges and universities. A machine learning approach then helped us to find out some features that differentiate between these two groups of people. This will help people to understand if a person is going to be addicted or not based on their health issues and social and familial behavior. Our questionnaire was constructed on the basis of Addiction Severity Index with the help of psychologists and specialists on drug addiction.

关键词： Drugs Neural networks Support vector machines Correlation Psychology Random forests

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intelligent data processing received from radio frequency identification system 9

Intelligent data processing received from radio frequency id...

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9th Annual international conference of the Biologically-Inspired-Cognitive-Architectures-Society (BICA) held as part of the Joint Multi-conference on Human-Level Artificial Intelligence (HLAI)

作者： Matrosova, Elena Tikhomirova, Anna Natl Res Nucl Univ MEPhI Moscow Engn Phys Inst Kashirskoe Shosse 31 Moscow 115409 Russia

The present article is devoted to the issue of improvement of utilization efficiency of modern information technologies in order to enhance the quality and safety of healthcare delivery. The introduction of Real-time Locating Systems (RTLS), in particular Radio Frequency Identification System (RFID), applied in conjunction with intelligent data processing system can reduce the severity of the consequences of event risks implementation, as well as avoidance of a number of such consequences. The authors of the present article suggest using mathematical tools of fuzzy logic theory for event analysis in online mode and recommendation formation on their basis for timely management decision-taking. (C) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-nd license Peer-review under responsibility of the scientific committee of the 9th Annual international conference on Biologically Inspired Cognitive Architectures.

关键词： decision-taking process automated decision support system machine learning intelligent information system Radio Frequency Identification Real-time Locating Systems fuzzy logic theranostic process

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Active learning Strategy at a Collegewide Level in NIT, Gifu College 22

Active Learning Strategy at a Collegewide Level in NIT, Gifu...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Ogawaa, Nobuyuki Gifu Coll Natl Inst Technol Dept Architecture 2236-2 Kamimakuwa Gifu 5010495 Japan

For more than fifteen years, National Institute of Technology (NIT), Gifu College has promoted the practice of education aimed at improving students' activeness through student-centered approaches such as ICT education and e-learning. The past educational practices led to the acquisition of the "Support Program for Contemporary Educational Needs (GP)" (2004) and the "Acceleration Program for Rebuilding of University Education (AP)" (2014) by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). It is effective to use digital educational materials in the practice of AL. As educational materials used in AL class, we are preparing them based on the viewpoints of our college senior graduates who have worked long and/or are currently working in Japanese high-tech industries as leading engineers. Also, our college has established a rule, a system for student guidance and a server system where students who have learned the educational materials can gain practical engineering credit points through extra curriculum activities. In our college, AL, which is practiced by all teachers, is categorized into three levels. Respective teachers are required to describe the type of AL in their syllabus. Moreover, our college is aiming for students to acquire the ability to describe matters related to engineering in English, while promoting the interactions between the teaching of technical English and that of the specialized subjects through active education as shown in subjects highly recommended by corporate engineers. 2018 The Authors. Published by Elsevier Ltd.

关键词： Active learning ICT Practical engineering credit points Educational materials

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On the development of an open and collaborative bioinformatics research environment 22

On the development of an open and collaborative bioinformati...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Kanterakis, Alexandros Karacapilidis, Nikos Koumakis, Lefteris Potamias, George Inst Comp Sci CBML Iraklion 70013 Crete Greece Univ Patras IMIS Lab MEAD Patras 26500 Greece

This paper reports on the development of a self-sustaining and community-responsive platform that streamlines the wealth of available open Bioinformatics resources to accelerate multi-disciplinary collaboration and boost innovation in post-genomics biomedical research. Our approach adopts the principles of reproducible, reusable and remixable computer-aided research, and builds on top of state-of-the-art concepts and converging technologies for simple, fast and scalable specification and execution of scientific workflows. The proposed platform enables innovative networking and community building among researchers, facilitates knowledge sharing and co-creation, assures better-informed collaboration, and expedites gaining of insights. Paying particular attention to the issues of data and research provenance and attribution, the platform integrates a set of innovative services for the management of research resources and competences. The overall approach ensures the interoperability of the abovementioned resources and services from a technical, conceptual and user interface point of view. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Bioinformatics Collaborative Systems Open-Science data Analytics

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Comparison of the efficiency of Machine learning algorithms on Twitter Sentiment Analysis of Pathao

Comparison of the efficiency of Machine Learning algorithms ...

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international conference on Computer and Information Technology (ICCIT)

作者： Mahamudul Islam Sajib Shoeib Mahmud Shargo Md. Alomgir Hossain Computer Science & Engineering IUBAT Dhaka Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

Nowadays, we are virtually living on social media, constantly sharing news and opinions regarding things that associate with us. As a result, social media sites are getting flooded with data in the form of opinions, especially on Twitter. However, all these tweets or opinions can be mined and used in various aspects of life and business for the betterment of human beings as a whole. Opinions on social media are unstructured and scattered and needs to be organized properly so that it contains valuable information regarding a specific subject. Sentiment analysis plays a big role here. In this paper, we have presented a way to automatically analyze the sentiment of tweets posted by Pathao (popular ride¬sharing service in South-East Asia) users. We have merged data mining with text mining and computational intelligence to label the tweets as negative or positive. We have used Twitter API to collect dataset from Twitter and followed the supervised learning approach for the development of training corpus. For text classification, we used three different machine learning algorithms-Naive Bayes, Support Vector Machine and Logistic Regression. We also made a comparative analysis of the efficiency of these three machine learning algorithms. Our proposed method is showing effective result and outcomes are satisfactory.

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