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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

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Q-Learning Energy Management System (Q-EMS) in Wireless Sensor Network

Q-Learning Energy Management System (Q-EMS) in Wireless Sens...

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Smart Computing, IoT and Machine Learning (SIML), International Conference on

作者： Deden Ardiansyah Mustafid Teddy Mantoro Doctoral Program of Information Systems School of Postgraduate Universitas Diponegoro Semarang Central Java Indonesia Computer Engineering Department Vocational School Universitas Pakuan Bogor West Java Indonesia Media-Tech-Lab Computer Science Department Sampoerna University DKI Jakarta Indonesia

ISBN: (数字)9798350364101

ISBN: (纸本)9798350364118

The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. These disparities result in suboptimal wireless sensor network performance. The reliance on batteries is a major factor contributing to this problem. The Q-Learning Energy Management System (Q-EMS) is designed to address these challenges and improve energy management strategies. The Q-EMS algorithm used a learning process resulting in optimal actions for sensor nodes in different situations. The rewards or punishments nodes receive determine their decisions, which are determined by the Q-EMS algorithm. The Q-learning model approach is aimed at reducing energy consumption and supply in WSNs. Energy supply is categorized into battery-based, transfer-based, and harvesting, while energy consumption can be classified into task-cycle, mobility-based, and data-driven. The development of the Q Learning algorithm model has three scenarios: determining energy needs for WSN, effective energy harvesting strategy, and effective energy transfer. As a result, effective energy demand, harvesting and transfer using the Q-learning algorithm are balanced. However, it needs to be studied in more depth using real data in the field. In future research, I will use real data and optimize the use of the Q-Learning algorithm.

关键词： Wireless communication Wireless sensor networks Energy consumption Q-learning Machine learning algorithms Energy resources Classification algorithms

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Monitoring System for Elderly Health Care Using Smart Band, Raspberry Pi, and Node-Red

Monitoring System for Elderly Health Care Using Smart Band, ...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Yudhi Ardiyanto Kusworo Adi Kurnianingsih Karisma Trinanda Putra Pradityo Utomo Doctoral Program of Information System School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Muhammadiyah Yogyakarta Yogyakarta Indonesia Department of Physics Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Politeknik Negeri Semarang Semarang Indonesia Department of Informatics Management Faculty of Engineering Universitas Merdeka Madiun Madiun Indonesia

ISBN: (纸本)9781665465427

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity.

关键词： Heart rate Electrical engineering Cloud computing Databases Medical services Receivers Older adults

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Predicting online item-choice behavior: A shape-restricted regression approach

arXiv

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arXiv 2020年

作者： Nishimura, Naoki Sukegawa, Noriyoshi Takano, Yuichi Iwanaga, Jiro Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki305-8577 Japan Recruit Lifestyle Co. Ltd. Tokyo100-6640 Japan Department of Information and Computer Technology Tokyo University of Science Tokyo125-8585 Faculty of Engineering Information and Systems University of Tsukuba Ibaraki305-8577 Doctoral Program in Policy and Planning Sciences University of Tsukuba Ibaraki305-8577 Erdos Inc. Kanagawa222-0033

This paper examines the relationship between user pageview (PV) histories and their item-choice behavior on an e-commerce website. We focus on PV sequences, which represent time series of the number of PVs for each user-item pair. We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences. This model imposes monotonicity constraints on item-choice probabilities by exploiting partial orders for PV sequences, according to the recency and frequency of a user's previous PVs. To improve the computational efficiency of our optimization model, we devise efficient algorithms for eliminating all redundant constraints according to the transitivity of the partial orders. Experimental results using real-world clickstream data demonstrate that our method achieves higher prediction performance than that of a state-of-the-art optimization model and common machine learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph theory

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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Working ratio estimation method for a long distance fixed wireless access (22 GHz band, 6.3 Mbit/s) link

Working ratio estimation method for a long distance fixed wi...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Y. Ohmiya K. Katagishi A. Vasilache T. Asami H. Murakami K. Toraichi Doctoral Program Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba-shi Ibaraki Japan Institute of Information Sciences and Electronics University of Tsukuba Tsukuba-shi Ibaraki Japan Japan Science and Technology Corporation University of Tsukuba TARA Center Ibaraki Japan KDDI R&D Laboratories Inc. Kamifukuoka-shi Saitama Japan KDDI Corporation KDDI bldg. Shinjuku-ku Tokyo Japan

This paper discusses a method to determine the optimal node location before a long distance FWA network (22 GHz band, 6.3 Mbit/s) is set. During our 3 years experiment, we found out that the long distance FWA net with 2 links (about 60 km) was down for approximately 42 hours per year due to heavy rain fall conditions. This means that the long distance FWA net is practicable to some extent. We discuss a method to estimate the working ratio between two unknown locations using AMeDAS weather data. By using this method, an appropriate node location can be decided before FWA net is deployed. This approach can improve the working ratio in the sense of link-up continuation.

关键词： Rain Space technology Costs Systems engineering and theory Research and development Laboratories Telegraphy Delay Interference Space stations

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Nonlinear internal wave run-up on impermeable steep slopes

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Journal of Offshore Mechanics and Arctic engineering 2009年第4期131卷 1-5页

作者： Tseng, I-Fan Chen, Chen-Yuan Kuo, Hui-Ming Department of Marine Environment and Engineering National Sun Yat-Sen University Kaohsiung 80424 Taiwan Department of Computer Science National Pingtung University of Education No. 4-18 Ming Shen Rd. Pingtung 90003 Taiwan Department of Management Information System Yung-Ta Institute of Technology and Commerce Lin-Luoh Pingtung 90941 Taiwan Doctoral Program in Management National Kaohsiung First University of Science and Technology Kaohsiung Taiwan Department of Logistics Management Shu-Te University Yen Chau Kaohsiung 82445 Taiwan

Laboratory experiments were conducted to investigate the run-up of internal solitary waves (ISWs) on steep uniform slopes in a two-layered fluid system with a free surface. A 12 m long wave flume, which incorporated a movable vertical gate for generating ISWs, was used in the experiments. A steep uniform slope was modeled at one end of the flume. In the present study we looked at internal waves with small and large amplitudes using a steep uniform slope (from one of Θ=30, 50, 60, 90, 120, and 130 deg) much longer than those previously published in the literature. Results collected from a wide range of experimental runs show that the run-up height depends on the planar slope, while the breaking depth is only antecedent to the amplitude of an ISW. The overall quantitative agreement with the linear feature aspects of the incident wave amplitude is encouraging. Copyright © 2009 by ASME.

关键词： Arctic engineering

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Empirical testing: The prototyper's evaluation mechanism

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NAVAL ENGINEERS JOURNAL 1997年第1期109卷 41-46页

作者： Hafner, AN Arnold N. Hafner Ph.D.:is founder and president of Information Systems Research (ISR). He has twenty-five years of experience in systems development and is published in the field of systems development management. He served as corporate research scientist at Systems Exploration Inc. from 1988 to 1991 program director at Computer Science Corporation from 1983 to 1988 director of operations at Republic Management Systems Corporation from 1981 to 1983 and program manager at Computer Science Corporation from 1972 to 1981. A 1962 graduate of the US. Naval Academy he holds a doctoral degree in human behavior and engineering degrees in electronics and communications. He has taught courses on information systems and systems management at most of the colleges in the San Diego area. Dr. Hafner has presented fourteen refereed research papers while publishing sixteen articles and a book A Manager's Guide to Software System Development.

Evaluating complex systems is the subject of this paper, the third in a series investigating prototyping. It provides an interesting and helpful overview of how to evaluate systems prototypes and outlines the iterative stream of developer-user interactions that is replacing older approaches to testing and evaluating new military systems, which promise to reduce the time required to develop and field future military capabilities. Changes to the acquisition process, such as those the paper sketches, will facilitate the nation's rapid transit through its current revolution in military affairs.

关键词： Systems analysis

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Letters to the Editor

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Journal of the Association for information science and Technology 1971年第4期22卷

作者： Carlos A. Cuadra P. O'N. Honey Mrs. Maria Patermann Elaine Caruso Editor Annual Review of Information Science and Technology Manager group Scientifc Information Service J. Lyons & co. Ltd. Head Sciences-Engineering Library University of California Santa Barbara Interdisciplinary Doctoral Program in Information Science University of Pittsburgh Pitsburgh Pa.

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REPEAT ANALYSIS program AS A QUALITY ASSURANCE SYSTEM FOR RADIOLOGY MANAGEMENT: CAUSAL REPEAT AND AN OVERVIEW

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Journal of Medical Imaging and Radiation sciences 2023年第3期54卷 S31-S31页

作者： Rochmayanti, Dwi Adi, Kusworo Widodo, Catur Edi Doctoral Program of Information System School of Postgraduate Diponegoro University Department of Radiodiagnostic and Radiotherapy Engineering Health Polytechnic Ministry of Health Semarang Central Java Department of Physics Faculty of Science and Mathematics Diponegoro University

OBJECTIVEThis paper aims to describe the image retakes program over several decades, and identify the causes of issues due to rejection rates. MATERIALS & METHODSThis study is a literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Articles were searched from Google Scholar (3140) and Scopus (18) using keywords " Reject" or " Repeat" or " Retakes" and " Analysis" and " in" and " Diagnostic" and " Imaging". The article range was expanded to describe the profile of repeat analysis from the use of film-screen to digital transformation systems. Based on the established criteria, 20 articles were extracted and analyzed. RESULTSFrom the literature, it was found that repeat rates above 5% were still found in some examination categories, with chest radiography contributing the highest rejection rate, reaching 51.67%, with the biggest contributing factor being positioning (84.8%). There are interesting findings related to this program, where subjective factors in determining the classification of rejection causes still exist. One study mentioned that AI can predict image rejection accuracy with a sensitivity of 93%. Therefore, the use of AI can be very helpful in classifying rejection causes, making data available for more efficient and easier radiology management. CONCLUSIONRepeat program is part of image quality management. The justification for rejection decisions still poses a challenge related to personnel's subjective factors and determining the causes of rejection/repeat must also be done to obtain concrete data related to radiography performance.

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