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A simple method for estimating lower limb muscle strength in the elderly and standing assistance according to the individual's physical weakness 48

A simple method for estimating lower limb muscle strength in...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chugo, Daisuke Miyazaki, Yuya Muramatsu, Satoshi Yokota, Sho She, Jin-Hua Hashimoto, Hiroshi Kwansei Gakuin University Graduate School of Sceince and Technology Hyogo Japan Tokai University School of Information Science and Technology Kanagawa Japan Toyo University Faculty of Science and Engineering Saitama Japan Tokyo University of Technology School of Engineering Tokyo Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Engineering Tokyo Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This paper proposes a new estimation method for the lower limb muscle strength of the elderly that can be easily performed in nursing care facilities. Furthermore, based on the estimated lower limb muscle strength information, this paper proposes a human model that simulates the physical conditions of the elderly and, using this model, proposes a new design method for standing assistance strategy adapted to the physical conditions of the elderly. The main advantage of the proposed method is that it can estimate and model leg muscle strength, which depends on the individual elderly person, in a simple way that can be carried out by staff in nursing care facilities who are not engineering specialists, without the use of specialized measuring equipment such as electromyography. © 2022 IEEE.

关键词： Nursing

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Feasibility Study of Semi-supervised Intrusion Detection Systems for Industrial Control Systems

Feasibility Study of Semi-supervised Intrusion Detection Sys...

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International Conference on Applied System Innovation (ICASI)

作者： Fu-Nie Loo Tung-Shou Chen Chia-Wei Tsai Jason Lin Chun-Wei Yang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung City Taiwan Master Program for Digital Health Innovation China Medical University Taichung City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

Industrial Control systems (ICS) automate industrial processes but also introduces cybersecurity threats. Intrusion Detection System (IDS) are crucial for detecting cyber-attacks on ICS, yet zero-day attacks are often inefficiently detecting with supervised learning. This study employs semi-supervised learning using one-class SVM, isolation forest, and Local Outlier Factor (LOF), to train IDS models. Utilizing dataset collected from a self-build virtual ICS environment, the study demonstrates the feasibility of these models in detecting common attack like Injection, ARP, and Man-in-the-Middle.

关键词： Training Support vector machines Technological innovation Supervised learning Intrusion detection Forestry Semisupervised learning

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Using LSTM and Regression Analysis for Railway Passenger Traffic Volume Prediction 11

Using LSTM and Regression Analysis for Railway Passenger Tra...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Tey, Fu Jie Tuan, Chih-Chi Cai, Cheng-En Wu, Ming-Xuan Wu, Yun-Sheng Wu, Tin-Yu National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan

ISBN: (纸本)9781665492324

The purpose of this paper is to investigate the factors affecting the passenger traffic volume on the high-capacity MRT, to analyze the traffic volume trends, and to make traffic volume predictions more accurately. This paper uses the LSTM (Long Short-term Memory) model and regression analysis for Taipei MRT passenger traffic volume prediction. The training data, including dates, temperatures and number of passengers, were collected, and a regression analysis was performed to find trends in the data. © 2022 IEEE.

关键词： Long short-term memory

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Using a Composite Catalyst in the Gasification Process, In-Situ Plastic Waste is Converted to Energy

SSRN

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SSRN 2023年

作者： Abdelrahman, Ameen Nedil, Mourad Erchiqui, Fouad Material science Département School of Engineering Canada Mechanical Engineering Department Université du Québec en Abitibi-Témiscamingue Canada Master of Engineering Program School of Genius Canada

The world is eager to find a solution to reduce the phenomenon of climate change and transition to sustainable and renewable resources in all facets of our lives as we live in this period. Climate change and the emission of greenhouse gases are closely related issues. In that study, we focused on finding ways to reduce municipal management of plastic solid waste and conversion to enhance gases and solid carbon instead of direct incineration or disposal. In order to fracture a variety of plastic debris (PEHD, PELD, PVC, and PS) in a fixed bed reactor system, we created composite materials made of three transition metal oxides (manganese, titanium, and iron). These materials were built using layers of bentonite clay. To enrich and improve gas yields and product quality, the gasification reaction has been set up with customizable, parameter-optimized heating rate, loading, and reaction time settings. For the distribution of composites and products, many characterization techniques including XRD, TEM, SEM, GC, and EDX have been used (gas yield, and char weight ratio). © 2023, The Authors. All rights reserved.

关键词： Climate change

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation 11

A Genetic Algorithm to Optimize Cargo Stability in Container...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Hsieh, Chi-Jung Wu, Kuang-Sung Wu, Tin-Yu Tey, Fu Jie Chen, Yi-Kai Ho, Jye Yi National Ilan University Department of Computer Science and Information Engineering Ilan County Taiwan National Ilan University Master Program of E-Learning for Multimedia and Network Communications Yilan Taiwan National Pingtung University of Science and Technology Department of Management Information Systems Pingtung County Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9781665492324

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method. © 2022 IEEE.

关键词： Genetic algorithms

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Reentrant hybrid flow shop scheduling with stockers in automated material handling systems using deep reinforcement learning

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Computers and Industrial engineering 2024年 189卷

作者： Lin, Chun-Cheng Peng, Yi-Chun Chang, Yung-Sheng Chang, Chun-Hsiang Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

In smart factories, automated material handling system (AMHSs) replace manual material handling to increase production efficiency, in which stockers serve as the temporary storage for work-in-process inventories. Furthermore, costly machines and complex processes in advanced manufacturing lead to the need for multiple reentrant processes of the same workstation. However, previous works on scheduling problems rarely considered the function of stockers and reentrant processes, and their approaches were mostly based on metaheuristic algorithms. Recent advances in artificial intelligence enable the possibility of solving scheduling problems using deep reinforcement learning (DRL). Therefore, this work investigates the reentrant hybrid flow shop scheduling problem with stockers (RHFS2) inspired by AMHSs, and solves it by DRL. Firstly, the states, actions, and rewards of the Markov decision process for this problem are designed;and then, two deep Q network (DQN) approaches based on the actions for determining machines and jobs, respectively, are proposed. Simulation results demonstrate that our proposed DQN approaches outperform for finding better solutions of different-scale problems than classical metaheuristic algorithms. © 2024 Elsevier Ltd

关键词： Reinforcement learning

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Edible coating from VCO, iota-carrageenan and alginate blends for pindang fish preservation

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Green Materials 2023年第1期13卷 3-16页

作者： Iswahyuni, Hana Suyatma, Nugraha Edhi Wulandari, Nur Irianto, Hari Eko Dewi, Fera Roswita Nurhayati Sinurat, Ellya Nurbayasari, Rodiah Giyatmi Fransiska, Dina Analyst Ministry for Marine Affairs and Fisheries IPB University Bogor Indonesia Master of Food Technology Study Program Graduate School IPB University Bogor Indonesia Department of Food Science and Technology Faculty of Agricultural Engineering and Technology IPB University Bogor Indonesia Research Center for Marine and Land Bioindustry National Research and Innovation Agency of the Republic of Indonesia Lombok Indonesia Food Technology Study Program Faculty of Food Technology and Health Sahid University Jakarta Indonesia Research Center for Applied Microbiology National Research and Innovation Agency of the Republic of Indonesia Cibinong Indonesia

This study aims to develop an edible coating based on virgin coconut oil (VCO) nanoemulsion, iota-carrageenan and alginate to prolong the shelf life of pindang (mildly salted cooked) fish products. The research involved three stages: material preparation, characterization and determination of factors and responses for experimental design;parameter optimization to achieve optimal conditions;and application and analysis of pre- and post-coating characteristics of pindang fish. The parameter ranges were 0.5–1.5% for alginate concentration, 0.5–1.5% for iota-carrageenan concentration and 0.5–1.0% for VCO nanoemulsion concentration. The result of this study was an optimized food coating solution that was clear and bright white in color. Pindang fish coated with this layer showed improved quality in terms of water content (48.46%), weight loss (0.39%), pH (5.84), total volatile base (47.01%), salt content (2.9%), appearance (7), texture (7) and slime (7). At the same time, the total bacterial count remained unchanged (8 log colony-forming units (CFU)/g), the mold count remained unchanged (5 log CFU/g) and the aroma and taste received a score of 6. Thus, this study successfully produced an optimized edible coating that can enhance the quality and extend the shelf life of pindang fish, making it suitable for broader market distribution. © 2023 ICE Publishing. All rights reserved.

关键词： Fish

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on Computer System, Information Technology, and Electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Preparation of LFP-based cathode materials for lithium-ion battery applications

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Materials Today: Proceedings 2022年 65卷 2347-2350页

作者： Suttison, Suchanat Pengpat, Kamonpan Intatha, Uraiwan Fan, Jinchen Zhang, Wei Eitssayeam, Sukum Master's Degree Program in Materials Science Faculty of Science Chiang Mai University Chiang Mai 50200 Thailand Graduate School Chiang Mai University Chiang Mai Thailand Department of Physics and Materials Science Faculty of Science Chiang Mai University Thailand Center of Innovative Materials for Sustainability School of Science Mae Fah Luang University Chiang Rai 57100 Thailand School of Materials and Chemistry University of Shanghai for Science and Technology Shanghai 200093 China Department of Engineering Mechanics Dalian University of Technology Dalian 116024 China

Lithium-ion batteries (LIB) have developed into the mainstream power source of energy storage devices due to their advantages: high power density, high power, long service life, and less pollution. This materials development for LFP batteries was considered successful in synthesizing powder. Here we show that three synthesis conditions were performed. LFP doped NiO, NMC, and (NH4)2HPO4 were synthesized by the nitrogen gas flow method. LFP-doped NiO was burned at 600 °C and 700 °C for five h. NMC doped with LFP burned at 550 °C for five h, and NMC-doped LFP and (NH4)2HPO4 were burned at 550 °C and 600 °C for five h. The structure and morphology of the material were examined by X-ray diffraction (XRD) and scanning electron microscopy (SEM). © 2022

关键词： Lithium-ion batteries

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