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IEEE/SICE International Symposium on system Integration

作者： Shijian Li Daisuke Chugo Satoshi Muramatsu Sho Yokota Jin-Hua She Hiroshi Hashimoto Uemura Takashi Hiroaki Kamishina Yoshiharu Hata Takayuki Yamada Takahiro Uchida Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan School of Information Science and Technology Tokai University Hiratsuka Japan Faculty of Science and Engineering Toyo University Kawagoe Japan School of Engineering Tokyo University of Technology Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Tokyo Japan KyotoAR Animal Advanced Medical Center Kyoto Japan Rinrei Co. Ltd. Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

The objective of this paper is to identify the stresses placed on the musculoskeletal system when small dogs walk on floors with low friction coefficients. In many households, and small dogs in particular are often kept indoors. According to veterinary experience, it is known that small dogs kept indoors that walk with their lower limbs slipping cause a great deal of stress on the musculoskeletal system, and that the incidence of joint disease in the lower limbs of such small dogs is high. In this study, we measured the body movements of small dogs walking on slippery floors using a three-dimensional motion capture system and force plate system. Furthermore, by inputting these measurements into the musculoskeletal simulation model we have developed to simulate the musculoskeletal system of dogs, we derived the load on the musculoskeletal system of small dogs on slippery and non-slippery floors. As a result, it was identified that walking on a slippery floor causes a strong load on the lower limb muscles of small dogs.

关键词： Legged locomotion Limbs Friction Dogs system integration Muscles Object recognition Floors Stress Paints

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Video-Based Container Tracking system Using Deep Learning

Video-Based Container Tracking System Using Deep Learning

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International Conference on Cyber Physical, Computer and Automation systems, CPCAS 2019

作者： Rahmat, Basuki Via, Yisti Vita Wasian, Abdullah Purbasari, Intan Yuniar Sari, Ni Ketut Wurjani, Widi Bandong, Steven Joelianto, Endra Siregar, Persaulian Faculty of Computer Science Universitas Pembangunan Nasional "Veteran" Jawa Timur Surabaya Indonesia Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9789813340619

The process of loading and unloading containers at the port is very important to be automated in order to increase productivity, revenues, efficiency and safety in the logistics transportation process especially in a maritime country like Indonesia. To achieve this, identification and tracking of container positions need to be done accurately so that container transfers can occur precisely and smoothly. In this study, YOLO deep learning is used to detect moving containers. The model is trained using container images, and then the validation and testing process are carried out on the model using other container images. The results of training are stored in several checkpoints which will be compared to get the most accurate model. Obtained YOLO gave good results for tracking containers with MAP value of 68.63% and LAMR of 0.31. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Ergometer-Based Lower Limb Rehabilitation Robot for Post-Stroke Hemiplegic Patients with Voluntary Movement Induction using Muscle Synergy

Ergometer-Based Lower Limb Rehabilitation Robot for Post-Str...

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Conference on Human system Interactions, HSI

作者： Daisuke Chugo Yuya Miyazaki Honoka Kubo Satoshi Muramatsu Sho Yokota Jin-Hua She Keio Ishiguro Hiroshi Hashimoto Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Engineering Kwansei Gakuin University Sanda Hyogo Japan School of Information Science and Technology Tokai University Hiratsuka Kanagawa Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan School of Health Sciences Tokyo University of Technology Kamata Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

This paper proposes a lower limb rehabilitation robot for normal walking in post-stroke hemiplegic patients. Ergometers are commonly used for lower limb rehabilitation, but their use has depended on the experience of physiotherapists. Therefore, this paper proposes a new rehabilitation robot based on an ergometer that leads the patient to voluntary muscle activity and learns how to use the muscles required for normal walking. The paper first compares how pedestrians walk between a normal person and a hemiplegic patient using muscle synergy analysis to identify the lower limb usage required for normal gait. The paper then proposes a method to approach the use of the lower limb required for normal gait by changing the load depending on the position of the pedals when a hemiplegic patient uses the ergometer. This will enable stroke hemiplegic patients to train for normal gait by pedaling the ergometer. The above proposed method has been implemented in our prototype robot and the effectiveness of our proposed method has been confirmed in experiments with post-stroke hemiplegic patients as subjects.

关键词： Legged locomotion Training Pedestrians Prototypes Muscles Stroke (medical condition) Assistive robots

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Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction 2

Alert and Response of Earthquake and Tsunami for Community B...

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2nd International Conference on Sciences and technology Applications, ICOSTA 2020

作者： Retno Susilorini, Rr M. I. Pamudji, Andre Kurniawan Sanjaya, Ridwan Ismail, Amrizarois Amasto, Ardhito Hayyu Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Indonesia Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Indonesia Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang Indonesia

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that "Smart Resilient City"model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that "Smart Resilient City"model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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Effects of Cr Atom Adsorption Configurations on Anatase Tio2(101) Toward Methane-to-Methanol Conversion Mechanism

SSRN

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SSRN 2024年

作者： Afifah, Hasna Mahyuddin, Muhammad Haris Saputro, Adhitya Gandaryus Shukri, Ganes Dipojono, Hermawan Kresno Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Quantum and Nano Technology Research Group Faculty of Industrial Technology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia Research Center for Nanoscience and Nanotechnology Institut Teknologi Bandung Jl. Ganesha 10 Bandung40132 Indonesia

The application of single atom catalysts (SAC) has been widely studied for various chemical reactions including the direct methane-to-methanol (DMTM) conversion. Here, by means of density functional theory (DFT) calculation coupled with the climbing-image nudged elastic band (CI-NEB) method, we investigate the methane-to-methanol reaction processes on the single Cr-decorated anatase TiO2(101). We analyze the DMTM activities and reactivities on two surface configurations i.e., the Cr either as a substitutional or adatom dopant on top of the TiO2(101) surface. While we find that both configurations energetically promote the dissociation of H2O2 that acts as the oxidant, the subsequent methane oxidation proceeds in considerably distinct mechanisms between the two Cr-decorated TiO2(101) systems. The configuration with Cr as an adatom favors the non-radical methane activation (i.e., C-H bond cleavage of CH4) with lower activation barrier relative to the Cr-substituted TiO2(101) system, but it falls short of methanol conversion due to the kinetically high barrier of the subsequent CH3* + OH* → CH3OH* reaction through the Langmuir-Hinshelwood mechanism. On the other hand, the Cr-substituted TiO2(101) configuration needs to overcome the higher activation barrier of C-H bond cleavage through the radical pathway, but it shows an overall more favorable energetic profile for methanol formation via the Eley-Rideal mechanism. We find that both as adatom and substitutional dopant, Cr plays an important role as an active site for H2O2 decomposition into either two adsorbed OH fragments or an oxygen atom and water molecule. In general, these findings can guide further design of reactive and selective DMTM conversion in particular when one utilizes the transition metal decorated TiO2 based catalyst. © 2024, The Authors. All rights reserved.

关键词： Catalysts

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Generating Synthetic Values on Automatic Weather Station Dataset using GAIN

Generating Synthetic Values on Automatic Weather Station Dat...

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International Conference on Instrumentation Control and Automation (ICA)

作者： Bayu Santoso Muhammad Ryan Faqihza Mukhlish Deddy Kurniadi Master Program in Instrumentation and Control Institut Teknologi Bandung Bandung Indonesia Department of Instrumentation and Calibration Badan Meteorologi Klimatologi dan Geofisika Medan Indonesia Center of Aviation Meteorology Badan Meteorologi Klimatologi dan Geofisika Jakarta Indonesia Engineering Physics Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

The Automatic Weather Station (AWS) is an automated instrument designed to gather meteorological parameters within a given environment. The dataset obtained from AWS often contains missing values due to various factors such as sensor malfunction or transmission issues. Nonetheless, the data produced by AWS plays a vital role in weather analysis and prediction. In this study, the authors employ the Generative Adversarial Imputation Network algorithm to generate synthetic values for the missing data in the AWS dataset. The dataset is divided into four schemes, each exhibiting different distributions of missing values at 5, 25, 50, and 80 gaps, respectively. The synthetic values generated by the algorithm are subsequently compared to the ground truth values using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. The findings demonstrate that for the air pressure parameter, both the 5 gaps and 25 gaps models exhibit RMSE and MAE values below 2 hPa. Similarly, the models for the air temperature parameter demonstrate RMSE and MAE values below 2°C. As for the relative humidity parameter, the models achieve RMSE and MAE values below 15%. Therefore, both the 5 gaps and 25 gaps models satisfy the error threshold established by the Badan Meteorologi Klimatologi, dan Geofisika for AWS data quality control.

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Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features

Traffic Flow Estimation using Graph Neural Network with Aggr...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Adiyana Putri Ferio Brahmana Endra Joelianto Herman Y. Sutarto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Artificial Intelligence Control and Automation Laboratory Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

ISBN: (纸本)9781665476881

The increasing vehicle volume every year affects the prediction of the traffic system. The purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or ages. In estimating traffic flow, obtaining and explaining the historical data from the nearest segment sensor is necessary. The graph model can describe the spatial relationship between segments by connecting nodes with edges. Graph Neural Network (GNN) has been used as a learning method to predict the traffic flow in the segment sensor. GNN gets input in the form of spatial data built through a graph model, and the features are traffic density, average vehicle speed, and delay time on the road network. This paper shows that GNN's capability performs better in extracting spatial features and predicting the traffic flow by aggregating the features through connectivity nodes and edges.

关键词： Learning systems Roads Estimation Feature extraction Robot sensing systems Graph neural networks Spatial databases

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Assessment of Electrocoagulation Control system Strategy in Textile Wastewater Treatment Plant

Assessment of Electrocoagulation Control System Strategy in ...

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2020 International Electronics Symposium, IES 2020

作者： Efendy, Bobby Ekawati, Estiyanti Nazuwatussyadiyah Budi, Eko Mursito Institut Teknologi Bandung Engineering Physics Program Faculty of Industrial Technology Bandung Indonesia Center for Instrumentation Technology and Automation Institut Teknologi Bandung Engineering Physics Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9781728195308

Electrocoagulation is a method of wastewater treatment that applies an electrical current into the liquid to coagulate the suspended particles. This study assessed two control strategies for electrocoagulation systems in wastewater treatment in terms of dynamic performances and energy consumptions. The first control strategy used the electrical current as a manipulated variable, and the second used the waste liquid flowrate instead. For either strategy, the controlled output was the turbidity of the product liquid. The assessment was conducted in an experimental setting, in a laboratory-scale, continuous pilot plant. The experiment began with the identification of the dynamic characteristics of the electrocoagulation process due to the combined changes in the electrical current between 1.8-5.2 Ampere and the changes in waste flowrate between 10.37-14.67 ml/s. First-Order-Process-with-Time-Delay equations approximated these processes. The statistical Analysis of Variance was used to select the best process condition to compare two control strategies. The associated Proportional Integral controllers were designed and applied for either strategy. The experiment showed that manipulating electrical current yield 7.48% longer settling time, but with significantly lower energy consumption throughout the electrocoagulation process. The result highlighted the benefit of using electrical current as of the manipulated variable in the electrocoagulation process in the pilot plant. © 2020 IEEE.

关键词： Wastewater treatment

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Optimal RTGC Controller using Robust PID H∞ Integral-Backstepping under Payload Mass and Rope Length Uncertainties

Optimal RTGC Controller using Robust PID H∞ Integral-Backst...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Steven Bandong Yul Yunazwin Nazaruddin Endra Joelianto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia National Center for Sustainable Transportation Technology Institut Teknologi Bandung Indonesia

ISBN: (纸本)9781665476881

The increase in goods traffic between countries and islands requires the availability of port automation so that the distribution of goods is faster and the quality is guaranteed. One of the control problems for automating the port is controlling the position and sway angle of the container on the Rubber Tyred Gantry Crane (RTGC). However, the RTGC system's model is often uncertain because of the uncertainty in the mass of the payload and the length of the hauling rope when operating. This paper proposes a robust PID controller based on $H$ ∞ integral backstepping to overcome this problem. Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithms (GA) are used to optimize the parameter of $H$ ∞ integral backstepping so that the system response follows the desired trajectory. The results show that the control system designed can provide a satisfactory solution to variations in the mass and length of the rope. PSO and SA give better optimal and robust performance than GA.

关键词： Backstepping Uncertainty Automation Simulated annealing Control systems Robustness Trajectory

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Container Detection system Using CNN Based Object Detectors 7

Container Detection System Using CNN Based Object Detectors

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7th International Conference on Instrumentation, Control, and Automation, ICA 2021

作者： Bandong, Steven Nazaruddin, Yul Yunazwin Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung Indonesia National Center for Sustainable Transportation Technology CRCS Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision NLP and Big Data Analytics Institut Teknologi Bandung Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (纸本)9781665432955

The increase in free trade will also amplify the exchange of goods between countries and islands, especially in the seaports. The manual operation of the gantry-crane at the seaports has a risk due to human negligence. For that reason, automation is strongly needed in the loading and unloading of the container. Camera-based object detection is implemented to achieve this goal. In the recent years, CNN gives better accuracy in term of image classification, recognition and detection compared to the traditional method that use handcrafted features. In this paper, several advanced detection methods using CNN-based object detection, namely MobileNet, ResNet, and Faster RCNN are compared to detect and track the movement of containers. The results of the experiment show that the SSD ResNet and Faster RCNN methods are superior in terms of detection accuracy based on the Precision, Recall, Average Precision, and IoU values. MobileNet v3 excels in speed detection compared to the other methods. © 2021 IEEE.

关键词： Containers

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