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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Chih-Chung Yang, Sophia Hou, Xiu-Yu Jhang, Yu-An Institute of Data Science National Cheng Kung University Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Taiwan

ISBN: (纸本)9798350324174

In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F-avg score of 0.514. © 2023 IEEE.

关键词： Unmanned surface vehicles

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of engineering & Technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

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Grating Coupled Attachment of Optical Fiber Arrays for in situ Photonics Experimentation

Grating Coupled Attachment of Optical Fiber Arrays for in si...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Arnold, Kellen P. Slaby, Joel B. Dattilo, Hannah M. Kaylor, C. Alex Schrimpf, Ronald D. Fleetwood, Daniel M. Ralph, Stephen E. Reed, Robert Weiss, Sharon M. Interdisciplinary Materials Science Program Vanderbilt University 2201 West End Ave NashvilleTN37235 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Electrical and Computer Engineering Vanderbilt University 2201 West End Ave NashvilleTN37235 United States

We report a simple, vacuum-compatible fiber attach process for in situ study of grating-coupled photonic devices. The robustness of this technique is demonstrated on grating-coupled waveguides exposed to multiple X-ra... 详细信息

关键词： Photonic devices

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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An IoT-based Parameter Extraction Platform for Powder Metallurgy Sintering Furnace 11

An IoT-based Parameter Extraction Platform for Powder Metall...

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11th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2024

作者： Chang, Shih-Man Lin, Hao Pu Han, Chin-Chuan Wu, Yu-Chi National United University Dept. of Computer Science and Information Engineering Miao-Li Taiwan National United University Program in Material and Chemical Engineering MiaoLi Taiwan National United University Dept. of Electrical Engineering Miao-Li Taiwan

ISBN: (纸本)9798350366501

Internet of Things (IoT) technology has widely used in industrial production. Most cases focused on designing new equipment or processes. However, small and medium enterprises especially the powder metallurgy manufacturers in Taiwan would not replace existing production equipment due to the finical problems. In this study, an IoT-based platform is conducted to extract the sintering parameters for technology to augment sintering furnaces by installing sensors and data transmission modules, setting a sintering parameter extraction platform for powder metallurgy sintering furnace. Long-term monitoring and remote control are the main technologies for solving three problems during the sintering process, i.e., temperature, atmosphere, and time. Monitoring targets include checking whether the heaters are damaged, ensuring stable air flow meter readings, and verifying the normal speed of the conveyor belt. Additionally, remote control technology has been developed to execute cooling and reheating processes on the sintering furnace. The staying time on-site for personnel is reduced and the sintering time is increased for production increase during working hours. © 2024 IEEE.

关键词： Belt conveyors

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Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

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Journal of engineering and Applied Science 2023年第1期70卷 56页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Chemical Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Electrical and Computer Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir5019 Tunisia

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%). © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Failure analysis

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A Nanostructure-Electrode-Based Electrochemical Sensor for Detection of Lactate Dehydrogenase

A Nanostructure-Electrode-Based Electrochemical Sensor for D...

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2024 IEEE Nanotechnology Materials and Devices Conference, NMDC 2024

作者： Uchayash, Sajid Que, Sunney Rubby, Md Fazlay Que, Long Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States Ames High School and Worked Partly on This Project NSF Outreach Program at ISU United States

ISBN: (纸本)9798331541439

This paper reports on the development of nanostructure-electrode-based electrochemical sensors for detecting lactate dehydrogenase (LDH). Using an ultrathin nanopore template, gold nanostructured electrodes were fabricated. An on-chip specific chemical surface functionalization process was developed for the sensors for detecting LDH for the first time. Experiments showed the sensitivity has been improved, compared to the electrochemical sensors with plain electrodes. It has been found that at least 5U/mL (61 nM) of LDH can be readily detected. © 2024 IEEE.

关键词： Nanopores

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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2023 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2023

作者： Chien, Shao-Yu Wong, Ching-Chang Tamkang University College of Engineering Doctoral Program in Robotics New Taipei City25137 Taiwan Tamkang University Electrical and Computer Engineering Department New Taipei City25137 Taiwan

ISBN: (纸本)9798350302714

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Reinforcement learning

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A Stable Discontinuous Galerkin Time-Domain Method for Simulation of Thin Conductive Layers

A Stable Discontinuous Galerkin Time-Domain Method for Simul...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Özakin, M. Burak Chen, Liang Ahmed, Shehab Bagci, Hakan Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia Ali I. Al-Naimi Petroleum Engineering Research Center Thuwal23955 Saudi Arabia

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A conductive layer that is much thinner than the skin depth is often incorporated into electromagnetic solvers as a resistive boundary condition (RBC) to avoid fine meshes (and consequently small time steps for time-domain methods). When the discontinuous Galerkin time-domain (DGTD) method, which relies on an explicit Runge-Kutta (RK) scheme for time integration, is used in a simulation involving an RBC surface with high conductivity, the solution becomes unstable. In this work, to circumvent this bottleneck, a locally-implicit time marching scheme is developed. The proposed method uses implicit Euler backward and explicit Euler forward schemes to discretize the time derivatives in local DG equations associated with the elements that "touch"and do not "touch"the RBC surface, respectively. This yields a locally-implicit Euler (LIME) time marching scheme that maintains its stability in the presence of a highly conductive layer. Indeed, numerical results demonstrate that LIME-DGTD is more stable than RK-DGTD for a transmission problem involving a thin metal sheet. © 2022 IEEE.

关键词： Lime

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to Bioengineering and Related Disciplines 54

Application of 3D Printing and Design in Healthcare: A Focus...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zelada-Quiroz, Cindy M. Reyes-Santiago, Brian L. Ortiz-Rivera, Eduardo I. University of Puerto Rico at Mayagüez Bio-Engineering Graduate Program Mayagüez Puerto Rico University of Puerto Rico at Mayagüez Department of Computer Science and Engineering Mayagüez Puerto Rico University of Puerto Rico at Mayagüez Department of Electrical and Computer Engineering Mayagüez Puerto Rico

ISBN: (纸本)9798350351507

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Students

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