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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Yuki Hosono Nico Surantha Department of Electrical Electronics and Communication Engineering Graduate School of Science and Engineering Tokyo City University Tokyo Japan Department of Electrical Electronics and Communication Engineering Faculty of Science and Engineering Tokyo City University Tokyo Japan

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Early damage detection and regular train overhead line equipment inspection are essential for safe and reliable train operation. Traditionally, these inspections have been conducted directly by power line engineers at night. However, such methods face challenges like a shrinking workforce and limited mobility. Attempts have been made to enhance mobility by installing cameras on test vehicles, yet the analysis of captured images still requires manual intervention. With the advancement of drones, high-resolution cameras, and deep learning technology, AI-based inspections using drones have become a promising solution. This study aims to develop an AI-based drone navigation system for automated inspection of train overhead line equipment, focusing on improving inspection efficiency and accuracy through model training for future drone deployment. In this study, a simulator was employed to generate images of overhead line facilities, and a machine learning model was trained using these images, based on the concepts of Dronet and FlowDronet from previous studies. The model's performance was evaluated using varying numbers of training images. A test dataset was created from images captured by a drone in flight and processed with optical flow techniques. The results revealed that the model trained with 22000 images achieved the highest performance, with an Average accuracy of 87.90%, a Collision Recall of 89.60%, and a Non-Collision Recall of 85.99%. These findings underscore the importance of larger datasets in enhancing model generalization capabilities and demonstrate the effectiveness of utilizing drones and AI for automated inspection of train overhead line equipment.

关键词： Training Deep learning Adaptation models Visualization Accuracy Navigation Transfer learning Inspection Cameras Drones

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A Dual-Band Circularly Polarized Bi-Sectoral Waveguide Horn Antenna for High Power LEOP TT&C Systems 3

A Dual-Band Circularly Polarized Bi-Sectoral Waveguide Horn ...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Alkin, Esra Turkmen, Ceyhan Secmen, Mustafa Yasar University Graduate School Izmir Turkey Yasar University Department of Electrical and Electronics Engineering Izmir Turkey

ISBN: (纸本)9789463968058

In this study, a dual-band circularly polarized bi-sectoral waveguide antenna is proposed, which is used launch and early orbit phases (LEOP) of telemetry, tracking and command (TT&C) applications of satellite systems to almost cover full space together with omni-directional (toroidal) antennas. The structure consists of four parts (pyramidal horn antennas) each having excited by a single inclined slot being cut over circular waveguide. However, because the overall antenna is only fed with a single rectangular waveguide, it works as transceiver (in TX and RX bands simultaneously). The parts belonging to each band have bi-directional patterns being opposite to each other. Therefore, instead of using four separate antennas each being fed by separate rectangular waveguide to cover both directions in dual band (TX and RX bands) in 3D space, one antenna with just one feed is demonstrated as being sufficient for same purpose. For this purpose, an antenna is designed for a Ku-band TT&C application. The results show that the antenna is dual-band over 500 MHz bandwidth around 11.75 GHz (TX) and 13.75 GHz (RX) center frequencies. The antenna has more than 9.5 dBi peak gain in both main lobes and 15 dB return loss at TX band;and has more than 10 dBi peak gain and 10 dB return loss at RX band. Besides, it has less than 3 dB and 3.5 dB axial ratio (AR) values at TX and RX bands within almost 45º and 60º azimuth and elevation beamwidth values, respectively. © 2022 URSI.

关键词： Orbits

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FPGA-based Implementation of Random Forest Classifier for sEMG Signal Classification 46

FPGA-based Implementation of Random Forest Classifier for sE...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Martins, Wagner Bagesteiro, Leia B. Weber, Tiago O. Balbinot, Alexandre Universidade Federal do Rio Grande do Sul Post-Graduate Program in Electrical Engineering Porto Alegre Brazil San Francisco State University Department of Kinesiology San FranciscoCA United States

This paper proposes a method that combines a hardware description language and Random Forests theory, to develop a motion recognition embedded system. To validate this system, the Ninapro database DB2 was used as trai... 详细信息

ISBN: (纸本)9798350371499

关键词： Random forests

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The Impact of Equipment Specifications on the Efficiency of Visual Inspection using Unmanned Aerial Vehicles

The Impact of Equipment Specifications on the Efficiency of ...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Weitong Wu Yuki Funabora Shinji Doki Kae Doki Department of Electrical Engineering and Computer Science Graduate School of Engineering Nagoya University Aichi Japan Department of Electrical and Electronics Engineering Aichi Institute of Technology Aichi Japan

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This manuscript investigates the impact of unmanned aerial vehicles (UAVs) and their cameras’ key specifications on the efficiency of infrastructure visual inspections using UAVs. The challenges of UAV-based inspection data collection include reducing necessary waypoints to improve inspection efficiency while collecting desired data for sure. Existing studies generally concentrated on optimizing the efficiency of UAVs’ inspection trajectories and waypoints when equipment is given. However, the impacts of equipment specifications and their selections have not been investigated, yet they also significantly affect inspection efficiency. This manuscript surveys the key specifications of current equipment available for inspection and investigates the variation in efficiency through inspection simulations with different equipment specification combinations. Simulation results reveal that with the highest pose accuracy achievable by existing UAV platforms, it is possible to achieve efficient inspection. However, such high pose accuracy may be difficult to achieve, underscoring the importance of targeted selection of camera focal lengths according to UAV specifications and the types of pose errors. Additionally, using higher-resolution cameras may substantially improve the efficiency of high-resolution data collection tasks, which is a more practical solution for enhancing efficiency than further improvements in UAV pose accuracy. The conclusions drawn from this research provide actionable guidelines for equipment selection and development and lay the foundations of efficiency modeling, ultimately contributing to the advancement of UAV-based inspection methodologies.

关键词： Surveys Visualization Accuracy Inspection Data collection Autonomous aerial vehicles Cameras Trajectory Standards Guidelines

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OSINT Methods in the Intelligence Cycle

OSINT Methods in the Intelligence Cycle

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International Conference on Computer Science, electronics, and Industrial engineering, CSEI 2022

作者： Tanabe, Roberto de-Oliveira-Albuquerque, Robson da-Silva-Filho, Demétrio Alves-da-Silva, Daniel Costa-Gondim, João-Jose Professional Post-Graduate Program in Electrical Engineering Department of Electrical Engineering University of Brasília Brasília70910-900 Brazil Institute of Physics University of Brasilia DF Brasilia70910-900 Brazil DF Brasília70910-900 Brazil

ISBN: (纸本)9783031305917

The process for producing intelligence is traditionally represented by a series of steps forming a cycle. Collection is one of these stages and is characterized by the application of a set of disciplines to obtain information that will be analyzed for the production of an intelligence product. These disciplines are characterized according to the type of source, its methods and techniques. Open-Source Intelligence (OSINT) is the collection discipline focused on publicly available information. Open-source collection methods are usually represented by working diagrams that draw a flow according to the type of information. This paper makes a comparative study of the intelligence cycles of some relevant actors in the international OSINT scene, locates open-source collection in the intelligence cycle, and presents a workflow that combines this cycle with the techniques that form a method for OSINT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Intelligence Methodology OSINT

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Implementation of a PID Controller using Online Tuning Applied to a Mobile Robot Obstacle Following/Avoidance

Implementation of a PID Controller using Online Tuning Appli...

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Argentine Conference on electronics (CAE)

作者： M. A. Pastrana L. H. Oliveira D. A. Mendes Douglas. L. Silva Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The implementation of a bioinspired PID-based controller with online tuning capabilities is developed for obstacle following/avoidance of a nonholonomic mobile robot. An innovative mixture of the conventional PID controller and particle swarm optimization (PSO), which has a strong capability of adaptation across unknown scenarios, reduced objective error, brings us a novel online tuning methodology that can derive optimal controller parameters for a given scenario. It is appropriate for a kind of plant without prior knowledge or established mobile robot model. Computer simulation for obstacle following/avoidance is carried out in four different scenarios using the CoppeliaSim software and the EVA mobile robot platform. The effectiveness of the proposed control algorithm is demonstrated trough the simulation experiment, which shows its superior performance in the training scenario, including an overshoot of 20.1 %, a settling time of 10 seconds, and a steady-state error of 0.76%.

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Highly efficient near-infrared thermally activated delayed fluorescence organic light-emitting diodes with emission beyond 800 nm

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Journal of Materials Chemistry C 2023年第21期11卷 6981-6988页

作者： Liang, Jing-Xing Tang, Yukun Wang, Xiaofei Zhang, Kai Shih, Yu-Wei Chen, Chia-Hsun Chiu, Tien-Lung Li, Pei Jin Lee, Jiun-Haw Wang, Chuan-Kui Wu, Chung-Chih Fan, Jian Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices Soochow University Jiangsu Suzhou215123 China Department of Electrical Engineering Graduate Institute of Electronics Engineering and Graduate Institute of Photonics and Optoelectronics National Taiwan University Taipei10617 Taiwan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Chemistry National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering Yuan Ze University Taoyuan32003 Taiwan Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan State Key Laboratory of Structural Chemistry Fujian Institute of Research on Structure of Matter Chinese Academy of Sciences Fujian Fuzhou35002 China

Near-infrared (NIR) emitters are employed in a wide range of applications such as bio-sensors, optical communication devices, and organic light-emitting diodes (OLEDs). The development of NIR thermally activated delayed-fluorescence (TADF) materials lags significantly behind that of platinum complexes and quantum dots. To achieve cost reduction and environmental sustainability, TADF OLEDs have attracted extensive attention in both the academic community and industry in recent years. Herein, a conjugated planar acceptor was applied in a TADF emitter (TCN-TPA) to enhance the intermolecular through-space electronic coupling and redshift the emission spectra at high doping levels. In addition, multiple sub-acceptor units were integrated into a rigid acceptor to narrow the band gap. Importantly, the overall robust configuration of the TCN-TPA molecule led to high photoluminescence quantum yield (PLQY). Consequently, TCN-TPA demonstrated a record-high external quantum efficiency (EQE) of 2.4% at 802 nm and 1.1% at 841 nm among TADF OLEDs in this spectral range. © 2023 The Royal Society of Chemistry

关键词： Energy gap

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A VCO-Based Readout ADC for Quasi-Static Sensing Applications in 3-µm Low-Temperature Poly-Silicon Thin-Film Transistor Technology

A VCO-Based Readout ADC for Quasi-Static Sensing Application...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chia-Hsiang Chang Pang-Cheng Chen Hsiang-Chi Cheng Chung-Hung Chen Chun-Yen Lin Chih-Ting Lin Tsung-Hsien Lin Department of Electrical Engineering Graduate Institute of Electronics Engineering National Taiwan University AUO Corporation (AUO) Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This paper presents a voltage-controlled oscillator (VCO)-based readout ADC for quasi-static sensor applications. The proposed VCO-based ADC is implemented in a low-temperature polycrystalline silicon thin-film transistor (LTPS-TFT) process. To mitigate the severe offset and flicker noise in the TFT technology, this design adopts a relaxation-based VCO and a system-level chopping scheme. To improve the linearity and widen the input range, a foreground 3rd-order polynomial-fitted compensation is adopted in this design. This readout ADC is fabricated in a 3-µm LTPS-TFT process and occupies a core area of only 3.6 mm 2 . With a 5-V supply voltage, the proposed VCO-ADC achieves a 48.2-dB SNR with a 0.6-V input at a bandwidth of 250 Hz. The total power consumption is 505 µW. The ADC’s dynamic range is 59.5 dB. The corresponding Walden’s figure of merit (FoMw) is 4.8 nJ/conversion-step.

关键词： Temperature measurement Voltage measurement Power measurement Voltage-controlled oscillators Measurement uncertainty Area measurement Energy measurement

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Impact of EV load uncertainty on optimal planning for electric vehicle charging station

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Science China(Technological Sciences) 2021年第11期64卷 2469-2476页

作者： LONG YiLin LI Yong WANG YaHui CAO YiJia JIANG Lin ZHOU YiCheng DENG YouYue NAKANISHI Yosuke College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693BXUK Graduate School of Environment and Energy Engineering Waseda UniversityTokyo 169-8050Japan

With the development of electric vehicles(EV), there is a huge demand for electric vehicle charging stations(EVCS). The utilization of renewable energy sources(RES) in EVCS can not only decrease the energy fluctuation by participating in peakload reduction of the grid, but also reduce the pollution to the environment by cutting down the use of fossil fuels. In this paper,the optimal planning for grid-connected EVCS with RES is studied by considering EV load uncertainty. Nine scenarios are set based on a different characteristic of EV load to reveal the impact of EV load on net present cost(NPC) and to express the relationship between the optimal capacity and energy flow. Moreover, since electricity price also plays an important role in EVCS planning, an economic comparison between different cases with different electricity prices for peak-valley-flat period is carried out. The results reveal the economic benefits of applying RES in EVCS, and demonstrate that EV load with different characteristics would influence the capacity of each device(PV, battery, converter) in the EVCS optimal planning.

关键词： electric vehicle charging station renewable energy optimal planning load uncertainty

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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