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Conference on Power, Energy and Innovations (ICPEI)

作者： Samten Thanapong Suwanasri Cattareeya Suwanasri Nattapon Panmala EPE Program The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

This paper provides an examination of the impact of lightning strikes on a 220 kV double circuit (D/C) high voltage transmission line in Bhutan. The study employs the ATP-EMTP software to identify the leading causes of transmission line faults, which are primarily attributed to lightning strikes. Since the transmission lines run through areas in Bhutan that are prone to severe weather conditions and lightning strikes, they are vulnerable to damage, leading to an unreliable power supply. To address this issue, the author proposes several solutions, such as enhancing the tower footing resistance and installing line surge arresters. The author also created models of different transmission towers and analyzed an 18 km stretch of the transmission line that is highly susceptible to lightning strikes.

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Pushing the LTE Capability towards 5G: The Analysis of Licensed Assisted Access (LAA) Implementation for High-Density Area in Indonesia 12

Pushing the LTE Capability towards 5G: The Analysis of Licen...

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12th International Conference on Information Technology and electrical engineering, ICITEE 2020

作者： Ksamawati, Ni Made Dwidhyana Gunawan, Dadang Nashiruddin, Muhammad Imam University of Indonesia Graduate Program of Telecommunication Management Dept. of Electrical Engineering Jakarta Indonesia Telkom University Center for Regulation Management of Telecommunication School of Electrical Engineering Bandung Indonesia

ISBN: (数字)9781728110974

ISBN: (纸本)9781728110974

The number of mobile network subscribers has increased over the past few years rapidly. 4G LTE, as a part of wireless technology, made a significant contribution through the coverage expansion around countries but still faces critical issues, namely spectrum scarcity. However, the growth of data users and data consumption seems to be increased. The Mobile Network Operator (MNO) tried to provide the infrastructures and services that capable of responding to the necessity of capacity, reliability, and availability. 3GPP answered the opportunity by introducing the LTE-Advanced Pro (4.9 G) technology with 3GPP Rel-13 that called Licensed Assisted Access (LAA). LAA uses carrier aggregation technology, both licensed and 5 GHz unlicensed band. Through utilizing the unlicensed band, MNO tried to face the necessity of improvement with the cost-efficiency. This paper tries to introduce the LAA implementation in a dense urban area of Indonesia. Based on the calculation result of capacity and coverage planning in Jakarta, LAA will increase the capacity from 41.95% to 61.67% in the time period 2020-2030 based on existing LTE capacity. Besides capacity planning, the coverage planning resulted in the amount of required LAA site to covered 40 Points of Interest (POI) area by 72 outdoor LAA and 119 indoor LAA. According to business feasibility analysis for economic aspects, LAA business implementation in the dense urban area is categorized as feasible with positive NPV value at IDR 390,653,517,937;IRR > rate at 40.61%, and three years and three months payback period (PP) on the baseline scenario. © 2020 IEEE.

关键词： Wireless networks

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Identification of D- and L-Phenylalanine Enantiomeric Mixtures by Employing Deep Neural Network Models

SSRN

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SSRN 2023年

作者： Nigdelioglu, Ebru Toprak, Elif Guney Akkurt, Melike Erol Barkana, Duygun Kazanci, Murat Uyaver, Sahin Calik, Nurullah Istanbul Medeniyet University Nanoscience and Nanoengineering Program Graduate School Istanbul Medeniyet University Istanbul34700 Turkey Istanbul Medeniyet University School of Engineering and Natural Sciences Department of Biomedical Engineering Istanbul Turkey Istanbul34700 Turkey Yeditepe University Faculty of Engineering Department of Electrical and Electronics Engineering Kayışdagı Istanbul34755 Turkey Marmara University Faculty of Technology Department of Computer Engineering Maltepe Istanbul34854 Turkey

Phenylalanine is an aromatic essential amino acid that exhibits the tendency to self-aggregate into fibrillar structures in its enantiomerically pure form. This observation was indicated as the underlying mechanism of phenylketonuria, which is a genetic condition associated with various neurological, physical, and developmental issues, characterized with phenylalanine buildup in the brain. The presence of D-phenylalanine was demonstrated previously to inhibit the formation of fibrils by L-phenlyalanine, indicating its potential use in phenylketonuria treatment. In this study, several combinations of D and L-phenylalanine were examined with the help of state-of-the-art deep learning methods for their fibril forming capacity, demonstrating the usefulness and accuracy of deep learning methods in distinguishing between different self-assembled structures. © 2023, The Authors. All rights reserved.

关键词： Self assembly

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ADHD Diagnosis through Resting-State EEG Frequency Analysis with Random Forest

ADHD Diagnosis through Resting-State EEG Frequency Analysis ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Guilherme R. Pedrollo Leia B. Bagesteiro Alexandre R. Franco Alexandre Balbinot Post-Graduate Program in Electrical Engineering Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Department of Kinesiology San Francisco State University San Francisco USA Child Mind Institute The Nathan Kline Institute for Psychiatric Research NYU Grossman School of Medicine New York USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Attention-deficit hyperactivity disorder (ADHD) affects about 5% of the population. In order to minimize ADHD effects, it is important to identify its biomarkers. We analyzed electroencephalographic (EEG) signals using a random forest (RF) classifier optimized with a genetic algorithm (GA) to find differences between control and ADHD groups. Data from 856 participants were analyzed with a preprocessing procedure that included artifact subspace reconstruction (ASR) and independent component analysis (ICA). After preprocessing and GA optimization, the RF classifier achieved 88.6% total average accuracy considering the theta frequency band. This outcome suggests that this approach has great potential as a biomarker analyzer for ADHD diagnosis.

关键词： Filters Quality control Independent component analysis Forestry Biomarkers Feature extraction Electroencephalography Optimization Genetic algorithms Classification tree analysis

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Evaluating Conveyor Belt Health With Signal Processing Applied to Inertial Sensing

Evaluating Conveyor Belt Health With Signal Processing Appli...

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International Workshop on Secure Internet of Things (SIoT)

作者： Saulo Neves Matos Otávio Ferracioli Coletti Fernando Ugucioni Filho Ricardo C. C. L. de Carvalho Thomas V. B. Pinto Luiz G. D. de Barros Caetano Mazzoni Ranieri Bruno Eduardo Lopes Jó Ueyama Gustavo Pessin Institute of Mathematics and Computer Science University of sao Paulo Brazil Sao Carlos School of Engineering University of Sao Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Graduate Program in Electrical Engineering Universidade Federal of Minas Gerais Belo Horizonte MG Brazil Vale -SA Vitoria Brazil

Conveyor belts are commonly used in the mining industry for efficient material transport. However, they are prone to failures such as idler anomalies, belt tears, and misalignment. Current monitoring systems only evaluate the conveyor or idle belt's condition at the installation point. This study proposes a method to detect faults in conveyor belts, specifically idler anomalies and belt deviation, using two IMUs installed at the lateral extremities of the belt to evaluate its health along its route. In a reduced-scale conveyor belt, we conducted experiments by collecting gyroscopes and accelerometer data in various scenarios, including normal conditions and anomaly situations. We modified the idlers' physical structure or changed the belt tracks to create these scenarios. We used similarity analysis techniques to compare the data collected and performed statistical hypothesis analysis to observe the influence of the anomaly scenarios on the gyroscope and accelerometer data. We used Dynamic Time Warping (DTW) for gyroscope data to compare ground truth with anomaly scenarios. For the acceleration data, we performed vibration analysis by converting the time domain data to the frequency using Fast Fourier Transform (FFT) and evaluating the difference in spectral density between the reference and anomaly data. Statistical hypothesis analysis revealed that anomaly scenar-ios influenced the gyroscope x-axis and z-axis more. According to the spectral density analysis of the accelerometer, it was revealed that belt misalignment on the y-axis may be detected.

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Immersive Modeling Framework for Training Applications

Immersive Modeling Framework for Training Applications

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International Conference on Advanced Learning Technologies (ICALT)

作者： Vinicius Chrisosthemos Teixeira Carlo Smaniotto Mantovani Alexandre Cardoso Carlos Alexandre dos Santos Márcio Sarroglia Pinho Computer Science Graduate Program School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil

This article describes a framework for modeling and executing training in augmented and virtual reality environments. The framework was designed based on characteristics observed in existing training applications for complex tasks, such as the use of tools, control panels and the need for step-by-step instructions. Unlike other frameworks, in the proposed system, it is possible to create the training entirely within the virtual/augmented environment, avoiding constant switching between 2D and 3D environments. The framework allows for the definition of steps in a training program, each of which includes textual instructions, videos, and 3D objects, static or animated, anchored in the real world. To demonstrate the capabilities of the framework, a training program for operating a Universal Testing Machine was created as a case study. Overall, the proposed framework allows for the creation of effective and efficient AR training programs for a variety of tasks and industries.

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Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection

arXiv

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arXiv 2024年

作者： Tushar, Fakrul Islam Vancoillie, Liesbeth McCabe, Cindy Kavuri, Amareswararao Dahal, Lavsen Harrawood, Brian Fryling, Milo Zarei, Mojtaba Sotoudeh-Paima, Saman Ho, Fong Chi Ghosh, Dhrubajyoti Harowicz, Michael R. Tailor, Tina D. Luo, Sheng Segars, W. Paul Abadi, Ehsan Lafata, Kyle J. Lo, Joseph Y. Samei, Ehsan Center for Virtual Imaging Trials Carl E. Ravin Advanced Imaging Laboratories Dept. of Radiology Duke University School of Medicine United States Dept. of Electrical & Computer Engineering Pratt School of Engineering Duke University United States Medical Physics Graduate Program Duke University United States Dept. of Biostatistics and Bioinformatics Duke University School of Medicine United States Dept. of Radiation Oncology Duke University School of Medicine United States

Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free environment. The Virtual Lung Screening Trial (VLST), an in silico study inspired by the National Lung Screening Trial (NLST), illustrates the potential of VITs to expedite clinical trials, minimize risks to participants, and promote optimal use of imaging technologies in healthcare. This study aimed to show that a virtual imaging trial platform could investigate some key elements of a major clinical trial, specifically the NLST, which compared Computed tomography (CT) and chest radiography (CXR) for lung cancer screening. With simulated cancerous lung nodules, a virtual patient cohort of 294 subjects was created using XCAT human models. Each virtual patient underwent both CT and CXR imaging, with deep learning models, the AI CT-Reader and AI CXR-Reader, acting as virtual readers to perform recall patients with suspicion of lung cancer. The primary outcome was the difference in diagnostic performance between CT and CXR, measured by the Area Under the Curve (AUC). The AI CT-Reader showed superior diagnostic accuracy, achieving an AUC of 0.92 (95% CI: 0.90-0.95) compared to the AI CXR-Reader’s AUC of 0.72 (95% CI: 0.67-0.77). Furthermore, at the same 94% CT sensitivity reported by the NLST, the VLST specificity of 73% was similar to the NLST specificity of 73.4%. This CT performance highlights the potential of VITs to replicate certain aspects of clinical trials effectively, paving the way toward a safe and efficient method for advancing imaging-based diagnostics. © 2024, CC BY-NC-ND.

关键词： Lung cancer

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The 2020 power blackout in the brazilian state of amapá: Lessons learned and opportunities

The 2020 power blackout in the brazilian state of amapá: Le...

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2021 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2021

作者： Do Prado, Josue Campos Santos, Marcos Vinicius Ramos, Lucas Feksa Canha, Luciane Neves Konzen, Gabriel School of Engineering Computer Science Washington State University VancouverWA98686 United States School of Engineering Newcastle University Newcastle Upon TyneNE1 7RU United Kingdom Federal University of Rondônia Department of Electrical Engineering Porto Velho Brazil Graduate Program in Electrical Engineering Federal University of Santa Maria Santa Maria Brazil Division of Energy Economics and Environmental Studies Rio de Janeiro Brazil

ISBN: (纸本)9781665444217

The Brazilian state of Amapa=-notable for having 70% of its area consisting of unexplored Amazonian forests-experienced a massive power blackout in November 2020. An internal short-circuit in a transformer led to an explosion followed by a fire at the Macapa substation. This event left about 90% of the Amapa's population without power for several days in the middle of the COVID-19 pandemic, disrupting the lives of hundreds of thousands of people and raising concerns from several electricity sector stakeholders. This paper discusses select lessons learned from the power blackout and presents future opportunities to improve power system reliability and resilience for the grids in Amapá, and other similar Brazilian states, with a special focus on the increasingly important role of of distributed energy resources and smart grid technologies. © 2021 IEEE.

关键词： Outages

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Exploring Multimodal Gait Rehabilitation and Assistance through an Adaptable Robotic Platform

Exploring Multimodal Gait Rehabilitation and Assistance thro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Sophia Otálora Sergio D. Sierra M. Felipe Ballén-Moreno Marcela Múnera Carlos A. Cifuentes Graduate Program of Electrical Engineering Federal University of Espirito Santo Vitoria Brazil Bristol Robotics Laboratory University of the West of England Bristol UK Robotics & Multibody Mechanics (R&MM) Reasearch Group Vrije Universiteit Brussel Brussels Belgium Flanders Make Brussels Belgium Biomedical Engineering Program Colombian School of Engineering Julio Garavito Bogotá D.C. Colombia The School of Engineering Science and Technology Universidad del Rosario Bogotá D.C. Colombia

Lower-limb exoskeletons and smart walkers are robotic devices to assist patients in regaining their autonomy after a stroke. The integration of these devices enables gait rehabilitation and functional compensation, promoting natural over-ground walking. This article presents the Adaptable Robotic Platform for Gait Rehabilitation and Assistance (AGoRA V2 platform), which integrates the new AGoRA V2 Smart Walker and the AGoRA V2 unilateral lower-limb exoskeleton. It was evaluated with 14 healthy subjects using physiological and kinematic variables and a perception assessment. The study entailed four conditions: Without exoskeleton (WOE), With Exoskeleton (WE&T), With Walker (WW), and With Platform (WP). Results indicate a reduction in the muscle activity of the Rectus Femoris (18%) and Vastus Lateralis (15%), comparing WE&T and WP, as well as walking without any device (WOE) and using any robotic device (WE&T, WW, WP). Results suggest the importance of combining the exoskeleton with the robotic walker and the assistance of each device independently. Moreover, using the complete platform induces slower gait patterns than the walker, as the mean impulse force and linear velocity decrease by 42% and 44%, respectively. These results demonstrate that the platform contributes to safety, and improvements in gait parameters and muscular activity, indicating the system's potential to act as a modular device according to users' conditions and therapeutic goals.

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