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检索条件"机构=Electrical Engineering PhD Program of the Faculty of Engineering"

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LoRa Network Design for Monitoring Landslides on 150 KV Delivery Tower 10

LoRa Network Design for Monitoring Landslides on 150 KV Deli...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Pancane, I Wayan Dikse Putra, I Putu Gelgel Astika Gede Suarjana, I Wayan Supranartha, Agus Universitas Pendidikan Nasional Electrical Engineering Study Program Denpasar Indonesia Universitas Negeri Manado Faculty of Sport Science and Public Health Department of Public Health Sulawesi Indonesia Electro Enginering Elektrical Engineering Program Study Politechnic Negeri Bali Badung Indonesia

ISBN: (纸本)9798350363104

The objective of this research is to design and develop an integrated landslide early detection system at Tower Number Eight, 150 kV Gianyar. The research is expected to result in the construction of a communication system based on LoRa technology that will serve to reduce the potential damage from landslides. In order to achieve this, several parameters must be considered as benchmarks. These include RSSI, SNR, BER and PDR. The device operates by monitoring changes in position, soil moisture and rainfall, and then transmitting the data via LoRa technology. At the sink, the data is recorded and monitored for RSSI, SNR, PDR and BER values. In the test, the source 1 node, situated 300 metres away, exhibited an RSSI value of -100 to -109 dBm and an SNR value of 6.75 to -6.7 in both sunny and rainy weather, with a PDR value of 100%. The source 2 node, situated 600 metres away, exhibited an RSSI value of -97 to -109 dBm and an SNR value of 8.5 to -11.75 dB, with a PDR value of 70 to 100%. It should be noted that the number of bits in the sensor data sent is always complete. This is evidenced by the bit error ratio (BER) value, which is always 0 on both source nodes. © 2024 IEEE.

关键词： Soil moisture

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Ant Colony Optimization-Based Path Planning for Autonomous Vehicle Navigation Systems

Ant Colony Optimization-Based Path Planning for Autonomous V...

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International Conference on electrical engineering and Computer Science (ICECOS)

作者： Desi Windi Sari Suci Dwijayanti Bhakti Yudho Suprapto Department of Electrical Engineering Doctoral Program in Engineering Science Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

A crucial aspect of autonomous vehicles is path planning, which involves using various algorithms to determine the most efficient routes. This study investigates the application of the Ant Colony Optimization (ACO) algorithm to create a system for identifying the shortest path. The process includes collecting coordinate data from specific test locations on the Universitas Sriwijaya Indralaya campus to generate a graph-based map for analysis. The study begins with gathering route data through the Google Maps application, which includes 182 routes. In simulation tests, the routes generated by ACO differ from those suggested by Google Maps; sometimes, the ACO route is longer and sometimes shorter, due to the probabilistic nature of the ACO algorithm in finding optimal routes. The average error compared to Google Maps is 11.7 meters. Additional tests introduce obstacles on the road, requiring the system to find alternative routes. For instance, the initial route from the auditorium to the faculty of Science and Math is 38-39-11-53-110-54-111 with a route length of 670 m. After adding a roadblock in front of the faculty of engineering, the route changes to 38-39-116-40-70-69-56-111, with a route length of 730 meters. These tests demonstrate that the ACO algorithm effectively identifies the best routes for autonomous vehicles, both under normal conditions and when obstacles are present.

关键词： Photography Meters Web services Navigation Simulation Roads Probabilistic logic Path planning Satellite images Autonomous vehicles

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Performance Evaluation of OSNR Sensitivity in DWDM-ROADM System for Data Center Interconnection

Performance Evaluation of OSNR Sensitivity in DWDM-ROADM Sys...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Nomarhinta Solihah Erna Sri Sugesti School of Electrical Engineering Telkom University Digital Connectivity Servcie Telkom Indonesia Bandung Indonesia PhD Program of Electrical Engineering School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350368086

ISBN: (纸本)9798350368093

The growing demand for handling sensitive data and high-bandwidth applications has made Data Center Interconnection (DCI) very important, as it provides high-bandwidth connectivity to improve efficiency. To address the increasing need for reliable, high-performance data transport, experts are evaluating the performance of Dense Wavelength Division Multiplexing (DWDM) integrated with Reconfigurable Optical Add-Drop Multiplexers (ROADM) system. Therefore, this study aimed to determine the optimal Optical Signal-to-Noise Ratio (OSNR) required for DWDM-ROADM system to transmit a 400 $\mathrm{Gb} / \mathrm{s}$ client interface operating at an $800 \mathrm{~Gb} / \mathrm{s}$ line-side rate. System configuration used a channel spacing of 150 GHz at a wavelength of $\mathbf{1 9 3. 1 5 0} \mathbf{~ T H z}$. Furthermore, OSNR performance evaluation was carried out by analyzing noise levels from Variable Optical Attenuator (VOA) and pre-Forward Error Correction (pre-FEC) setting. The results showed that DWDM-ROADM system required OSNR sensitivity value of $\mathbf{2 3. 1 5} \mathbf{d B}$ to ensure reliable $400 \mathrm{~Gb} / \mathrm{s}$ traffic transmission. This value was achieved at pre-FEC bit error rate (BER) of 0.02 and VOA attenuation of 8 dB, while capable of transmitting $400 \mathrm{~Gb} / \mathrm{s}$ of traffic. System performance was more stable at VOA level of 8 dB compared to 6 dB, where the traffic experiences errors at BER $1.63 \times 10^{-11}$. Finally, this study proposed a new test methodology for determining the minimum OSNR required for DWDM-ROADM system to reliably transmit $400 \mathrm{~Gb} / \mathrm{s}$ of traffic.

关键词： Performance evaluation Data centers Sensitivity Bit error rate Optical attenuators Wavelength division multiplexing Optical noise Reliability Monitoring Signal to noise ratio

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Java Bali Grid Mean Time To Failure Prediction using Machine Learning 1

Java Bali Grid Mean Time To Failure Prediction using Machine...

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1st International Conference on Technology, engineering, and Computing Applications, ICTECA 2023

作者： Sesotyo, Priyo Adi Pandjaitan, M.M. Lanny W. Lukas Universitas Katolik Indonesia Atma Jaya Professional Engineer Study Program Faculty of Engineering Jakarta12930 Indonesia Universitas Semarang Electrical Engineering Dept. Semarang50196 Indonesia Universitas Katolik Indonesia Atma Jaya Electrical Engineering Master Program Jakarta12930 Indonesia Jakarta12930 Indonesia

ISBN: (纸本)9798350360554

The role of Mean Time To Failure (MTTF) is critical to maintain cost-efficient in smart grids, as the predictive maintenance means for ensuring their reliability. Java Bali grid is the most important power network in Indonesia, because it holds 75% energy sales and the foundation of the Indonesian government, business and industrial sectors. One method for prediction in the machine learning is the Linear Regression, as part of the Supervised Learning technique. The regression model extracts the grid overload variable from MV distribution system, where connected load (MVA) divided by the number of MV customers. The model constructs the Linear Regression function, where the slope and intercept values found from the correlation between predicted response and actual response. After confronting the function f(x) and the coefficient of determination (R2), the test results for the MTTF prediction values for Java-Bali's region shows the higher accuracy when the R2 shows highest value. © 2023 IEEE.

关键词： Forecasting

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Transfer Learning Evaluation of Convolutional Neural Network Architectures for Thermal Image Classification of Solar Modules

Transfer Learning Evaluation of Convolutional Neural Network...

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International electrical engineering Congress (iEECON)

作者： Chutinun Phungket Prasit Nangtin The Graduate Program Faculty of Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Accurate anomaly classification in solar modules is crucial for efficient solar power system monitoring and maintenance. This study explores deep learning, specifically Convolutional Neural Networks (CNNs), for automated defect classification in thermal images. We employed transfer learning with pre-trained AlexNet and GoogleNet architectures, comparing static feature extraction and fine-tuning. Using a dataset of thermal images categorized as no anomaly, cracked, or soiled modules, the fully fine-tuned GoogleNet model achieved the highest classification accuracy 95.5%, outperforming AlexNet and traditional feature-based methods. GoogleNet's superior performance stems from its Inception modules, which learn more discriminative features by employing multiple convolutional filter sizes within the same layer, capturing complex thermal image patterns. Fine-tuning further boosts performance by adapting pre-trained knowledge to the specific task. These results demonstrate deep learning's potential for enhancing solar system maintenance reliability and efficiency.

关键词： Deep learning Adaptation models Accuracy Transfer learning Solar energy Feature extraction Maintenance Solar panels Convolutional neural networks Image classification

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Arduino and LabVIEW Based Ping-Pong Ball Position Control using PID Controller 17

Arduino and LabVIEW Based Ping-Pong Ball Position Control us...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Ramelan, Agus Rohman, Febri Abdul Sulistyo, Meiyanto Eko Adriyanto, Feri Sartika, Nike Zakaria, Diky Faculty of Engineering Universitas Sebelas Maret Dept. of Electrical Engineering Surakarta Indonesia UIN Sunan Gunung Djati Bandung Dept. of Electrical Engineering Bandung Indonesia Mechatronics and Artificial Intelligence Study Program Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (纸本)9798350309164

Industrial development requires accurate and reliable control technology. One of the popular control systems is PID control. This paper discusses the implementation of the PID method for ping-pong ball position control by utilizing the theory of the Coanda effect. The control system was designed using Arduino as the controller and LabVIEW as the control interface. Tests were carried out by combining set point values to determine whether the position control system worked properly or not. The test results show that the PID method combined with LabVIEW can work well with the results of the PID control test at successive heights of 15 cm, 20 cm, 25 cm, 30 cm, and 35 cm obtained rise time (tr) 3.49 seconds, 4.58 seconds, 9.08 sec, 9.92 sec, and 8.84 sec. The average tolerance is 1.5 cm for the upper limit and 1 cm for the lower limit. © 2023 IEEE.

关键词： Position control

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IoT and Peer-to-peer Signal Notification Methods Reduce the Time Needed to Detect Wildfire Heat Points 7

IoT and Peer-to-peer Signal Notification Methods Reduce the ...

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Panlawan, Nattawat Somsak, Teerasak Thongpron, Jutturit Muangjai, Worrajak Ngaodet, Montri Assanavijit, Visut Thanin, Phiched Sungkham, Suriyon Oranpiroj, Kosol Patcharaprakiti, Noppom Somsak, Peeraya Nakaiam, Kan Program in Electrical Engineering Faculty of Engineering Rmutl Chiang Mai Thailand College of Integrated Science and Technology Rmutl Chiang Mai Thailand Research and Development Institute Rmutl Chiang Mai Thailand Faculty of Business Administration and Liberal Arts Rmutl Chiang Mai Thailand

ISBN: (纸本)9798350386073

The issue of dust and smoke resulting from forest fires in northern Thailand. This is caused by people burning the forest to get forest products. Individuals can start forest fires by intentionally burning the forest to extract forest resources. Deforestation negatively affects the well-being of vulnerable patients and has repercussions on public health. The decrease in tourist numbers impacts the economy. Individuals setting fire to the forest to gather forest resources impact government and public sector connections. The education sector has implemented many strategies to educate and mitigate forest fires. There is a fire suppression system. Remove garbage, leaves, and branches that are flammable. However, forest fires remain a concern. Collaboration among three entities in the community can aid in preventing unauthorized forest fires through communal procedures. From multiple ministries, the local organization department, and the central government Collaborating to safeguard Forest fire suppression and afforestation This study is a component of the forest fire surveillance division. If the forest fire's location is not clearly indicated, participating in combined operations will lead to issues because of the overlap in operational responsibilities. Previously, thermal imaging satellites were utilized for coordinate determination. If the satellite completes two orbits each day and identifies a hot location, it initiates a forest fire. Firefighters cannot reach the area to put out forest fires;thus, they need to enter from a nearby, accessible location. Joining the forest fire prevention volunteer organization is challenging and will require approximately 24 to 25 hours. This study aims to conserve energy by utilizing an loT system that transmits peer-to-peer signals. Individuals are prepared to dismantle this system. The villagers can develop it themselves due to its modest cost of $10. During a forest fire (temperature exceeding 50 degrees Celsius), specific oper

关键词： Fire extinguishers

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Sheikh, Mohamed M. Bayoumy, Aya Ahmed, Nada M. Mohamed Eldeeb, Amany Rabea, Fatma I. Nawar, Amira A. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Design and Analysis of an Optical Fiber Network for Vehicles Sensing System

Design and Analysis of an Optical Fiber Network for Vehicles...

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electrical and Information Technology (IEIT), International Conference on

作者： Muhammad Athallah Adriansyah Yus Natali Catur Apriono Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok Indonesia Telecommunication Engineering Program Faculty of Engineering Telkom University Jakarta Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

The increasing number of accidents on toll roads, predominantly caused by human error, underscores the critical need for enhanced vehicular safety systems. This research introduces a novel optical fiber network design that utilizes polymer optical fiber (POF) as both a sensor and communication medium to significantly improve the reliability and response time of vehicle safety mechanisms. The system was carefully developed and simulated using OptiSystem software, incorporating key components such as a continuous-wave laser operating at 1310 nm, NRZ encoding, and a Mach-Zehnder modulator, all chosen for their ability to optimize data transmission and minimize signal degradation. Comprehensive simulations were conducted to evaluate the system's performance, focusing on parameters like bit error rate (BER), signal integrity, and response time. The results demonstrate that the proposed design achieves a Q factor of 5.2628, an output power of -14.049 dBm, and a rise time of 0.07 ns. Additionally, the system's power margin of 13.951 dB confirms its robustness and suitability for real-world implementation with the potential to be a viable solution for advancing vehicular safety technologies.

关键词： Q-factor Optical fibers Vehicle safety Optical fiber networks Encoding Time factors Polymers Optical signal processing Power generation Fiber lasers

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review 11

Multi Task Classification Using Deep Learning Approaches for...

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11th International Conference on Information Technology, Computer and electrical engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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