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2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Amadeus, Clarence Syafalni, Infall Sutisna, Nana Adiono, Trio School of Electrical Engineering and Informatics Bandung Institute of Technology Indonesia University Center of Excellence on Microelectronics Bandung Institute of Technology Indonesia

ISBN: (纸本)9798350302103

Keyword spotting is a problem in Natural Language Processing which aim is to classify spoken words from a dataset. With the recent emergence of CNN, researchers have attempted to perform keyword spotting using CNN. However, CNN architectures are complex, thus making them not compatible with real-time applications. In this paper, SpectroNet, a low complexity hybrid CNN-LSTM approach is proposed to perform keyword spotting with less complexity. This architecture reaches 94.07% accuracy, with only 142,877 trainable parameters needed. The trainable parameters needed are significantly less than CNN networks while achieving comparable results. © 2023 IEEE.

关键词： Convolutional Neural Network Keyword Spotting Long Short Term Memory Spectrograms

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Comparison of Visual Occupancy Detection Approaches for Parking Lots and Dedicated Containerized REST-API Server Application 9

Comparison of Visual Occupancy Detection Approaches for Park...

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9th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2022

作者： Petričko, Andrej Dakić, Pavle Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

With the development of new technology, the urban infrastructure, which is necessary to meet the social, economic, and physical needs of the population, is also gradually improving. Therefore, cities face significant urban planning challenges. With the increasing population and number of cars on the road parking space occupancy detection techniques are of great importance for efficient parking space management. Knowing the availability of parking spaces within a city in real-time and informing residents can be of great help in reducing the number of car park councils, reducing traffic congestion in cities, or improving the scalability of car parks and the time needed to find empty parking space. In many car parks, ground sensors are used to determine occupancy status. However, this requires the installation and maintenance of these sensors in every parking space. This can be expensive, especially in car parks with a large number of available spaces. With increasing computing power, new approaches to parking space occupancy detection using computer vision are emerging. However, these approaches are tailored to the site of application. In this work, we propose a comparison of two general approaches: classification and object detection. The reliability, precision, and scalability of these approaches are compared using a REST-API server application implemented in Python. In order to get as close as possible to general use, we use docker, which enables us easier develop and deploy this application independently of the hardware. © 2022 Copyright for this paper by its authors.

关键词： Object detection

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TiO2/SiO2-Filled Composite with Spatial Permittivity Distribution for Application in FGM Spacer 10

TiO2/SiO2-Filled Composite with Spatial Permittivity Distrib...

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10th International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Zaputra, Satia Fabio, Muhammad R. Rachmawati Hidayat, Syarif Suwarno School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Electrical Engineering Indonesia Kebangsaan University of the Republic of Indonesia Indonesia

ISBN: (纸本)9798350304299

This research paper presents a comparative study of non-linear permittivity distribution models for spacers with functionally graded materials (FGM). FGM is an effective solution for controlling the electric field in the Triple Junction (TJ) area between conductors, spacers and SF6 gas. An increase in the electric field strength often occurs at the junction of the conductor with the spacer. This electric field concentration can be reduced by using the spatial distribution of permittivity of the U-FGM type. To obtain the FGM spacer model, the desired spacers relative permittivity (?r) distribution model has been designed to be between 3.5 and 7.5. The results of the analysis show that spatial permittivity distribution model have been able to reduce the electric field concentration by 27% in the Triple Junction - High Voltage (TJ-HV) region from 11.596 kV/cm to 8.458 kV/cm. Furthermore, this study made FGM specimens using TiO2/SiO2 - filled composite can obtain the desired permittivity distribution of the U-FGM model. When the filler loading weight is 30% of the total weight of the epoxy resin material, the ?r value is between 3.7 to 6.8. While the composite filler loading has 40% of the total weight of the epoxy resin material, the measured ?r is 4.1 to 12.5. © 2023 IEEE.

关键词： Permittivity

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Technologies for Detecting Nutrient Deficiencies in Plants: A Review

Technologies for Detecting Nutrient Deficiencies in Plants: ...

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2024 International Conference on Information technology Systems and Innovation, ICITSI 2024

作者： Ramsari, Nopi Suhardi Faizal, Ahmad Andrianto, Heri Hidayat, Teddy Institute of Technology Bandung School of Electrical Engineering and Informatics Bandung Indonesia Institute of Technology Bandung School of Life Sciences and Technology Bandung Indonesia Maranatha Christian University Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798331511470

The increasing global population and rising demand for food, coupled with changing environmental conditions such as weather and soil quality, significantly impact the quality and quantity of agricultural yields. This article provides a literature review of technologies used to detect nutrient deficiencies in plants, focusing on the application of Artificial Intelligence (AI) and the Internet of Things (IoT) in agriculture. The review begins by presenting the background of plant nutrient deficiencies and the conventional methods used for their detection. It then explores state-of-the-art technologies such as computer vision, machine learning, deep learning, IoT, and remote sensing, which are applied in detecting nutrient deficiencies in plants. These technology based approaches offer more efficient, accurate, and non destructive solutions compared to traditional methods. Additionally, this article identifies research opportunities for further studies in this field. © 2024 IEEE.

关键词： Smart agriculture

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Adding of SiO2Nanocomposite on Insulation Properties for Epoxy Resin Insulating Material 4

Adding of SiO2Nanocomposite on Insulation Properties for Epo...

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4th International Conference on High Voltage engineering and Power Systems, ICHVEPS 2023

作者： Syakur, Abdul Facta, Mochammad Aulia Khairunnas, Ibrahim Septian, Desta Rossi Sigiro, Trinita Khayam, Umar Diponegoro University Department of Electrical Engineering Semarang Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350318678

Insulator is a component that has a very important role in the electrical energy system. The insulator functions as a separator between a conductor and the part that has no voltage. Currently, polymer insulators have been developing, one of which is epoxy resin polymer material. The quality of the epoxy resin insulator is influenced by the mixture of manufacturing materials and filler. This paper discusses the effect of adding of SiO2 filler onthe quality of epoxy resin insulator. Epoxy resin insulator material testing was carried out at high voltage laboratory using an AC Voltage of 3,5 kV 50Hz. The epoxy resin insulator materials tested in this study by adding SiO2. The observed electrical parameter is the leakage current. The contact angle is an observed physical parameter and the degradation time of the insulator surface. The result showed that the addition of SiO2 to the epoxy resin reduced the value of the leakage current, degradation time and increased the contact angle. © 2023 IEEE.

关键词： Insulation

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad Randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%. © 2023 IEEE.

关键词： Machine learning

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A Real Time Implementation of SpectroNet for Music Player Application

A Real Time Implementation of SpectroNet for Music Player Ap...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

ISBN: (纸本)9798350381290

Keyword Spotting(KWS), which refers to the task of recognizing spoken words out of a small database of words, has become one of the most popular research topics. After the emergence of deep learning architectures, such as Convolutional Neural Network(CNN) and Reccurent Neural Network(RNN), works have been done to apply these architectures to Keyword Spotting problems. Keyword Spotting can have different topics/themes, such as home automation, robot control, etc. In this paper, music player is chosen as the topic of the KWS. A novel database will be created to test the system. The network chosen to implement this is SpectroNet, which is a low-complexity hybrid CNN-RNN architecture based on the DenseNet architecture. © 2023 IEEE.

关键词： Convolution

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Sentiment Analysis of Game Reviews on STEAM using BERT, BiLSTM, and CRF

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLS...

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2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Al Mursyidy Fadhlurrahman, Jalaluddin Herawati, Neng Ayu Widya Aulya, Hayyu Rachma Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context. © 2023 IEEE.

关键词： Sentiment analysis

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Introduction to Heterogeneous Robotic Systems in 5G and Beyond Networks

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IEEE WIRELESS COMMUNICATIONS 2023年第2期30卷 64-65页

作者： Lu, Huimin Ho, Pin-Han Khalfi, Bassem Verikoukis, Christos Kyushu Institute of Technology Japan Department of Electrical and Computer Engineering The University of Waterloo Canada Oregon State University CorvallisOR United States University of Patras Department of Computer Engineering & Informatics Greece

Remote intelligence in the application of robotics and the autonomous system relies heavily on seamless wireless connections. The 5G mobile network technology meets traditional manufacturing enterprises' application requirements for wireless networks based on robot transformation and upgrading, robot interconnection, and remote interactive applications in production. However, there exists many challenging 5G communication issues, such as different communication protocols in the system varies with different robots and no dis-ruptive changes to the physical layer.

关键词： Special issues and sections Robots 5G mobile communication Physical layer Autonomous systems Heterogeneous networks

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Non-IT Program Students Learn Basic Web Language;The Preliminary Study of Affective and Cognitive Response on IVML Prototype 17th

Non-IT Program Students Learn Basic Web Language;The Prelimi...

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17th International Conference on Information technology and Applications, ICITA 2023

作者： Naim, Nur Faraha Mohd Ibrahim, Ag. Asri Ag. Baharum, Aslina Moung, Ervin Gubin Salimun, Carolyn Multimedia Technology Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kampus Antarabangsa Labuan Federal Territory of Labuan Malaysia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia User Experience Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia

ISBN: (纸本)9789819983230

This paper examines the response of affective and cognitive processes under human–computer interaction (HCI) toward interactive video mobile learning known as the IVML prototype. The idea is to determine the potential of the interactive elements in the educational video as the key factor that can influence learners’ engagement and comprehension of the presented topic. This paper aims to measure the level of affective and cognitive response of learners toward the subject learning that are not from their field, not exposed to, or not familiar with. The data analyze based on user experience, user interface, and human–computer interaction. The statistical result may produce a significant outcome that could contribute to the hyper video platform. The findings may confirm the significant role of technical aspects and psychological aspects included in video production style, hence heightening the hyper video platform in teaching and learning activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

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