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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Hermanus, Davy Ronald Supangkat, Suhono Harso Hidayat, Fadhil Bina Nusantara University Research Interest Group in Educational Technology Computer Science Department Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350389654

With regard to Agriculture 5.0, the goal of this research is to create an intelligent platform for multi-drone collaboration that will improve situational awareness. This study examines the crucial phases in the Sensing, Understanding, and Action processes by fusing cutting-edge drone technology with creative coordination methods. Creating the architecture and foundation for drone collaboration, utilizing artificial intelligence for real-time data processing, and creating user interfaces that work are all part of the study technique. Area coverage, data quality, processing speed, and decision-making effectiveness are some of the performance evaluation measures. The goal of the research is to identify strategies for enhancing large-scale farming methods' resource management and operational effectiveness. The results will greatly enhance Agriculture 5.0 technologies and create avenues for the deployment of multi-drone collaboration platforms in a variety of high-situational awareness businesses. © 2024 IEEE.

关键词： Data quality

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Enhancing Voxel-Based Human Pose Classification Using CNN with Modified VGG16 Method 8

Enhancing Voxel-Based Human Pose Classification Using CNN wi...

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Rahmanti, Farah Zakiyah Pradnyana, Gede Aditra Priyadi, Ardyono Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya60111 Indonesia Universitas Pendidikan Ganesha Department of Informatics Bali81116 Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9798350344691

Human pose classification based on 3D point cloud is a challenging problem in pattern recognition and computer vision. The human pose prediction based on a 3D point cloud is a first step in human monitoring because the advantages are robustness to light and having an accurate location in 3D space. This research proposed a novel method for human pose classification based on a 3D point cloud to overcome that condition. However, with the proposed method of voxel-based feature extraction and Convolutional Neural Network (CNN) modified VGG16 achieved great success in classifying human poses with 3D point cloud inputs. This research proposes a CNN with modified VGG16 network to classify 3D point cloud human poses by present voxel-based feature extraction. This work uses our primary 3D point cloud data from LiDAR 32-channel. Before 3D point cloud learning, the first step is pre-processing data with normalization and extracting features with voxelization. Our experiment uses two types of classification cases, namely the classification for binary-class and multi-class 3D point cloud human poses. Experimental results show that our proposed method performs well and excellently, obtaining an accuracy value of 90% for the binary-class case and outperforming other existing methods. With our proposed method, it will be possible to recognize human poses better. © 2023 IEEE.

关键词： Feature extraction

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Head Pose Feature for Prediction Pedestrian Intention to Crossing the Road Using LSTM 38

Head Pose Feature for Prediction Pedestrian Intention to Cro...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Sidharta, Hanugra Aulia Ilham Perdana, Muhammad Mulyanto Yuniarno, Eko Al Kindhi, Berlian Hery Purnomo, Mauridhi Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Muhammadiyah Malang Department of Informatics Malang Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Automation Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350302196

Understanding pedestrian behaviour when crossing the road is an important key to the development of autonomous vehicles. Because pedestrians are considered Vul-nerable Road Users (VRUs), they are likely to be killed if they are involved in an accident. To ensure their safety, it is then necessary to predict the pedestrian's intention based on their behaviour. In this experiment, we propose head pose observation for predicting their intention, by observing pedestrians' head pose data, we can then predict their intention to cross the road. To achieve this purpose, we use human head detector and head pose extraction feature, and the resulting yaw, pitch and roll as three head pose features. To select the most optimal feature is important for predicting pedestrian intention, then we make 7 combination scenarios based on these three features and compare it with the same model. Based on this scenario, it is proved that all these three data are optimal to observe pedestrian intention. There are three behavioural annotation that have been used, there are crossing, not crossing and will crossing. We derive will crossing from the annotation looking and not crossing while waiting at the roadside. Prediction of pedestrian behaviour is done by using LSTM model, and the resulting precision on crossing and not crossing with 0.98, while will crossing is 0.94. © 2023 IEEE.

关键词： Forecasting

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

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computers, Materials & Continua 2023年第9期76卷 2959-2978页

作者： Muhammad Firdaus Harashta Tatimma Larasati Kyung-Hyune Rhee Department of Artificial Intelligence Convergence Pukyong National UniversityBusan48513Korea School of Electrical Engineering and Informatics Institut Teknologi BandungBandung40132Indonesia College of Information Technology and Convergence Division of Computer Engineering and AIPukyong National UniversityBusan48513Korea

The enormous volume of heterogeneous data fromvarious smart device-based applications has growingly increased a deeply interlaced cyber-physical *** order to deliver smart cloud services that require low latency with strong computational processing capabilities,the Edge Intelligence System(EIS)idea is now being employed,which takes advantage of Artificial Intelligence(AI)and Edge Computing Technology(ECT).Thus,EIS presents a potential approach to enforcing future Intelligent Transportation Systems(ITS),particularly within a context of a Vehicular Network(VNets).However,the current EIS framework meets some issues and is conceivably vulnerable tomultiple adversarial attacks because the central aggregator server handles the entire ***,this paper introduces the concept of distributed edge intelligence,combining the advantages of Federated Learning(FL),Differential Privacy(DP),and blockchain to address the issues raised *** performing decentralized data management and storing transactions in immutable distributed ledger networks,the blockchain-assisted FL method improves user privacy and boosts traffic prediction ***,DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing *** model has been deployed in two strategies:First,DP-based FL to strengthen user privacy by masking the intermediate data during model ***,blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular *** simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget(ε)of 4.03,1.18,and 0.522,achieving model accuracy of 95.8%,93.78%,and 89.31%,respectively.

关键词： Edge intelligence federated learning differential privacy blockchain vehicular networks

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Optimizing Optical Attocells Positioning of Indoor Visible Light Communication System

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computers, Materials & Continua 2023年第2期74卷 3607-3625页

作者： Mohammed S.M.Gismalla Asrul I.Azmi Mohd R.Salim Farabi Iqbal Mohammad F.L.Abdullah Mosab Hamdan Muzaffar Hamzah Abu Sahmah M.Supa’at School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi MalaysiaSkudai81310Malaysia Lightwave Communications Research Group(LCRG) Faculty of Electrical EngineeringUniversiti Teknologi Malaysia(UTM)SkudaiJohorMalaysia Department of Electronics and Electrical Engineering Faculty of EngineeringInternational University of AfricaKhartoum12223Sudan Department of Communication Engineering Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Parit RajaBatu Pahat86400JohorMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Department of Computer Science University of São PauloSão PauloBrazil

Visible light communication(VLC),which is a prominent emerging solution that complements the radio frequency(RF)technology,exhibits the potential to meet the demands of fifth-generation(5G)and beyond *** random movement of mobile terminals in the indoor environment is a challenge in the VLC *** model of optical attocells has a critical role in the uniform distribution and the quality of communication links in terms of received power and signal-to-noise ratio(SNR).As such,the optical attocells positions were optimized in this study with a developed try and error(TE)*** optimized optical attocells were examined and compared with previous *** novel approach had successfully increased minimum received power from−1.29 to−0.225 dBm,along with enhanced SNR performance by 2.06 *** bit error rate(BER)was reduced to 4.42×10−8 and 6.63×10−14 by utilizing OOK-NRZ and BPSK modulation techniques,*** optimized attocells positions displayed better uniform distribution,as both received power and SNR performances improved by 0.45 and 0.026,*** the results of the proposed model are optimal,it is suitable for standard office and room model applications.

关键词： Visible light communication(VLC) optical attocell received power signal-to-noise ratio(SNR) bit error rate(BER) coefficient of variation(CV)

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One-Shot Verification for Paper Certificates in Public Service System

One-Shot Verification for Paper Certificates in Public Servi...

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

作者： Aditama, Raditya Maulana, Maulvi Ziadinda Harisman, M. Rifki Virziadeili Hariyanto, Agus Susanto, Teguh T. D Universitas Indonesia Faculty of Computer Science Depok Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Universitas Kristen Teknologi Solo Department of Informatics Engineering Surakarta City Indonesia Universitas Negeri Jakarta Department of Educational Management Jakarta Indonesia

ISBN: (纸本)9798331511470

Even if an explosive number of proposals and prototype systems of blockchain based certificates and verification have been suggested and implemented since 2015, still in the public service areas paper certificates are prevalent. Because of advancement of color duplication and editing tools obtainable at almost no price in the internet, paper certificates are easily forged and modified. For the reason, the verification technology of the paper certificates needs need regressive research and can provide abundant research opportunities. In this research, various research suggestions and technical implementation are reviewed and a new system, called 'one-shot' verification is briefly described. The one-shot verification captures the image and QR code of the printed certificates with mobile devices before sending them to block-chained server. The block-chained server decides whether the printed certificates are tampered or not, with the OCR content by comparing hash value with hash value of the original content. The image OCR of the paper was trained with more than 1,700 images using YOLOv8. In the testing with preprocessing, the technology showed 96% accuracy. © 2024 IEEE.

关键词： Blockchain

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Classification of Tuna Meat Grade Quality Based on Color Space Using Wavelet and k-Nearest Neighbor Algorithm

Classification of Tuna Meat Grade Quality Based on Color Spa...

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2023 International Conference on Smart-Green Technology in electrical and Information Systems, ICSGTEIS 2023

作者： Gede Sujana Eka Putra, I. Ketut Gede Darma Putra, I. Sudarma, Made Sudana, Anak Agung Kompiang Oka Institute of Business and Technology Indonesia Department of Computer Informatics Denpasar Indonesia Udayana University Department of Information Technology Denpasar Indonesia Udayana University Department of Electrical and Computer Engineering Denpasar Indonesia

ISBN: (纸本)9798350382822

Tuna products are one of Indonesia's leading export commodity products. Accuracy in determining the quality grade of tuna is necessary to ensure food safety and product quality. Several cases of rejection of Indonesian fishery products by the United States and cases of food poisoning show a lack of food safety management in Indonesia. Determination of fish quality grade was done manually by identifying the eyes condition, gills, and meat color, taking a sample of tuna meat using a spike, which is the manual grading process causing human error. Previous studies had been carried out to identify the freshness and quality of fish using odor profiles, and color profiles from color sensors, measuring fish freshness and quality based on fish eye images. According to Robert DiGregorio, five parameters determine the grade quality of tuna, namely freshness, fish size and shape, meat color, texture, and fat content. Fish grades are grouped into grade 1, grade 2+, grade 2, and grade 3. This study aims to determine the grade quality of tuna meat based on color space through image preprocessing, dataset training, and classification. Image preprocessing consists of image cropping, converting images from RGB to HSV, and feature extraction using a wavelet. The training phase uses the k-NN algorithm using k=4 based on the number of classes. The result shows the correlation coefficient between grades of feature extraction using wavelet Symlet better than Haar. Classification of 65 images test dataset using Symlet wavelet and k-NN has a better accuracy of 81.8% compared to the Haar wavelet k-NN with an accuracy of 80.3%. © 2023 IEEE.

关键词： Feature extraction

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Supervised Machine Learning Algorithms for the Analysis of Ship Engine Data 16th

Supervised Machine Learning Algorithms for the Analysis of S...

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16th EAI International Conference on Wireless Internet, WiCON 2023

作者： Dimitriou, Theodoros Skondras, Emmanouil Hitiris, Christos Gkola, Cleopatra Papapanagiotou, Ioannis S. Vergados, Dimitrios J. Papapanagiotou, Stavros I. Koumantakis, Stratos Michalas, Angelos Vergados, Dimitrios D. Department of Informatics University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Informatics University of Western Macedonia Kastoria Greece Internet Business Hellas Athens Greece MAS S.A. Athens Greece

ISBN: (纸本)9783031580529

Supervised Machine Learning (ML) algorithms are used for making predictions or decisions based on labeled data. In this paper, an overview about existing supervised ML algorithms is performed. In particular, the algorithms that are studied comprehend the Linear Regression (LR), the Ridge Regression (RR), the Decision Tree (DT), as well as Ensemble algorithms. Subsequently, a comparative analysis of the algorithms is performed using a dataset containing data about ship engines. Effective management of ship engines is important for their robust operation, which can then bring significant economic and environmental benefits. Inferences about the condition of engines and predictions about their performance could prove crucial for specifying optimal cruise parameters, early fault detection and timely service planning. The analysis demonstrates the strength and the weaknesses of each algorithm in terms of predicting decay factors of the ship engine by taking into consideration the data included to the aforementioned dataset. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Forecasting

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A Tutorial on Hybrid Feedback Control 63

A Tutorial on Hybrid Feedback Control

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Casau, Pedro Ferrante, Francesco Sanfelice, Ricardo G. University of Aveiro Department of Electronics Telecommunications and Informatics Portugal University of Perugia Department of Engineering Italy University of California Faculty of Electrical and Computer Engineering Santa Cruz United States Instituto de Telecomunicações Portugal

ISBN: (纸本)9798350316339

This tutorial paper introduces hybrid feedback control through a self-contained examination of hybrid control systems modeled by the combination of differential and difference equations with constraints. Using multiple examples, it illustrates the power of hybrid feedback control, which stems from the integration of continuous and discrete dynamics, where state variables update instantaneously at specific events while flowing continuously otherwise. The paper defines hybrid closed-loop systems as interconnected hybrid plants and controllers with designated inputs and outputs, and formalizes their solutions. It summarizes key properties of hybrid systems and reviews various control strategies, including supervisory control with logic variables to select feedback controllers, event-triggered control to minimize control input updates, and strategies using multiple Lyapunov-like functions for stabilization. Pointers to further reading and other strategies in the literature are provided. © 2024 IEEE.

关键词： Feedback control

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