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1st International Conference on Smart Mechatronics, ICSMech 2024

作者： Haiyunnisa, Triya Mahayana, Dimitri Research Center For Smart Mechatronics National Research and Innovation Agency Bandung Indonesia School of Electrical and Engineering Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350368918

Underactuated systems, characterized by fewer control inputs compared to their degrees of freedom, pose significant challenges in control design. Stabilization of underactuated nonlinear systems presents several challenges mainly due to their complex dynamic behavior and the presence of uncertainties. In this study, the representation of Lyapunov functions is done using artificial neural networks. The neural network parameters are optimized iteratively using random forest and gradient boosting algorithms. Based on the research results, the iteration process to obtain the Lyapunov function is also achieved quickly and the derivative of the Lyapunov function consistently decreases in all systems, becoming negative as the iteration progresses. In addition, several tests were carried out to assess the effectiveness of the method used to determine the Lyapunov function. From the analysis results, it was found that the area of the optimized Lyapunov function RoA expanded significantly more than 41.14% compared to previously estimated RoA area. The expansion of RoA through optimization indicates that the system is stronger and potentially more stable, assuming the Lyapunov condition applies in a wider area. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving LoRaWAN Networks Performance Through Optimized Radio Resource Management 4th

Improving LoRaWAN Networks Performance Through Optimized Rad...

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4th International Conference on Recent Innovations in Computing, ICRIC 2021

作者： Rajab, Husam Tiansheng, Xi Cinkler, Tibor Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9789811688911

The massive number of Internet of Things (IoT) applications is allowed by Low-Power Wide-Area Networks (LPWAN) with an extensive geographical area, low bit rate, and longevity specifications. The LoRa technology is a notable LPWAN that utilizes a proprietary Chirp Spread Spectrum (CSS) physical layer. Simultaneously, the LoRaWAN is defined as the media access control (MAC) protocol for large-scale deployment. It is intended to let low-powered applications to interact with massive requests that are connected through extensive wireless links. The aimed dense network distribution will inescapably lead to a scarcity of radio resources and have an issue with scalability when massive devices are connected to the network. In this paper, we propose a mathematical model to improve the link budget and scalability of LoRaWAN networks. Simulations using Matlab for different network scenarios illustrate the effectiveness of the optimization procedure. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Time Series Classification for Eye Tracking-Based Visual-Verbal Learning Style 16

Time Series Classification for Eye Tracking-Based Visual-Ver...

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16th International Conference on Information technology and Electrical engineering, ICITEE 2024

作者： Nurlatifa, Hafzatin Adji, Teguh Bharata Ardiyanto, Igi Sidhawara, A.G. Pradnya Wibirama, Sunu Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia Universitas Atma Jaya Faculty of Industrial Technology Department of Informatics Yogyakarta Indonesia

ISBN: (纸本)9798350375817

Classifying learning styles is crucial for personalized education. Eye tracking has been used successfully to classify visual-verbal learning style. However, previous studies on classification of visual-verbal learning style still relied on proprietary eye movement analysis software. Thus, limiting feature extraction from eye movement data. To address this research gap, we investigated the use of raw time series eye tracking data and machine learning to classify visual-verbal learning style using the Felder-Silverman Learning Style Model (FSLSM). This study evaluated several machine learning methods to classify visual-verbal learning styles based on eye tracking data. We also systematically evaluated the effect of various window sizes on the classification accuracy of machine learning models. Experimental results indicated that the Supervised Time Series Forest (STSF) as the best performing classifier with an accuracy of 97.70% (Z = -2.812, p = 0.005). We also found a window size of 10 demonstrated superior performance compared to other window sizes. Our findings demonstrate that features extracted from raw eye tracking data significantly enhance classification accuracy, highlighting the potential of time series eye tracking data for classifying visual-verbal learning styles. This study serves as an initial step toward enhancing data-driven personalized education and adaptive learning technologies. © 2024 IEEE.

关键词： Contrastive Learning

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Depth Map Estimation of Single-View Image using Smartphone Camera for a 3-Dimension Image Generation in Augmented Reality 6

Depth Map Estimation of Single-View Image using Smartphone C...

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6th International Symposium on Computer, Consumer and Control, IS3C 2023

作者： Takarabe, Jun'Nosuke Sari, Irawati Nurmala Du, Weiwei Kyoto Institute of Technology Department of Information Science Kyoto Japan University of Brawijaya Malang Department of Informatics Engineering Indonesia

ISBN: (纸本)9798350301953

Immersive experience exhibits such as augmented reality (AR) are a way to enjoy museums and art galleries because this way not only may update information easily but also does not require physical existence. However, 3-dimension (3D) images of AR are difficult to apply with one image as depth information either is unknown, or is cost and time consuming. This paper designs a depth map estimation method from single-view image by using smartphone camera to generate a 3D image in AR. Some functions are added into AR Depth Lab [7] in a personal computer (PC). Then, the improve AR Depth Lab [7] is installed into the smartphone. The depth map can be obtained by using patch-based depth estimation [8] and saved into the smartphone. The appropriate parameters of the proposal such as patch size, the cropped image size and the appropriate image models are set by experiments. © 2023 IEEE.

关键词： Augmented reality

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Skeleton Detection Using MediaPipe as a Tool for Musculoskeletal Disorders Analysis 1

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7th Computational Methods in Systems and Software, CoMeSySo 2023

作者： Böhm, Josef Chen, Taotao Štícha, Karel Kohout, Jan Mareš, Jan Faculty of Electrical Engineering and Informatics University of Pardubice Studentská 95 Pardubice 2532 10 Czech Republic Department of Mathematics Informatics and Cybernetics University of Chemistry and Technology in Prague Technická 1905/5 Praha 6166 28 Czech Republic

ISBN: (数字)9783031535499

ISBN: (纸本)9783031535482

Skeleton detection, also known as human pose estimation (HPE), is becoming more and more popular as it can be applied in a range of applications such as game entertainment, human-machine interaction, VR-based projects, medical rehabilitation, etc. Thanks to the booming development of deep learning, HPE solutions can be implemented using deep learning methods which require standard 2D RGB images or video sequences as input. That is, technology nowadays is making HPE solutions more and more lightweight and fast which is possible to run on mobile devices for the daily use of skeleton detection. This article covers a brief survey of current deep learning-based human pose estimation approaches in the first place. Then, a lightweight deep learning model – MediaPipe – will be illustrated from all the perspectives of its structure, working flow, strengths & weaknesses and the more concerned compatibility in platforms and programming languages. As a result, a multi-platform application for collecting movement data from patients suffering from musculoskeletal diseases relying on MediaPipe is introduced. Finally, there is a summary of achievements and obstacles of application development, which is significant as it can be a signpost for teams who are doing or about to do an application based on the MediaPipe library. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Diseases

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Driver Fatigue Detection Based on Face Mesh Features Using Deep Learning 24

Driver Fatigue Detection Based on Face Mesh Features Using D...

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24th International Seminar on Intelligent technology and Its Applications, ISITIA 2023

作者： Nuralif, Imam Yuniarno, Eko Mulyanto Suprapto, Yoyon Kusnendar Wicaksono, Alif Aditya Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya60111 Indonesia

ISBN: (纸本)9798350313956

The number vehicles and road users increases every year, this also has the potential to increase the risk of traffic accidents. Fatigue is the most dominant cause of accidents compared to several other factors. This research focuses on detecting driver fatigue. We use the mediapipe face mesh model to extract the key points on the face, next is to utilize the deep learning model, Long Short Term Memory (LSTM), which has been trained previously and implemented into mediapipe to detect driver fatigue. The data that is trained is the data point movement of facial features, so that the system can not only process one frame but several frames. The data given by the camera will be processed using the LSTM model's ability to detect long-term information, dynamically process data, and handle picture data using mediapipe in order to achieve low computational and high accuracy. The LSTM model has better accuracy in predicting facial features than the conventional random forest model. © 2023 IEEE.

关键词： Long short-term memory

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Research on Interpolation Method for Missing Electricity Consumption Data

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Computers, Materials & Continua 2024年第2期78卷 2575-2591页

作者： Junde Chen Jiajia Yuan Weirong Chen Adnan Zeb Md Suzauddola Yaser A.Nanehkaran Department of Electronic Commerce Xiangtan UniversityXiangtan411105China School of Information Engineering Yancheng Teachers UniversityYancheng224000China Department of Information and Electrical Engineering Ningde Normal UniversityNingde352100China College of Engineering Southern University of Science and TechnologyShenzhen518005China School of Informatics Xiamen UniversityXiamen361005China

Missing value is one of the main factors that cause dirty *** high-quality data,there will be no reliable analysis results and precise ***,the data warehouse needs to integrate high-quality data *** the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss *** the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity ***,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data *** missing data are randomly interpolated within the upper and lower ***,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption *** last,this process is implemented iteratively until the missing values do not *** a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete ***,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

关键词： Data interpolation GMDH electricity consumption data distribution system

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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An improved method to detect coherence time in wireless communications channel based on auto correlation functions

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4039-4055页

作者： Pamungkas, Wahyu Suryani, Titiek Wirawan Affandi, Achmad Ananda, Ridho Hendry, Jans Faculty of Telecommunications and Electrical Engineering Institut Teknologi Telkom Purwokerto Indonesia Department of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Sekolah Vokasi UGM Yogyakarta Indonesia

Coherence Time of a wireless communication channel is needed to determine the duration of time during which the channel will be static. If the receiving end is aware of this Coherence Time parameter, another Coherence Time related parameter, Doppler Shift, will be recognized. Very few of the numerous Doppler Shift studies use Coherence Time as the basis for detecting Doppler Shift. This is because the Coherence Time parameters for each channel model will have different values and be hard to detect. Our previous studies have been carried out to determine the Coherence Time detection method on a V2V channel but its correlation coefficient was below 0.5, which is not compatible with the theory. Therefore, this study aims to enhance the method with better correlation coefficient value above 0.5. This method was applied on V2V channel with moving scatterers and Correlated Double-Ring (CDR) channel models. This study can be therefore utilized as a guide for detecting the duration of Coherence Time on wireless communications channels, with the findings of Coherence Time detection being compared to the Coherence Time value calculated theoretically to get the accuracy rate. The accuracy rate on the V2V channel with moving scatterer has an accuracy value of up to 80.2%, while the accuracy rate on the CDR channel has an average accuracy value of up to 90.2%, according to the test results of the Coherence Time detection method that we present. Further, our proposed method could be implemented to determine the data symbol sent in a single frame in order to obtain a higher BER value in wireless communications such as Wi-fi, Cellular or V2V. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Doppler effect

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Individual Health Profiling Using Multimodal Sensors on Smart Health Care 17

Individual Health Profiling Using Multimodal Sensors on Smar...

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

作者： Ikawati, Vidya Saepudin, Didin Susanto, Bambang Munandar, Ahmad Nurba, Hardy Purnama Prihatmanto, Ary Setijadi Doctoral Program of School of Electrical and Informatics Engineering Institute of Technology Bandung Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309164

Health Service, also referred to as Health Care System, plays a pivotal role in maintaining and enhancing health by preventing illnesses and injuries. In this context, electronic health records are essential for both inpatients and outpatients, serving as a crucial tool for healthcare providers and policymakers. However, the potential of these records extends beyond individual care to support broader health policy and research initiatives. Personal Smart Health Care, an individual-based digital health technology, bridges the gap between personal health management and medical records, enabling comprehensive healthcare improvements. Smart healthcare, driven by technologies like IoT, mobile applications, cloud computing, big data, 5G, artificial intelligence, and biotechnology, offers a holistic approach to healthcare, emphasizing diagnosis, treatment, disease prevention, risk monitoring, and virtual assistance. An intelligent framework for smart healthcare includes four layers: The instrumentation layer, information system layer, business intelligence layer, and gamification layer, each contributing to enhanced patient outcomes, streamlined processes, and improved patient care. The gamification layer incentivizes healthy behaviors and fosters patient engagement, especially in healthcare scenarios. This comprehensive framework underscores the transformative potential of smart healthcare, where technology-driven solutions can revolutionize the healthcare landscape, improving patient care, health management, and the overall well-being of individuals and populations. © 2023 IEEE.

关键词： Big data

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