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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Samaras, V. Tatas, K. Lontos, A. Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus Frederick University Department of Mechanical Engineering Nicosia Cyprus

ISBN: (纸本)9798350321074

This paper presents the development of proof of concept for a low-cost, real-time Cyber Physical System (CPS)/IoT that deals with observed excess braking issues in Formula Student (FS) race cars. The goal is to utilize telemetry aspects through a smart monitoring system under the FS low budget competition restrictions, with low-cost materials, open-source software, Long Range wireless communication (LoRa) and cloud services. The paper also introduces an algorithm for implementing an adaptive sampling rate based on the proximity of monitored parameters to warning levels to minimize unnecessary data transmission. The system prototype was tested under realistic driving conditions, obtaining useful preliminary data that can be used to guide both driving behavior and future vehicle design. The results demonstrate that the proposed approach can be useful for the race team in FS conditions and may be possible to expand it to other similar driving scenarios outside FS. © 2023 IEEE.

关键词： Cyber Physical System

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The Using of Mixed Reality Technology for Digital Transformation in Tourism 10

The Using of Mixed Reality Technology for Digital Transforma...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Tresnawati, Dewi Latifah, Ayu Mahpudin, Indra Pratama, Vitradisa Sukoco, Agus Prihatmanto, Ary Setijadi Informatics Institut Teknologi Garut Department of Computer Science Garut Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science Informatics Universitas Bandar Lampung Garut Indonesia

ISBN: (纸本)9798350339543

The tourism industry in Indonesia experienced a decrease in the number of tourist visits that occurred at the end of 2020. This was due to the outbreak of the COVID-19 outbreak that occurred at the end of January 2020. Apart from having an impact on decreasing the number of foreign tourist visits, it also had an impact on domestic tourists or local tourists. especially Bandung residents who are reluctant to travel because they are worried about the spread. Bandung has several popular tourist destinations such as history, culinary, fashion, handicrafts, arts, and interesting amusement parks to visit. One of them is Gedung Sate. For this reason, media promotion is needed to attract tourists to Bandung. The purpose of this research is to implement mixed reality technology that can be used to support tourism. This study uses the multimedia development life cycle which consists of six stages, namely concept, design, material collection, assembly, testing, and distribution. The results of this study are mixed reality application prototypes as promotional media for tourist attractions in Bandung that can be used by domestic and foreign tourists. Additionally, by using mixed reality technology as a promotional tool for Bandung's tourism sites, users may interact with 3D objects in real-time, making the information offered more engaging and dynamic. © 2023 IEEE.

关键词： Life cycle

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XLM-RoBERTa Model for Key Information Extraction on Military Document 10

XLM-RoBERTa Model for Key Information Extraction on Military...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Rachmadi, Muhammad Faza Ilmanuddiin Shalahuddin, Salman Permana, Heru Setijohatmo, Urip Teguh Hutahaean, Jonner Politeknik Negeri Bandung Department of Computer and Informatics Engineering Bandung Indonesia Bandung Indonesia

ISBN: (纸本)9798350339543

Key Information Extraction (KIE) is the process of extracting important data in a text or an image, such as the content of a military document. One popular method for performing KIE is by utilizing pattern matching with Regular Expression on texts from documents. However, this method is not efficient due to the fixed nature of RegEx, where we need an exclusive RegEx rule to extract certain data entities. To solve this flexibility issue, a deep learning method with Named Entity Recognition (NER) has been proposed. NER is applied to the scanned document using a transformer-based model called XLM-RoBERTa. This model has been chosen for the development of the KIE module as it supports Indonesian and has demonstrated good accuracy in previous studies. The model has been trained on a military document dataset in the Indonesian language, achieving an f1-score of 86.55%. This method has also been proven to be flexible, as the model can extract the same entities across different document types. © 2023 IEEE.

关键词： Pattern matching

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Realizing Predictive Maintenance in Production Machinery Through Low-Cost IIoT Framework and Anomaly Detection: A Case Study in a Real-World Manufacturing Environment 19

Realizing Predictive Maintenance in Production Machinery Thr...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Filios, Gabriel Karatzas, Stylianos Krousarlis, Marios Nikoletseas, Sotiris Panagiotou, Stefanos H. Spirakis, Paul University of Patras Computer Engineering and Informatics Department Greece Greece University of Patras Civil Engineering Department Greece University of Liverpool Department of Computer Science United Kingdom

ISBN: (纸本)9798350346497

Digital transformation and predictive maintenance are still some of the key challenges faced by the industrial sector as it moves towards Industry 4.0. However, the lack of resources, experienced personnel, and the absence of digital-savvy culture in manufacturing companies, often hinder the adoption of the required technologies. In this context, maintenance is a required process to ensure the smooth and continuous operation of the production, but to this day, engineers are still required to physically inspect equipment on site. By applying digital technologies, a predictive maintenance framework will enable the industry to maximize the life of the equipment and to act on time in order to prevent failures and thus reduce costs and optimize maintenance efficiency. In this effort, integrating such technologies and the required modern computing devices for monitoring legacy industrial equipment remains a challenge. Therefore, in this paper, we present a low-cost IIoT system for an AI-based predictive maintenance of machinery in a real-world industrial environment. In specific, a data collection system, consisting of a Raspberry Pi device and several sensors (namely, an accelerometer, temperature sensors and a microphone), was designed and developed. The collected data was then utilized to feed a deep-learning LSTM-autoencoder model for anomaly detection through a semi-supervised learning approach. Despite the small amount of data collected, the model is able to effectively detect anomalies. Finally, ways in which the existing system can be improved are proposed. © 2023 IEEE.

关键词： Internet of things

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Real-Time-Based Blood Wastage Management Using IoT and Blockchain Technology

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SN computer Science 2024年第3期5卷 310页

作者： Kumar, Ajay Chatterjee, Indranath Pallavi Sharma, Komal Thakur, Muskan Department of Computer Science and Informatics Central University of Himachal Pradesh Dharamshala India Department of Computer Engineering Tongmyong University Busan South Korea Department of Computing and Mathematics Manchester Metropolitan University Manchester United Kingdom

The rapid shift in population density increased the need for blood in medical treatments, emphasizing the importance of appropriate blood supply chain management. Most countries have institutions that run centralized blood management information networks. However, these centralized solutions are limited as they do not provide complete blood information. In the healthcare industry, supply chains are crucial, ranging from the prompt delivery of vital medications to patients to the traceability of these medications. To address the limitations of current systems, we recommend the use of Blockchain technology to offer real-time information on donors and patients. The primary advantage of Blockchain is that information is secure and less susceptible to tampering. The blood supply chain uses private Blockchain solutions, ideal for commercial transactions due to the limited number of participants, who are both rapid and trustworthy. This article also explores how the Internet of Things (IoT) can be used to store blood information in QR codes. These codes, attached to blood packets, provide easy access to critical data such as blood group, cell count, etc. The proposed method aims to reduce blood waste, facilitate real-time identification of suitable donors, and improve the distribution of blood units to all needed locations. Ultimately, implementing this approach will be beneficial for both donors and patients. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Blockchain Internet of Things Smart contracts Supply chain

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Color image restoration by filtering methods: a review

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Soft Computing 2024年第13-14期28卷 7755-7782页

作者： Salamat, Nadeem Missen, Malik Muhammad Saad Akhtar, Nadeem Mustahsan, Muhammad Surya Prasath, V.B. Department of Mathematics Khawaja Fareed University of Engineering and Information Technology Rahim Yar Khan64200 Pakistan Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Computer Science Pediatrics and Biomedical Informatics University of Cincinnati CincinnatiOH45221 United States

Digital images are corrupted with noise, and image denoising is an important step in image processing modules. In this review, the latest developments in filtering methods for color image restoration are analyzed. These algorithms are compared in terms of objective image quality measures and divided into major classes, such as spatial domain, switching and wavelet filtering methods. These classes are based on the particular methodology used in image denoising algorithms and further subdivided to show their classification in terms of noise models utilized, application style, and stages the filters applied in images. In particular, we present a review of filtering methods in color image denoising, published over the past two decades. Our classification and succinct descriptions of color image restoration by these mathematical filtering techniques and their characterizations can help choose the appropriate ones for various downstream image processing tasks. © The Author(s) 2024.

关键词： Restoration

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Evaluation of RIS-Enabled B5G/6G Indoor Positioning and Mapping using Ray Tracing Models 11

Evaluation of RIS-Enabled B5G/6G Indoor Positioning and Mapp...

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11th Workshop on Satellite Navigation Technology, NAVITEC 2024

作者： Kompostiotis, Dimitris Vordonis, Dimitris Paliouras, Vassilis Alexandropoulos, George C. Grec, Florin University of Patras Electrical and Computer Engineering Department Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Noordwijk Netherlands

ISBN: (纸本)9798350356854

A Reconfigurable Intelligent Surface (RIS) can significantly enhance network positioning and mapping, acting as an additional anchor point in the reference system and improving signal strength and measurement diversity through the generation of favorable scattering conditions and virtual line-of-sight paths. In this paper, we present a comprehensive framework aimed at user localization and scatterer position estimation in an indoor environment with multipath effects. Our approach leverages beam sweeping through codebook-based beamforming at an 1-bit RIS to scan the environment, applies signal component extraction mechanisms, and utilizes a super-resolution algorithm for angle-based positioning of both connected users and scatterers. To validate the system's effectiveness, accurate 3D ray tracing models are employed, ensuring the robustness and effectiveness of the proposed approach in practical scenarios. © 2024 IEEE.

关键词： Mapping

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Improving SVM Performance in Determining Cost and Kernel Parameter Values Through Grid Search 2

Improving SVM Performance in Determining Cost and Kernel Par...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Sylvia, Visca Lydia, Maya Silvi Nasution, Benny Benyamin Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350351613

The purpose of this study is to improve the performance of Support Vector Machine (SVM) algorithm in sentiment analysis of trainee reviews through parameter optimization using Grid Search. Trainee reviews were taken from the Vocational and Productivity Training Center (BBPVP) Medan, which is the context of this study. The method used includes collection and preprocessing of review data, followed by the application of SVM for sentiment analysis. Grid Search is used to optimize the cost and kernel parameters of SVM. The results show that after optimization, the resulting SVM model shows significant performance improvement compared to the model before optimization. Accuracy increased from 68.75% to 71.96%, with improvements in other metrics such as precision, recall, and F1 score. These findings suggest that Grid Search is an effective method to optimize SVM parameters in sentiment analysis, providing a more accurate evaluation of the effectiveness of training programs at BBPVP Medan. This research makes an important contribution by applying hyperparameter optimization to SVM and suggests the application of this method for similar analysis in the future. © 2024 IEEE.

关键词： Support vector machines

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Non-stationary Domain Generalization: Theory and Algorithm 40

Non-stationary Domain Generalization: Theory and Algorithm

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40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Pham, Thai-Hoang Zhang, Xueru Zhang, Ping Department of Computer Science and Engineering The Ohio State University United States Department of Biomedical Informatics The Ohio State University United States

Although recent advances in machine learning have shown its success to learn from independent and identically distributed (IID) data, it is vulnerable to out-of-distribution (OOD) data in an open world. Domain generalization (DG) deals with such an issue and it aims to learn a model from multiple source domains that can be generalized to unseen target domains. Existing studies on DG have largely focused on stationary settings with homogeneous source domains. However, in many applications, domains may evolve along a specific direction (e.g., time, space). Without accounting for such non-stationary patterns, models trained with existing methods may fail to generalize on OOD data. In this paper, we study domain generalization in non-stationary environment. We first examine the impact of environmental non-stationarity on model performance and establish the theoretical upper bounds for the model error at target domains. Then, we propose a novel algorithm based on adaptive invariant representation learning, which leverages the non-stationary pattern to train a model that attains good performance on target domains. Experiments on both synthetic and real data validate the proposed algorithm. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Adversarial machine learning

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