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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Rakprasertsuk, Sorawich Wannaboon, Chatchai Thai-Nichi Institute of Technology Intelligent Electronics Systems Research Laboratory Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798350383010

Sign language classification is a challenging task due to the complexity and diversity of sign gestures, and hand shapes. This paper presents deep learning approach for sign language classification using a mixed-features method. Two distinct datasets derived from a common source, i.e., a conventional image and a transformed histogram, are used for the input of deep learning model. The image dataset serves as input for a convolutional neural network (CNN) model, capturing spatial dependencies and features, while the histogram dataset is adapted for input to a long short-term memory (LSTM) model, emphasizing temporal aspects of hand gestures. This integration not only enables a comprehensive analysis of both spatial and temporal characteristics but also facilitates a reduction in the overall model size. The experimental results demonstrate notable improvements in efficiency metrics. The proposed method can achieve a reduction in model size, alleviating resource-intensive computational requirements. Furthermore, the need for extensive data augmentation is eliminated, streamlining the training process. Consequently, the proposed approach significantly enhances the overall efficiency of the sign language classification system. © 2024 IEEE.

关键词： Convolutional neural networks

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Photonics Applications and Web engineering – WILGA 2024

Photonics Applications and Web Engineering – WILGA 2024

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024

作者： Romaniuk, Ryszard S. Smolarz, Andrzej Wójcik, Waldemar Warsaw University of Technology Institute of Electronic Systems Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Poland

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

Wilga 2024 Summer Symposium on Photonics Applications and Web engineering was the 52th edition of the research and technical meetings series. Traditionally, the annual series of technical conferences and topical sessions lasted the whole week. They usually were split to one winter meeting in January and one or two summer events in May/June, and in September. The Covid epidemic and the war in Ukraine has changed a lot. The hybrid meetings were held in Wilga resort near Warsaw, owned by the Warsaw University of technology. Now the meetings since 2022 were shifted to Lublin. Nearly 100 participants took part in all of the Wilga 2022 events, most of them being young researchers active in all aspects of photonics, electronics and ICT science and technology. Over 60 participants took part in Wilga 2024 meetings. Wilga Symposium embraces hardware and software technologies associated with photonics like optics, optical engineering, optoelectronics, electronics and electrical engineering, mechatronics, chemical and material engineering, applied physics, industrial solutions and applications. Around 50 papers were presented during Wilga 2024, oral and poster. Out of this number, some papers chosen by authors are published in this volume of Proc. SPIE mostly related to photonics. The major change was that during Wilga 2020, 2021, 2022 and 2024 Symposia, during summer editions, no traditional large young researcher sessions at large were possible to be organized. We hope that this will change and Wilga 2025 will return to a good tradition to organize concurrent social research sessions full of young researchers like was during Wilga 2019 and earlier. © 2024 SPIE.

关键词： Photonic devices

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Improvement of Music Removal Accuracy from Environmental Sounds 23

Improvement of Music Removal Accuracy from Environmental Sou...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Hagiwara, Hyuga Ogawa, Tomomi Matsumoto, Hiroki Maebashi Institute of Technology Systems Life Engineering Maebashi Japan Tokyo Denki University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9798350353952

Recently, by proliferating video-sharing sites and social networking services, the posting-videos has increased on the internet. However, it needs to be careful when the video contains copyrighted music. One method of music removal is the method to use of speech enhancement. However, this method removes environmental sounds other than music, and the sense of realism for the video is lost. Therefore, the method is proposed to remove only music from speech data containing various sounds by using machine learning. However, the removal accuracy is not sufficient. Then, in this paper, to improve music removal accuracy, we propose a method to feed for the learning model to augmented the existing data. The augmenting is assumed to keep the length of the data in order to unify the data length given to the learning model. Specifically, we use the pitch shift method which changes only the pitch of the sound while maintaining the length of the data. Then, we compare music removal precision on the non-augmentation data and the augmentation data. As a result, it is confirmed that the augmented data is removing music more accurately than the non-augmentation data. Thus, in this paper, the augmentation method proposed is effective. © 2024 IEEE.

关键词： Economic and social effects

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A consideration on improvement of music removal accuracy from environmental sounds

A consideration on improvement of music removal accuracy fro...

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2024 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Hagiwara, Hyuga Ogawa, Tomomi Matsumoto, Hiroki Systems Life Engineering Maebashi Institute of Technology Maebashi Japan Faculty of Science and Engineering Tokyo Denki University Tokyo Japan

ISBN: (纸本)9798350389210

Recently, the demand for video content has been increasing due to the growing number of diverse social networking users. On the other hand, it is necessary to be aware of copyrights when uploading videos. If copyrighted music is included in the video, the music must be removed from the video. There is a method using audio enhancement techniques to remove music from video, but this method removes the environmental sounds in the video, which reduces the sense of realism of the video. Therefore, a method has proposed to remove music only from multiple mixtures of sounds using machine learning. However, the music removal accuracy of the method is not sufficient. In contrast, augmenting the training data is expected to improve accuracy, however, there is no suitable augmentation methods for music data. Therefore, we propose applying pitch shifting as a data augmentation method for music data to improve the music removal accuracy. Then, we conduct a simulation using both the original data without augmentation and the data augmented with our proposed method, and compare the music removal accuracy. As a result, we confirm that music is removed with higher accuracy when learning with augmented data. Therefore, the data augmentation method proposed in this paper is effective. © 2024 IEEE.

关键词： Conv-TasNet Copyright protection Data augmentation Environmental sounds Machine learning Music removal

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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Computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Organizational Transformation for Business Sustainability and Growth in Public Service Corporations 1

Organizational Transformation for Business Sustainability an...

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1st IEEE International Symposium on Consumer technology, ISCT 2024

作者： Widiastuti, Rini Rusdiansyah, Ahmad Widyaningrum, Retno Doctoral of Management Technology Interdisciplinary School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Industrial and Systems Engineering Faculty of Industrial Technology and Systems Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

Public service companies are essential in society, providing reliable, affordable, high-quality services focused on customer satisfaction while meeting stakeholder expectations and regulations for sustainable business growth. Strategy innovation is necessary to achieve these goals. In Indonesia, public service companies are particularly consolidated by state-owned enterprises (SOEs), which the government fully owns. SOEs innovate by forming holding companies, which the government sees as the most effective way to create synergy. Studies show that 60%-70% of organizational changes fail, highlighting the importance of successful transformation in public companies. Ensuring smooth transitions with minimal disruption while maintaining service quality is crucial for good organizational performance in public service corporations. Efficiency in transformations and transitions can be achieved through change management. This study evaluates various change management factors and frameworks, examining their impact on organizational performance using the 7s McKinsey Change Model. The research involves a literature review, surveys, and descriptive statistical analysis. The results indicate that change management factors in the McKinsey model influence organizational performance. However, the optimal change management model for public companies remains undetermined, presenting opportunities for further research. © 2024 IEEE.

关键词： Service industry

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The Inhibition Activity of Fatty Imidazoline Acetate Derived from Waste Cooking Oil on the AISI 1018 Mild Steel Corrosion

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-11页

作者： Cholidah, Tsana Kusumasari, Lutfiah Lila Febriyanti, Tria Firdaus, Achmad Fadjar Maulana Fuadi, Raihana Putri Nariswari Madurani, Kartika A. Chemistry Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia

Imidazoline and its derivatives have been proposed as effective organic corrosion inhibitors for AISI 1018 mild steel, though challenges remain regarding their production processes and efficiency in CO2/H2S-saturated brine solutions. In this study, fatty imidazoline acetate (FIA) was synthesized from waste cooking oil and evaluated for its ability to protect AISI 1018 steel in corrosive environments. The chemical composition of the synthesized FIA was confirmed through gas chromatography-mass spectroscopy (GC–MS) and Fourier transform infrared (FTIR) spectroscopy. Corrosion inhibition performance was assessed using potentiodynamic polarization at room temperature, with the most promising results further examined via weight loss measurements and contact angle analyses. A commercial inhibitor commonly used in pipelines with CO2/H2S-saturated brine was also evaluated for comparison. Surface characterization of the steel samples, both with and without inhibitors, was conducted using scanning electron microscopy (SEM). The FIA demonstrated an inhibition efficiency (IE%) of 99.89% at a concentration of 40 mg/L, closely matching the performance of the commercial inhibitor (IE% = 99.96%). This study underscores the potential of FIA as a sustainable and effective corrosion inhibitor in CO2/H2S-saturated environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Gas chromatography

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Performance Improvement of a Virtual Network Embedding Algorithm based on Temporal-difference Learning by Resource-Constraint-Aware Candidate Solution Selection

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IEIE Transactions on Smart Processing and Computing 2024年第2期13卷 158-166页

作者： Fukushima, Yukinobu Sagawa, Yuta Tarutani, Yuya Yokohira, Tokumi Faculty of Natural Science and Technology Okayama University Okayama Okayama city Japan Graduate school of Natural Science and Technology Okayama University Okayama Okayama City Japan Faculty of Interdisciplinary Science and Engineering in Health Systems Okayama University Okayama Okayama city Japan

In this paper, we tackle a virtual network embedding problem in network virtualization. For this problem, an algorithm (VNE-TD) based on temporal difference learning has been proposed. VNE-TD, however, does not consider the node and link resource constraints in selecting the candidate solutions. Therefore, when attempting to construct a virtual network, the embedding of a virtual network may fail due to insufficient resources. In this paper, we modify VNE-TD to select only those candidate solutions that satisfy the node and link resource constraints. We add a function to check the satisfiability of the node and link resource constraints to VNE-TD. The simulation results show that our modified methods reduce the blocking ratio of virtual network requests by up to 80% compared to VNE-TD. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Network embeddings

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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Computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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LoRaWAN Sensors Integration for Manufacturing Applications via Edge Device Model with OPC UA 28

LoRaWAN Sensors Integration for Manufacturing Applications v...

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28th International Conference on Applied Electronics, AE 2023

作者： Peniak, Peter Holečko, Peter Bubeniková, Emilia Kanáliková, Alžbeta University of Žilina Žilina Faculty of Electrical Engineering Engineering and Information Technology Department of Control and Information Systems Slovakia University of Žilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Žilina Slovakia

ISBN: (纸本)9798350335538

The article deals with the integration of Lo(ng) Ra(nge) Wide Area Network (LoRaWAN) sensors with generic manufacturing applications and Industrial Internet of Things (IIoT) use-cases. The focus is on creating a suitable Edge device model that enables the integration of specific Long Range (LoRa) sensors together with the other generic Internet of Things (IoT/IIoT). The model is based on the Open Platform Communications Unified Architecture (OPC UA) standard, which is the most suitable integration platform for various control systems and manufacturing applications. The model was implemented in Node-RED software and subsequently validated to confirm the required integration of LoRaWAN sensors within OPC UA models. In contrast to other approaches based on integration gateway or LoRaWAN/OPC UA protocol convertors, the proposed model utilizes extended capabilities of Edge computing. It enables Edge processing and data transformation of LoRaWAN sensors data transferred by Message Queuing Telemetry Transport (MQTT) protocol from the network server, based on requirements of manufacturing applications for cloud solutions. © 2023 IEEE.

关键词： Wide area networks

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