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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

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Neural Computing and Applications 2024年第18期36卷 10501-10525页

作者： Zitar, Raed Abu Alhadhrami, Esra Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah Thales/SCAI Sorbonne University Abu Dhabi United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Research Center University of Tabuk Tabuk71491 Saudi Arabia Thales Land and Air Systems Paris France Mohammed VI Polytechnic University-Ai movement Rabat Morocco Sorbonne University Paris France College of Engineering Yuan Ze University Taoyuan Taiwan School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia Applied science research center Applied science private university Amman Jordan

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to

关键词： Clutter (information theory)

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TreeXGNN: can gradient-boosted decision trees help boost heterogeneous graph neural networks? 48

TreeXGNN: can gradient-boosted decision trees help boost het...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hong, Ming-Yi Chang, Shih-Yen Hsu, Hao-Wei Huang, Yi-Hsiang Wang, Chih-Yu Lin, Che Academia Sinica Research Center for Information Technology Innovation Taiwan National Taiwan University and Academia Sinica Graduate Program of Data Science Taiwan National Taiwan University Graduate Institute of Communication Engineering Taiwan National Taiwan University Department of Electrical Engineering Taiwan

ISBN: (纸本)9781728163277

Graph neural networks are a promising deep learning method that can apply graph structures to various tasks. In real-world scenarios, we often have heterogeneous graphs, wherein different node and edge types capture complex interactions between nodes. High-dimensional node features provide rich information about the target nodes. Conventional heterogeneous graph neural networks (HGNN) focus more on graph structures than node features and may have difficulties extracting knowledge from complex node features. In this study, we propose a novel framework, the tree-boosted heterogeneous graph neural network abbreviated as TreeXGNN, which could efficiently and automatically extract target node features via gradient-boosted decision trees (GBDT). It integrates community structure information with proper fusion modules and a shared feature space design on HGNN. We achieved state-of-the-art performance on the three well-known heterogeneous graph benchmark datasets, IMDB, DBLP, and ACM, and significantly improved performance compared to previous studies. Our work paves the foundation for integrating tree-based models to boost HGNNs for general community analysis. © 2023 IEEE.

关键词： Classification (of information)

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Bicolour Pulse Encoding in a Polarization-Maintaining Laser by a Twistable Tapered-Fiber Lyot Filter 16

Bicolour Pulse Encoding in a Polarization-Maintaining Laser ...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Liu, Bowen Dai, Maolin Ma, Yifan Yamashita, Shinji Set, Sze Yun The University of Tokyo Research Center for Advanced Science and Technology 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan The University of Tokyo Department of Electrical Engineering and Information Systems 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

An effective method to control dichromatic pulse encodings is reported. The spectral ripples are enhanced via a twistable tapered-fiber Lyot filter, thereby degrading the actual saturable absorption and thus causing m... 详细信息

ISBN: (纸本)9798350372076

关键词： Signal encoding

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A glance over the past decade: road scene parsing towards safe and comfortable autonomous driving

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Autonomous Intelligent systems 2025年第1期5卷 1-15页

作者： Fan, Rui Li, Jiahang Li, Jiaqi Wang, Jiale Long, Ziwei Jia, Ning Liu, Yanan Wang, Wenshuo Bocus, Mohammud J. Vityazev, Sergey Chen, Xieyuanli Xiao, Junhao Andreev, Stepan Lu, Huimin Dvorkovich, Alexander College of Electronics & Information Engineering Shanghai Institute of Intelligent Science and Technology Shanghai Research Institute for Intelligent Autonomous Systems the State Key Laboratory of Intelligent Autonomous Systems and the Frontiers Science Center for Intelligent Autonomous Systems Tongji University 4800 Caoan Rd Shanghai201804 China College of Electronics & Information Engineering Tongji University 4800 Caoan Rd Shanghai201804 China School of Microelectronics Shanghai University 20 Chengzhong Rd Shanghai201800 China School of Mechanical Engineering Beijing Institute of Technology 5 Zhongguancun South Street Beijing100081 China Department of Electrical and Electronic Engineering University of Bristol Woodland Road England BristolBS8 1UB United Kingdom Faculty of Radio Engineering and Telecommunications Ryazan State Radio Engineering University Ulitsa Gagarina 59/1 Ryazan Oblast Ryazan390005 Russia College of Intelligence Science and Technology National University of Defense Technology 109 Deya Road Changsha410073 China Microwave Photonics Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia Multimedia Technology and Telecom Department Telecommunications Center Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141701 Russia

Road scene parsing is a crucial capability for self-driving vehicles and intelligent road inspection systems. Recent research has increasingly focused on enhancing driving safety and comfort by improving the detection of both drivable areas and road defects. This article reviews state-of-the-art networks developed over the past decade for both general-purpose semantic segmentation and specialized road scene parsing tasks. It also includes extensive experimental comparisons of these networks across five public datasets. Additionally, we explore the key challenges and emerging trends in the field, aiming to guide researchers toward developing next-generation models for more effective and reliable road scene parsing. © The Author(s) 2025.

关键词： Semantics

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Temporal phase characterization of picosecond pulsed squeezed vacuum

Temporal phase characterization of picosecond pulsed squeeze...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Xu, Zicong Nitanai, Sho Ozeki, Yasuyuki Research Center of Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan Department of Electrical Engineering and Information Systems The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

We propose a method to measure the temporal phase of ultrashort pulsed squeezed vacuum. We verified that the temporal chirp has limited influence on pulsed squeezing level. © Optica Publishing Group 2024

关键词： Ultrahigh vacuum

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Temporal phase characterization of picosecond pulsed squeezed vacuum

Temporal phase characterization of picosecond pulsed squeeze...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Fiber lasers

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Conceptual Model of Graph-based Individual Tree and Its Utilization in Digital Twin and Metaverse of Urban Forest

Conceptual Model of Graph-based Individual Tree and Its Util...

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Ambarwari, Agus Suwardhi, Deni Rani, Medria Shekar Husni, Emir Junaidy, Deny Willy Agirachman, Fauzan Alfi Murtiyoso, Arnadi Griess, Verena Christiane Doctoral Program of Electrical Engineering and Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia Faculty of Earth Sciences and Technology Institut Teknologi Bandung Indonesia School of Architecture Planning and Policy Development Institut Teknologi Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia Faculty of Art and Design Institut Teknologi Bandung Indonesia Department of Environmental Systems Science Forest Resources Management Institute of Terrestrial Ecosystems Zurich8092 Switzerland

3D city models are an important cornerstone in the development of digital twin cities, allowing for various analyses and simulations. CityGML, as an open standard for 3D city models, emphasizes five main aspects: scale or level of detail (LoD), semantics, geometry, topology, and appearance. One of the important elements in CityGML 3D city models is representation of vegetation. Vegetation in CityGML, especially individual trees, is still limited in terms of detail and resolution, reducing its effectiveness for specific applications. This paper proposes a graph-based conceptual model for individual trees that conforms to the CityGML v2.0 specification. The model refers to the morphological structure of a tree consisting of roots, trunk, and crown (including branches, twigs, and leaves), and considers the five main aspects of CityGML. By enhancing semantic information and geometric details, the model aims to provide an information-rich and realistic representation of individual trees in a 3D city environment. The 3D tree model created based on this conceptual model may be applicable in the development of digital twin urban forests and virtual forest simulations that utilize immersive technologies such as the metaverse. This research opens up new opportunities in the development of solitary vegetation objects through CityGML ADE by including more detailed semantic and topological information. © Author(s) 2024.

关键词： Digital elevation model

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