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5th International Conference on Advancements in Computing, ICAC 2023

作者： Dilshan, K.N. Rathnayake, H.A.C.K. Pathirana, L.P.T.L. Dhanapala, D.H.S.V. Fernando, Harinda Abeygunawardhana, Pradeep K.W Sri Lanka Institute of Information Technology Faculty of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

关键词： Blockchain

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Learning a Coordinated Network for Detail-Refinement Multiexposure Image Fusion

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IEEE Transactions on Circuits and systems for Video Technology 2023年第2期33卷 713-727页

作者： Li, Jiawei Liu, Jinyuan Zhou, Shihua Zhang, Qiang Kasabov, Nikola K. Ministry of Education School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Dalian116622 China Dalian University of Technology School of Software Technology Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1010 New Zealand Ulster University Intelligent Systems Research Center LondonderryBT52 1SA United Kingdom

Nowadays, deep learning has made rapid progress in the field of multi-exposure image fusion. However, it is still challenging to extract available features while retaining texture details and color. To address this difficult issue, in this paper, we propose a coordinated learning network for detail-refinement in an end-to-end manner. Firstly, we obtain shallow feature maps from extreme over/under-exposed source images by a collaborative extraction module. Secondly, smooth attention weight maps are generated under the guidance of a self-attention module, which can draw a global connection to correlate patches in different locations. With the cooperation of the two aforementioned used modules, our proposed network can obtain a coarse fused image. Moreover, by assisting with an edge revision module, edge details of fused results are refined and noise is suppressed effectively. We conduct subjective qualitative and objective quantitative comparisons between the proposed method and twelve state-of-the-art methods on two available public datasets, respectively. The results show that our fused images significantly outperform others in visual effects and evaluation metrics. In addition, we also perform ablation experiments to verify the function and effectiveness of each module in our proposed method. The source code can be achieved at https://***/lok-18/LCNDR. © 1991-2012 IEEE.

关键词： Extraction

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Establishing Pattern Sequences Using Artificial Neural Networks with an Application to Organizational Patterns 10

Establishing Pattern Sequences Using Artificial Neural Netwo...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Matovič, Viktor Vranić, Valentino Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava84216 4 Slovakia

Explicit relationships included in textual description of patterns are insufficient to establish pattern sequences. This article brings a new method of establishing pattern sequences using artificial neural networks. The method is based on extracting implicit relationships between organizational patterns using the softmax regression model by Zhang et al., which are learned by the artificial neural network and encoded in its weights. The implementation of the method is based on the mini-batch stochastic gradient descent method by Zhang et al. and the Keras application programming interface of the TensorFlow framework. To evaluate the method, we conducted four experiments on selected organizational patterns documented by Coplien and Harrison. Two pattern sequences were established in each experiment. For each pattern sequence, a pattern story was created in order to check its meaningfulness. © 2023 Copyright for this paper by its authors.

关键词： artificial neural networks organizational pattern pattern relationships pattern sequence softmax regression

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A set of study cases for the massive integration of solar renewables in non-interconnected areas

A set of study cases for the massive integration of solar re...

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2023 ISES Solar World Congress, SWC 2023

作者： David, Mathieu Andriamandroso, Mirhado Nahary Poulsen, Peter Behrensdorff Castaing-Lasvignottes, Jean Cutululis, Nicolaos Das, Kaushik Durif-Aboukali, Chloé Francou, Johann Lauret, Philippe Le Gal La Salle, Josselin Lorenz, Elke Marc, Olivier Melgar, David Spataru, Sergiu PIMENT University of La Reunion Saint-Denis France Department of Electrical and Photonics Engineering Technical University of Denmark Roskilde Denmark Department of Wind and Energy Systems Technical University of Denmark Roskilde Denmark Fraunhofer Institute for Solar Energy Systems ISE Freiburg Germany

ISBN: (纸本)9783982040899

The massive integration of solar renewable energies is challenging in non-interconnected areas such as remote islands or isolated regions. Indeed, their power grid, which cannot rely on the support of larger electricity networks, is more vulnerable to the inherent variability of the solar resource and grid failures, such as sudden breakdown of production units or transmission lines. The TwInSolar project, funded by the European Commission, aims to provide support and solutions to overcome the problems faced by island territories not connected to continental electricity grids. As a part of this project, four study cases are presented to the scientific community, each highlighting specific issues observed at different scales on the island of La Reunion. This article aims to provide a detailed description of the four selected systems, the corresponding challenges, and the data available. © 2023. The Authors.

关键词： Power grids

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Haptic-Aided Navigation Vehicle: Enhancing Obstacle Detection in Blind Spots and Transparent Object Scenarios

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Journal of Shanghai Jiaotong University (Science) 2025年 1-9页

作者： Li, Mingwang Li, Xinde Zhang, Zhentong Zhang, Zeyu Zhao, Haoming School of Software Southeast University Suzhou Jiangsu215000 China School of Automation Southeast University Nanjing210018 China School of Cyber Science and Engineering Southeast University Nanjing211102 China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education Nanjing214135 China Nanjing Center for Applied Mathematics Nanjing211135 China Southeast University Shenzhen Research Institute Shenzhen Guangdong518063 China

As autonomous mobile robots are increasingly deployed in complex environments, traditional vision sensors and LiDAR encounter considerable limitations, particularly in detecting obstacles in blind spots or transparent objects. To address the issue of blind spots, we design a specialized haptic sensing structure and develop the haptic-aided navigation vehicle (HANV). This system integrates haptic sensors and LiDAR to deliver comprehensive perception, significantly enhancing close-range obstacle detection in areas that are typically beyond the range of conventional sensors. To tackle the challenge of transparent obstacles, which are often undetected by both vision and LiDAR sensors, we employ a fusion of haptic sensors and LiDAR. The haptic system provides physical contact feedback, ensuring reliable detection of transparent obstacles such as glass, while LiDAR offers long-range sensing capabilities. This combination enables HANV to navigate effectively in environments with transparent obstacles, overcoming the limitations of traditional sensing systems. Experiment results indicate that the proposed haptic and LiDAR integration substantially improves obstacle detection in both blind spots and environments with transparent obstacles. HANV achieves high success rates, minimal collisions, and efficient obstacle avoidance, particularly excelling in complex, confined spaces where conventional systems prove inadequate. These findings emphasize the efficacy of our approach in enhancing navigation performance in dynamic and challenging environments. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： Mobile robots

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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2023 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2023

作者： Zhou, Yikui Wang, Jie Tang, Junnan Gou, Chao Jiang, Zigui Li, Dan Ng, See-Kiong Sun-Yat Sen University School of Software Engineering Zhuhai China Sun-Yat Sen University School of Intelligent Systems Engineering Shenzhen China National University of Singapore Institute of Data Science School of Computing Singapore

ISBN: (纸本)9798350312270

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted. © 2023 IEEE.

关键词： Cyber Physical System

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Phase Re-Service in Reinforcement Learning Traffic Signal Control

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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International Conference on Intelligent Transportation

作者： Zhiyao Zhang George Gunter Marcos Quinones-Grueiro Yuhang Zhang William Barbour Gautam Biswas Daniel Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University USA Institute for Software Integrated Systems Vanderbilt University USA Department of Computer Science Institute for Software Integrated Systems Vanderbilt University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation.

关键词： Shock waves Reinforcement learning Delays Logic Standards Queueing analysis Optimization Intelligent transportation systems

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A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving systems

arXiv

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arXiv 2024年

作者： Aslam, Iqra Buragohain, Abhishek Bamal, Daniel Aniculaesei, Adina Zhang, Meng Rausch, Andreas Institute for Software and Systems Engineering Technische Universität Clausthal Clausthal-Zellerfeld Germany

Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications. © 2024, CC BY.

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Performance of VoNR and OTT Voice in Congested and Uncongested 5G Networks 2

Performance of VoNR and OTT Voice in Congested and Uncongest...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Modroiu, Elena-Ramona Mwangama, Joyce Corici, Marius Malila, Bessie Magedanz, Thomas University of Cape Town Electrical Engineering Cape Town South Africa Technische Universität Berlin Next Generation Networks Dept. Berlin Germany Fraunhofer Fokus Institute Software-based Networks Dept. Berlin Germany

ISBN: (纸本)9798331530099

Open-source projects implementing components of 5G systems are the catalyst of the evolution of current telecommunication networks towards the 6th generation of mobile networks, driving prototyping and standardization. They also play a main role in the wide industrial adoption of private 5G solutions by small and medium-size businesses. In this paper, we conduct an experimental comparison of the performances of voice-over-new-radio and over-the-top voice services added to a 5G stand-alone testbed in congested and uncongested network conditions. At the heart of the experiments is the world's first open-source voice-over-new-radio prototype, which was recently implemented and demonstrated at the Technische Universität Berlin. Measurements of performance metrics, including call setup time, jitter and packet loss, are presented and evaluated. © 2024 IEEE.

关键词： 5G mobile communication systems

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