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Artificial Intelligence (CAI), IEEE Conference on

作者： Jun Huang Zongze Li Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

The learning-based underwater image enhancement, which is suitable for batch processing, is a pivotal research direction in underwater image processing. Extensive paired image data are required in existing learning-based methods, which necessitate considerable preprocessing and hinder the application of these methods. To address these limitations, we propose a semi-supervised approach called UWM-Net: firstly, we use a compact dataset of underwater image pairs to train the Mixture Density Network (MDN) with an underwater scene setting; subsequently, U-Net can learn underwater image enhancement more efficiently. The MDN can transform standard images into underwater scenes, reducing the reliance on paired data and making much smaller training datasets. In experimental studies, UWM-Net using only 18 pairs of underwater image data achieves highly competitive results in terms of 3 metrics compared with advanced models.

关键词： Measurement Training Learning systems Image quality Image color analysis Refining Training data

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Decomposition-Based Memetic Algorithm for Multi-Objective Fleet Size and Mix Vehicle Routing Problem

Decomposition-Based Memetic Algorithm for Multi-Objective Fl...

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Congress on Evolutionary Computation

作者： Yunpeng Ba Ruihao Zheng Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

The heterogeneous fleet vehicle routing problem (HFVRP) is of great significance in logistics and transportation. This paper considers a crucial and challenging HFVRP variant, namely multi-objective fleet size and mix vehicle routing problem with unlimited fleet, fixed cost, and dependent cost (MO-FSMVRPFD). A decomposition-based memetic algorithm called MOEA/D-ALS is proposed to cope with MO-FSMVRPFD. MOEA/D-ALS uses the multi-objective evolutionary algorithm based on decomposition (MOEA/D) as the backbone. Beyond that, it adopts a replacement strategy based on maximal fitness improvement (MFI). In addition, the local search is conducted on difficult subproblems for further refinement. The experimental studies on 18 instances show that MOEA/D-ALS exhibits a significant performance superiority over two other representative algorithms. The effectiveness of both the MFI strategy and the adaptive local search procedure is also validated.

关键词： Memetics Measurement Costs Vehicle routing Transportation Evolutionary computation Logistics

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important computer Science, engineering, and Decision Sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural Science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Towards Lightweight Underwater Depth Estimation

Towards Lightweight Underwater Depth Estimation

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Artificial Intelligence (CAI), IEEE Conference on

作者： Keyu Zhou Jin Chen Shuangchun Gui Zhenkun Wang School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Underwater depth estimation is crucial in the applications of marine robotics. It can provide environment information for target tracking, robot navigation, and 3D reconstruction of underwater terrain. Existing works transform underwater images into in-air conditions to adapt methods that are designed for natural images. However, this may result in expensive computational resources. To overcome this limitation, we propose a lightweight knowledge distillation framework for underwater depth estimation. We utilize a powerful model designed for underwater images as the teacher model and a lightweight CNN model as the student model. We distill global features to enable the student to acquire both local and global information, thereby improving estimation performance. Our framework includes a global transformation module for efficient global feature distillation and a global-local fusion module to combine local and global information for final estimation. Experimental results on the FLSea dataset demonstrate that our student model is lighter than the teacher model while outperforming lightweight in-air models. Our network is 60% lighter than the teacher model and achieves a 3.1% improvement in the δ 1 metric compared to the lightweight in-air model.

关键词： Measurement Adaptation models Three-dimensional displays Target tracking Navigation Computational modeling Estimation

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Super-resolution GANs for upscaling unplanned urban settlements from remote sensing satellite imagery - the case of Chinese urban village detection

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International Journal of Digital Earth 2023年第1期16卷 2623-2643页

作者： Alessandro Crivellari Hong Wei Chunzhu Wei Yuhui Shi Department of Computer Science and Engineering SouthernUniversity of Science and TechnologyShenzhenPeople’s Republic of China Ministry of Education Ecological Field Station for East Asian Migratory Birds Department of Earth System ScienceInstitute for Global Change StudiesTsinghua UniversityBeijingPeople’s Republic of China School of Geography and Planning Sun Yat-sen UniversityGuangzhouPeople’s Republic of China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiPeople’s Republic of China

The semantic segmentation of informal urban settlements represents an essential contribution towards renovation strategies and reconstruction *** this context,however,a big challenge remains unsolved when dealing with incomplete data acquisitions from multiple sensing devices,especially when study areas are depicted by images of different *** practice,traditional methodologies are directed to downgrade the higher-resolution data to the lowest-resolution measure,to define an overall homogeneous dataset,which is however ineffective in downstream segmentation activities of such crowded unplanned urban *** this purpose,we hereby tackle the problem in the opposite direction,namely upscaling the lower-resolution data to the highest-resolution measure,contributing to assess the use of cutting-edge super-resolution generative adversarial network(SR-GAN)*** experimental novelty targets the particular case involving the automatic detection of‘urban villages’,sign of the quick transformation of Chinese urban *** aligning image resolutions from two different data sources(Gaofen-2 and Sentinel-2 data),we evaluated the degree of improvement with regard to pixel-based landcover segmentation,achieving,on a 1 m resolution target,classification accuracies up to 83%,67%and 56%for 4x,8x,and 10x resolution upgrades respectively,disclosing the advantages of artificially-upscaled images for segmenting detailed characteristics of informal settlements.

关键词： SR-GAN resolution upscaling informal settlements urban villages cross-sensor analytics

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Advanced IOT Traffic Light Control system 5

Advanced IOT Traffic Light Control System

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Hamad, Martin Abou El Hajj, Antonio Daou, Roy Abi Zeid Hayek, Ali Haidar, Gaby Abou Lebanese German University Faculty of Public Health Biomedical Technologies Department Lebanon MART Learning Education and Research Center Lebanon University of Kassel Department of Computer Architecture and System Programming Kassel Germany American University of Science and Technology Engineering Faculty Zahle Lebanon

ISBN: (纸本)9798350309959

For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more and more critical, and if not treated smartly this issue will negatively affect drivers by wasting time and fuel gas while waiting for hours in lanes. This paper presents a new and smart way to mitigate this issue in an affordable cost, minimum processing power, and low power consumption. This concept takes into consideration the majority of the cases that may cause congestion and presents a smart and accurate outputs to ease traffic flow leading to the prediction of the peak hours of traffic congestion for smarter control. A model is designed to study the case of a four lanes crossroad with two traffic lights and two LCD monitors. The strategy in reading data is divided into two parts: real data from sensors and pre collected data from google maps to create a kind of a predicted pattern over a certain time interval. The responsiveness of the system is analyzed thoroughly, and the accuracy of all possible cases is carefully considered and evaluated. Each part of the system was tested alone, and the overall system is still in an ongoing testing phase. The results have shown minimum faulty errors and accepted outputs that can lead to safe traffic control decisions. Finally, integrating more IoT devices and sensors between V2V, V2P, V2I with the help of artificial intelligence will definitely optimize this system with higher accuracy. © 2023 IEEE.

关键词： Traffic congestion

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AI-aided power electronic converters automatic online real-time efficiency optimization method

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Fundamental Research 2025年第3期5卷 1111-1116页

作者： Tang, Yuanhong Cao, Di Xiao, Jian Jiang, Chenying Huang, Qi Li, Yunwei Chen, Zhe Blaabjerg, Frede Hu, Weihao Power System Wide-area Measurement and Control Sichuan Provincial Key Laboratory School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China MORNSUN Guangzhou Science & Technology Co. Ltd. Guangzhou 510663 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu 611731 China Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou 324000 China Department of Chengdu State Grid Corporation of China Chengdu 610599 China Southwest University of Science and Technology Mianyang 621010 China Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6G 2V4 Canada Department of Energy Technology Aalborg University Aalborg 9220 Denmark

Energy losses during the conversion and supply of electric power are considered a significant issue and cannot be estimated. Improvement in the efficiency of energy conversion systems is highly restricted because of their internal nonlinearity and complexity. Thus, inspired by the successful utilization of robotic chemists, we demonstrate a pioneering concept of artificial intelligence (AI)-aided automatic online real-time optimization of a power electronics converter using a dual active bridge (DAB) converter as an example. An optimal modulation strategy was obtained through repeated automatic exploration experiments on a practical DAB converter platform. Specifically, the DAB experimental platform operated autonomously around the clock for approximately 71 h. It performed 120,000 consecutive experiments (12,000 episodes) within a six-variable experimental space driven by a deep deterministic policy gradient (DDPG) algorithm. The proposed AI-aided automatic online real-time optimization method achieved significantly improved efficiency of power conversion and supply. Consequently, zero carbon emissions may be obtained in the future. © 2023

关键词： Artificial intelligence Automatic online real-time exploration DC-DC converter Dual active bridge converter Energy conversion system Power electronics

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Global mitigation efforts cannot neglect emerging emitters

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National Science Review 2022年第12期9卷 82-89页

作者： Can Cui Dabo Guan Daoping Wang Jing Meng Vicky Chemutai Paul Brenton Shaohui Zhang Yuli Shan Qiang Zhang Steven J.Davis Department of Earth System Science Tsinghua University The Bartlett School of Sustainable Construction University College London Department of Computer Science and Technology University of Cambridge Centre for Nature and Climate World Economic Forum School of Urban and Regional Science Shanghai University of Finance and Economics The World Bank School of Economics and Management Beihang University International Institute for Applied Systems Analysis School of Geography Earth and Environmental Sciences University of Birmingham Department of Earth System Science University of California Irvine Department of Civil and Environmental Engineering University of California Irvine

International efforts to avoid dangerous climate change have historically focused on reducing energy-related CO2emissions from countries with either the largest economies（*** EU and the USA）and/or the largest populations（*** and India）.However,in recent years,emissions have surged among a different and much less-examined group of countries,raising concerns that a next generation of high-emitting economies will obviate current mitigation ***,we analyse the trends and drivers of emissions in each of the 59 countries where emissions in 2010-2018 grew faster than the global average（excluding China and India）,project their emissions under a range of longer-term energy scenarios and estimate the costs of decarbonization *** emissions from these 'emerging emitters' reach as much as 7.5 GtCO2/year in the baseline 2.5° scenari—substantially greater than the emissions from these regions in previously published scenarios that would limit warming to 1.5℃ or even 2℃.Such unanticipated emissions would in turn require non-emitting energy deployment from all sectors within these emerging emitters,and faster and deeper reductions in emissions from other countries to meet international climate ***,the annual costs of keeping emissions at the low level are in many cases 0.2%-4.1% of countries' gross domestic production,pointing to potential trade-offs with poverty-reduction goals and/or the need for economic support and low-carbon technology transfer from historically high-emitting *** results thus highlight the critical importance of ramping up mitigation efforts in countries that to this point have been largely ignored.

关键词： emerging emiters developing countries climate change mitigation CO_2 emissions net zero

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Enhanced Congestion Control Model Based on Message Prioritization and Scheduling Mechanism in Vehicle-to-Infrastructure (V2I) 3

Enhanced Congestion Control Model Based on Message Prioritiz...

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3rd International Conference on Emerging Electrical Energy, Electronics and Computing Technologies 2021, ICE4CT 2021

作者： Ismail, Noraini Hossain, Mohammad Asif Md.noor, Rafidah Wahab, Ainuddin Wahid Abdul Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Darul Iman Kuala Nerus21030 Malaysia

Vehicular Ad Hoc Networks (VANETs) is an origination of Mobile Ad-Hoc Network (MANET), where road vehicles will distribute messages and provide safety alerts to notice hazardous circumstances to the drivers. One of its greatest challenges is ensuring Quality of Service (QoS) owing to channel sharing, high traffic and topology changes in VANETs. These challenges cause of degradation of network performance. Congestion control should be properly considered to improve the network performance and transmission message over VANETs. However, there seem significant limitations to most of the other current congestion control mechanisms. In this paper, an enhanced congestion control model based on prioritization and scheduling-based strategy is proposed. This strategy assigns priorities of the safety messages and non-safety messages based of the message type and network condition. Further, safety message can be classified into event-driven messages and beacon messages. At that point, all messages transmit into the appropriate queue based on their priority and schedule all messages in each queue. However, when new message enqueue, reschedule in each queue may cause additional unwanted delay to be processed cause of inefficient scheduling technique. Thus, an improved dynamic scheduling algorithm also proposed to schedule the messages in this circumstance. © Published under licence by IOP Publishing Ltd.

关键词： Vehicular ad hoc networks

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Noise-Resilient Homomorphic Encryption: A Framework for Secure Data Processing in Health care Domain

arXiv

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arXiv 2024年

作者： Shuriya, B. Kumar, S. Vimal Bagyalakshmi, K. Department of Computer Science and Engineering PPG Institute of Technology Coimbatore641035 India Department of Defense System Engineering Sejong University Seoul05006 Korea Republic of Department of Mathematics SNS College of Technology Coimbatore641035 India

In this paper, we introduce the Fully Homomorphic Integrity Model (HIM), a novel approach designed to enhance security, efficiency, and reliability in encrypted data processing, primarily within the health care industry. HIM addresses the key challenges that noise accumulation, computational overheads, and data integrity pose during homomorphic operations. Our contribution of HIM: advances in noise management through the rational number adjustment;key generation based on personalized prime numbers;and time complexity analysis details for key operations. In HIM, some additional mechanisms were introduced, including robust mechanisms of decryption. Indeed, the decryption mechanism ensures that the data recovered upon doing complex homomorphic computation will be valid and reliable. The healthcare id model is tested, and it supports real-time processing of data with privacy maintained concerning patients. It supports analytics and decision-making processes without any compromise on the integrity of information concerning patients. Output HIM promotes the efficiency of encryption to a greater extent as it reduces the encryption time up to 35ms and decryption time up to 140ms, which is better when compared to other models in the existence. Ciphertext size also becomes the smallest one, which is 4KB. Our experiments confirm that HIM is indeed a very efficient and secure privacy-preserving solution for healthcare applications. © 2024, CC0.

关键词： Data integrity

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