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Optimized Autonomous Computing with Trusted Resilient Data Management for iot Emerging Networks

IEEE Sensors Journal 2025年

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (iot) network. © 2025 IEEE.

关键词： Autonomous Computing Emerging Networks Mobile devices Smart network Trusted Data Management

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Using NSGA-Ⅲ for optimising biomedical ontology alignment

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CAAI Transactions on Intelligence technology 2019年第3期4卷 135-141页

作者： Xingsi Xue Jiawei Lu Junfeng Chen College of Information Science and Engineering Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Intelligent Information Processing Research Center Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhouFujianPeople’s Republic of China Fujian Key Laboratory for Automotive Electronics and Electric Drive Fujian University of TechnologyFuzhouFujianPeople’s Republic of China College of IOT Engineering Hohai UniversityChangzhouJiangsuPeople’s Republic of China

To support semantic inter-operability between the biomedical information systems, it is necessary to determine the correspondences between the heterogeneous biomedical concepts, which is commonly known as biomedical ontology matching. Biomedical concepts are usually complex and ambiguous, which makes matching biomedical ontologies a challenge. Since none of the similarity measures can distinguish the heterogeneous biomedical concepts in any context independently, usually several similarity measures are applied together to determine the biomedical concepts mappings. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on the similarity measures significantly reduces the alignment’s quality. In this study, a non-dominated sorting genetic algorithm (NSGA)-III-based biomedical ontology matching technique is proposed to effectively match the biomedical ontologies, which first utilises an ontology partitioning technique to transform the large-scale biomedical ontology matching problem into several ontology segment-matching problems, and then uses NSGA-III to determine the optimal alignment without tuning the aggregating weights. The experiment is conducted on the anatomy track and large biomedic ontologies track which are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI’s participants show the effectiveness of the authors’ approach.

关键词： ontology ontologies transform together Ontology complex context effects

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A novel contact engineering method for transistors based on two-dimensional materials

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Journal of Materials Science & technology 2021年第10期69卷 15-19页

作者： Yaochen Sheng LuFang Zhang Feng Li Xinyu Chen Zhijian Xie Haiyan Nan Zihan Xu David Wei Zhan Jianhao Chen Yong Pu Shaoqing Xiao Wenzhong Bao State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics&SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433China Engineering Research Center of IoT Technology Applications(Ministry of Education) Department of Electronic EngineeringJiangnan UniversityWuxi 214122China International Center for Quantum Materials School of PhysicsPeking UniversityBeijing 100871China New Energy Technology Engineering Laboratory of Jiangsu Province&School of Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Six Carbon Tech ShenzhenShenzhen 518106China

Contact engineering is of critical importance for two-dimensional(2D)transition metal dichalcogenide(TMD)-based ***,there are only a few solutions to overcome this obstacle because of the complexity of the TMD-contact *** this work,we propose a novel method using a soft plasma treatment followed by the seamless deposition of a metal electrode to reduce the contact resistance of MoS_(2)field effect transistors(FETs).The treated FETs exhibit three times higher mobility than the control FETs without plasma *** soft plasma treatment can remove the facial sulfur atoms and expose the middle Mo atoms so that they come into direct contact with the metal electrode,thus greatly improving the contact ***-principles calculation is also performed to support the experimental *** potentially scalable strategy can be extended to the whole family of TMD based FETs to provide a possible route of device processsing technology for 2D device application.

关键词： Electrical contact resistance MoS_(2) Plasma

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A Gender Recognition Method for Chinese Mitten Crab in Low Light Environment Based on Machine Vision

A Gender Recognition Method for Chinese Mitten Crab in Low L...

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2024 China Automation Congress, CAC 2024

作者： Tian, Junlong Guo, Ya Liu, Yingfei Zhang, Jianguo Jiang, Yongnian International Joint Research Center for Intelligent Optical Sensing and Applications Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Aquatic and Livestock Station of Yixing Agriculture and Rural Bureau Yixing China Wuxi Agricultural Equipment Technology Extension Center Wuxi China Jiangsu Zhongnong IoT Technology Co. LTD Yixing China

ISBN: (纸本)9798350368604

The acquisition of gender information of Chinese mitten crabs is crucial for survival rate assessment and biomass estimation. Machine vision is commonly used for the automatic gender recognition of Chinese mitten crabs, but the low light conditions caused by deeper containers reduce the accuracy of existing algorithms. To improve the accuracy of gender recognition of Chinese mitten crabs in low light environments, this study incorporates the GAM into the backbone of YOLOv7, improving the capability to distinguish gender-specific features. A lightweight upsampling operator, CARAFE, replaces the original upsampling module, expanding the model’s receptive field and better aggregating global information. Using Focal-CIoU to mitigate the impact of low-quality prediction boxes, enabling the model to better optimize high-quality ones. By constructing an image dataset of Chinese mitten crabs in low-light environments to validate the model’s performance, experimental results demonstrate that the average precision of the model established in this study is 0.870, 10.6% higher than the most update algorithm GMNet-YOLOv4, effectively performing the classification task of Chinese mitten crabs in low-light environments. © 2024 IEEE.

关键词： Machine vision

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A hybrid NSGA-II for matching biomedical ontology 14th

A hybrid NSGA-II for matching biomedical ontology

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14th International Conference on intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Xue, Xingsi Chen, Jie Chen, Junfeng Chen, Dongxu College of Information Science and Engineering Fujian University of Technology FuzhouFujian350118 China Intelligent Information Processing Research Center Fujian University of Technology FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology FuzhouFujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology FuzhouFujian350118 China College of IOT Engineering Hohai University ChangzhouJiangsu China Fujian Medical University Union Hospital FuzhouFujian350001 China

ISBN: (纸本)9783030037444

Over the recent years, ontologies are widely used in the biomedical domains. However, biomedical ontology heterogeneity problem hamper the cooperation between intelligent applications based on biomedical ontologies. It is crucial to establish correspondences between the heterogeneous biomedical concepts in different ontologies, which is so-called biomedical ontology matching. Approaches based on Multi-Objective Evolutionary Algorithm (MOEA), such as NSGA-II, are emerging as a new methodology to solve the ontology matching problem. In this paper, to further improve the quality of biomedical ontology alignments, a hybrid NSGA-II is proposed, which modifies the knee solutions in the Pareto front by using a local search method. Experiment utilizes two biomedical ontology matching tracks provided by Ontology Alignment Evaluation Initiative (OAEI 2017). The experimental results show that our approach outperforms the participants of OAEI 2017 and NSGA-II based ontology matching technique. © Springer Nature Switzerland AG 2019.

关键词： Ontology

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A Gender Recognition Method for Chinese Mitten Crab in Low Light Environment Based on Machine Vision

A Gender Recognition Method for Chinese Mitten Crab in Low L...

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Chinese Automation Congress (CAC)

作者： Junlong Tian Ya Guo Yingfei Liu Jianguo Zhang Yongnian Jiang International Joint Research Center for Intelligent Optical Sensing and Applications Jiangnan University Wuxi China Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Aquatic and Livestock Station of Yixing Agriculture and Rural Bureau Yixing China Wuxi Agricultural Equipment Technology Extension Center Wuxi China Jiangsu Zhongnong IoT Technology Co. LTD Yixing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The acquisition of gender information of Chinese mitten crabs is crucial for survival rate assessment and biomass estimation. Machine vision is commonly used for the automatic gender recognition of Chinese mitten crabs, but the low light conditions caused by deeper containers reduce the accuracy of existing algorithms. To improve the accuracy of gender recognition of Chinese mitten crabs in low light environments, this study incorporates the GAM into the backbone of YOLOv7, improving the capability to distinguish gender-specific features. A lightweight upsampling operator, CARAFE, replaces the original upsampling module, expanding the model's receptive field and better aggregating global information. Using Focal-Clo U to mitigate the impact of low-quality prediction boxes, enabling the model to better optimize high-quality ones. By constructing an image dataset of Chinese mitten crabs in low-light environments to validate the model's performance, experimental results demonstrate that the average precision of the model established in this study is 0.870, 10.6% higher than the most update algorithm GMNet-YOLOv4, effectively performing the classification task of Chinese mitten crabs in low-light environments.

关键词： Biometrics Accuracy Target recognition Machine vision Estimation Object detection Predictive models Containers Prediction algorithms Classification algorithms

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Interactive Ontology Matching Based on Evolutionary Algorithm

Interactive Ontology Matching Based on Evolutionary Algorith...

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International Conference on Computational Intelligence and Security

作者： Xingsi Xue Junfeng Chen Aihong Ren College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of IOT Engineering Hohai University ChanZhou China School of Mathematics and Information Science Baoji University of Arts and Sciences Baoji China

ISBN: (数字)9781728160924

ISBN: (纸本)9781728160931

Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.

关键词： Ontologies Graph theory Pattern matching Evolutionary computation

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第Sup1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

Shaoqing Wang1, Xiancun Yang2, Meixia Su1, Qiang Liu1 1Department of MRI, Shandong Medical Imaging Research Institute Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China; 2Department of Interventional Radiology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong, 250021, People's Republic of China Correspondence: Qiang Liu (2002md@***) Aims To evaluate the diagnostic value of three- dimensional rotational angiography (3D-RA) of intracranial micro-aneurysms (diameter ≤ 3 mm) and provide guidance on the value of endovascular treatment. Materials and methods 43 patients with intracranial micro-aneurysms were analyzed retrospectively, all patients had undergone angiography with both conventional 2D-DSA(Two-Dimensional Digital Subtraction Angiography) and rotational angiography with three-dimensional reconstruction; the frequency of detection of aneurysms, depiction of aneurysm neck, radiation dose, and the dosage of contrast agent were recorded respectively. Results 55 pieces of aneurysms were detected out from the 43 cases with intracranial micro-aneurysms by 3D-RA. But only 39 cases were detected out using 2D-DSA from the 55 samples, there were significant differences with regards to detection rate (P < 0.05). There were significant differences in radiation dose and dosage of contrast agent (P < 0.05) between the two methods of using 3D-RA can improve the detection rate of micro-aneurysms, which bestows obvious advantages on displaying the shape of aneurysms, the aneurysm neck at the best angle, and the relationship with the parent artery, at the same time, the amount of contrast agent and radiation dose are reduced in 3D-RA compared to 2D-DSA.

关键词： Convolutional Neural Network High Resolution Compute Tomography Intensity Inhomogeneity Kernel Support Vector Machine Clonal Selection Algorithm

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