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The SDN-Governed Ad Hoc Swarm for Mobile Surveillance of Meteorological Facilities 16th

The SDN-Governed Ad Hoc Swarm for Mobile Surveillance of Me...

16th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2020

作者： Chen, Xi Wu, Tao Tan, Ying The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu University Chengdu China School of Computer Science Chengdu University of Information Technology Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9783030775681

Accurate meteorological observation relies heavily on the proper and precise working of meteorological facilities. Nevertheless, a big portion of meteorological facilities are deployed in outdoor environments hardly within the reach of convenient monitoring and reliable networking infrastructure, hence the surveillance challenge. Given such an infrastructure-less/-poor environment (deserts, oceans, etc.) for meteorological facilities, Ad Hoc nodes are possible candidates for mobile surveillance. However, pure Ad Hoc networking without a logical centric node can barely provide consistent collaboration between mobile nodes during monitoring. This paper proposes a tunnelled overlay structure that bridges the Ad Hoc protocol stack and the SDN (Software-Defined Networking) protocol stack based on network virtualization techniques, so that robust distributed Ad Hoc mobile nodes are grouped in the form of an SDN-governed swarm to conduct the mobile surveillance task with joint efforts under the consistent control of the centric SDN controller. In addition, mobile nodes are equipped with TensorFlow-based image recognition feature, capable of transmitting the recognized results of harmful creatures that might cause facility damages, to suggest proper protection measures, control/avoid facility loss, etc. Experiments on the prototype SDN-governed Ad Hoc swarm are carried out in a real-world university campus meteorological station to demonstrate its feasibility and functionalities. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Image recognition

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The Power of Age-based Reward in Fresh Information Acquisition

The Power of Age-based Reward in Fresh Information Acquisiti...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhiyuan Wang Qingkai Meng Shan Zhang Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Institute of Artificial Intelligence Beihang University Beijing China State Key Laboratory of Software Development Environment

Many Internet platforms collect fresh information of various points of interest (PoIs) relying on users who happen to be nearby the PoIs. The platform will offer reward to incentivize users and compensate their costs incurred from information acquisition. In practice, the user cost (and its distribution) is hidden to the platform, thus it is challenging to determine the optimal reward. In this paper, we investigate how the platform dynamically rewards the users, aiming to jointly reduce the age of information (AoI) and the operational expenditure (OpEx). Due to the hidden cost distribution, this is an online non-convex learning problem with partial feedback. To overcome the challenge, we first design an age-based rewarding scheme, which decouples the OpEx from the unknown cost distribution and enables the platform to accurately control its OpEx. We then take advantage of the age-based rewarding scheme and propose an exponentially discretizing and learning (EDAL) policy for platform operation. We prove that the EDAL policy performs asymptotically as well as the optimal decision (derived based on the cost distribution). Simulation results show that the age-based rewarding scheme protects the platform’s OpEx from the influence of the user characteristics, and verify the asymptotic optimality of the EDAL policy.

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A Property-Based Hybrid Genetic Algorithm and Tabu Search for Solving Order Acceptance and Scheduling Problem with Trapezoidal Penalty Membership Function

SSRN

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SSRN 2022年

作者： An, Youjun Zhao, Ziye Chen, Xiaohui Li, Yinghe Gao, Kaizhou College of Mechanical and Vehicle Engineering Chongqing University Chongqing400030 China Macau Institute of Systems Engineering Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology 999078 China School of Computer Science Liaocheng University Liaocheng252000 China

Under a highly competitive make-to-order environment, the limited production capacity and strict delivery requirement make the manufacturer needs to simultaneously consider which orders should be accepted and how to arrange these accepted orders for production so as to ensure the profitability and improving the customer satisfaction. In this context, this paper studies an order acceptance and scheduling (OAS) problem in a single machine environment with the primary purpose of partitioning orders into two subsets (i.e., the subsets of accepted and rejected orders) and scheduling the accepted orders to maximize the total net profit (TNP), and the earliness / tardiness penalty under the common due window is considered. Specifically, (1) a trapezoidal earliness/tardiness penalty membership function under the common due window is designed, and a mathematical model is established to characterize the concerned problem;(2) six important problem properties are derived for determining which orders to be accepted or rejected, arranging the processing sequence of accepted orders and deciding the start processing time for each accepted orders;(3) an effective property-based hybrid algorithm (GATS-SSRIR) is designed to deal with the concerned problem, which hybridizes the genetic algorithm, tabu search and six important properties;and (4) eleven initialization rules are proposed to generate the high-quality initial population and a greedy selection method based on the similarity of individuals is applied to enhance the diversity of solutions. In the numerical experiments, the Taguchi method is first employed to optimize the parameter combination for the proposed algorithm under different initialization rules. Second, the effectiveness and superiority of proposed properties are verified by comparing with other strategies. Next, the performance and competitiveness of the proposed GATS-SSRIR algorithm are demonstrated by comparing with the variants of three intelligent algorithms. F

关键词： Membership functions

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BinaryAI: Binary Software Composition Analysis via Intelligent Binary Source Code Matching

BinaryAI: Binary Software Composition Analysis via Intellige...

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International Conference on Software engineering (ICSE)

作者： Ling Jiang Junwen An Huihui Huang Qiyi Tang Sen Nie Shi Wu Yuqun Zhang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China Tencent Security Keen Lab Shanghai China Department of Computer Science and Engineering Southern University of Science and Technol-ogy Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

While third-party libraries (TPLs) are extensively reused to enhance productivity during software development, they can also introduce potential security risks such as vulnerability propagation. Software composition analysis (SCA), proposed to identify reused TPLs for reducing such risks, has become an essential procedure within modern DevSecOps. As one of the mainstream SCA techniques, binary-to-source SCA identifies the third-party source projects contained in binary files via binary source code matching, which is a major challenge in reverse engineering since binary and source code exhibit substantial disparities after compilation. The existing binary-to-source SCA techniques leverage basic syntactic features that suffer from redundancy and lack robustness in the large-scale TPL dataset, leading to inevitable false positives and compromised recall. To mitigate these limitations, we introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. First, BinaryAI trains a transformer-based model to produce function-level embeddings and obtain similar source functions for each binary function accordingly. Then by applying the link-time locality to facilitate function matching, BinaryAI detects the reused TPLs based on the ratio of matched source functions. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task. Specifically, our embedding model outperforms the state-of-the-art model CodeCMR, i.e., achieving 22.54% recall@l and 0.34 MRR compared with 10.75% and 0.17 respectively. Additionally, BinaryAI outperforms all existing binary-to-source SCA tools in TPL detection, increasing the precision from 73.36% to 85.84% and recall from 59.81% to 64.98% compared with the well-recognized commercial SCA product Black Duck. E-https://***

关键词： Codes Source coding Semantics Redundancy Syntactics Transformers Software

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A NEW APPROACH ON CONDUCTING FLEXIBILITY STUDIES BASED ON NEURAL NETWORKS - APPLICATION ON THE HELLENIC TRANSMISSION SYSTEM 14

A NEW APPROACH ON CONDUCTING FLEXIBILITY STUDIES BASED ON NE...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Kaskouras, Christos D. Krommydas, Konstantinos F. Baltas, Ioannis Papaioannou, George P. Papayiannis, Georgios I. Yannacopoulos, Athanasios N. Department of Statistics Athens University of Economics and Business Athens Greece Stochastic Modelling and Applications Laboratory Athens University of Economics and Business Athens Greece Research Technology and Development Department Independent Power Transmission Operator S.A. Athens Greece Department of Electrical and Computer Engineering University of Patras Rion Greece Department of Financial and Management Engineering University of Aegean Chios Greece Center for Research and Applications of Nonlinear Systems CRANS University of Patras Rion Greece Department of Statistics and Insurance Science University of Piraeus Piraeus Greece

ISBN: (纸本)9781837242689

In order to meet the environmental targets, a large scale integration of renewable energy sources (RESs) has been taking place in power systems. Although those sources contribute to more clean energy and help to reduce the green house gasses emissions, they pose significant challenges to the system's stability due to the intermittency and the high level of dependence to the weather conditions. One of the main challenges is to sustain the system's balance and to guarantee the flexibility adequacy. For this purpose, the need for more comprehensive flexibility studies will increase in the future, since different scenarios should be examined according to various level of RESs, demand and weather conditions. In this work, we propose a novel approach for the unit commitment (UC) algorithm that is implemented in the conventional flexibility studies that aims to reduce the computational time and simultaneously sustain the results' accuracy in a satisfactory level. For this scope, we designed a nested-neural network system of two recurrent neural networks with long short-term memory, which are integrated and aim to reproduce the results of the complex conventional UC algorithm. Results show that the predicted insufficient ramping resource expectation of the two solutions are similar, while the computational time is being reduced by almost 99%. © The Institution of engineering & Technology 2024.

关键词： FLEXIBILITY RECURRENT NEURAL NETWORKS TRANSMISSION SYSTEM

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PCB Defect Detection Method Based on G-YOLOv8

PCB Defect Detection Method Based on G-YOLOv8

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Chao Gao Mengting Bai Xiaoqin Lian Xiangyu Hu Shuangshuang Han Shichao Chen School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing Huairou Academy of Parallel Sensing Beijing China

ISBN: (数字)9798350376739

ISBN: (纸本)9798350376746

In the electronics industry, PCB is crucial for ensuring the product quality. However, these defects of PCB generally have the characteristics of complex backgrounds, small sizes and similar features, resulting in the issues of high computational complexity or low detection precision of PCB defect detection algorithms based on deep learning. To resolve the aforementioned concerns, this paper proposed a lightweight PCB defect detection model named G-YOLOv8 which was based on the YOLOv8. The G-YOLOv8 can effectively reduce the computational complexity by integrating the Ghost Conv into the Backbone and Neck networks. Furthermore, the G-C2f module was introduced into Neck network to strengthen the capability of G-YOLOv8 to perceive the feature information at different scales and semantic levels, thus improving the detection performance of the model for the PCB defect. Meanwhile, the performance of G-YOLOv8 model was evaluated on the public data set. The experimental results show that the parameter number of G-YOLOv8 is 2.6 million, which is decreased by 89.7% and 13.3% compared with SSD and YOLOv8, respectively. The computational load of G-YOLOv8 is 7.5 GFLOPs, which is decreased by 93.5% and 7.4% compared with SSD and YOLOv8, respectively. The mAP@0.5 of G-YOLOv8 is 94.4%, which is increased by 4.8% and 1.3% compared with SSD and YOLOv8, respectively. Hence, in the task of PCB defect detection, the G-YOLOv8 model can reduce the computational complexity while preserving a high detection precision, which can meet the requirements of PCB defect detection in industrial scenarios.

关键词： Computational modeling Semantics Feature extraction Data models Neck Social intelligence Quality assessment Computational complexity Defect detection Load modeling

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Enhancing Visual Reasoning with LLM-Powered Knowledge Graphs for Visual Question Localized-Answering in Robotic Surgery

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Hao, Pengfei Wang, Hongqiu Yang, Guang Zhu, Lei Hong Kong University of Science and Technology ROAS Thrust Systems Hub Guangzhou China Imperial College London Bioengineering/Imperial-X London United Kingdom Guangdong Guangzhou511400 China Hong Kong University of Science and Technology Henan Key Laboratory of Imaging and Intelligent Processing Department of Electronic and Computer Engineering SAR Hong Kong Hong Kong

Expert surgeons often have heavy workloads and cannot promptly respond to queries from medical students and junior doctors about surgical procedures. Thus, research on Visual Question Localized-Answering in Surgery (Surgical-VQLA) is essential to assist medical students and junior doctors in understanding surgical scenarios. Surgical-VQLA aims to generate accurate answers and locate relevant areas in the surgical scene, requiring models to identify and understand surgical instruments, operative organs, and procedures. A key issue is the model's ability to accurately distinguish surgical instruments. Current Surgical-VQLA models rely primarily on sparse textual information, limiting their visual reasoning capabilities. To address this issue, we propose a framework called Enhancing Visual Reasoning with LLM-Powered Knowledge Graphs (EnVR-LPKG) for the Surgical-VQLA task. This framework enhances the model's understanding of the surgical scenario by utilizing knowledge graphs of surgical instruments constructed by the Large Language Model (LLM). Specifically, we design a Fine-grained Knowledge Extractor (FKE) to extract the most relevant information from knowledge graphs and perform contrastive learning with the extracted knowledge graphs and local image. Furthermore, we design a Multi-attention-based Surgical Instrument Enhancer (MSIE) module, which employs knowledge graphs to obtain an enhanced representation of the corresponding surgical instrument in the global scene. Through the MSIE module, the model can learn how to fuse visual features with knowledge graph text features, thereby strengthening the understanding of surgical instruments and further improving visual reasoning capabilities. Extensive experimental results on the EndoVis-17-VQLA and EndoVis-18-VQLA datasets demonstrate that our proposed method outperforms other state-of-the-art methods. We will release our code for future research. © 2013 IEEE.

关键词： Knowledge graph

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Correction to: Loss-based differentiation strategy for privacy preserving of social robots

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The Journal of Supercomputing 2022年第1期79卷 1159-1160页

作者： Yang, Qinglin Wang, Taiyu Zhu, Kaiming Wang, Junbo Han, Yu Su, Chunhua School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China Department of Computer Science and Engineering the University of Aizu Aizuwakamatsu Japan

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WING: Wheel-Inertial Neural Odometry with Ground Manifold Constraints

arXiv

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arXiv 2024年

作者： Jiang, Chenxing Zhang, Kunyi Yang, Sheng Shen, Shaojie Xu, Chao Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Alibaba DAMO Academy Hangzhou China Huzhou Institute Zhejiang University Huzhou China

In this paper, we propose an interoceptive-only odometry system for ground robots with neural network processing and soft constraints based on the assumption of a globally continuous ground manifold. Exteroceptive sensors such as cameras, GPS and LiDAR may encounter difficulties in scenarios with poor illumination, indoor environments, dusty areas and straight tunnels. Therefore, improving the pose estimation accuracy only using interoceptive sensors is important to enhance the reliability of navigation system even in degrading scenarios mentioned above. However, interoceptive sensors like IMU and wheel encoders suffer from large drift due to noisy measurements. To overcome these challenges, the proposed system trains deep neural networks to correct the measurements from IMU and wheel encoders, while considering their uncertainty. Moreover, because ground robots can only travel on the ground, we model the ground surface as a globally continuous manifold using a dual cubic B-spline manifold to further improve the estimation accuracy by this soft constraint. A novel space-based sliding-window filtering framework is proposed to fully exploit the C2 continuity of ground manifold soft constraints and fuse all the information from raw measurements and neural networks in a yaw-independent attitude convention. Extensive experiments demonstrate that our proposed approach can outperform state-of-the-art learning-based interoceptive-only odometry methods. © 2024, CC BY.

关键词： Navigation systems

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Optimal Power Control Based-Metaheuristic Algorithm Based Variable Speed Wind Energy System of DFIG

Optimal Power Control Based-Metaheuristic Algorithm Based Va...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Boussaid Ibrahim Harrouz Abdelkader Merahi Farid Ibrahim Yaichi Ilhami Colak Korhan Kayisli Virgil Dumbrava Electrical Engineering Department Laboratory of sustainable development and computer science (LDDI) University of Adrar Adrar Algeria Laboratory of sustainable development and computer science (LDDI) University of Adrar Adrar Algeria Electrical Engineering Department Automatic Laboratory of Setif University of Ferhat Abbas Setif1 Setif Algeria Department of Electrical Electronics Eng. Faculty of Engineering Architecture Nisantasi University Istanbul Turkey Department of Electric Power Systems Polytechnic University of Bucharest Bucharest Romania

This article presents a novel method for controlling a doubly fed induction generator (DFIG) wind energy system in the presence of variable wind speeds. In order to achieve decoupling control of the active and reactive power, the rotor-side converter (RSC) of the DFIG is controlled using field-oriented control (FOC). The system is very complicated and nonlinear, making it challenging to adjust the PI gains appropriately. The present research proposes the utilization of the Grey Wolf Optimization (GWO) algorithm as an approach to optimize the gains of a Proportional-Integral (PI) regulator. The simulation results demonstrate that the GWO-PI provides a lower criterion value and an improved response than the conventional PI.

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