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SSRN 2024年

作者： Xie, Tingting Chen, Hui Liu, Wanquan Zhou, Rongyu Li, Qilin College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China School of Electrical Engineering Computing and Mathematical Sciences Curtin University BentleyWA Australia

Extracting surfaces from 3D point clouds is significant in reconstructing and transforming these discrete points into their corresponding models. Scannedpoint clouds are often accompanied by noise, and the existing methods mainly rely on local feature similarities for surface extractions. Errors in estimatingthe feature information may lead to incorrect surface detection. In this paper we propose a surface extraction and boundary detection method based onclustering technique. The method can be described in three steps: In the first step, a normal correction is carried out using the information from theneighbourhood of points with sharp features. The second step is to cluster the points that meet the coplanar condition of the local quadric surface (LQS).In the third step, surface merger is performed by merging the local surfaces satisfying the merging conditions. Experimental validation is carried outto determine the effectiveness of the proposed method. The experimental results show improved surface extraction accuracy of the proposed methodin comparison to RANSAC, RG, LCCP, C2NO and HT methods. © 2024, The Authors. All rights reserved.

关键词： Merging

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Heavy-Ball Momentum Accelerated Actor-Critic With Function Approximation

arXiv

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arXiv 2024年

作者： Dong, Yanjie Zhang, Haijun Wang, Gang Cui, Shisheng Hu, Xiping The Artificial Intelligence Research Institute Guangdong-Hong Kong Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Shenzhen MSU-BIT University Shenzhen518172 China The Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing China The State Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China

By using an parametric value function to replace the Monte-Carlo rollouts for value estimation, the actor-critic (AC) algorithms can reduce the variance of stochastic policy gradient so that to improve the convergence rate. While existing works mainly focus on analyzing convergence rate of AC algorithms under Markovian noise, the impacts of momentum on AC algorithms remain largely unexplored. In this work, we first propose a heavy-ball momentum based advantage actor-critic (HB-A2C) algorithm by integrating the heavy-ball momentum into the critic recursion that is parameterized by a linear function. When the sample trajectory follows a Markov decision process, we quantitatively certify the acceleration capability of the proposed HB-A2C algorithm. Our theoretical results demonstrate that the proposed HB-A2C finds an ǫ-approximate stationary point with O(ǫ−2) iterations for reinforcement learning tasks with Markovian noise. Moreover, we also reveal the dependence of learning rates on the length of the sample trajectory. By carefully selecting the momentum factor of the critic recursion, the proposed HB-A2C can balance the errors introduced by the initialization and the stoschastic approximation. © 2024, CC BY.

关键词： Stochastic systems

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Fast ADMM algorithm with efficient communication for distributed optimization of multi-agent systems

Fast ADMM algorithm with efficient communication for distrib...

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2021 China Automation Congress, CAC 2021

作者： Cao, Shuang Jiang, Xiaowei Chi, Ming Zhang, Xianhe School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Ministry of Education Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9781665426473

This paper proposes a distributed optimization algorithm based on alternating direction method of multipliers (ADMM) for the distributed optimization problem of multi-agent systems, called ADMM with adaptive penalty terms and conditional communication (for short, AAPCC), by using the local information of agents to update the adaptive penalty terms to speed up the convergence speed while by restricting the communication conditions of agents to reduce the communication cost of agents in the iterative process. Thus, the communication cost of the system is reduced while the convergence speed is ensured. Numerical simulation experiments show that in a multi-agent system, compared with the classical ADMM, AAPCC can better balance the convergence speed and communication cost, suitable for multi-agent systems that require fast convergence speed and less communication cost. © 2021 IEEE

关键词： Multi agent systems

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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Few-Shot Small Vessel Segmentation Using a Detail-Preserving Network Enhanced by Discriminator

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SSRN 2024年

作者： Huang, Yan Yang, Jinzhu Sun, Qi Yuan, Yuliang Hou, Yang Shang, Jin Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Computer Science and Engineering Northeastern University Liaoning Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Department of Radiology Shengjing Hospital of China Medical University Liaoning Shenyang China

Accurate segmentation of small vessels, such as coronary and pulmonary arteries, is crucial for early detection and treatment of vascular diseases. However, challenges persist due to the vessel's small size, complex structures, morphological variations, and limited annotated data. To address these challenges, we propose a detail-preserving network enhanced by a discriminator to improve the few-shot small vessel segmentation performance. The detail-preserving network constructs a complex module with multi-residual hybrid dilated convolution, which can enhance the network's receptive field while preserving the image's full detail features, enabling it to better capture the small vessel's structural features. Simultaneously, discriminator enhancement is incorporated into the training process through adversarial learning, effectively utilizing large amounts of unlabeled data to boost the generalization and robustness of the segmentation model. We validate the proposed method on in-house and public coronary artery datasets and public pulmonary artery datasets. Experimental results demonstrate that the proposed method significantly improves segmentation accuracy, particularly for small vessels. Compared with other state-of-the-art methods, the proposed method achieves higher accuracy, a lower false positive rate, and superior generalization capability, effectively assisting the clinical diagnosis of vessel diseases. © 2024, The Authors. All rights reserved.

关键词： Semi-supervised learning

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Fast accurate position control of light-responsive liquid crystal elastomer actuator

Fast accurate position control of light-responsive liquid cr...

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Asian Control Conference

作者： Xinyu Nie Jundong Wu Yawu Wang Chun-Yi Su Daiki Sato Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada Laboratory for Future Interdisciplinary Research of Science and Technology (FIRST) Tokyo Institute of Technology Yokohama Kanagawa Japan The School of Engineering Tokyo University of Technology Tokyo Japan

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Light-responsive liquid crystal elastomers (LCEs) are promising for soft robot actuators due to their ability to respond to light stimuli and produce deformation. In order to realize their application in soft robots, fast and accurate position control is essential. Based on the intrinsic physical nature of LCE actuator's deformation, a terminal sliding mode controller (TSMC) is designed to efficiently adjust the temperature of the actuator, thus directly controlling the actuator deformation. In order to achieve the target temperature, a prediction model is established to predict the target temperature corresponding to the target displacement. Moreover, a proportional integration controller is applied to eliminate model errors and disturbances. The feasibility of the control strategy is experimentally verified, and compared with conventional PID controller. The results prove that the proposed control strategy can achieve fast and accurate control of displacement.

关键词： Heating systems Actuators Accuracy Deformation Position control Process control Elastomers

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive Control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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Mental Calculation Drives Reliable and Weak Distant Connectivity While Music Listening Induces Dense Local Connectivity

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Phenomics 2021年第6期1卷 285-298页

作者： Gaoxing Zheng Yuzhu Li Xiaoying Qi Wei Zhang Yuguo Yu State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Human Phenome Institute and Research Institute of Intelligent and Complex SystemsInstitute of Science and Technology for Brain-Inspired IntelligenceShanghai 200433China Department of Neurology Zhongshan Hospital and Shanghai Medical CollegeFudan UniversityShanghai 200032China

Mathematical calculation usually requires sustained attention to manipulate numbers in the mind,while listening to light music has a relaxing effect on the *** differences in the corresponding brain functional network topologies underlying these behaviors remain rarely ***,we systematically examined the brain dynamics of four behaviors(resting with eyes closed and eyes open,tasks of music listening and mental calculation)using 64-channel electroencephalogram(EEG)recordings and graph theory *** developed static and dynamic minimum spanning tree(MST)analysis method and demonstrated that the brain network topology under mental calculation is a more line-like structure with less tree hierarchy and leaf fraction;however,the hub regions,which are mainly located in the frontal,temporal and parietal regions,grow more stable over *** contrast,music-listening drives the brain to exhibit a highly rich network of star structure,and the hub regions are mainly located in the posterior *** then adopted the dynamic dissimilarity of different MSTs over time based on the graph Laplacian and revealed low dissimilarity during mental *** results suggest that the human brain functional connectivity of individuals has unique dynamic diversity and flexibility under various behaviors.

关键词： Dynamic minimum spanning tree Spatiotemporal variability Dissimilarity Graph Laplacian Mental calculation Music listening

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