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Spatio-Temporal Context Modeling for Road Obstacle Detection 1

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wu, Xiuen Wang, Tao Liang, Lingyu Li, Zuoyong Ching, Fum Yew College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing211189 China Guangzhou510320 China Penang11800 Malaysia

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling. Firstly, a data-driven spatial context model of the driving scene is constructed with the layouts of the training data. Then, obstacles in the input image are detected via the state-of-the-art object detection algorithms, and the results are combined with the generated scene layout. In addition, to further improve the performance and robustness, temporal information in the image sequence is taken into consideration, and the optical flow is obtained in the vicinity of the detected objects to track the obstacles across neighboring frames. Qualitative and quantitative experiments were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and Found dataset. The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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House Price Prediction System Using Ensemble Learning Approach

House Price Prediction System Using Ensemble Learning Approa...

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International Conference on Parallel Distributed and Grid computing (PDGC)

作者： Abhishek Kumar Paras Jain Priyank Pandey Shyamalendu Das Vishan Kumar Gupta Dhiraj Kumar Gupta Department of CSE MMHA & PU Patna Bihar India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kothrikalan Bhopal MP India Department of CSE Graphic Era Deemed to be University Dehradun India Humanities Department SRMCEM Lucknow India Amity School of Engineering and Technology Amity University Mohali Punjab India Department of Quality Control Sun Pharmaceutical Industrial Limited Halol Vadodara Gujrat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The real estate industry is among the most active and critically important industries for any economy. An accurate forecast of the prices of houses is very important to the buyers, sellers, investors and the policy makers. In years gone by, other methods of valuation have been used, but new advancements have been made in machine learning meaning the prediction speeds and the level of accuracy have been boosted. In this article, I would like to know how the development of various sophisticated models of ML can be used to forecast house prices: the most used models, the most favorite datasets and informative features, as well as the appropriate evaluation measures. Real estate is a continually expanding business that is prominently involved in the world economy. To precisely estimate house prices is always a daunting task due to various factors that vary from physical to non-physical factors include size, location, market, and economic conditions. Digital advancements especially, the incorporation of machine learning in the analysis of financial markets, housing and other sectors has enabled real estate specialists, banks and other institutions and government bodies analyze trends and develop sophisticated models to predict housing prices more effectively than conventional analytical models. In the context of this article’s expanse, we provide a deeper look into the components of house price prediction, with additional information on datasets, feature engineering, specific algorithms, optimization tactics, known difficulties, and future developments.

关键词： Training Analytical models Accuracy Biological system modeling Computational modeling Machine learning Predictive models Stakeholders Optimization Testing

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An Algorithm of Object Detection Based on Regression Learning for Remote Sensing Images

An Algorithm of Object Detection Based on Regression Learnin...

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2021 International Conference on Applied Mathematics, Modelling and Intelligent computing, CAMMIC 2021

作者： Wang, Jiajia Wu, Ran Yu, Xiaqiong Beijing Jinghang Research Institute of Computing and Communication Beijing100076 China Applied Mathematics Research Center of CASIC Beijing100076 China Beijing Air Traffic Control Engineering Technology Co. Ltd Beijing100621 China 32021 Troops Beijing100094 China

Saliency detection method is widely used in the field of object detection. It can greatly improve the efficiency of detection by extracting the region that may contain the objects and reduce the processing of background area. It is very difficult to detect the airports in remote sensing image for its changeable structure and complex background. Considering this situation, this paper proposes a object detection method of saliency model based on regression learning, this method extracts salient regions that may contain airports, and then detects salient regions through feature bag model. Our method uses super-pixel segmentation, minimization of absolute shrinkage and selection operator and hierarchical learning theory based on back-propagation. The salient regions that contain potential objects are extracted and sent to the feature bag model for classification. and it can location the final objects. This paper constructs the airport training dataset and test dataset, and the experimental results fully prove the effectiveness and superiority of this method. © Published under licence by IOP Publishing Ltd.

关键词： Airports

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

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6th International Symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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Data-Driven Predictive control for Robust Exoskeleton Locomotion

arXiv

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arXiv 2024年

作者： Li, Kejun Kim, Jeeseop Xiong, Xiaobin Hamed, Kaveh Akbari Yue, Yisong Ames, Aaron D. Department of Computation and Neural Systems Caltech PasadenaCA91125 United States Department of Mechanical and Civil Engineering Caltech PasadenaCA91125 United States Department of Mechanical Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Mechanical Engineering Virginia Tech VA24061 United States Department of Computing and Mathematical Sciences Caltech PasadenaCA91125 United States Department of Control and Dynamical Systems Caltech PasadenaCA91125 United States

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations. © 2024, CC BY.

关键词： Inverse kinematics

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Optimal control of linear Gaussian quantum systems via quantum learning control

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Physical Review A 2024年第6期109卷 063508-063508页

作者： Yu-Hong Liu Yexiong Zeng Qing-Shou Tan Daoyi Dong Franco Nori Jie-Qiao Liao Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Quantum Computing Center Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics School of Engineering and Information Technology Department of Physics Institute of Interdisciplinary Studies

Efficiently controlling linear Gaussian quantum (LGQ) systems is a significant task in both the study of fundamental quantum theory and the development of modern quantum technology. Here, we propose a general quantum-learning-control method for optimally controlling LGQ systems based on the gradient-descent algorithm. Our approach flexibly designs the loss function for diverse tasks by utilizing first- and second-order moments that completely describe the quantum state of LGQ systems. We demonstrate both deep optomechanical cooling and large optomechanical entanglement using this approach. Our approach enables the fast and deep ground-state cooling of a mechanical resonator within a short time, surpassing the limitations of sideband cooling in the continuous-wave driven strong-coupling regime. Furthermore, optomechanical entanglement could be generated remarkably fast and surpass several times the corresponding steady-state entanglement, even when the thermal phonon occupation reaches one hundred. This work will not only broaden the application of quantum learning control, but also open an avenue for optimal control of LGQ systems.

关键词： Optomechanics Quantum optics

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Research on Reconstruction of CT Images Based on AA-R2Unet in Inferior Mesenteric Artery Classification 7th

Research on Reconstruction of CT Images Based on AA-R2Unet i...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and 7th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Xu, Peixia Wang, Meirong Han, Yu Chen, Jinghao Zhu, YuanFan Zhang, Kun He, Bosheng School of Electrical Engineering Nantong University Nantong China Department of Radiology The Second Affiliated Hospital of Nantong University Nantong China Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong China School of Management Science and Engineering ChongqingTechnology and Business University Chongqing China

ISBN: (纸本)9789811672064

In the process of colorectal cancer surgery, dispute on whether to retain the left colonic artery continues to this day. The classification of the inferior mesenteric artery has instructional significance for clinic which has been proved. Therefore, realizing relative position among vessels is an essential step for the diagnosis and therapy of colorectal cancer disease. The method of 3D reconstruction based on CT sequence images in hospital is time-consuming and laborious. In this paper, we propose a kind of AA-R2Unet based on Unet to realize automatic segmentation of the inferior mesenteric artery. We use attention augment rather than the convolution to achieve the global feature, stacking predictions with 2D features to achieve the function of 3D visualization. The improved Focal Loss function we proposed focuses more on our target vessel. AA-R2Uet achieved a per case precision of 93.34%, recall of 90.28%, and 90.06% of F1 score. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Application of 3D vision intelligent calibration and imaging technology for industrial robots

Application of 3D vision intelligent calibration and imaging...

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2021 Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2021

作者： Yang, Rui Mo, Qinglong Li, Yuhong Liu, Yizhi Hu, Ruihan Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing Guangzhou China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery Shihezi University Shihezi China

During modern flexible lean manufacturing, flexible operation of irregular and complex workpieces with different specifications and arbitrary placement is an essential ability of industrial robots, while it cannot be met by traditional clamping methods. Vision technology brings flexibility and convenience to industrial robots, but the common two-dimensional technology only involves three degrees of freedom (plane displacement and rotation), which hinders the positioning of arbitrarily placing workpieces (often six degrees of freedom) and disorderly sorting. In addition, for typical visual tasks in industrial environments like defect detection, accurate distinguishing of such defects as pits and scratches is challenging under two-dimensional plane imaging. The introduction of three-dimensional information provides an effective solution to this problem. Thus, in the face of increasingly complex, flexible, intelligent and personalized manufacturing needs, the acquisition and processing of 3D visual information are of much importance. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Vision

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Application of three-dimensional vision perception technology to industrial robots

Application of three-dimensional vision perception technolog...

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2021 Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum, AIBDF 2021

作者： Yang, Rui Mo, Qinglong Li, Yuhong Gan, Lin Hu, Ruihan Guangdong Institute of Intelligent Manufacturing Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Cloud Computing Center Chinese Academy of Sciences Dongguan523808 China Guangdong Key Laboratory of Modern Control Technology Guangdong Institute of Intelligent Manufacturing Guangzhou China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery Shihezi University Shihezi China

The three-dimensional vision system can improve the active perception ability of the robot, and then guide its flexible operation. This system has been widely used in industrial production processes, such as disorderly sorting, assembly, flexible welding, and defect detection. In sorting, assembly and other applications, accurate perception in a complex and changeable industrial environment is essential. Moreover, the control and other operations should be completed under the guidance of feedback information based on the collected three-dimensional perception results. Nonetheless, improvements are still required, such as accurate three-dimensional detection and positioning of work-in-progress and autonomous guidance in a complicated industrial context with continuous changes. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Industrial robots

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Research on CT Image Grading of Superior Mesenteric Artery Based on AA Res-Unet 7th

Research on CT Image Grading of Superior Mesenteric Artery B...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and 7th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Han, Yu Chen, Jinghao Xu, Peixia Wang, Meirong Zhu, YuanFan Zhang, Kun He, Bosheng School of Electrical Engineering Nantong University Nantong China Department of Radiology The Second Affiliated Hospital of Nantong University Nantong China Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong China School of Management Science and Engineering ChongqingTechnology and Business University Chongqing China

ISBN: (纸本)9789811672064

In recent years, the incidence of Crohn’s disease (CD) is on the rise, which calls for more accurate and less invasive diagnostic tools. Crohn’s disease usually need to be confirmed by colonoscopy, and this paper puts forward a network model AA Res-Unet based on the fundamental Unet, can realize automatic of superior mesenteric artery CT image segmentation, and further to realize the automatic grading function, help doctors by observing the superior mesenteric artery features, early diagnosis of disease. Image segmentation achieved a per case precision of 98.76%, recall of 98.00%, and 98.34% of F1 score. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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