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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Zhang, Zhixuan Zhang, Chi Liu, Yuehu Su, Yuanqi Li, Ping Zheng, Jinzi School of Software Engineering Xi'An Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'An Jiaotong University Xi'an China Shaanxi Key Lab of Digital Technology and Intelligent Systems Xi'an China School of Computer Science and Technology Xi'An Jiaotong University Xi'an China China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (纸本)9781450387835

With increasing hardware computing power and model capacity, visual tasks for scene cognitive understanding have attracted more attention, such as visual relationships inference. The scene graph representation formed by a coupling of objects, attributes and relationships nodes displayed by different modalities of information, including original image, foreground things, background stuff and scene attributes, strongly promotes the progress of research area. In this paper, we address the scene graph representation of traffic scenarios for autonomous driving. It should be noted that the universal representation are the specific needs of cognitive understanding of traffic scenes: on the one hand, there is a lack of fine-grained description of key objects and attributes;on the other hand, there are redundant descriptions of objects and relationships. To tackle these problems, we take advantage of the fine-grained instance-level annotation of the traffic scene, proposing a bottom-up representation paradigm. It makes full use of the hierarchical structure of the traffic scene and the sparsity of element classes. In addition, on the basis of the existing methods, we optimize the relationship list of traffic scene graph representation. Moreover, we improve the scene graph annotation methods, proposing a "ground-vision joint location method"to better describe the spatially-distributed visual knowledge. The case analysis showed that compared with existing methods, our paradigm for scene graph can represent more abundant traffic scene information. © 2020 ACM.

关键词： Knowledge representation

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The research of the expansion property and chain mixing property of topological group 12

The research of the expansion property and chain mixing prop...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

作者： Zhan-Jiang, Ji Wei, Shi School of Information and Electronic Engineering Wuzhou University China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University China Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University China Party Committee Office Wuzhou Vocational College China

ISBN: (纸本)9781728170817

The expansion property and chain mixing property have an important significance in terms of theory and application. The concept of the expansion property and chain mixing property are introduced in this paper. We study their dynamical properties. The following result are obtained: (1)Letbe the hyperspace of. Ifis expansive, thenis expansive;(2)Letbe the hyperspace of. Ifis chain mixing, thenis chain mixing;(3)Letbe the hyperspace of. Ifis chain transitive, thenis chain transitive;These results enriched the theory of the expansion property and chain mixing property of topological group. © 2020 IEEE.

关键词： Topology

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The research of G-chain transitive and G-chain mixing on the hyperspace of topological group action 12

The research of G-chain transitive and G-chain mixing on the...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-chain transitive and G-chain mixing have an important significance in terms of theory and application. According to the definition of chain transitive and chain mixing, we give the concept of G-chain transitiveand G-chain mixing in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace of. Ifis a G-chain transitive map, thenis a G-chain transitive map;(2)Letbe the hyperspace of. Ifis a G-chain mixing map, thenis a G-chain mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. All non-empty open set are invariable for. Thenis a G-exact map if and only ifis a G-exact map. These results enriched the theory of the G-chain transitive and G-chain mixing on the hyperspace of topological group action. © 2020 IEEE.

关键词： Group theory

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The research of g-mixing and g-equicontinuity on the hyperspace of topological group action 12

The research of g-mixing and g-equicontinuity on the hypersp...

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12th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2020

ISBN: (纸本)9781728170817

G-mixing and G-equicontinuity have an important significance in terms of theory and application. According to the definition of mixing and equicontinuity, we give the concept of G-mixing and G-equicontinuity in this paper. By inference, some conclusions of the hyperspace were extended to the hyperspace of topological group action. We have the following result: (1)Letbe the hyperspace ofandbe a pseudo equivariant map. All non-empty open set are invariable for. Ifis a G-mixing map, thenis a G-mixing map;(2)Letbe the hyperspace of andbe a equivariant map. Thenis a G-mild mixing map if and only ifis a G-mild mixing map;(3)Letbe the hyperspace ofandbe a equivariant map. Thenis a G-equicontinuity map if and only ifis a G-equicontinuity map. These results enriched the theory of the G-mixing and G-equicontinuity on the hyperspace of topological group action. © 2020 IEEE.

关键词： Mixing

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A Bottom-up Paradigm for Traffic Scene Graph Representation 2020

A Bottom-up Paradigm for Traffic Scene Graph Representation

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Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

作者： Zhixuan Zhang Chi Zhang Yuehu Liu Yuanqi Su Ping Li Jinzi Zheng School of Software Engineering Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China and Shaanxi Key Lab of Digital Technology and Intelligent Systems Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (纸本)9781450387835

With increasing hardware computing power and model capacity, visual tasks for scene cognitive understanding have attracted more attention, such as visual relationships inference. The scene graph representation formed by a coupling of objects, attributes and relationships nodes displayed by different modalities of information, including original image, foreground things, background stuff and scene attributes, strongly promotes the progress of research area. In this paper, we address the scene graph representation of traffic scenarios for autonomous driving. It should be noted that the universal representation are the specific needs of cognitive understanding of traffic scenes: on the one hand, there is a lack of fine-grained description of key objects and attributes; on the other hand, there are redundant descriptions of objects and relationships. To tackle these problems, we take advantage of the fine-grained instance-level annotation of the traffic scene, proposing a bottom-up representation paradigm. It makes full use of the hierarchical structure of the traffic scene and the sparsity of element classes. In addition, on the basis of the existing methods, we optimize the relationship list of traffic scene graph representation. Moreover, we improve the scene graph annotation methods, proposing a "ground-vision joint location method" to better describe the spatially-distributed visual knowledge. The case analysis showed that compared with existing methods, our paradigm for scene graph can represent more abundant traffic scene information.

关键词： visual relationship description traffic knowledge representation autonomous driving Scene graph

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Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action

arXiv

引用

arXiv 2020年

作者： Heris, Mostapha Kalami Rahnamayan, Shahryar Intelligent Systems Lab. Research and Development Department FaraDars Online Education Organization Tehran Iran Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences. Originally a single-objective optimal control problem has been defined on DICE dynamics, which is aimed to maximize the social welfare. In this paper, a bi-objective optimal control problem defined on DICE model, objectives of which are maximizing social welfare and minimizing the temperature deviation of atmosphere. This multi-objective optimal control problem solved using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) also it is compared to previous works on single-objective version of the problem. The resulting Pareto front rediscovers the previous results and generalizes to a wide range of non-dominant solutions to minimize the global temperature deviation while optimizing the economic welfare. The previously used single-objective approach is unable to create such a variety of possibilities, hence, its offered solution is limited in vision and reachable performance. Beside this, resulting Pareto-optimal set reveals the fact that temperature deviation cannot go below a certain lower limit, unless we have significant technology advancement or positive change in global conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Climate change

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A Review of Artificial Fish Swarm Algorithms: Recent Advances and Applications

arXiv

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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Adversarial Learning with Cost-Sensitive Classes

arXiv

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arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

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Improved dynamic window approach with ellipse equations for autonomous navigation of unmanned surface vehicle

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Journal of Institute of Control, robotics and Systems 2020年第8期26卷 624-629页

作者： Kim, Hyogon Yun, Sung-Jo Choi, Young-Ho Ryu, Jae-Kwan Won, Byong-Jae Suh, Jin-Ho Korea Institute of Robotics and Technology Convergence Korea Republic of Unmanned/Intelligent Robotic Systems R&D Lab. LIG Nex1 Korea Republic of Department. of Mechanical System Engineering Pukyoung National University Korea Republic of

Autonomous navigation technology for Unmanned Surface Vehicles (USVs) has seen many recent advancements. In order for a USV to operate autonomously, the path to move from its current location to its destination must be planned, and local path planning must be applied for tracking the path and avoiding obstacles. The Dynamic Window Approach (DWA) is a well-known navigation algorithm used for local path planning. The DWA algorithm derives the linear velocity and angular velocity by evaluating the object function defined as destination direction, velocity, and distance from the obstacle. However, because DWA does not consider the velocity and direction of obstacles, USVs may be in danger when avoiding high-speed obstacles. In this paper, we propose an improved DWA algorithm that considers the velocity and direction of obstacles. Whereas the existing DWA algorithm defines the obstacle recognition area as a circle, the proposed DWA algorithm defines the obstacle recognition area as an ellipse and reflects the speed of the obstacle in the ellipse. In this study, we implemented a simulation to compare the existing DWA algorithm with the improved DWA algorithm proposed here. The results confirm that the proposed DWA algorithm can avoid dynamic obstacles more safely than the existing DWA algorithm. © ICROS 2020.

关键词： Unmanned surface vehicles

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Incorporating Hidden Layer representation into Adversarial Attacks and Defences

arXiv

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arXiv 2020年

In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adversarial perturbation. And this defence strategy can be regarded as an activation function which can be applied any kind of neural networks. We also prove theoretically the effectiveness of this defense strategy under certain conditions. Besides, incorporating hidden layer representation we propose three types of adversarial attacks to generate three types of adversarial examples, respectively. The experiments show that our defence method can significantly improve the adversarial robustness of deep neural networks which achieves the state-of-the-art performance even though we do not adopt adversarial training. © 2020, CC BY.

关键词： Deep neural networks

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