检索结果-内蒙古大学图书馆

Chinese automation Congress (CAC)

作者： Xiao Liu Luefeng Chen Chengda Lu Haipeng Fan Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust $H_{\infty}$ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller's effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction.

关键词： Drilling Robust control Friction Simulation Geology Velocity control Stability criteria Process control Coal Output feedback

来源：评论

学校读者我要写书评

暂无评论

Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

Direct Shooting Method for Numerical Optimal Control: A Modi...

引用

European Control Conference (ECC)

作者： Jiawei Tang Yuxing Zhong Pengyu Wang Xingzhou Chen Shuang Wu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Water Bay Hong Kong SAR Department Of Electronic And Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Noah's Ark Lab Huawei

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle high-order systems. This paper fills the research gap by considering the direct shooting method for high-order systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.

关键词： Upper bound Accuracy System dynamics Optimal control Europe Programming Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Corrections to “Intelligent Traffic Data Transmission and Sharing Based on Optimal Gradient Adaptive Optimization Algorithm”

引用

IEEE Transactions on Intelligent Transportation Systems 2024年第3期25卷 3202-3204页

作者： Xing Li Haotian Zhang Yajing Shen Lina Hao Wanfeng Shang School of Electrical Engineering and Intelligentization Dongguan University of Technology Dongguan China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Jilin University Changchun China Department of Biomedical Engineering City University of Hong Kong Hong Kong China School of Mechanical and Engineering Northeastern University Shenyang China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

In [1] , inaccuracies in several critical equations along with their accompanying descriptions appear in the article. Furthermore, some references are missing, and certain analyses of experiments are flawed.

关键词： Adaptive systems Data communication Optimization Intelligent transportation systems

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Optimal Multiple Waypoint Navigation

Reinforcement Learning-Based Optimal Multiple Waypoint Navig...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Christos Vlachos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos Department of Electrical and Computer Engineering University of Patras Greece School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this paper, a novel method based on Artificial Potential Field (APF) theory is presented, for optimal motion planning in fully-known, static workspaces, for multiple final goal configurations. Optimization is achieved through a Reinforcement Learning (RL) framework. More specifically, the parameters of the underlying potential field are adjusted through a policy gradient algorithm in order to minimize a cost function. The main novelty of the proposed scheme lies in the method that provides optimal policies for multiple final positions, in contrast to most existing methodologies that consider a single final configuration. An assessment of the optimality of our results is conducted by comparing our novel motion planning scheme against a RRT* method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

arXiv

引用

arXiv 2023年

作者： Yang, Jing Chen, Yuehai Du, Shaoyi Chen, Badong Principe, Jose C. School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong Xi'An710049 China Institute of Articial Intelligence and Robotics College of Articial Intelligence Xi'an Jiaotong University Shanxi Xi'An710049 China The Computational NeuroEngineering Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module including this data-driven mechanism is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance. © 2023, CC BY-NC-ND.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Single Frame Lidar-Camera Calibration Using Registration of 3D Planes

Single Frame Lidar-Camera Calibration Using Registration of ...

引用

IEEE International Conference on Robotic Computing (IRC)

作者： Ashutosh Singandhupe Hung Manh La Quang Phuc Ha Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA School of Electrical and Data Engineering University of Technology Sydney Australia

ISBN: (纸本)9781665472616

This work focuses on finding the extrinsic parameters (rotation and translation) between Lidar and an RGB camera sensor. We use a planar checkerboard and place it inside the Field-of-View (FOV) of both sensors, where we extract the 3D plane information of the checkerboard acquired from the sensor’s data. The plane coefficients extracted from the sensor’s data are used to construct a well-structured set of 3D points. These 3D points are then ’aligned,’ which gives the relative transformation between the two sensors. We use our proposed Correntropy Similarity Matrix Iterative Closest Point (CoSMICP) Algorithm to estimate the relative transformation. This work uses a single frame of the point cloud data acquired from the Lidar sensor and a single frame from the calibrated camera data to perform this operation. From the camera image, we use the projection of the calibration target’s corner points to compute the 3D points, and along the process, we calculate the 3D plane equation using the corner points. We evaluate our approach on a simulated dataset with complex environment settings, making use of the freedom to assess under multiple configurations. Through the obtained results, we verify our method under various configurations.

关键词： Point cloud compression Three-dimensional displays Laser radar Transmission line matrix methods Robot vision systems Cameras Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Learning a Better Control Barrier Function

arXiv

引用

arXiv 2022年

作者： Dai, Bolun Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam. © 2022, CC BY.

关键词： Controllers

来源：评论

学校读者我要写书评

暂无评论

Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

引用

12th International Conference on computer engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

A Continual Learning Method for Reducing Class Interference Based on Replay 42

A Continual Learning Method for Reducing Class Interference ...

引用

42nd Chinese Control Conference, CCC 2023

作者： Xu, Zhibo Wang, Tian Wang, Jian Li, Ce Fu, Yao Snoussi, Hichem School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Sklsde Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China School of Computer Science Wuhan University Wuhan430072 China China University of Mining and Technology Department of Computer Science and Technology Beijing100083 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

ISBN: (纸本)9789887581543

Although deep neural networks perform well on many individual tasks, they suffer from catastrophic forgetting when learning new tasks continually. Recently, various continual learning methods have been proposed, and some approaches based on replaying memory data achieve promising performance. Maximally Interfered Retrieval is a strong replay-based baseline, however, exists class interference due to class imbalance and lacks the ability to generalize to the real class-incremental scenario. In this paper, aiming at these problems, we design Class cumulative Classifier to replace the shared output layer of the original network, which makes it closer to practical applications, and Class balanced Buffer to address the class imbalance of stored samples. In addition, we propose Retrospect strategy to further improve the accuracy. Experimental results on benchmark datasets show that our method outperforms several strong baselines and is more suitable for complex datasets with more classes. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Feature Selection Based on a Modified Adaptive Human Learning Optimization Algorithm 7th

Feature Selection Based on a Modified Adaptive Human Learnin...

引用

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

作者： Yu, Siyue Jia, Yihao Hu, Xuelian Ni, Haoqi Wang, Ling Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronics Engineering and Automation Shanghai University Shanghai200072 China Department of Electrical and Computer Engineering North Carolina State University RaleighNC27606 United States

ISBN: (纸本)9789811672125

This paper proposes a new wrapper feature selection method based on the modified adaptive Human Learning Optimization (MAHLO) algorithm and Support Vector Machine (SVM). To achieve better results, the initialization and random learning strategies are modified in MAHLO to solve the feature selection problem more efficiently and the adaptive strategies are used to enhance the performance and relieve the effort of parameter setting. Besides, a two-stage evaluation function is adopted to eliminate the useless and redundant features, which is easier to operate in applications. The simulation results indicate that MAHLO can solve high-dimensional feature selection problems more efficiently, and the classification results on the UCI benchmark problems further demonstrate the efficiency and the advantage of the proposed MAHLO-based wrapper feature selection method. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：