检索结果-内蒙古大学图书馆

An Improved Ship Classification Method Based on YOLOv7 Model with Attention Mechanism

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Cen, Jian Feng, Hao Liu, Xi Hu, Yongjian Li, Haoliang Li, Haisheng Huang, Weisheng School of Automation Guangdong Polytechnic Normal University China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory China School of Electronic and Information Engineering South China University of Technology China Department of Electrical Engineering City University of Hong Kong Hong Kong Hong Kong Guangdong Xixun Intelligent Technology Co. Ltd. China

Deep learning (DL) is widely used in ship detection, but there are still some problems in the effective classification, such as inaccurate object feature extraction and inconspicuous feature information in deep layers. To address these problems, we propose a YOLOv7-residual convolutional block attention module (YOLOv7-RCBAM) by combining the convolutional attention mechanism and residual connections to the YOLOv7. First, to accelerate the training speed, the parameters in the backbone network of the pretrained model are frozen by using transfer learning, and the model is fine-tuned for training. Second, to enhance the information relevance of channel dimensional features, an attention mechanism with residual connectivity is adopted. Finally, a feature fusion attention mechanism is introduced to improve the effective feature extraction. The effectiveness of the proposed method is fully validated on the SeaShips dataset. The results show that the YOLOv7-RCBAM model achieves better performance with a 97.59% in mAP and effectively extracts object feature in deep layers. Meanwhile, the YOLOv7-RCBAM model can accurately locate ship in complex environments with darkness and noise with the mAP reaching 96.13% to achieve effective ship classification detection. © 2023 Jian Cen et al.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Empirical Based Irrigation Model Using Predicted Soil Moisture for Durian Plantation 3rd

Empirical Based Irrigation Model Using Predicted Soil Moistu...

引用

3rd International Conference on control, Instrumentation and Mechatronics engineering, CIM 2022

作者： Ramli, Muhammad Shahrul Azwan Abidin, Mohamad Shukri Zainal Hean, Pui Boon Rahman, Mohd Amiruddin Abd Perumal, Thinagaran Reba, Mohd Nadzri Md Division of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai Malaysia IoT Department MIE Industrial Sdn. Bhd Selangor Puchong Malaysia Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Seri Kembangan Malaysia Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Skudai Malaysia

ISBN: (纸本)9789811939228

It is vital to the agricultural activities to have sufficient water supply for its operation and maintenance mainly for cultivation to keep it in good condition Therefore, it is important to determine the soil moisture levels existing while designing a precise irrigation system. Installing soil moisture sensors in each tree is complicated or excessively expensive. Forecasting the value using climate data is a viable solution in this scenario. Climate data are used to forecast soil moisture and then utilized in this irrigation model. This study uses an Artificial Neural Network (ANN) to forecast soil moisture values. The statistical method is used to determine the predicted values’ correctness. After the process, the irrigation volume and schedule are calculated based on the most accurate prediction findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Soil moisture

来源：评论

学校读者我要写书评

暂无评论

Peak Time-Windowed Mean Estimation using Convex Optimization

Peak Time-Windowed Mean Estimation using Convex Optimization

引用

IEEE Conference on Decision and control

作者： Jared Miller Niklas Schmid Matteo Tacchi Didier Henrion Roy S. Smith Department of Information Technology and Electrical Engineering (D-ITET) ETH Zürich Physikstrasse 3 Automatic Control Laboratory (IfA) and NCCR Automation Zürich Switzerland Univ. Grenoble Alpes CNRS Grenoble INP (Institute of Engineering Univ. Grenoble Alpes) GIPSA-lab Grenoble France LAAS-CNRS Université de Toulouse CNRS Toulouse France and the Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper presents an algorithmic approach towards bounding the peak time-windowed average value attained by a state function along trajectories of a dynamical system. An example includes the maximum average current flowing across a power line in any 5 -minute window. The peak time-windowed mean estimation task may be posed as a finite-dimensional but nonconvex optimization problem in terms of an initial condition and stopping time. This problem can be lifted into an infinite-dimensional linear program in occupation measures, where no conservatism is introduced under compactness and dynamical regularity assumptions. The peak time-windowed mean estimation linear program is in turn truncated into a convergent sequence of semidefinite programs using the moment-Sum-of-Squares hierarchy. Bounds of the time-windowed mean are computed for example systems.

关键词： Heuristic algorithms Current measurement Estimation Convex functions Trajectory Dynamical systems Optimization

来源：评论

学校读者我要写书评

暂无评论

Trajectory Selection Optimization for Behavior-based Aerial-Ground Robots Heterogeneous System: A Predictive Approach

Trajectory Selection Optimization for Behavior-based Aerial-...

引用

2021 China automation Congress, CAC 2021

作者： Chen, Jianfei Chen, Yutao Liu, Zihang Huang, Jie College of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350108 China Fuzhou No.1 High School Fuzhou35108 China

ISBN: (纸本)9781665426473

To address the problem of unpredictable trajectory caused by sensing limitation of UGV, in this paper, a heterogeneous multi-agent behavioral control architecture with predictive function is proposed, in which UAV intervenes in the decision-making of UGV. The basic task functions of UGV and UAV are defined respectively and the intervention signal of UAV is connected to the task function of UGV in null-space-based control framework to solve the problem of multi-task conflict. The UAV platform adopts the nonlinear model predictive control to predict the trajectory according to the environment data collected in advance and the current state of the UGV. The prediction results are input into the decision model to produce the final intervention signal and sent to the UGV platform. The UGV adjusts the task strategy in time to ensure the selection of the optimal trajectory for the UGV. Finally, the simulation results show the effectiveness of the proposed cooperative control method. © 2021 IEEE

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

VDBblox: Accurate and Efficient Distance Fields for Path Planning and Mesh Reconstruction

VDBblox: Accurate and Efficient Distance Fields for Path Pla...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yinlong Bai Zhiqiang Miao Xiangke Wang Yong Liu Hesheng Wang Yaonan Wang Department of Electrical and Information Engineering Hunan University Changsha China College of Intelligence Science and Technology National University of Defense Technology Changsha China Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Automation Shanghai Jiao Tong University Shanghai China

Highly accurate and efficient map in unknown and complex environments is essential for robotics navigation. Traditionally, mobile robot platforms are often computationally constrained when using multiple sensors to process large amounts of input data. In previous works, some of them have been deployed to embedded platforms in real-time. However, how to balance accuracy and efficiency while reducing the computational resources and the memory footprint is still the bottleneck. Motivated by these challenges, we proposed a mapping framework called VDBblox to incrementally build Euclidean Signed Distance Fields (ESDFs) map from Truncated Signed Distance Fields (TSDFs) mapping. We use a novel weight function to update the non-projective TSDFs, thus improving the quality of the mesh reconstruction with higher accuracy than up-to-date methods. Meanwhile, the generated ESDFs map is maintained by the least recently used (LRU) cache to dynamically handle the obstacle changes with less runtime than state-of-the-art. We show VDBblox performance in terms of accuracy and efficiency by benchmark comparison on RGB-D and LiDAR public datasets. Moreover, we demonstrate that VDBblox can be integrated into a completed quadrotor system as a sub-module. Then we validate it through online obstacle avoidance and high-quality mesh reconstruction in real-world experiments. Finally, we release our method as open-source code to the community 1 1 Code - https://***/yinloonga/vdbblox.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors

引用

IFAC-PapersOnLine 2023年第2期56卷 270-275页

作者： Zishuo Li Muhammad Umar B. Niazi Changxin Liu Yilin Mo Karl H. Johansson Department of Automation Tsinghua University Beijing 100084 China Division of Decision and Control Systems Digital Futures School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm SE-100 44 Sweden Laboratory for Information and Decision Systems Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139 USA

This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.

关键词： Estimation and filtering Design of fault tolerant/reliable systems Estimation and fault detection

来源：评论

学校读者我要写书评

暂无评论

Intelligent Networked control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

引用

Computer Systems Science & engineering 2023年第10期47卷 721-739页

作者： Mohamed Esmail Karar Amged Sayed A.Mahmoud College of Computing and Information Technology(CCIT) Shaqra UniversityShaqra11961Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering(FEE)Menoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportP.O.Box 12577Smart VillageSmart Village-B 2401Egypt

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability *** paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status *** infusion of vasoactive drugs to patients endures various issues,such as variation of sensitivity and noise,which require effective and powerful systems to ensure robustness and good *** developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy(IT2F)logic and teaching-learning-based optimization(TLBO)*** networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs *** avoid the manual selection of control parameter values,the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F *** simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances.A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy ***,the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’sensitivity to infused vasoactive drugs in a noisy environment.

关键词： Intelligent medical systems telemedicine fuzzy control teaching learning-based optimization

来源：评论

学校读者我要写书评

暂无评论

Stochastic Optimal Investment Strategy for Net-Zero Energy Houses

arXiv

引用

arXiv 2023年

作者： Li, Mengmou Tanaka, Taichi Carnerero, A. Daniel Wasa, Yasuaki Hirata, Kenji Fujisaki, Yasumasa Ushifusa, Yoshiaki Hatanaka, Takeshi Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Electrical Engineering and Bioscience School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan Department of Electrical and Electronic Engineering School of Engineering University of Toyama Toyama930-8555 Japan Department of Information and Physical Sciences Graduate School of Information Science and Technology Osaka University Osaka565 0871 Japan Faculty of Economics and Business Administration The University of Kitakyushu Fukuoka802-8577 Japan

In this research, we investigate Net-Zero Energy Houses (ZEH), which harness regionally produced electricity from photovoltaic (PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This paper examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. We propose two potential scenarios: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing;the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, we apply the Monte Carlo sample average approximation (SAA) to the problems. To demonstrate the practical applicability of these models, we implement the proposed scenarios in the Jono neighborhood in Kitakyushu, Japan. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Designing of A Plastic Garbage Robot With Vision-Based Deep Learning Applications

Designing of A Plastic Garbage Robot With Vision-Based Deep ...

引用

International Conference on System Science and engineering (ICSSE)

作者： Le Tien Thanh Le Hoang Lam Thanh Nha Nguyen Duc Thien Tran Automation Control Department Ho Chi Minh City University of Technology and Education Ho Chi Minh City Vietnam Faculty of Electrical and Electronics Engineering Ho Chi Minh City University of Technology and Education Ho Chi Minh City Vietnam

To address the issue of plastic waste, a robot using deep learning technology for visual recognition to classify plastic waste has been developed. This system includes a 3DOF robot arm, a conveyor, a camera, an electrical cabinet, and a computer. The object detection component of the system is designed using transfer learning with a pre-trained YOLOv5 model to ensure the system operates in real time. Selecting the best model by evaluating and comparing the results of models trained using labeling by bounding box and polygon methods. Then, the real-world coordinates for the origin of the robot arm are determined by utilizing matrices obtained from MATLAB through chessboard images. The computer processes the data and transmits commands to the robot arm system and conveyor, which is controlled by a PLC and 3 different Servo Drivers, for object sorting on the conveyor. The best-performing model has a Precision of 92.1% and a Recall of 87.3%, and the success rate of picking up an object is 91.5%. While the experimental results indicate complete stability in inter-device connectivity, implementing it would necessitate hardware improvements to leverage its potential.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Delivering Improved Care through Remote Health Monitoring Systems by Using Microservices. Case Study: Fall Detection Encompassed into Connected Care 24

Delivering Improved Care through Remote Health Monitoring Sy...

引用

24th International Conference on control Systems and Computer Science, CSCS 2023

作者： Ianculescu, Marilena Alexandru, Adriana Giura, Ionut-Emanuel Vasilevschi, Ana-Mihaela National Institute for Research and Development in Informatics Communication Digital Applications and Systems Department Bucharest Romania University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania Counselors Department National Institute for Research and Development in Informatics Târgovişte Romania Faculty of Electrical Engineering Electronics and Information Technology Târgovişte Romania Valahia University of Târgovişte Târgovişte Romania

ISBN: (纸本)9798350313390

Based on today's modern technologies, patient care can be provided remotely, in a connected way, being, at the same time, personalized, patient-centered and proactive. Such an approach can be achieved using Remote Health Monitoring Systems (RHMS). In case of elderly people, dramatic permanent deteriorations of their health status can be caused by falls. The seniors' health and life style monitoring and the detection and/or prevention of falls can be done by such RHMS, an example being the RO-SmartAgeing system developed inside 'Non-invasive Monitoring System and Health Assessment of the Elderly in a Smart Environment (RO-SmartAgeing)' research project. The paper presents the problem of using microservices for optimizing the architecture and functionalities of RO-SmartAgeing system, and the way in which the problems of ensuring the security of patient data can be solved. © 2023 IEEE.

关键词： Fall detection

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：