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Remote Sensing Identification and Evaluation Method for Oyster Resources in Marine Pasture Rafting

Remote Sensing Identification and Evaluation Method for Oyst...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Guangping Zhang Chenxiao Zhang Jinzhan Wei School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China College of Food Engineering Beibu Gulf University Qinzhou China College of Electronic Information and Automation Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In response to the problems of large sea areas, scattered breeding points and areas, time-consuming and labor-intensive manual statistical methods, and low accuracy in current marine ranch aquaculture, a set of artificial intelligence remote sensing recognition and evaluation methods is proposed to quickly and accurately evaluate the resources of oysters raised on large, scattered, and small target marine pasture rafts. Based on machine learning object recognition technology, a detection box generation method for rotation angle elements is proposed, and a pixel based intersection and union method is used to calculate the PIoU loss value of the target. The OysterRE model is proposed to comprehensively calculate and evaluate the resources of oyster farming in ocean pastures. The total resources of oyster farming in ocean pastures are calculated by multiplying the number of oyster farms by the biomass of individual oyster farms. The results indicate that: 1) A method for generating detection boxes based on rotation angle elements has been proposed, which enables the detection boxes to better match irregular targets under complex backgrounds; 2) Propose a pixel based intersection union method to calculate the IoU of the target, making the loss value sensitive to the size, position, and rotation angle of the annotated box, which is more conducive to non-maximum suppression control of the selection of detection boxes; 3) The AVG Recall of the OysterRE model is 0.73, which is 0.12 higher than the average recall of the YOLOv5 model of 0.61. This shows that the proposed model has better recognition performance for oyster farming rafts and consistent with the actual field investigation.

关键词： YOLO Target recognition Biological system modeling computational modeling Oceans Data models Biomass Remote sensing Farming Aquaculture

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An Adaptive Evolutionary Algorithm for Bi- Level Multi-objective VRPs with Real-Time Traffic Conditions

An Adaptive Evolutionary Algorithm for Bi- Level Multi-objec...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Baojian Chen Changhe Li Sanyou Zeng Shengxiang Yang Michalis Mavrovouniotis School of Automation China Univsersity of Geosciences Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781728190495

The research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multiobjective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms.

关键词： Heuristic algorithms Roads Evolutionary computation Prediction algorithms Search problems Routing Electric vehicles

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Learning to Solve Nonlinear Optimization Problem with Deep Reinforcement Learning

Learning to Solve Nonlinear Optimization Problem with Deep R...

引用

ieee International Conference on Robotics and Biomimetics

作者： Yue Gao Qiyue Yang Huajian Wu Ming Sun MoE Key Lab of Artificial Intelligence and AI Institute Shanghai Jiao Tong University Shanghai P.R. China Department of Automation Shanghai Jiao Tong University Shanghai P.R. China

ISBN: (纸本)9781665481106

Nonlinear least-squares problems (NLS) are pop-ular in engineering and scientific fields. Traditional optimization methods such as Newton's method and Gaussian-Newton method (GN) suffer from the sensibility to initial values and the high computational complexity. In this paper, we propose LS-DDPG, a robust optimization method utilizing deep rein-forcement learning algorithms to solve nonlinear least-squares problems. The experiment results on synthetic data demonstrate that the proposed method outperforms Newton's method in terms of computation cost, convergence speed and initial values sensibility. In addition, LS-DDPG is utilized on model predictive control (MPC) problems for trajectory planning and tracking tasks in self-driving with longer prediction horizon and higher accuracy than baseline methods.

关键词： Trajectory planning Optimization methods Reinforcement learning Robustness Newton method Task analysis Robots

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On the Performance of Machine Learning at the Network Edge to Detect Industrial IoT Faults

On the Performance of Machine Learning at the Network Edge t...

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ieee International symposium on Network Computing and Applications

作者： Yuri Santo Bruno L. Dalmazo Roger Immich André Riker Federal University of Pará Brazil Federal University of Rio Grande Brazil Federal University of Rio Grande do Norte Brazil

ISBN: (纸本)9798350397314

Industrial Internet-of-Things (IoT) massively deploys intelligent computing in industrial production and manufacturing environments seeking automation, reliability, and control. Machine Learning models provide intelligent decisions to drive manufacturing systems to the next level of productivity, efficiency, and safety. One of the critical challenges that must be faced is the deployment of Machine Learning models at the network edge to detect data anomalies caused by Industrial IoT hardware failures, since industrial IoT devices are prone to errors and failures. These anomalies can harm the industrial IoT system by producing false alarms, consuming network resources, and affecting productivity. Because of that, it is critical to rely on low latency and high precision detection systems to verify the data received from industrial IoT devices. In light of this, we assessed key performance indicators of five machine learning models running at edge computing, to provide in-depth discussions. The performance results were obtained from an oil refinery scenario using a real industrial IoT dataset. The performance was measured in terms of (a) Accuracy, (b) Precision, (c) Recall, (d) F1 score, (e) Training time, and (f) Response time.

关键词： Productivity Training computational modeling Image edge detection Machine learning Time measurement Safety

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 ieee International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 ieee.

关键词： Image segmentation

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Normalized Canonical Correlation Analysis for Calibrating the Background EEG Activity in SSVEP Detection

Normalized Canonical Correlation Analysis for Calibrating th...

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ieee symposium Series on computational intelligence (SSCI)

作者： Chun-Shu Wei Institute of Education and the Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (纸本)9781728190495

The brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been one of the most stable BCI that is able to transfer commands effectively by the aids of canonical correlation analysis (CCA) for frequency recognition. Nevertheless, CCA-based SSVEP detection encounters the spectral non-uniformity of spontaneous background EEG activity which deteriorates the performance. In this study, we found the performance of SSVEP-based BCI highly predictable by the standard deviation of resting-state response using CCA. Therefore, the proposed normalized CCA (nCCA) aims to calibrate the spectral non-uniformity of background activity which causes bias in classification, and outperforms standard CCA significantly in simulated online tests. In addition, nCCA features near-zero calibration making it a nice fit to practical applications of SSVEP-based BCI spellers.

关键词： Training Visualization Correlation Electroencephalography Brain-computer interfaces Calibration Steady-state

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Handover Process Modeling in Autonomous Vehicles-a control Engineering Approach 19

Handover Process Modeling in Autonomous Vehicles-a Control E...

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ieee Joint 19th International symposium on computational intelligence and Informatics / 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics

作者： Drexler, Daniel Andras Takacs, Arpad Nagy, Tamas Daniel Haidegger, Tamas Obuda Univ Antal Bejczy Ctr Intelligent Robot Univ Res Innovat & Serv Ctr Becsi Ut 96-b H-1034 Budapest Hungary

ISBN: (纸本)9781728156255

Automated driving and advanced driver assistance technologies represent an emerging field. The difference in maturity of various commercial and prototype systems causes ambiguities in the use and reliance for the users, who generally are not trained or educated for autonomous system management and efficient handover processes. While numerous models for shared vehicle control and driver behavior have been introduced recently, not much is known about the effect of situation awareness in a closed-loop human driver-vehicle system. This paper proposes a novel driver model based on a control theory approach, taking both situation awareness and vehicle dynamics into consideration.

关键词： handover process takeover time autonomous vehicle car safety

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Inpainting Electrical Logging Images Based on Deep CNN with Attention Mechanisms

Inpainting Electrical Logging Images Based on Deep CNN with ...

引用

ieee symposium Series on computational intelligence (ieee SSCI)

作者： Du, Chunyu Xing, Qiang Zhang, Jinyan Wang, Jun Liu, Baodi Wang, Yanjiang China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Well Logging Co Sinopec Shengli Petr Engn Co LTD Dongying 257000 Peoples R China

ISBN: (纸本)9781728125473

Microresistivity imaging logging is a crucial technique for evaluating the glutenite, carbonate and other complex reservoirs in oil exploration field. Because the size of the electrode pads in the electric imaging logging instrument can not match the borehole size well, it is hard to get full borehole covering images, which leads to many blank strips in the logging images. Thus it is necessary to fill the blank strips to ensure the accuracy of the subsequent processing and interpretation. The existing filling methods work well only in simple formation structures, but for images whose structure is very complex, for example, glutenite formation, the filled regions appear blurred and indistinct. To remedy the drawbacks, in this paper, we propose a machine learning method to inpaint the blank strips in electrical logging images based on deep convolutional neural network with the attention mechanisms embedded, which can capture a large number of low-level image statistical prior information. The attention mechanisms here refer to both the channel attention and the spatial attention. The channel attention focuses on the information to be attended in the intermediate feature maps while the spatial attention serves determining where to attend. Extensive experiments validate and confirm the effectiveness of the proposed filling method, which produce more compelling results than the existing filling approaches.

关键词： deep convolutional neural network attention mechanism image inpainting Microresistivity imaging logging

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Quadcopter control implemented on FPGA 19

Quadcopter control implemented on FPGA

引用

ieee Joint 19th International symposium on computational intelligence and Informatics / 7th International Conference on Recent Achievements in Mechatronics, automation, Computer Sciences and Robotics

作者： Szekely, Istvan Zsolt Brassai, Sandor Tihamer Sapientia Hungarian Univ Transylvania Dept Elect Engn Cluj Napoca Romania

ISBN: (纸本)9781728156255

The aim of the paper is to solve the horizontal stabilization of a quadcopter on a Field Programmable Gate Array circuit. There are similar implementations, many of them implemented on microcontroller or embedded processors. Our implementation focuses on Zybo Development Board Zynq-7000 family, which integrates a dual-core ARM Cortex-A9 processor and Xilinx 7-series FPGA logic. The essence of the work is the implementation in FPGA of the critical control loops: sensors reading, control algorithm, filters, motor control and remote control. One of the main aim of our project is to apply the implemented modules in education as well. High level control algorithm is proposed to be implemented on the embedded processor. The various subunits of the task are integrated in the Xilinx System Generator. Critical parts, the control and synchronization of the modules, are implemented in VHDL as finite state machine with datapath. Hardware co-simulation allows system parameter update and measurement evaluation during the operation and simplifies testing of implemented circuits. The paper details the measurements made under laboratory conditions for one degree of freedom of the system.

关键词： FPGA control quacopter UAV hardware implementation

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Two Time Scale Partial Unknown Dynamics System Tracking control Based on Off-Policy Inverse Reinforcement Learning 11th

Two Time Scale Partial Unknown Dynamics System Tracking Cont...

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11th International symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Shen, MinYin Liu, Fei Institute of Automation Jiangnan University Wuxi214122 China

ISBN: (纸本)9789819647552

This article, integrating the singular perturbation technique with inverse reinforcement learning, proposes a novel linear two time scale system tracking control method grounded in off-policy inverse reinforcement learning. This method addresses the challenge of unknown cost functions prevalent in industrial processes. First, the singular perturbation method is leveraged to decompose the original problem into fast and slow subsystem issues. Without manually designing a cost function, the method learns from known optimal behavioral data by reconstructing cost functions tailored to each subsystem, enabling the system to mimic optimal behaviors. Then, for the fast time scale system, a model-based inverse reinforcement learning method is adopted, while for the slow time scale system, a model-free off-policy inverse reinforcement learning strategy is employed, which reconstructs the system’s cost function solely using measured expert behavioral data inputs. Finally, using a mixed separation thickening industrial process to illustrate the effectiveness of this method in two time scale tracking. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cost functions

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