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Image Recognition of Chinese Herbal Medicine Using Adaptive Gamma Correction Based on Convolutional Neural Network

Image Recognition of Chinese Herbal Medicine Using Adaptive ...

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data driven control and learning systems (ddcls)

作者： Honghai Ji Xinjing Liu Li Wang Lingling Fan Shida Liu School of Electronics & Control Engineering North China University of Technology Beijing China School of Automation Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Chinese herbal medicine (CHM) has gained significant attention due to its potential in treating various diseases. CHM image classification is crucial in fields such as smart medical care and medical teaching. However, accurately classifying CHM images has been a challenging task due to their fine-grained variability. To achieve automatic identification and classification of Chinese herbal medicines, we designed a method that combines image processing and deep learning. We established a dataset of CHM images, which includes 10 kinds of common Chinese herbal medicines, with 15,719 images in total. the dataset covers medicinal materials in various natural backgrounds and provides ample samples for training deep learning models. Regarding image processing, we proposed a method based on adaptive gamma correction(AGC) and compared it with CLAHE(Contrast Limited Adaptive Histogram Equalization)and traditional gamma correction(TGC) methods. Subsequently, we employed three pre-training models of convolutional neural networks based on transfer learning, namely GoogLeNet, ResNet-18, and MobileNetV2, to identify CHM images. the results of the verification indicate that the AGC-CNN model outperforms the CLAHE-CNN and TGC-CNN models in terms of classification accuracy and image quality improvement.

关键词： Training Image quality Deep learning Adaptation models Accuracy Image recognition data models

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Dynamic Personalized Graph Neural Ordinary Differential Equation Network for Multivariate Time Series Prediction of Chemical Processes

Dynamic Personalized Graph Neural Ordinary Differential Equa...

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data driven control and learning systems (ddcls)

作者： Nan Wang Yanming Cao Lei Wang Fei Luo Xueming Li Yongming Han Xuan Hu Key Laboratory of Oil & Gas Business Chain Optimization CNPC PetroChina Planning and Engineering Institute Beijing China PetroChina Dalian Petrochemical Company Liaoning China Research Center of Intelligent PSE Ministry of Education in China College of Information Science and Technology Beijing University of Chemical Technology Engineering Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the prediction of multivariate time series (MTS) has increasingly captured substantial interest in practical domains such as chemical process flow and data analysis, which representing a profoundly challenging task. Numerous traditional MTS forecasting models demand substantial computational resources and may fall short of fulfilling real-time requirements. In this paper, given the intricate interdependencies within and between variables, a novel Multivariate Time Graph Neural Network (MTGNN) integrated with an ordinary differential equation (ODE) (MTGNN-ODE) approach is proposed to address the challenges of MTS prediction in chemical engineering. First, MTGNN represents the dependencies among variables in MTS data using the adjacency matrix to learn of spatio-temporal features. then, the ODE is utilized to achieve the continuous propagation of the graph structure to a deeper level, and solves the problem of over-smoothing. In comparison with the baselines on a petrochemical process dataset, proposed method demonstrates a 15.6% enhancement in RSE and a 5.1% improvement in CORR. the prediction of key points in the chemical process is of great significance to achieve closed-loop control and real-time optimization of product quality.

关键词： Training Time series analysis Predictive models Ordinary differential equations Real-time systems Product design Chemical processes

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A Novel Intelligent Online Fire Monitoring Approach for Underground Pipe Galleries

A Novel Intelligent Online Fire Monitoring Approach for Unde...

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data driven control and learning systems (ddcls)

作者： Yongjing Wang Chenying Wen Zheren Zhu Xinming Zhang Zhihuan Song Zugang Zhu Xueer Fan The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou Hangzhou Underground Pipeline Development Co. Ltd Hangzhou

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Underground pipe galleries are constructed beneath urban areas, providing pathways for the installation of various municipal pipelines. these galleries help protect pipelines and efficiently utilize underground space, serving as a significant facilitator for the construction of smart cities. Due to the confined space inside the galleries and the simultaneous presence of multiple pipelines, there is a potential safety hazard of fire incidents. To ensure the safe operation of pipelines, online monitoring and timely warning of fire incidents are valuable tasks. Current traditional computer vision and machine learning approaches for object detection often experience slow response time. therefore the You Only Look Once (YOLO) series due to its fast response time, has become a popular method in real-time detection. Nonetheless, YOLOv8 still suffer from information fusion problems. In response to this issue, this paper proposed YOLOH algorithm, which improves YOLOv8 by employing the Gather-and-Distribute (GD) mechanism for intelligent online monitoring of fire incidents in underground pipe galleries. Finally, the effectiveness of YOLOH was validated using a combination of publicly available fire datasets and simulated fire datasets in underground pipe galleries. the YOLOH attains a 63.2 mAP, surpassing the YOLOv8-N baseline by +2.15 mAP.

关键词： YOLO Machine learning algorithms Smart cities Pipelines Propagation losses Real-time systems Neck

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An Effective Crop Diseases Detector via CropDetDiff Model with Multi-Sized Selective Kernels

An Effective Crop Diseases Detector via CropDetDiff Model wi...

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data driven control and learning systems (ddcls)

作者： Chenghao Hua Jianlei Kong National Engineering Research Center for Agri-Product Quality Traceability Beijing Technology and Business University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Crop disease detection is an integral aspect of smart agricultural practices, instrumental in augmenting crop yield and quality. Contemporary mainstream approaches are heavily reliant on deep learning paradigms whose enhancements are contingent upon substantial volumes of meticulously annotated datasets. However, in the domain of agricultural pathology detection, the arduous task of dataset creation and the inherent challenges associated with the detection of small targets pose formidable obstacles to existing methodologies. In response to these challenges, we introduce a detector named CropDetDiffthat employs diffusion learning to minimize reliance on training samples, thus delivering superior detection performance in agricultural settings with limited training data availability. To address the detection of small-sized crop targets, we have developed a lightweight graph neural network, LCSP-ViG, with an integrated Large-Sized Selective Kernel (LSK) network for sparse detection of small objects. We have also designed a One-to-Few (O2F) head featuring a label assignment strategy to enhance the precision of small object bounding boxes. Finally, a new bounding box loss function, HIoU, has been introduced to focus the model's supervised learning specifically on small objects. We conducted a series of experiments that verified the optimal detection performance of our model compared to other leading detectors. In particular, on the agricultural object detection datasets Strawberry, our model demonstrated APs of 73.16%, notably, there is a pronounced enhancement in detection performance of small targets. the performance of this model proves its effectiveness for the operation on agricultural robots.

关键词： Training Supervised learning Training data Process control Detectors Object detection data models

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Collaborative Decision-Making Technique for Wireless Communication Means Based on Multi-Agent Reinforcement learning

Collaborative Decision-Making Technique for Wireless Communi...

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data driven control and learning systems (ddcls)

作者： Kun Zhang Chao Hu Deqing Huang Jiachen Shen School of Electrical Engineering Southwest Jiaotong University Chengdu P. R. China Key Laboratory of Advanced Energy Traction and comprehensive energy saving railway industry Southwest Jiaotong University Chengdu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Wireless communication is a pivotal domain within modern information and communication technology. With the proliferation of mobile devices and the Internet of things, wireless communication methods have become ubiquitous across diverse industries. Challenges arise in collaborative decision-making technology that leverages wireless communication, including heightened information variability, increased time sensitivity, and a significant need for manual intervention. this paper introduces, for the first time, the application of multi -agent reinforcement learning technology to develop wireless communication agents. this approach involves distributed training, real-time simulation interaction, and other key technologies rooted in the pertinent wireless communication mechanism. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method. By leveraging a mechanism of iterative trial and error, the optimal collaborative decision-making strategy is derived, significantly enhancing efficiency. Experimental validation demonstrates the effectiveness of the proposed method.

关键词： Wireless communication Training Technological innovation Target tracking Sensitivity Decision making Collaboration

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Dynamic Optimization for SP of control Loops Based on Both Rapid Intelligent Optimization and UniSim/EPKS Environments

Dynamic Optimization for SP of Control Loops Based on Both R...

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data driven control and learning systems (ddcls)

作者： Hehui Zhang Zhu Wang Shao Xian Wang Department of automation China University of Petroleum (Beijing) Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Advanced Process control is frequently applied in industrial chemical processes, where Dynamic Matrix control is widely employed for its capability to handle complex optimization control problems with multiple variables and constraints. However, during prolonged operation of industrial processes, factors such as changes in operating conditions and external environmental variations may lead to a mismatch between the predictive model and the actual plant in DMC. To address the Model-plant Mismatch, this paper proposes a dynamic trajectory rapid intelligent optimization method based on online reconstruction of the predictive model. Constructing the system model based on the known linear dynamic model of the control loop and PID parameters, the new dynamic matrix is obtained using the closed-loop step response of the system model. then, updating the prediction model online and simultaneously updating the optimization-constrained conditions in real time ensures the long-term effectiveness of the advanced control scheme. To rapidly solve the optimization objective, this paper utilizes an improved GA-PSO algorithm to compute the Set Point trajectory of the control loop. Finally, this study conducted simulations by integrating Honeywell's UniSim Design simulation platform with the EPKS DCS control system, verifying the effectiveness of dynamic matrix online updating and demonstrating the long-term effectiveness of the advanced control scheme.

关键词： Monte Carlo methods Computational modeling Simulation Process control Optimization methods Predictive models control systems

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Design of a Cough Detection System Based on Vibration and Audio Signals for Post- thoracic Surgery Patients

Design of a Cough Detection System Based on Vibration and Au...

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data driven control and learning systems (ddcls)

作者： Zhilin Qiu Quan Liu Guanbin Gao Jun Peng Yashan Xing Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming China Department of Thoracic Surgery First People's Hospital of Yunnan Province Affiliated to Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Cough is a vital rehabilitation indicator which can exhibit many complications after thoracic surgical procedures. In this paper, a portable bimodal cough detector that can achieve consecutive detection is designed to recognize postoperative cough. the detector collects vibration and audio signals of cough and sends the raw data to the base station for data processing. the time domain and frequency domain features of vibration data are extracted. Meanwhile, 13 dimensions of Mel-frequency Cepstral Coefficients (MFCC) features and two dimensions of physical features are extracted from audio data. the threshold distinguish method and the bagged tree model are applied to classify the vibration and audio signals features respectively. the fusion discrimination method determines the result of the two signals and then gives the final judgment based on the two classifiers. the bagged tree model training results show that the audio classifier achieved an accuracy of 94.20% with an Area Under the Receiver Operating Characteristics Curve (AUC) of 0.984. We evaluate the performance of the fusion discrimination in actual use scenarios. the experiment results show that the detector achieved a sensitivity of 85.54% and a specificity of 98.76% in the silent environment. the detector in the interference environment can achieve a sensitivity of 83.44% and a specificity of 98.50% which exhibits a strong interference resisting ability.

关键词： Vibrations Sensitivity Accuracy Surgery Detectors Interference Vibration measurement

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Fault-Tolerant control for Electromagnetic Suspension System of Maglev Train: Comprehensive Research and Design

Fault-Tolerant Control for Electromagnetic Suspension System...

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data driven control and learning systems (ddcls)

作者： Junhao Liu Chunhui Dai Shangrongxi Sun Hao Wang Xiaolong Li College of Intelligence Science and Technology National University of Defense Technology Changsha Hunan China Hunan Provincial Key Laboratory of Electromagnetic Levitation and Propulsion Technology Changsha Hunan China Northwest Institute of Electromechanical Technology Xian Shanxi China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Maglev technology, as an advanced transit technology, have garnered global attention due to their remarkable high-speed performance, energy efficiency, environmental friendliness, and comfortable ride experience. they have been actively promoted and put into practical operation in numerous countries and regions worldwide. Given the complexity of magnetic levitation train systems and their sensitivity to external disturbances, enhancing their reliability and robust stability is of paramount importance. this paper takes the conventional electromagnetic suspension (EMS) maglev train developed by the National University of Defense Technology as a reference model, and systematically investigates the fault-tolerant control techniques for magnetic levitation trains based on accurate sensor fault detection and localization. the study encompasses the implementation of active fault tolerance through dynamic switching control strategies and sensor failures, as well as simultaneous passive fault-tolerant control schemes that exploit the inherent redundancy characteristics of the joining-structure within the magnetic levitation train system. Furthermore, the effectiveness and practicability of these fault-tolerant control methods were validated using a Digital Signal Processor (DSP) digital controller. the experimental results demonstrated that the summarized fault-tolerant control system meets the predefined performance criteria, thereby substantiating the validity and practicality of the relevant fault-tolerance control technologies.

关键词： Fault tolerance Magnetic levitation vehicles Fault tolerant systems Redundancy Stability criteria Digital signal processors Switches

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Graph-model Based Optimization of Shelter Hospitals: Integrating MILP for Efficient Layout and Deployment

Graph-model Based Optimization of Shelter Hospitals: Integra...

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data driven control and learning systems (ddcls)

作者： Cai Daomeng Liu Jinyuan E Ertai Wang Tianyue Xu Haoran Pang Jinhui Fu Yongling Beihang University Beijing P.R. China Systems Engineering Institute China State Shipbuilding Corporation Beijing P.R. China Beijing Institute of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper introduces a novel approach to optimizing the layout and deployment of mobile shelter hospitals, a concept that has gained prominence due to the increasing need for rapid medical response in diverse scenarios, including natural disasters and pandemics. Our approach integrates Mixed Integer Linear Programming (MILP) and the genetic algorithms to develop a graph model that represents the different functional areas of a shelter hospital as vertices. In this model, network edges are defined by capacity and transit time, employing a discrete time approach for patient movement and queuing. We establish a Nash equilibrium for the model to ensure optimal patient flow and treatment efficiency. Utilizing the genetic algorithms, the model's hyperparameters are refined, and we demonstrate its practical application through a case study. the results from our experiments indicate a significant improvement in layout planning and deployment strategies. In one specific instance, by optimizing the graph model parameters, the total time for all patients to receive services was reduced from 190 minutes to 170 minutes, a 10.5% reduction. this approach addresses the challenges of diverse and complex medical scenarios in shelter hospitals, enhancing their emergency response capabilities and operational efficiency.

关键词： Hospitals Pandemics Layout Optimization methods Nash equilibrium Emergency services Planning

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Two-Dimensional Statistical Pattern Analysis for Effective Modeling and Monitoring of Limited Batch Process data in Food Industry

Two-Dimensional Statistical Pattern Analysis for Effective M...

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data driven control and learning systems (ddcls)

作者： Jinlin Zhu Zheng Zhang State Key Laboratory of Food Science and Resources Jiangnan University Wuxi P.R.China School of Food Science and Technology Jiangnan University Wuxi P.R.China Department of Chemical and Biological Engineering Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this study presents a novel two-dimensional statistical pattern analysis (2D-SPA) method, specially designed to address the challenges inherent in monitoring and modeling processes within low-volume, high-quality food manufacturing settings. By ingeniously integrating robust statistical techniques within a two-dimensional sliding window framework, this approach significantly expands the analytical dataset by operating across both batch and time dimensions, effectively overcoming the constraints posed by limited batch sizes. this innovative strategy not only augments the detection and diagnosis capabilities for subtle process deviations and emerging trends but also capitalizes on the dynamic statistical variances observed across batches and over time, thus offering a comprehensive view of the process variability. the application of 2D-SPA is meticulously validated through an industrial case study focused on lactic acid bacteria fermentation, where it demonstrates marked improvements in accuracy and sensitivity for process monitoring. the results underscore the method's potential to significantly enhance quality assurance measures in food batch production.

关键词： Temperature sensors Temperature measurement Analytical models Quality assurance Food manufacturing Batch production systems Market research

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