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Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying

International Journal of Agricultural and Biological engineering 2023年第1期16卷 260-272页

作者： Dalong Jiang Congcong Li Zifan Lin Yuntian Wu Hongjuan Pei Magdalena Zielinska Hongwei Xiao School of Computer and Control Engineering Yantai UniversityYantai 264005ShandongChina Hebei Agricultural University Baoding 071001HebeiChina Hebei Key Laboratory of Agricultural Big Data Baoding 071001HebeiChina Department of Electrical and Electronic Engineering University of Western AustraliaPerth 6000Australia BeiGene Guangzhou Biologics Manufacturing Co. LtdGuangzhou 510555China Neuroscience and Intelligent Media Institute Communication University of ChinaBeijing 100024China Department of Systems Engineering University of Warmia and MazuryOlsztyn 004889Poland College of Engineering China Agricultural UniversityBeijing 100083China

In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slices under constant and controlled temperature and relative humidity were carried *** results were validated with experimental *** results of the simulation show that the Quadratic model fitted well to the moisture ratio and the material temperature data trend with average relative errors of 5.9%and 8.1%,***,the results of the simulation considering shrinkage show that the moisture and temperature distributions during drying are closer to the experimental data than the results of the simulation disregarding *** material moisture content was significantly related to the shrinkage of dried *** and relative humidity significantly affected the volume shrinkage of carrot *** volume shrinkage increased with the rising of the constant temperature and the decline of relative *** model can be used to provide more information on the dynamics of heat and mass transfer during drying and can also be adapted to other products and dryers devices.

关键词： carrot drying numerical simulation heat and mass transfer shrinkage

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Super-resolution adversarial generative fish classification based on Swin-Transformer 22

Super-resolution adversarial generative fish classification ...

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Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer engineering

作者： Yaohan Gong Shenming Gu Linting Guan School of Information Engineering Zhejiang Ocean University China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University China

ISBN: (纸本)9781450397148

The development of fish detection and identification technology is crucial in fishery production. Due to the large intra-class difference and high inter-class similarity, fish classification tasks are considered to be fine-grained. In addition, the acquisition of fish images is often affected by lumps, angles, complex backgrounds, weather, and other factors, which makes the classification task difficult. We present a fish classification method that combines super-resolution reconstruction, multi-scale feature information, and self-attention to improve image quality and classification. Through super-resolution reconstruction and data augmentation, image quality can be improved. A feature extraction network based on Swin-Transformer is used to extract the image's multi-scale feature information and self-attention. The method was evaluated using the Nature Conservancy's publicly available fisheries monitoring dataset. The results of the experiments reveal that the approach may significantly increase image quality while completing the fish classification. At the same time, we compared our results to those of other approaches.

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Helmet Detection Algorithm of Electric Bicycle Riders Based on YOLOv5 with CBAM Attention Mechanism Integration 8th

Helmet Detection Algorithm of Electric Bicycle Riders Based ...

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8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023

作者： Fu, Si-Yue Wei, Dong Zhou, Liu-Ying School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Beijing100044 China

ISBN: (纸本)9789819975921

Object detection algorithms can assist in detecting the helmet-wearing status of electric bicycle riders, thereby saving regulatory manpower costs. However, there is currently a lack of standardized and publicly available datasets. Additionally, the basic YOLOv5s object detection algorithm, due to its limited feature extraction capabilities, may lead to numerous instances of both false negative and false positive. To enhance the model’s focus on critical information within the feature maps, this paper introduces the CBAM attention mechanism module into the Backbone section of YOLOv5s. This module sequentially infers attention maps from the input feature maps along both channel and spatial dimensions independently, and then multiplies these attention maps with the input feature maps to achieve adaptive feature optimization. This paper have established self-built dataset for experimental research, and the results indicate that compared to the original YOLOv5s model, the proposed method has improved the model’s overall mAP score by 1.89%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Characterization Of Dim Light Response In DVS Pixel: Discontinuity of Event Triggering Time

Characterization Of Dim Light Response In DVS Pixel: Discont...

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IEEE International Conference on Image Processing

作者： Xiao Jiang Fei Zhou College of Electronic and Information Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Dynamic Vision Sensors (DVS) have recently generated great interest because of the advantages of wide dynamic range and low latency compared with conventional frame-based cameras. However, the complicated behaviors in dim light conditions are still not clear, restricting the applications of DVS. In this paper, we analyze the typical DVS circuit, and find that there exists discontinuity of event triggering time. In dim light conditions, the discontinuity becomes prominent. We point out that the discontinuity depends exclusively on the changing speed of intensity of light. Experimental results on real event data validate the analysis and the existence of discontinuity that reveals the non-first-order behaviors of DVS in dim light conditions.

关键词： Delay effects Image processing Vision sensors Cameras High dynamic range Voltage control Low latency communication

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Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models

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Advances in Climate Change Research 2024年第4期15卷 669-679页

作者： Jin-Lin ZHA Ting CHUAN Yuan QIU Jian WU De-Ming ZHAO Wen-Xuan FAN Yan-Jun LYU Hui-Ping JIANG Kai-Qiang DENG Miguel ANDRES-MARTIN Cesar AZORIN-MOLINA Deliang CHEN Key Laboratory of Atmospheric Environment and Processes in the Boundary Layer over the Low-Latitude Plateau Region Department of Atmospheric ScienceYunnan UniversityKunming 650091China Key Laboratory of Regional Climate and Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Center for Energy&Environmental Policy Research Beijing Institute of Technology Beijing 100081China Key Laboratory of Regional Sustainable Development Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China School of Atmospheric Sciences Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Centro de Investigaciones Sobre Desertificacion Consejo Superior de Investigaciones Cientificas(CIDECSIC-UV-Generalitat Valenciana)ClimateAtmosphere and Ocean Laboratory(Climatoc-Lab)Moncada 46113Spain Regional Climate Group.Deparment of Earth Sciences University of GothenburgGothenburg 40530Sweden

Wind energy development in Central Asia can help alleviate drought and fragile ***,current studies mainly used the global climate models(GCMs)to project wind speed and *** simulated biases in GCMs remain prominent,which induce a large uncertainty in the projected *** reduce the uncertainties of projected near-surface wind speed(NSW)and better serve the wind energy development in Central Asia,the Weather Research and Forecasting(WRF)model with bias-corrected GCMs was *** with the outputs of GCMs,dynamical downscaling acquired using the WRF model can better capture the high-and low-value centres of NSWS,especially those of Central Asia's ***,the simulated NSWS bias was also *** future changes in wind speed and wind energy,under the Representative Concentration Pathway 4.5(RCP4.5)scenario,NSWS during 2031-2050 is projected to decrease compared with that in *** magnitude of NSwS reduction during 2031-2050 willreach 0.1 m s^(-1).and the maximum reduction is projected to occur over the central and western regions(>0.2 m s^(-1)).Furthermore,future wind power density(WPD)can reveal nonstationarity and strong volatility,although a downward trend is expected during *** addition,the higher frequency of wind speeds at the turbine hub height exceeding 3.0 m s^(-1)can render the plain regions more suitable for wind energy development than the mountains from 2031 to *** study can serve as a guide in gaining insights into future changes in wind energy across Central Asia and provide a scientific basis for decision makers in the formulation of policies for addressing climate change.

关键词： Near-surface wind speed Wind power density Dynamical downscaling Central Asia WRF

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A Clustering-Based Color Reordering Method for Reversible data Hiding in Palette Images

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IET Image Processing 2025年第1期19卷

作者： Deng, Jianxuan Chen, Yi Wang, Hongxia Guo, Chun Cui, Yunhe Shen, Guowei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China School of Cyber Science and Engineering Sichuan University Chengdu China

A recent research work pointed out that the reversible data hiding algorithms proposed for gray-scale images can be implemented on the reconstructed palette images to improve embedding capacity and visual quality by reordering the color table. However, the reordering effect has a significant impact on performance improvement. Therefore, we propose a clustering-based color reordering method for reversible data hiding in palette images to improve the reordering effect and further enhance the performance. In this method, we first design a centroid initialization method to select the initial centroids and then exploit the K-means algorithm to generate (Formula presented.) clusters for the colors in the original color table. In the following, our proposed method, respectively, reorders the colors of these clusters by a greedy strategy and concatenates them into the reordered color table. Based on the relationship between the original and the reordered color tables, a novel index matrix can be reconstructed. Finally, state-of-the-art reversible data hiding algorithms can be implemented on the reconstructed index matrix for performance improvement. Since our proposed method improves the reordering effect, enhances the correlation of the reconstructed index matrix, and reduces the length of the encoded location map, the maximal embedding capacities and the visual quality under the fixed embedding capacities are improved. We conducted experiments on two image datasets and six standard images to verify that the performance improvement of our proposed reordering method is better than that of the state-of-the-art methods. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Embeddings

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Antigen epitopes of animal coronaviruses:a mini-review

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Animal Diseases 2024年第1期4卷 19-26页

作者： Mingjun Su Guanghui Zheng Xiangwen Xu Houhui Song Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Research Center for Animal Health Diagnostics&Advanced TechnologyZhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health ManagementChina-Australia Joint Laboratory for Animal Health Big Data AnalyticsCollege of Animal Science and Technology&College of Veterinary Medicine of ZhejiangA&F University666 Wusu StreetLin'an DistrictHangzhou 311300Zhejiang ProvinceChina

Coronaviruses are widespread in nature and can infect mammals and poultry,making them a public health ***,prevention and control of emerging and re-emerging animal coronaviruses is a great *** mecha-nisms of virus-mediated immune responses have important implications for research on virus prevention and *** antigenic epitope is a chemical group capable of stimulating the production of antibodies or sensitized lympho-cytes,playing an important role in antiviral immune ***,it can shed light on the development of diagnos-tic methods and novel ***,we have reviewed advances in animal coronavirus antigenic epitope research,aiming to provide a reference for the prevention and control of animal and human coronaviruses.

关键词： Animal coronavirus Antigen epitope B-cell epitope T-cell epitope Immune responses Vaccines

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples

A multi-target UAV fault diagnosis method based on feature d...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Xue An Yizong Zhang Shaobo Li Xuan Xiong School of Mechanical Engineering Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data.

关键词： Dimensionality reduction Fault diagnosis Deep learning Information science Accuracy Autonomous aerial vehicles Long short term memory

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Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition 25

Global Feature Enhancing and Fusion Framework for Strain Gau...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

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An Intelligent Design Method Based on Case-based Reasoning and Reinforcement Learning

An Intelligent Design Method Based on Case-based Reasoning a...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Yu Huang Ru Wang Zhuqin Wei Guoxin Wang Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology School of Machanical Engineering Beijing Institute of Technology Beijing China

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.

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