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Analysis of the spatiotemporal trends and influencing factors of Hyphantria cunea in China

Journal of Forestry Research 2024年第6期35卷 294-305页

作者： Hongwei Zhou Chaoqun Gong Xiaodong Li Yue Wang Yunbo Yan College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040People’s Republic of China Key Laboratory of National Forestry and Grassland Administration On Forest and Grassland Pest Monitoring and Warning Center for Biological Disaster Prevention and ControlNational Forestry and Grassland AdministrationShenyang 110034People’s Republic of China

In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the spa-tial distribution trends and influencing factors of *** analysis involved integrating administrative division and boundary data,distribution data of ***,and envi-ronmental variables for *** and gravity analysis techniques were employed for data processing and *** results show that *** exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern *** these years,the focal point of the infestation was in Shandong Province with a spread towards the *** such as high vegetation density in eastern China provided favorable situations for growth and development of *** China,the spatial distribution of the moth is primarily influenced by two critical factors:precipitation during the driest month and *** play a pivotal role in determining the spread of the *** on these results,suggestions are provided for a mul-tifaceted approach to prevention and control of *** infestation.

关键词： Hyphantria cunea Temporal trends GeoDetector spatial analysis Spatial heterogeneity

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Multimodal Fusion Indoor Positioning Method for Smartphones 5

Multimodal Fusion Indoor Positioning Method for Smartphones

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5th International Conference on computer engineering and Intelligent control, ICCEIC 2024

作者： Zhuge, Huixin Xue, Can Xu, Dongyang Wang, Zhi Huzhou University College of Engineering Huzhou China Zhejiang University Key Laboratory of Industrial Control Technology Hangzhou China Huzhou Institution of Zhejiang University College of Control Science and Engineering Huzhou China

ISBN: (纸本)9798331507992

The capacity of smartphones to provide high-precision indoor positioning is an increasingly important application of these devices. Nevertheless, in complex indoor environments, it is challenging to achieve high-precision real-time mobile phone positioning through the use of a single sensor. This paper presents an analysis of the error sources and performance bottlenecks associated with smartphone indoor positioning and outlines the design of a multimodal fusion positioning method. Firstly, initial coarse positioning of the mobile phone is determined at the base station by estimating the angle of arrival (AOA) of the Bluetooth signal broadcast by the mobile phone. Secondly, in the region of coarse positioning, the time difference of arrival (TDOA) of the acoustic signals transmitted from the base station to the mobile phone is combined based on an improved indoor signal propagation model and a geometric dilution of precision (GDOP) factor. Finally, the optimal TDOA set is selected, and the weights of the two sensors are adjusted by updating the GDOP value of the device's current position in real time. Simulation results demonstrate that the proposed multimodal fusion method enhances the efficiency of position computation by 18.7% in comparison to the single-sensor positioning method. © 2024 IEEE.

关键词： Bluetooth

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Yolo-S: Texture-Less Object Pose Estimation with Shape Prior 13

Yolo-S: Texture-Less Object Pose Estimation with Shape Prior

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Danzhu Liu, Shan Polytechnic Institute Zhejiang University Hangzhou310015 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (纸本)9798350361674

This paper aims to address the challenge of pose estimation for texture-less objects under conditions of stacking, occlusion, and complex backgrounds. The proposed method, called Yolo-S, consists of two parts: a pre-estimation network based on Yolov8 backbone and a key point post-refinement network based on Graph Neural Network. The first part uses off-the-shelf detectors result as inputs (e.g. MaskRCNN) and outputs the pre-estimate keypoints at the pixel coordinates. The second part encodes the roughly estimated keypoints and the priori knowledge of the object's CAD model, uses a graph neural network to intergrate feature and predict the final keypoints. PNP method is used to obtain the object's pose. The main advantage of Yolo-S is that is has a prior knowledge embedded GNN model for estimating the pose of texture-less objects, which greatly improves prediction quality and accuracy and achieves zero-shot sim-to-real model transfer. Experimental results on the LINEMOD dataset demonstrate the effectiveness of the proposed method and its significant competitiveness with other state-of-the-art methods. © 2024 IEEE.

关键词： Graph neural networks

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Enhancing deformation characteristics prediction of coarse-grained soils with time-series generative adversarial network-based data augmentation and pre-training

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Frontiers of Structural and Civil engineering 2025年第3期19卷 396-410页

作者： Ying ZHANG Meng JIA Xuedong ZHANG Liping CAO Ziying AN Hongchao WANG Jinyu WANG School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China The State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing 100038China

Coarse-grained soils are fundamental to major infrastructures like embankments,roads,and *** their deformation characteristics is essential for ensuring structural *** methods,such as triaxial compression tests and numerical simulations,face challenges like high costs,time consumption,and limited generalizability across different soils and *** address these limitations,this study employs deep learning to predict the volumetric strain of coarse-grained soils as axial strain changes,aiming to obtain the axial strain(ε_(a))-volumetric strain(ε_(v))curve,which helps derive key mechanical parameters like cohesion(c),and elastic modulus(E).However,the limited data from triaxial tests poses challenges for training deep learning *** propose using a Time-series Generative Adversarial Network(TimeGAN)for data ***,we apply feature importance analysis to assess the quality of the numerical augmented data,providing feedback for improving the TimeGAN *** further enhance model performance,we introduce the pre-training strategy to reduce bias between augmented and real *** results demonstrate that our approach effectively predictscurve,with the mean absolute error(MAE)of 0.2219 and the R^(2) of *** analysis aligns with established findings in soil mechanics,underscoring the potential of our method in engineering applications.

关键词： coarse-grained soils deformation characteristics TimeGAN data augmentation pre-training

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An End-to-end Campus Fire Detection Algorithm Incorporating Priori Features 6

An End-to-end Campus Fire Detection Algorithm Incorporating ...

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6th International Conference on Robotics, Intelligent control and Artificial Intelligence, RICAI 2024

作者： Liang, Yu Xie, Xinlin Liu, Xuan Wang, Jie Shanxi Key Laboratory of Advanced Control and Industrial Intelligence Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798331541699

To address the imbalance in detection speed and accuracy caused by smoke, flame, and background confusion in campus video surveillance scenarios, an end-to-end campus fire detection algorithm incorporating priori features is proposed. First, a feature pyramid fusion module guided by priori features is constructed to cascade the multi-scale feature fusion module. The priori features are individually pre-trained by Convolutional Neural Network (CNN) classification network. The feature complementarity between the two is utilized to improve the feature representation of the detection network. Second, a lightweight set prediction detection head is designed. The head does not require manually designed anchors and post-processing strategies, enabling end-to-end detection while balancing speed and accuracy. Finally, a joint loss function that integrates the losses of categorical and bipartite matching is proposed. Focus on the drawbacks of the slow convergence associated with bipartite matching, both classification information and bipartite bit matching are considered in the detection results. Experimental results on the fire detection dataset demonstrate that the proposed algorithm improved average accuracy by 7.8% while maintaining detection speed, effectively balancing speed and accuracy in fire detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on Retrieval-augmented Generation Algorithms for Large Language Models 4

Research on Retrieval-augmented Generation Algorithms for La...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Qiao, Yi Du, Shuxin Qiu, Yi Huzhou University School of Engineering Huzhou313000 China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Zhejiang China

ISBN: (纸本)9798350363586

Large language model in the field of text generation shows excellent language skills, combined with the retrieval of generated algorithm is enhanced, the language model is more accurate and timely in a professional field. Therefore, retrieval enhanced generation (Retrieval Enhanced Generation, RAG) has become an important framework for building large language models, but the processing capacity of simple RAG is limited and highly dependent on the quality of retrieval. Therefore, this paper proposes an optimized RAG, adding additional processing steps in the whole process to improve the relevance and overall quality of optimized replies. Our validation shows that the improved RAG has a significant increase in its ability to perform on large models, and by comparing it with fine-tuning techniques, verifies that the combination of RAG and fine-tuning is better than what can be achieved with simple RAG or fine-tuning. © 2024 IEEE.

关键词： Finetune Hybrid Search LLM RAG

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Prediction of Multi-Converter Gas Holders Level in Steel Enterprises based on Mechanism and Data 43

Prediction of Multi-Converter Gas Holders Level in Steel Ent...

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43rd Chinese control Conference, CCC 2024

作者： Yan, Yaliang Chen, Long Zhao, Jun Wang, Wei Dalian University of Technology School of Control Science and Engineering Dalian116024 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Linz Donawitz converter gas (LDG) is an important secondary energy source in iron and steel enterprises. The accurate prediction of converter gas holder level is of great significance for the optimal scheduling of the LDG systems. Considering that there are multi-operating conditions caused by the affection of different gas holders from each other in a multiple gas holders system, this paper proposes a mechanism and data-based converter gas holder level prediction method, in which via physical mechanism analysis the operating conditions as well as their switching logics are built according to different pressures in each gas holder and valve states. Then, the least squares support vector machine (LSSVM) is employed to establish gas holders level prediction models for each operating condition. The actual operation data in an iron and steel plant of China are employed for simulation experiments. The results show that the proposed method not only can effectively identify the operating conditions of dual gas holders system, but also can achieve higher prediction accuracies compared to those of other prediction methods, providing effective guidance for the scheduling of the LDG systems. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Iron and steel plants

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Transformer-Based Differential Superpixel Selection Network for Remote Sensing Images Change Detection 4

Transformer-Based Differential Superpixel Selection Network ...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Cui, Tihao Xie, Xinlin Liang, Jingjing Zhang, Haoxue Shanxi Provincial Key Laboratory of Advanced Control and Industrial Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798331541729

In remote sensing images change detection, the results of Transformer often face the low precision and edge fitting problems, which are caused by the highly complex background of building categories and the imbalance of sample ratio. To address these challenges, we propose a Transformer-based differential superpixel selection network. First, a differential superpixel selection module is proposed which introduces difference selecting to refine the edge feature representation of the object region by using the boundary information of the selected superpixel. Second, we propose a multi-scale feature-aware enhancement module, which fuses multi-scale feature differences with the features extracted by the hierarchical Transformer encoder. This module enhances difference awareness while suppressing interference from complex backgrounds. Finally, we construct a composite loss function that combines the focal loss function with the binary cross-entropy loss function. The composite loss function can pay more attention to hardly classifying samples and suppress the influence of easily classifying samples. Our proposed algorithm is tested on the widely used LEVIR-CD and DSIFN-CD datasets. The results show that it effectively improved detection accuracy and edge fitting compared to existing methods. © 2024 IEEE.

关键词： Change detection

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Doped-nitrogen enhanced the performance of Nb_(2)CT_(x)on the electrocatalytic synthesis of H_(2)O_(2)

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Nano Research 2023年第5期16卷 6120-6127页

作者： Shijie Zhang Jingnan Zheng Junming Lin Yuanan Li Zhikang Bao Xiaoge Peng Wenkai Ji Lei Ding Zaixiang Xu Guoliang Wang Xing Zhong Jianguo Wang Institute of Industrial Catalysis State Key Laboratory Breeding Base of Green-Chemical Synthesis TechnologyCollege of Chemical EngineeringZhejiang University of TechnologyHangzhou 310014China Zhejiang Center for Disease Control and Prevention Hangzhou 310051China

Electrochemical oxygen reduction reaction(ORR)for preparing hydrogen peroxide(H_(2)O_(2))is a promising way to replace the anthraquinone *** key to H_(2)O_(2)production is the development of catalysts to regulate the oxygen reduction reaction ***,nitrogen-doped Nb_(2)CT_(x)was prepared by NH3 annealing *** with precursor Nb_(2)AlC(67.01%)and pure Nb_(2)CT_(x)(75.70%),nitrogen-doped Nb_(2)CT_(x)exhibited excellent performance for 2e−ORR with>90%H_(2)O_(2)selectivity(at 0.4 V *** hydrogen electrode(RHE)).Faradic efficiency of nitrogen-doped Nb_(2)CT_(x)reached 80.75%,whereas those for Nb2AlC and Nb_(2)CT_(x)were 60.35%and 39.27%,respectively.A desirable catalytic stability for 50 h was *** functional theory(DFT)calculations indicated excellent activity of the nitrogen-doped Nb_(2)CT_(x)was attributed to the introduction of *** excellent activity was conducive to the adsorption of oxygen and promoted the formation of the OOH *** work can serve as an important reference for regulating the electronic structure of MXene to expand the application area in the electrochemical field.

关键词： Nb_(2)CT_(x) nitrogen-doped electrocatalysis oxygen reduction reaction hydrogen peroxide

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Efficient production of 14α‑OH‑AD by engineered Mycolicibacterium neoaurum via coupled cofactor and reconstructed electron transport system

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Systems Microbiology and Biomanufacturing 2023年第2期3卷 358-369页

作者： Chenwei Zhang Yanbing Shen Yuanyuan Gao Zehui Zan Min Wang Key Laboratory of Industrial Fermentation Microbiology Ministry of EducationTianjin Key Laboratory of Industrial MicrobiologyTianjin University of Science and TechnologyTianjinChina Engineering Research Center of Microbial Metabolism and Fermentation Process Control College of BiotechnologyTianjin University of Science and TechnologyTianjin 300457People’s Republic of China

14α-hydroxy-androst-4-ene-3,17-dione(14α-OH-AD)is an important precursor for the synthesis of steroid drugs with anticancer and carcinolytic ***,14α-OH-AD was mostly synthesized by whole-cell fermentation of mold fungi using androstenedione(AD)as a substrate,which had difficulties in product isolation and purification as well as problems of high production *** this study,the source of the 14α-hydroxylase gene was *** 14α-hydroxylase genes were heterologously expressed in Mycolicibacterium neoaurum(MNR)M3ΔksdD,which enabled the one-step biotransformation from the cheap substrate phytosterols(PS)to 14α-OH-AD,reducing the difficulty of product purification and production *** is more,to alleviate the problem of poor activity of 14α-hydroxylase,the 14α-hydroxylase gene was co-expressed with the electron transport chain element genes and the coenzyme regeneration genes,and a superior engineered strain MNR M3ΔksdD/pMV261-14α-G6PDH was ***,the transformation conditions were optimized for the transformation of PS by the engineered *** molar yield of 14α-OH-AD reached to 60.4±2.3%(about 0.22 g/L productivity).This study investigated for the first time the effects of the tandem electron transport chain element genes and the tandem coenzyme regeneration genes on the 14α-hydroxylation reaction,providing a theoretical basis for the industrial production of 14α-OH-AD.

关键词： 14α-hydroxy-androst-4-ene-3,17-dione Mycolicibacterium neoaurum 14α-hydroxylase Electron transportation Coenzyme regeneration

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