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Automatic fusion method for perceptual data of internet of things based on Kalman filter

International Journal of Reasoning-based intelligent Systems 2024年第6期16卷 427-436页

作者： Gao, Andi Guo, Xiaojing Liu, Ketong Meng, Kuntai Department of Information Engineering Hebei Institute of Mechanical and Electrical Technology Xingtai054000 China Intelligent Sensor Network Application Technology Research and Development Center Xingtai054000 China

Due to the low fusion accuracy of traditional internet of things (IoT) sensing data automatic fusion methods, an automatic fusion method of IoT sensing data based on Kalman filter is proposed. Firstly, we adopt the error correction mechanism and time registration to deal with the structure deviation of the IoT sensing data and the asynchronous problem of the IoT sensing data. Then, the Kalman filtering algorithm is used to fuse the time data and space data at the terminal node and gateway layer. Finally, the Kalman filtering algorithm is optimised by using the distribution diagram method to determine the abnormal or missing data in the fused data, so as to obtain the optimised data automatic fusion results. The test results show that the precision of this method for automatic fusion of IoT sensing data is above 94%, and the fusion effect is good. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Wiener filtering

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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Personalized intelligent Job Recommendation Based on Deep Learning in Elasticsearch Environment

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 339-352页

作者： Cui, Xiu-Yan Hwang, David Hebei Software Institute Hebei Province University Intelligent Interconnection Equipment and Multimodal Big Data Application Technology Research and Development Center Baoding071000 China College of Engineering St. Paul University Philippines Tuguegarao3500 Philippines

Aiming at the issue that the jobs predicted by the existing job recommendation methods cannot be precisely matched with the job seekers, this paper designs a personalized intelligent job recommendation method based on deep learning in the Elas-ticsearch environment. Firstly, to deal with the data sparsity and cold start issue of traditional collaborative filtering algorithms, the similarity calculation formula of Pearson’s correlation in the algorithm is modified to enhance the accuracy of similarity calculation. Secondly, on the ground of the history of different job seekers, the keywords of job seekers are constructed in terms of various weights, and input into ElasticSearch to recall the set of jobs that are strongly related to the job seekers. Then adopting Convolutional Neural Network (CNN) to extract the text data features of job seekers and positions, and meanwhile, integrating the attention mechanism to get the auxiliary features of job seekers and positions, and finally, integrating the implicit feedback data with other at-tribute features of job seekers and positions as the input of the improved collaborative filtration algorithm, so that we can realize the intelligent recommendation of jobs for job seekers. Simulation outcome indicates that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Root Mean Square Percentage Error (RMSPE) of the suggested method are 0.0681, 0.0519, 0.0834 and 0.0628 respectively, which are lower than those of the comparison method, and the accuracy of the recommendation is effectively enhanced. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Context-Aware Augmented Reality Using Human-Computer Interaction Models

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Journal of Control and Decision 2024年第1期11卷 1-14页

作者： Ying Sun Qiongqiong Guo Shumei Zhao Karthik Chandran G.Fathima Henan intelligent education and intelligent technology application engineering technology research center Zhengzhou Railway Vocational and technical collegeZhengzhouHenanPeople’s Republic of China Henan Provincial Research Center of Wisdom Education and Intelligent Technology Application Engineering Technology Zhengzhou Railway Vocational and technical collegeZhengzhouHenanPeople’s Republic of China Department of Mechatronics Engineering Jyothi Engineering CollegeKeralaIndia CSE Adhiyamaan College of Engineering HosurTamilnaduIndia

Augmented Reality is a technique that allows users to overlap digital information with their physical *** Augmented Reality(AR)displays have an exceptional characteristic from the Human–Computer Interaction(HCI)*** to its increasing popularity and application in diverse domains,increasing user-friendliness and AR usage are ***-aware is one approach since an AR application can adapt to the user,environment,needs and enhance ergonomic principles and *** paper proposes the intelligent Contextaware Augmented Reality Model(ICAARM)for Human–Computer Interaction *** study explores and reduces interaction uncertainty by semantically modeling user-specific interaction with context,allowing personalised *** information is captured from an AR device to understand user interactions and *** depictions carry semantics to Augmented Reality applications about the user’s intention to interact with a specific device ***,this study describes personalised gesture interaction in VR/AR applications for immersive/intelligent environments.

关键词： Augmented Reality Context-aware Human–Computer Interaction

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Fundamentals and applications Related to Data Science, Machine Learning, and Statistical Processing V: applications of Machine Learning at Kanadevia Corporation

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Zairyo/Journal of the Society of Materials Science, Japan 2024年第11期73卷 881-887页

作者： Umano, Motohide Miyake, Toshihide Ioka, Ryota Wada, Takahiro Intelligent Machinery Research Center Technical Research Institute Research and Technology Development Headquarters Kanadevia Corporation Funamachi Taisho-ku Osaka551-0022 Japan

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Review of the detasseling techniques for maize(Zea mays L.)hybrid seed production

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 1-11页

作者： Ruirui Zhang Jiaxuan Yang Liping Chen Chenchen Ding Longlong Li Linhuan Zhang National Research Center of Intelligent Equipment for Agriculture Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China National Center of International Research on Agricultural Aerial Application Technology Beijing 100097China Research Center of Intelligent Equipment Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China

Maize(Zea mays L.)is a critical staple crop globally,integral to human consumption,food security,and agricultural product *** quality and purity of maize seeds,essential for hybrid seed production,are contingent upon effective *** study investigates the evolution of detasseling technologies and their application in Chinese maize hybrid seed production,with a comparative analysis against the United States.A comprehensive examination of the development and utilization of detasseling technology in Chinese maize hybrid seed production was undertaken,with a specific focus on key *** from the United States were included for comparative *** analysis encompassed various detasseling methods,including manual,semi-mechanized,and cytoplasmic male sterility,as well as more recent innovations such as detasseling machines,and the emerging field of intelligent detasseling driven by unmanned aerial vehicles(UAVs),computer vision,and mechanical *** detasseling methods were predominantly employed by *** the challenges of inflexible and occasionally overlooked,applying detasseling machines is efficient and *** present,China’s detasseling operations in hybrid maize seed production are mainly carried out by manual work,which is labor-intensive and *** order to address this issue,China is dedicated to developing intelligent detasseling *** study emphasizes the critical role of detasseling in hybrid maize seed *** United States has embraced mechanized *** application and development of manual and mechanized detasseling were applied later than those in the United States,but latest intelligent detasseling technologies first appeared in *** detasseling is expected to be the future direction,ensuring the quality and efficiency of hybrid maize seed production,with implications for global food security.

关键词： detasseling technique detasseling machine UAVs intelligent agriculture maize hybrid seed production

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Discriminative low-rank embedding with manifold constraint for image feature extraction and classification

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Optoelectronics Letters 2024年第5期20卷 299-306页

作者： YAN Chunman WEI Shuhong School of Physics and Electronic Engineering Northwest Normal UniversityLanzhou 730070China Engineering Research Center of Gansu Province for Intelligent Information Technology and Application Lanzhou 730070China

The robustness against noise, outliers, and corruption is a crucial issue in image feature extraction. To address this concern, this paper proposes a discriminative low-rank embedding image feature extraction algorithm. Firstly, to enhance the discriminative power of the extracted features, a discriminative term is introduced using label information, obtaining global discriminative information and learning an optimal projection matrix for data dimensionality reduction. Secondly, manifold constraints are incorporated, unifying low-rank embedding and manifold constraints into a single framework to capture the geometric structure of local manifolds while considering both local and global information. Finally, test samples are projected into a lower-dimensional space for classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 95.62%, 95.22%, 86.38%, and 86.54% on the ORL, CMUPIE, AR, and COIL20 datasets, respectively, outperforming dimensionality reduction-based image feature extraction algorithms.

关键词： manifold embedding dimensionality

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A Method for Fast Feature Selection Utilizing Cross-Similarity within the Context of Fuzzy Relations

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Computers, Materials & Continua 2025年第4期83卷 1195-1218页

作者： Wenchang Yu Xiaoqin Ma Zheqing Zhang Qinli Zhang School of Big Data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education Big Data Intelligent Perception and Application Engineering Research Center Anhui Provincial Joint Construction Key Laboratory of Intelligent Education Equipment and TechnologyChizhou247000China

Feature selection methods rooted in rough sets confront two notable limitations:their high computa-tional complexity and sensitivity to noise,rendering them impractical for managing large-scale and noisy *** primary issue stems from these methods’undue reliance on all *** overcome these challenges,we introduce the concept of cross-similarity grounded in a robust fuzzy relation and design a rapid and robust feature selection ***,we construct a robust fuzzy relation by introducing a truncation ***,based on this fuzzy relation,we propose the concept of cross-similarity,which emphasizes the sample-to-sample similarity relations that uniquely determine feature importance,rather than considering all such relations *** studying the manifestations and properties of cross-similarity across different fuzzy granularities,we propose a forward greedy feature selection algorithm that leverages cross-similarity as the foundation for information *** algorithm significantly reduces the time complexity from O(m2n2)to O(mn2).Experimental findings reveal that the average runtime of five state-of-the-art comparison algorithms is roughly 3.7 times longer than our algorithm,while our algorithm achieves an average accuracy that surpasses those of the five comparison algorithms by approximately 3.52%.This underscores the effectiveness of our *** paper paves the way for applying feature selection algorithms grounded in fuzzy rough sets to large-scale gene datasets.

关键词： Fuzzy rough sets feature selection cross-similarity fuzzy relations

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A Double-Compensation-Based Federated Learning Scheme for Data Privacy Protection in a Social IoT Scenario

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Computers, Materials & Continua 2023年第7期76卷 827-848页

作者： Junqi Guo Qingyun Xiong Minghui Yang Ziyun Zhao School of Artificial Intelligence Beijing Normal UniversityBeijing100875China Engineering Research Center of Intelligent Technology and Educational Application Ministry of EducationBeijing100875China

Nowadays,smart wearable devices are used widely in the Social Internet of Things(IoT),which record human physiological data in real *** protect the data privacy of smart devices,researchers pay more attention to federated *** the data leakage problem is somewhat solved,a new challenge has *** federated learning shortens the convergence time,while it has time delay and data heterogeneity *** of the two problems harm the *** overcome these issues,we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time delay and data heterogeneity *** scheme improves the Delay Compensated Asynchronous Stochastic Gradient Descent(DC-ASGD)algorithm based on the second-order Taylor expansion as the delay *** adds the FedProx operator to the objective function as the heterogeneity ***,the proposed scheme motivates the federated learning process by adjusting the importance of the participants and the central *** conduct multiple sets of experiments in both conventional and heterogeneous *** experimental results show that our scheme improves the accuracy by about 5%while keeping the complexity *** can find that our scheme converges more smoothly during training and adapts better in heterogeneous environments through numerical *** proposed double-compensation-based federated learning scheme is highly accurate,flexible in terms of participants and smooth the training *** it is deemed suitable for data privacy protection of smart wearable devices.

关键词： Social Internet of Things smart wearable devices data privacy asynchronous federated learning

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2D–3D dual carbon layer confined ultrasmall VN nanoparticles for improving lithium-ion storage in hybrid capacitors

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Rare Metals 2024年第1期43卷 65-75页

作者： Zhao-Wei Hu Hui-Fang Li Peng Wang Wan-Li Wang Lei Yang Xiao-Jun Wang Zhi-Ming Liu College of Electromechanical Engineering Shandong Engineering Laboratory for Preparation and Application of High-Performance Carbon-MaterialsQingdao University of Science&TechnologyQingdao 266061China Research Center for Intelligent and Wearable Technology College of Textiles and ClothingQingdao UniversityQingdao 266071China

Lithium-ion capacitors(LICs) of achieving high power and energy density have garnered significant attention. However, the kinetics unbalance between anode and cathode can impede the application of LICs. Vanadium nitride(VN) with a high theoretical specific capacity(~ 1200 m Ah·g^(-1)) is a better pseudocapacitive anode to match the response of cathode in LICs. However, the insertion/extraction of Li-ions in VN's operation results in significant volume expansion. Herein, the VN/N-r GO-5composite that three-dimentional(3D) dicyandiamidederived-carbon(DDC) tightly wrapped VN quantum dots(VN QDTs) on two-dimentional(2D) reduced graphene oxid(r GO) was prepared by a facile strategy. The VN QDTs can reduce ion diffusion length and improve charge transfer kinetics. The 2D r GO as a template provides support for nanoparticle dispersion and improves electrical conductivity. The 3D DDC tightly encapsulated with VN QDTs mitigates agglomeration of VN particles as well as volume expansion. Correspondingly, the LICs with VN/Nr GO-5 composite as anode and activated carbon(AC) as cathode were fabricated, which exhibits a high energy density and power density. Such strategy provides a perspective for improving the electrochemical properties of LIC anode materials by suppressing volume expansion and enhancing conductivity.

关键词： Vanadium nitride(VN)quantum dots Reduced graphene oxide(rGO)nanosheets Dicyandiamide-derived-carbon 2D–3D dual carbon layer Lithium-ion capacitors

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