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8th International Conference on System Reliability and Safety, ICSRS 2024

作者： Zhao, Zhiqian Wang, Yuefeng Jiao, Yinghou School of Mechatronics Engineering Harbin Institute of Technology Harbin China Beijing Jingdiao Technology Group Beijing China

ISBN: (纸本)9798350354508

The success of traditional fault diagnosis methods depends on obtaining a large number of training samples in the form of full faults, which may not be available in practical applications. Recent studies have shown the feasibility of constructing virtual representations reacting to fault situations by building physical models when fault samples are insufficient. In this paper, a virtual data-driven domain-based adaptive fault diagnosis method based on adaptive regularisation consistency, ARC, is proposed to ensure the consistency between the virtual and real domains. Firstly, the bearing dynamic model is constructed to obtain the simulation data, and the simulation data and the real data are input into the trained original model and the target model at the same time. The data distributions of the simulated and real samples are adaptively brought closer by adding weights to the maximum mean discrepancy (MMD);Kullback-Leibler Divergence (KLD) is used to make the feature representations extracted from the target model similar to each other and between the original model and the feature representations extracted from the target model, and regularization is used to fine-tune the feature extraction of the model. An entropy-based adaptive pseudo-labelling selection method is proposed to filter low-quality samples and prevent negative transfer. The diagnostic results of the case study show that the proposed method is able to utilize the diagnostic knowledge from the simulation data to achieve fault diagnosis of mechanical equipment and outperforms the commonly used unsupervised cross-domain fault diagnosis methods. © 2024 IEEE.

关键词： Virtual environments

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Fuzzy Logic Based Obstacle Avoidance Algorithm for Unmanned Systems 8

Fuzzy Logic Based Obstacle Avoidance Algorithm for Unmanned ...

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8th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Palaniappan, Rajkumar Nataraj, Sathees Kumar Ismail, Zeyad Noaman, Noaman M College of Engineering University of Technology Bahrain Department of Mechatronics Engineering Salmabad Bahrain

ISBN: (纸本)9798350327090

Researchers that create autonomous systems are very interested in obstacle avoidance. An autonomous system's ability to avoid obstacles more intelligently is a key component. Only artificial intelligence approaches can accomplish this. In this research, a smart obstacle avoidance system based on fuzzy inference system is proposed. The distance from the obstacle and the angle at which the autonomous system is facing the obstacle are the inputs to the fuzzy inference system. The suggested system's outputs include steering and speed adjustments for the autonomous vehicle to better avoid collision with obstacles. With the help of the correlation coefficient and root mean squared error, the developed fuzzy system was assessed. A correlation coefficient was obtained for the suggested system. For steering and speed adjustment, the proposed system received correlation coefficients of 0.983 and 0.974, respectively. Comparable results were found for steering and speed adjustment, with root mean squared errors of 0.0865 and 0.08633, respectively. The results of the performance review were quite successful, and the system that was created was dependable. The similar mechanism will be implemented in hardware in the future, and testing will take place. © 2023 IEEE.

关键词： Fuzzy systems

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Research on IIoT Cloud-Edge Collaborative Stream Processing Architecture for Intelligent Factory 9

Research on IIoT Cloud-Edge Collaborative Stream Processing ...

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9th IEEE Smart World Congress, SWC 2023

作者： Lee, Ruizhi Zhang, Mingyang Yan, Jihong Harbin Institute of Technology School of Mechatronics Engineering Heilongjiang China

ISBN: (纸本)9798350319804

Intelligent factories (IF) represented by unmanned workshops and light out workshops point out the development direction of future industrial production which is known as Industrial Internet of Things (IIoT) and cloud-edge collaborative services. IIoT must concurrently send various types of data in real time to the cloud for adequately analyzing and processing the data to support the intelligent decision-making needs of IF, which makes massive highly concurrent heterogeneous data streams. The traditional IIoT platform of directly connecting devices to the cloud, which cannot satisfy the high concurrency heterogeneous data stream transmission requirements of IF, result in data loss and high latency. In this article, cloud-edge collaborative stream processing architecture is proposed for efficiently processing high concurrency heterogeneous data streams generated by IIoT in the production process with reliable, confidential, and low latency data transmission from devices to edges and clouds. The real-time display of AGV status and workshop environment through the cloud-edge collaborative stream processing platform prototype system has demonstrated that the latency of data stream transmission processing is millisecond level, fully satisfying the practical application requirements of IF. © 2023 IEEE.

关键词： cloud-edge collaboration industrial internet of things intelligent factory stream processing

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Development and Practice of Ball Sports Robotics System Based on Competition-Integrated Teaching 12

Development and Practice of Ball Sports Robotics System Base...

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12th IEEE International Conference on engineering Education, ICEED 2023

作者： Jiang, Yu Zeng, ZhaoYang Liu, Lu School of Mechatronics Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350307429

Currently, there are 301 robotics engineering programs in universities across the country. With the development of robotics majors in higher education institutions, universities have begun to combine engineering maker ideas with robotics technology to carry out corresponding higher engineering education. This paper extends and improves the robotics technology practice course in Harbin Institute of technology's engineering Innovation Practice Center. Based on the competition-integrated teaching model, it demonstrates the development of a ball sports robotics teaching system and competition assessment methods. The practice goal is set as the competition tasks, and the self-developed robotics teaching system and processing equipment are used to achieve practical results as well as to cultivate students' ability to learn independently through practice. This paper aims to build a coordinated and unified system with distinctive features, incorporating education, competition, intelligence, and innovation, and to cultivate a large number of engineering makers among the new generation of university students. © 2023 IEEE.

关键词： Robotics

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Scheduling Optimization of Flexible Workshop Dynamics Based on Big Data 28

Scheduling Optimization of Flexible Workshop Dynamics Based ...

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28th International Conference on Automation and Computing, ICAC 2023

作者： Ji, Siyang Yan, Jihong School of Mechatronics Engineering Harbin Institute of Technology Heilongjiang China

ISBN: (纸本)9798350335859

Modern machining has the characteristics of 'small batch, multiple machining types, and complex processes'. The production process generates a large amount and variety of data, and the production strategy is complex and variable. The generation of scheduling strategies needs to consider the actual production status of the workshop and the disturbances in the current status to ensure that the scheduling results meet the actual order requirements. This paper proposes a dynamic scheduling optimization strategy based on workshop big data. We design a big data-based scheduling optimization computing framework, which integrates big data resources such as workshop machine tool processing status, workpiece processing information, and order information to obtain relevant data affecting processing in real-time, and quickly respond to changes in workshop factors to ensure consistency between scheduling constraints and actual situations. In addition, we have improved the traditional genetic algorithm and the method of calculating the fitness value, solving the problem of deviation from the original scheduling plan caused by the disturbance of urgent order insertion, rework and repair, machine tool maintenance, and scheduling time. This improves the real-time and dynamic decision-making of machining scheduling. The proposed scheduling optimization strategy has been applied to a certain aviation engine machining workshop, reducing the overall machining time and improving machining efficiency compared to the original scheduling strategy of the workshop. © 2023 IEEE.

关键词： Genetic algorithms

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Magnetic Resonance Image Fusion using Bilateral Filter and PCA 9

Magnetic Resonance Image Fusion using Bilateral Filter and P...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kannan, K. Sakthibala, K. Department of Mechatronics Engineering KAMARAJ College of Engineering and Technology Vellakulam K India

ISBN: (纸本)9798350383201

Three bands of magnetic resonance image-PD, T1 and T2-produce complimentary data about the human body portion being scanned. Medical image fusion combines complementary information of all three bands in the fused image with all relevant information needed by a physician to do medical analysis. This paper uses the edge-preserving bilateral filter and principal component analysis to combine complimentary data from PD, T1 and T2 bands of magnetic resonance images. Bilateral filter is used to decompose each of the three bands of magnetic resonance images into detail and approximation images. Detail image contains the high frequency information whereas approximation image contains the low frequency information. Principal component analysis and maximum absolute value are selected as the activity measure to fuse detail and approximation images. Then, the fused image is obtained by adding these fused detail and approximation images. Fusion metrics like Mutual information, Non-linear correlation information entropy, Structural similarity index measure, Gradient metric, Phase congruency metric and Human perception metric are used in this paper to compare the results of proposed method with image fusion using various pyramids, Wavelet and Curvelet Transforms. © 2023 IEEE.

关键词： Principal component analysis

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Recent Advancements in Pest-Repellent Monitoring Technologies in Precision Agriculture: A Comprehensive Review

Recent Advancements in Pest-Repellent Monitoring Technologie...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Udofa, Emaediong Sylvanus Mohamed Ali, Mohamed Sultan Jack, Kufre Esenowo Innocent, Chika Abdulbaki, Abdulkadir Olayinka Ekanem Ekanem, Udeme Universiti Teknologi Malaysia Department of Mechatronics and Automatic Control Engineering Johor Malaysia Federal University of Technology Minna Department of Mechatronics Engineering Niger State Nigeria Federal University of Technology Department of Telecommunication Engineering Niger State Minna Nigeria Akwa Ibom State Polytechnic Ikot Osurua Department of Mechatronics Engineering Technology Akwa Ibom State Ikot Ekpene Nigeria

ISBN: (纸本)9798350358155

With the global population increasing at a rapid pace, reaching an estimated 9.6 billion by 2050, the demand for food is expected to surge significantly. However, there is significant limitation in crop production as a result of factors such as lack of proper irrigation and pest. To meet this demand, it is essential to modernize the agricultural sector and enhance crop yields. One effective approach to overcoming the current limitations in crop production is adopting pest-repellent and monitoring technology. This paper comprehensively reviews recent advancements and modifications in commercialized and underdeveloped pest-repellent devices and monitoring technologies specifically tailored for the agricultural sector. The study focuses on the integration of pest repellent and crop monitoring in precision agriculture, examining advancements in drone design, the development of multiple sensors, and innovative techniques for farming purposes. Additionally, the report explores the integration of artificial intelligence (AI) and deep learning algorithms in remote crop monitoring, aligning with the current trend in agricultural technology. The findings of this study established and contribute to understanding the potential benefits and challenges associated with implementing advanced pestrepellent and monitoring technologies in precision agriculture, paving the way for improved agricultural practices and sustainable food production. © 2024 IEEE.

关键词： Smart agriculture

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Precipitation evolution of Al-Zn-Mg-Cu-(Ag) alloys with a low Zn/Mg ratio

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Journal of Materials Science & technology 2024年第28期195卷 177-196页

作者： Qianqian Zhu Xiaodong Wu Lingfei Cao Yan Zou Hui Song Yahui Liu Kexing Song Malcolm J.Couper School of Materials Science and Engineering Henan University of Science and TechnologyLuoyang 471023China International Joint Laboratory for Light Alloys(Ministry of Education) College of Materials Science and EngineeringChongqing UniversityChongqing 400044China School of Mechatronics Engineering Henan University of Science and TechnologyLuoyang 471023China Department of Materials Science and Engineering Monash UniversityClaytonVIC3800Australia

The influence of minor Ag on the precipitation evolution of the Al-4.2Zn-2.8Mg-1.0Cu(wt.%)alloy from early stages to over-aged stages at 150°C was ***,co-precipitation of strengthening phases T′andη′are found in both Ag-free and Ag-added *** Ag addition,precipitation of both T′andη′is refined and increased,such that the age-hardening capabilities and peak-aged tensile strength are *** addition,the quantitative proportion ofη′precipitates increases with the increase of Ag content due to the increase in the(Zn+Cu)/Mg ratio of nucleating *** narrowed precipitate-free zones(PFZs)are considered responsible for the undiminished fracture elongation in Ag-added ***,these effects of Ag are closely related to the strong Ag-vacancy and Ag-solute *** over-aged stages,the Ag-added alloys still possess higher hardness values compared to the Ag-free alloy,which is related to precipitate coarsening *** Ag-free alloy follows classical coarsening behavior by solid solution mediated diffusion,while the Ag-added alloy follows two possible coarsening mechanisms,coalescence of aggregates and diffusion of *** smaller average size and higher residual number density of precipitates benefited from the slow diffusion-controlled coarsening behavior depending on the precipitate composition characteristics of the two-stage differentiation and the precipitate distribution characteristics of high-density dispersion in early-aged stages could explain why the hardness of Ag-added alloy keeps at a higher level than that of Ag-free alloy even after 1000 h ***,the transformation of metastable phases to stable phases is inhibited due to the addition of Ag,such that GP zones,T′,η′,ηand T phases coexist even after 14 d of *** terms of phase composition,the addition of Ag decreases the ratio of Mg/(Al+Zn)in T-type *** the Ag-added alloy,the sum concentration of Zn+Mg inη′phase is about 10 at

关键词： Al-Zn-Mg-Cu alloy with low Zn/Mg Trace Ag Co-precipitation Evolution of precipitation Atom probe tomography

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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An improved 3D U-Net-based deep learning system for brain tumor segmentation using multi-modal MRI

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Multimedia Tools and Applications 2024年第37期83卷 85027-85046页

作者： Ali, Saqib Khurram, Rooha Rehman, Khalil ur Yasin, Anaa Shaukat, Zeeshan Sakhawat, Zareen Mujtaba, Ghulam Faculty of Information Technology Beijing University of Technology Beijing100124 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Department of Radiology Advance Diagnostic Center Islamabad Pakistan

Cancer is a lethal illness that requires an initial stage prognosis to enhance patient survival rate. Accurate brain tumor and their sub-structure segmentation through Magnetic Resonance Images (MRIs) is a tough endeavor. Owing to the heterogeneous tumor areas, automatically segmenting brain tumors has proved to be a critical task even for neural network-based algorithms, some tumor regions remain unidentified due to their small size and the variation in area occupancy among tumor sub-classes. Current progress in the area of neural networks has been employed to enhance the segmentation performance. This study designed an intelligent 3D U-Net encoder-decoder-based system for automatic detection and brain tumor sub-structure segmentation. Our proposed 3D model constitutes neural units (the basic building blocks) followed by transition layer blocks and skip connections. BraTS 2018 and private local datasets are used to evaluate the proposed model which segments the Whole Tumor (WT), Tumor Core (TC), and the Enhancing Tumor (ET). The training accuracy, validation accuracy, dice score, sensitivities, and specificities of WT, CT, and ET zones are computed. The experimental results demonstrate that dice scores are 0.913, 0.874, and 0.801 for the BraTS 2018 dataset. The developed models performance was further evaluated by utilizing the dataset from a local hospital containing 71 subjects. The dice scores of 0.891, 0.834, and 0.776 are achieved by the proposed model on the private dataset. The practicability of the proposed model was assessed by the comparative studies of our model with existing literature. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance

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