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Internet of things-based automation design and organizational innovation of manufacturing enterprises

Materials Today: Proceedings 2022年 56卷 1769-1775页

作者： Babu, T. Roopa, H. Shukla, Arvind Kumar Stalin David, D. Jayadatta, S. Rajesh, A.S. Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Application IFTM University Moradabad India Department of CSE IFET College of Engineering Villupuram India Department of Management Studies KLE's IMSR BVB Campus Hubli India Department of Mechanical Engineering JSS S&T University Mysore India

The Internet of Things (IoT) foresaw a transparent network between the real and virtual worlds. IoT offers a promising potential to develop strong market and industrial applications. The export-oriented economy of industrial manufacturers relies on product and service creativity to retain a competitive advantage. The IoT-based applications of innovative manufacturing companies (IOTs), where most companies are involved and engaged in many of the case IoT-based applications, is motivated by computing capacity, networking, cost reduction and electronic components. A common motive is the Internet of Things applications and the improvement of after-sales service activities. The suggested Linear Product Design Regression (LPDR) algorithm classifies business product innovation design based on consumer input to support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. IoT offers one of the latest innovations in manufacturing to allow us to meet key challenges. The product design and development engineering are engaged in utilizing the Internet of Things (IoT) to create comprehensive policies and policy creation for manufacturing *** support business development. Businesses, companies and the technology industry: the inhibitors hinder the development of the four IT applications. To allow us to meet key challenges and to create comprehensive policies and policy creation for manufacturing companies. © 2021

关键词： Product design

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Robust control for Bidirectional Thrust Quadrotors under Instantaneously Drastic Disturbances

Robust Control for Bidirectional Thrust Quadrotors under Ins...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zujian Chen Shaolin Mo Botao Zhang Jiyu Li Hui Cheng School of Computer Science and Engineering Sun Yat-sen University Guangzhou China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China College of Engineering South China Agricultural University Guangzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Quadrotors may crash and cause severe accidents under instantaneously drastic disturbances. To mitigate the effect of such disturbances, these critical issues should be considered: efficient disturbance observation and compensation, full attitude controllability, and instant output power generation of the quadrotor. In this paper, to keep the quadrotor stable even under suddenly drastic disturbances, a novel control framework is presented to by integrating the advantages of active disturbance rejection control (ADRC) as well as geometric control for a quadrotor with bidirectional thrust capabilities. Moreover, to strengthen the adaptability under significant disturbances, a novel switching strategy is introduced into the control framework by virtue of the quadrotor’s bidirectional thrust capabilities. The ADRC scheme is performed when the disturbances are within a range; alternatively, if the disturbances surpass the preset range and the desired control is beyond the ultimate output of the quadrotor, the quadrotor compliantly responds by executing a 180° flip reverse flight to handle such drastic disturbances. Numerical and real-world experiments demonstrate that the proposed robust control strategy has superior performance adapts to instantaneously drastic disturbances.

关键词： Robust control Attitude control Switches Numerical simulation controllability computer crashes Robotics and automation

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Motion planning for highly-dynamic unconditioned reflexes based on chained Signed Distance Functions

arXiv

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arXiv 2025年

作者： Lin, Ken Ye, Qi Lam, Tin Lun Li, Zhibin Chen, Jiming Li, Gaofeng College of Control Science and Engineering Zhejiang University Hangzhou China Robotics and Artificial Intelligence Laboratory The Chinese University of Hongkong Shenzhen China Department of Computer Science University College London London United Kingdom

The unconditioned reflex (e.g., protective reflex), which is the innate reaction of the organism and usually performed through the spinal cord rather than the brain, can enable organisms to escape harms from environments. Accordingly, it is essential to endow robots the highly-dynamic unconditioned reflexes to humans and/or environments, such that it can work collaboratively with humans or serve people in daily life. Although various sampling-based and optimization-based motion planning methods have been developed for robots, their limited computational speed is not suitable for rapid motion planning in highly-dynamic environments. In this paper, we propose an online, highly-dynamic motion planning algorithm to enable highly-dynamic unconditioned reflexes for manipulators. Our method is based on a chained version of Signed Distance Functions (SDFs), which can be pre-computed and stored. Our proposed algorithm is divided into two stages. In the offline stage, we create 3 groups of local SDFs to store the geometric information of the manipulator and its working environment. The SDFs in the first group are to describe the geometric information of the static environment. The SDFs in the second group are to describe the geometric information of each link. While the SDFs in the last group are to describe the reachability of each link’s end of the manipulator. In the online stage, the precomputed local SDFs are chained together according the configuration of the manipulator, to provide global geometric information about the environment. While the point clouds of the dynamic objects serve as query points to look up these local SDFs for quickly generating escape velocity. Then we propose a modified geometric Jacobian matrix and use the Jacobian-pseudo-inverse method to generate real-time reflex behaviors to avoid the static and dynamic obstacles in the environment. The benefits of our method are validated in both static and dynamic scenarios. In the static scenario, our meth

关键词： Jacobian matrices

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Tingting Zhao Zuoyong Li Li Quan Kun Zeng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance.

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A Dynamic Time Slot Algorithm Based on Throughput Optimization in IEEE 802.11 Wireless Networks 12

A Dynamic Time Slot Algorithm Based on Throughput Optimizati...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Jin, Guonian Lu, Shejie Hu, Siping Xu, Haihong Gao, Jinfeng Liu, Fang Wu, Haitao Hubei University of Science and Technology School of Computer Science and Technology Institute of Engineering and Technology Xianning437100 China Huanghuai University International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Zhumadian463000 China

ISBN: (纸本)9798350310153

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance. © 2022 IEEE.

关键词： IEEE Standards

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Towards an Integrated Framework for Fugitive Methane Source Localization and Monitoring in a Wireless Sensor Network

Towards an Integrated Framework for Fugitive Methane Source ...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Amira Al Jawhari Yasmine Souissi Jawhar Ghommam Yassine Jamoussi Faisal Mnif Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Department of Engineering GUtech University Muscat Oman Laboratory of Control Energy Management (Cem-Lab) ENIS Tunisa Departement of Computer Science College of Science SQU

Gas leaks are the main cause of industrial fires and accidents. These cause countless fatalities, equipment damage, and other severe environmental effects. In this paper, we provide a framework for the monitoring and detection of methane leakage using a diffusion model based on the gas diffusion theory. Given that centralized Least Square methods are not efficient and robust as they require the gathering and processing of large-scale measurements on a central node. We propose a detection technique which makes use of the distributed (Non-linear) least squares method to overcome this problem. Then, a network of connected methane sensors is used to detect gas leaks. In order to estimate the parameters of the diffusive model for the gas leakage on each sensor node, a distributed recursive estimator of the consensus plus an innovation type technique is used. The characteristics being estimated include the gas source’s distance, which will be effectively triangulated to determine the source’s precise location. The targeted location is subsequently estimated using a location dispersed algorithm-based LS.

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems 10

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Santosh, Kathari Kholmukhamedov, Timur Kumar, M. Sandeep Aarif, Mohd Muda, Iskandar Bala, B Kiran Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Turin Polytechnic University In Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350353068

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4's robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot's enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support. © 2024 IEEE.

关键词： Electronic commerce

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Dynamical Analysis of Discrete Monochamus Alternatus-Dastarcus Helophoroides Model with Artificially Released and Spatial Diffusion

SSRN

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SSRN 2024年

作者： Liu, Xiaoxiao Zhang, Chunrui College of Computer and Control Engineering Northeast Forestry University Harbin 150040 China School of Science Northeast Forestry University Harbin 150040 China

In this paper, we consider a discrete-time Monochamus alternates-Dastarcus helophoroides model with artificially released and spatial diffusion. When model without diffusion, we prove that Neimark-Sacker bifurcation and Flip bifurcation occur. The eradication of Monochamus alternatus can be achieved by incorporating sufficient quantities of artificially released Dastarcus helophoroides. And we obtain the normal form of Neimark-Sacker bifurcation. When considering spatial diffusion, taking the parameter regulating the growth of Dastarcus helophoroides c as a bifurcation parameter, we prove that Neimark-Sacker bifurcation, 1:2 Resonance and Turing instability occur. Monochamus alternatus and Dastarcus helophoroides populations generally exhibit complex periodic oscillatory behaviour in two-dimensional space. © 2024, The Authors. All rights reserved.

关键词： Diffusion

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Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

arXiv

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arXiv 2025年

作者： Gu, Yan Liu, Zhaoze Dai, Shuhong Liu, Cong Wang, Ying Wang, Shen Theodoropoulos, Georgios Cheng, Long The School of Control and Computer Engineering North China Electric Power University Beijing China The School of Computer Science and Technology Shandong University of Technology China The Institute of Computing Technology Chinese Academy of Sciences Beijing China The School of Computer Science University College Dublin Ireland Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing. © 2025, CC BY.

关键词： Job shop scheduling

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Differentially Private Singular Value Decomposition for Training Support Vector Machines

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Computational Intelligence and Neuroscience 2022年第0期2022卷 2935975页

作者： Sun, Zhenlong Yang, Jing Li, Xiaoye College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Computer and Control Engineering Qiqihar University Qiqihar161006 China

Support vector machine (SVM) is an efficient classification method in machine learning. The traditional classification model of SVMs may pose a great threat to personal privacy, when sensitive information is included in the training datasets. Principal component analysis (PCA) can project instances into a low-dimensional subspace while capturing the variance of the matrix A as much as possible. There are two common algorithms that PCA uses to perform the principal component analysis, eigenvalue decomposition (EVD) and singular value decomposition (SVD). The main advantage of SVD compared with EVD is that it does not need to compute the matrix of covariance. This study presents a new differentially private SVD algorithm (DPSVD) to prevent the privacy leak of SVM classifiers. The DPSVD generates a set of private singular vectors that the projected instances in the singular subspace can be directly used to train SVM while not disclosing privacy of the original instances. After proving that the DPSVD satisfies differential privacy in theory, several experiments were carried out. The experimental results confirm that our method achieved higher accuracy and better stability on different real datasets, compared with other existing private PCA algorithms used to train SVM. © 2022 Zhenlong Sun et al.

关键词： Singular value decomposition

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