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检索条件"任意字段=International Conference on Process Automation, Control and Computing"

共 28200 条记录，以下是71-80 订阅

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A research on optimization of fog estimation process in Peplography 24

A research on optimization of fog estimation process in Pepl...

引用

24th international conference on control, automation and Systems

作者： Ono, Seiya Ha, Jin-Ung Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Iizuka Fukuoka 8208502 Japan Hankyong Natl Univ Sch ICT Robot & Mech Engn IITC Anseong 17579 Kyonggi Do South Korea

ISBN: (纸本)9798331517939;9788993215380

In recent years, imaging techniques are essential in our lives, such as security cameras and drive recorders. However, the visualization quality of these techniques sometimes is affected by scattering media, such as smoke at a fire or fog during driving. To solve this problem, Peplography has been proposed. Peplography is an optical algorithm that emphasizes the object information presented in the scattering media, even when the density of the scattering media varies within the image. On the other hand, Peplography estimates the scattering media in each local region, and when the region is not defined appropriately, the estimation of the scattering media will not be visualized. Therefore, the purpose of this research is to find the optimal processing for each environment by experiment with various situations, such as when the size of the object in the image is small, when the density of the scattering media is heavy, and when the target object is various.

关键词： Scattering removal Image processing Computational imaging Visualization

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Safe Stabilization with Model Uncertainties: A Universal Formula with Gaussian process Learning 18

Safe Stabilization with Model Uncertainties: A Universal For...

引用

IEEE 18th international conference on control and automation (ICCA)

作者： Li, Ming Sun, Zhiyong Eindhoven Univ Technol Dept Elect Engn POB 513 NL-5600 MB Eindhoven Netherlands Eindhoven Artificial Intelligence Syst Inst POB 513 NL-5600 MB Eindhoven Netherlands

ISBN: (纸本)9798350354416;9798350354409

A combination of control Lyapunov functions (CLFs) and control barrier functions (CBFs) forms an efficient framework for addressing control challenges in safe stabilization. Developing an analytical control strategy, known as the universal formula approach, which integrates CLF and CBF conditions, is recognized as a computationally efficient method for achieving safe stabilization. However, successful implementation of this universal formula relies on an accurate model, as any mismatch between the model and the actual system can compromise stability and safety. In this paper, we propose a new universal formula that leverages Gaussian processes (GPs) learning to address the safe stabilization problem in the presence of model uncertainty. By utilizing the results related to bounded learning errors, we achieve a high probability of stability and safety guarantees with the proposed universal formula. Additionally, we introduce a probabilistic compatibility condition to evaluate conflicts between the modified CLF and CBF conditions with GP learning results. In cases where compatibility assumptions fail, we propose a modified universal formula that relaxes stability constraints. We illustrate the effectiveness of our approach through a simulation of adaptive cruise control (ACC), highlighting its potential for practical applications in real-world scenarios.

关键词： Stabilization

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TinyStressNet: On-device Stress Assessment with Wearable Sensors on Edge Devices

TinyStressNet: On-device Stress Assessment with Wearable Sen...

引用

IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Jaiswal, Dibyanshu Mukhopadhyay, Shalini Sharma, Varsha Tata Consultancy Serv Ltd TCS Res Kolkata India

ISBN: (纸本)9798350304367;9798350304374

Wearable sensor-based stress detection is a well-explored area of research in the domain of Affective computing and can be performed with the help of non-invasive sensing modalities like Electrodermal Activity (EDA). The EDA sensors with a wearable form factor are commonly available on commercial off-the-shelf devices. In recent years, with the increased availability of such wearable devices to end-users, these applications have become more pervasive and thus require a greater level of optimization for continuous usage on resource-constrained and battery-powered devices. While several research works have focused on designing Machine Learning and Deep Learning models for these tasks, very few focus on the resource footprint of these models. The balance between classification accuracy and computational resources is difficult to achieve with a manual design process and takes a long time. This is where automation plays a key role. In this work, we explore the applicability of automation Techniques such as Neural Architecture Search (NAS), to create tiny Stress Detection models suitable for round-the-clock inference, with minimal resource requirements, and low latency to perform in real-time, and show the trade-off between the various metrics on four publicly available datasets combined, and achieve an accuracy of 85.98% with a model size of 49.40 kB, which is comparable to the state-of-the-art counterparts.

关键词： Stress Detection Affective computing TinyML Neural Architecture Search Wearable Sensing Edge Devices

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Robotic process automation Applications Across Industries: An Exploration 7

Robotic Process Automation Applications Across Industries: A...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Yadav, Shiv Shankar Kumar Mishra, Gaurav Bennett University School of Management Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Amity School of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Robotic process automation (RPA) as one of the ramifications of the Artificial Intelligence, is emerging rapidly because of its adaptation across numerous industries day by day. As a result, organizations are able to operate with greater efficiency and speed, reduced costs, and improved delivery. While practitioner's landscape of various industries is rich with its applications related discussions, extant scholarly literature is not promising by amount equating to the usage span of RPA technology. To fill this gap, this article, with the help of content analysis, explores industry specific and process specific applications. The industries covered (rich in term of RPA engagements) are banking, healthcare, manufacturing, retail, telecommunications, insurance, logistics, government, energy and utilities, and human resources. The paper also discusses the challenges associated with RPA adoption across industries. In the end, considering the potential of RPA technology, the paper suggests exploration of less explored industries as a gap for further research. By examining organisational cases, white and scholarly papers, this work highlights how RPA is utilized to streamline processes such as customer onboarding, patient data management, inventory control, and claims processing in banking, healthcare, manufacturing and insurance industry respectively. The usage findings across different processes justify the potential of RPA in driving innovation and operational excellence. © 2024 IEEE.

关键词： Inventory control

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Conflict Management based on Deep Reinforcement Learning for Edge computing in Intent-Driven Networks 20

Conflict Management based on Deep Reinforcement Learning for...

引用

20th IEEE international Wireless Communications and Mobile computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Li, Zhen Gong, Jialong Wang, Dong China Telecom Res Inst Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350361261;9798350361278

In recent years, the Intent-driven Network (IBN) has been proposed to further enhance the intelligence of communication systems. In IBN, users can express their resource expectations through intents while the IBN performs resource scheduling to fulfill these intents. Many challenges of complex networks can be tackled by IBN such as the mobile edge computing (MEC) system. In a MEC system, limited computing resources are competed by the users which may cause the intent conflict. To solve this, in this paper, we introduce the IBN concept to the MEC system, where an intent conflict detection module is proposed. The proposed module is based on the Open Network automation Platform (ONAP) architecture. Moreover, by formulating the computing resource conflict problem as a Markov decision process (MDP) model, we employ an improved deep Qnetwork (DQN) algorithm to improve the efficiency of resource utilization. Simulation results demonstrate the completion time of intents is remarkably reduced in the proposed intent conflict resolution scheme.

关键词： Intent conflict MEC Reinforcement learning

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A Method Based on a Combination of Ontological Data Analysis and Cognitive Modeling Tools for Organizing the Computational process in Peer-to-Peer Networks

A Method Based on a Combination of Ontological Data Analysis...

引用

2024 international Russian automation conference, RusAutoCon 2024

作者： Melnik, Eduard Safronenkova, Irina The Southern Scientific Center of the Russian Academy of Sciences Federal Research Center Rostov-on-Don Russia

Despite the rapid development of edge and fog computing technologies, including significant improvements in the characteristics of communication channels and computing devices themselves, the problems associated with the organization of the computing process in distributed systems remain relevant. This is due to the high dynamism of the environment and the large number of computing devices that operate in it. One of the mentioned problems in the organization of the computing process is the workload relocation problem. It includes the selection of placement options and requires significantly more time resources for the case of coupled tasks in comparison with uncoupled tasks. The need to solve coupled problems is associated with the development of such areas as loT, the Internet of Robotic Things, collaborative robotics, augmented reality, etc. One of the ways to reduce the total solution time of a coupled problem is to reduce the search space of candidate nodes, which can potentially be assigned to subtasks. In this paper, we propose to use a method for reducing the search space based on a combination of ontology analysis and cognitive modeling tools. The ontological model and a cognitive map of the computing infrastructure are developed. Computational experiments are conducted. Application of ontological modeling along with the use of cognitive analysis tools has shown its effectiveness in solving the workload relocation problems in highly dynamic environments. This is confirmed by the results of the conducted experiments. (Abstract) © 2024 IEEE.

关键词： Fog computing

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Synergizing Environmental computing, Agricultural Engineering, and AI for Sustainable ICT Applications in Agriculture 8

Synergizing Environmental Computing, Agricultural Engineerin...

引用

8th IEEE international conference on computing, Communication, control and automation, ICCUBEA 2024

作者： Usmani, Usman Ahmad Sulaiman, Suliana Watada, Junzo Universiti Pendidikan Sultan Idris Perak Tanjong Malim35900 Malaysia Waseda University Tokyo Shinjuku City169-8050 Japan

ISBN: (纸本)9798350391770

This conference paper delves into the transformative synergy of Environmental computing, Agricultural Engineering, and Artificial Intelligence (AI) to forge sustainable ICT applications in agriculture. By amalgamating these domains, the paper unravels how AI-driven insights are harnessed to optimize resource allocation, mitigate environmental impact, and empower precision farming practices. This fusion not only enhances agricultural productivity and efficiency but also lays the foundation for ecologically mindful agricultural activities. The integration of AI further enriches Agricultural Engineering's potential by enabling data-driven decision-making through sensor data analysis, satellite imagery, and climate models, thus fostering resilience and productivity across the agricultural value chain. In sum, this exploration underscores the pivotal role of AI in shaping a sustainable future for ICT applications in agriculture through the harmonious interplay of Environmental computing and Agricultural Engineering. © 2024 IEEE.

关键词： Agricultural Engineering Artificial Intelligence (AI) Environmental computing Precision farming practices Sustainable ICT applications

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Embedded database SQLite application based on ARM platform

Embedded database SQLite application based on ARM platform

引用

2024 international conference on automation control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Yong, Wan China Coal Technology and Engineering Group Chongqing Research Institute Chongqing400039 China State Key Laboratory of Gas Disaster Monitoring and Emergency Technology Chongqing400037 China

ISBN: (纸本)9781510682597

This paper discusses the theory of embedded database SQLite and its application development on ARM platform, analyzes the characteristics of embedded database in detail, and expounds the architecture of embedded database on this basis. Based on the analysis of SQLite's architecture, powerful function, simple interface, fast speed, small space and other features, this paper introduces its development technology and the method of SQLite configuration and transplantation on ARM platform, and explains its application process with examples. © 2024 SPIE.

关键词： Databases Operating systems Embedded systems Data storage computing systems Data communications Computer architecture Data processing

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Deep Learning Based Soft Sensors in process Industry and Application 9

Deep Learning Based Soft Sensors in Process Industry and App...

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9th international conference on Smart computing and Communications, ICSCC 2023

作者： Neethu, A.M. Ajayan, K.K. College of Engineering Trivandrum Department of Electronics and Communication Trivandrum India

ISBN: (纸本)9798350314090

Industries can profit from automation by increasing rate of production and decreasing errors, improving safety and stabilising the manufacturing process. High levels of profitability, dependability and safety come with industrial automation. Machine learning(ML) has a place in the field of industrial automation because it makes it easier to monitor and maintain industrial machines. The pursuit of automating industrial procedures in all currently possible ways is intimately related to rational industrial development. Recent advancements in industrial automation have reduced the cost of hardware and energy use. Soft sensors provide numerous advantages including adaptability and affordability. Due to their accuracy and durability, deep learning(DL) algorithms are well known for the construction of soft sensors in difficult non-linear *** application of soft sensor for industrial automation is presented in this paper i.e, design of a soft sensor based on Deep Neural Network(DNN) for metabolic growth control of bioprocess in industry. © 2023 IEEE.

关键词： Profitability

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Intelligent Fire Detection and Response System with Dynamic Nozzle control and Evacuation Planning 3

Intelligent Fire Detection and Response System with Dynamic ...

引用

3rd international conference on automation, computing and Renewable Systems, ICACRS 2024

作者： Manorathna, A.Y.S. Gayashan, Sachintha W.K. Tharushika, W.A.V. Rathnayaka, R.M.S.T. Weerasinghe, Lokesha De Zoysa, Revoni Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331532420

The intelligent fire detection and response system integrates three key components: Fire Type Identification, Fire Tracking, and Dynamic Nozzle control. The Fire Identification component uses a multi-sensor setup (temperature, smoke, and gas sensors) and machine learning models to classify fire types into categories A, B, and C. The Fire Tracking component combines realtime video monitoring and temperature sensors to predict and visualize fire spread dynamically. Dynamic Nozzle control adjusts water pressure and nozzle angles based on fire severity, ensuring precise and efficient suppression. This integrated approach enhances fire management by ensuring accurate detection and targeted suppression. © 2024 IEEE.

关键词： Fires

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