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检索条件"任意字段=Conference on Advanced Process Control and Automation"

共 4412 条记录，以下是21-30 订阅

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

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26th International conference on Industrial Technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： Clustering algorithms

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Mitigating Security Vulnerabilities in Cloud-Based Home automation through advanced Edge Computing: A Comprehensive Framework for Privacy Enhancement and Data Protection

Mitigating Security Vulnerabilities in Cloud-Based Home Auto...

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International conference on Communication Systems and Network Technologies (CSNT)

作者： Biresh Kumar Shreya Kumari Ritesh Kumar Vineet Kumar Singh Binod Kumar Sharma Kamaldeep Gupta Amity Institute of Information Technology Amity University Jharkhand Ranchi Jharkhand India Cambridge Institute of Technology Tatisilwai Ranchi Jharkhand India Dept Of IT Dr. R.M.L. Avadh University Ayodhya UP Dept. of Computer Sci. & Engg. BIT Mesra Ranchi Jharkhand St. Xavier’s College Ranchi Jharkhand India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the soaring adoption of cloud-constructed home automation systems with CAGR of 8.5% from 2023 to 2030, security and privacy concerns have become critical considerations. This paper examines the security risks in cloud-based home automation systems and proposes leveraging edge computing to improve data security and privacy. We examine weaknesses including unapproved access, data leaks and highlight limitations of cloud-centric architectures. By integrating edge computing, we reduce data exposure, enable local processing, and improve control within the home environment. We explore how edge computing addresses specific attacks and offline operation during network disruptions. Challenges of incorporating edge computing into cloud-based home automation are discussed. This research contributes insights for achieving a more secure and privacy-enhanced home automation system.

关键词： Cloud computing Privacy Home automation Filtering Data protection process control Real-time systems Internet of Things Smart devices Edge computing

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Intelligent Monitoring Based on Integrated Temporal Dictionary Learning with Isometric Mapping for Geological Drilling process

Intelligent Monitoring Based on Integrated Temporal Dictiona...

引用

IEEE International conference on Industrial Technology (ICIT)

作者： Tianyu Ma Sheng Du Haipeng Fan Cheng Huang School of Future Technology China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Geological drilling process, owing to Complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process.

关键词： Drilling Smoothing methods Accuracy Geology Velocity control Feature extraction Robustness Kernel Monitoring Principal component analysis

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RFID based Smart Trolley Billing System using IoT

RFID based Smart Trolley Billing System using IoT

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International conference on ent Computing and control Systems (ICICCS)

作者： Manikandan S Harish Murugan A Kamal S Udhayakumar S Yokesh D Department of ETE Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Customers expect to experience better retail interactions while operations become more efficient in the modernizing retail industry. Shopping through traditional methods requires lengthy manual scanning for products and billing operations leading retailers to require automation solutions for streamlining these processes. It is not bad thing to mention about but why going a lengthy process when technology is all we have implement to make it better with a help of designed system. This system consists of RFID detection alongside 8051 microcontroller processing and load cells monitoring and uses IoT communications and relay controls to power an intelligent automated shopping cart. Real-time data tracking functions alongside cloud service readiness to deliver advanced monitoring and management capabilities as the system's standout feature. A Smart Trolley consumes RFID technology to recognize products that go inside the cart through unique product tags linked with RFID. Product details that the 8051 microcontroller retrieves are displayed through an LCD screen interface. The system delivers automated operations together with precise product tracking along with weight measurement functionality and billing support. Data transmission to the cloud enables distant storage functions along with distant system monitoring that produces meaningful information about trolley utilization while bolstering inventory handling capabilities. The system allows product tracking and billing automation and IoT integration for data management which enables its deployment into multiple retail settings to deliver enhanced customer service alongside operational efficiency. It is an attempt to make the shopping experience lighter and making the automation process a part of the shopping Eco system and to interpreted much more easily than with the trouble of going through all lengthy billing process by also ensuring the safety and security of the purchased product and also to neglect the idea of the

关键词： Weight measurement Cloud computing automation Microcontrollers Smart cities Real-time systems Security Monitoring Radiofrequency identification Ultra wideband technology

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International conference on Medical Imaging and Computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Integrative automation of Cybersecurity Systems through advanced Data Science Methodologies: Enhancing Predictive Analytics and Real-Time Threat Mitigation

Integrative Automation of Cybersecurity Systems through Adva...

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Power, control and Computing Technologies (ICPC2T), conference on

作者： Subhranil Das Rashmi Kumari Raghwendra Kishore Singh School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand School of Computer Science Engineering and Technology Bennett University Greater Noida India School of CS & AI SR University Warangal India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

Due to emergence in Corona disease, the digital revolution become more intensified which makes more prevalent in day-to-day activities. As a result, cloud service providers become more frequent. Therefore, system integration is essential throughout a partner ecosystem. Unbelievably many edge devices are sending IoT data to the cloud. The system becomes automated due to the organizational structures which resulted in developing new technologies. This new” Integration ecosystem” is where there is just one platform forall the system. According tothe survey of 4,500 decisionmakers, data protection—rather than business process optimization or processautomation—was the primary argument for employing AI within organizations. Using ML algorithmsto automatically identify cyberattacks is referred to as automating cyber security systems. By leveraging automation, cyber security systems can swiftly identify and respond to attacks, reducing the amount of time needed to avert possible harm. With the considerations in mind, we proposed Automating Cyber Security Systems with Data Science Practices in which we will be talking about how various cyber security systems can effectively utilize current data science practices to automate their workflow.

关键词： Surveys automation Prevention and mitigation Ecosystems Power control System integration Data science Real-time systems Computer crime Predictive analytics

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Application of Particle Swarm Optimization Algorithm Based on Adaptive Inertia Adjustment Strategy in Scheduling of the Coking process

Application of Particle Swarm Optimization Algorithm Based o...

引用

IEEE International conference on Industrial Technology (ICIT)

作者： Yiheng Chen Fan Yin Xiaochong Chen Yi Ren Jie Hu Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

The sccheduling for pushing plan during the coking process critically affects the efficiency and stability of production. However, the complexity with mutiple-stage during production makes it difficult to design an efficient coke pushing plan. To address this issue, this paper proposes a scheduling method based on the particle swarm optimization algorithm. Firstly, the fuzzy c-means clustering is utilized to categorize actual operating conditions as either normal or abnormal, thereby facilitating the scheduling of pushing plan under disparate conditions. Subsequently, the scheduling problem for the pushing plan is transformed into a traveling salesman problem, and scheduling models under various conditions are established. Finally, to accelerate the convergence and enhance the algorithm's global search capability, an adaptive inertia adjustment strategy is employed to dynamically regulate the velocity and position of particles. The proposed method has been implemented in the coking process. Through the analysis of application results, the completion coefficient of pushing plan has been increased by 4.25%, demonstrating that the proposed has advantages in scheduling the pushing plan during the actual coking process.

关键词： Job shop scheduling Heuristic algorithms Clustering algorithms Production Traveling salesman problems Stability analysis Robustness Complexity theory Particle swarm optimization Convergence

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AI-Driven Micro Defect Detection for Aerospace-Grade Metal Surface Quality control in Smart Manufacturing

AI-Driven Micro Defect Detection for Aerospace-Grade Metal S...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Murat Bakirci Irem Bayraktar Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

In the context of Industry 4.0 and Smart Industry, automated and precise quality control systems are essential for maintaining high standards in manufacturing processes. This paper introduces an advanced approach for detecting micro defects on metal surfaces, leveraging the YOLO11 object detection algorithm to meet the stringent demands of modern industrial environments. Micro defects, such as cracks and fractures on metal surfaces, can compromise structural integrity and lead to costly failures, making their detection crucial. In this study, we categorize micro defects into four distinct types, linear, curved, point, and branched, and employ a synthetic defect generation process to create an extensice set of defect variations, subsequently augmented to enhance training diversity. The YOLO11 algorithm is trained and tested across this extensive dataset, achieving high precision and recall rates for each defect category. Detailed precision-recall analysis indicates the model's strengths and areas for improvement, particularly with smaller, more challenging defect types. The results show YOLO11's capability to accurately detect and localize defects, identifying isolated defects, recurring process-related defects, and potential error-prone zones on the metal surfaces. These capabilities align with Industry 4.0's goals of leveraging AI-driven automation to enhance quality control processes, enabling predictive maintenance and operational efficiency.

关键词： Industries Training Surface morphology Metals process control Quality control Prediction algorithms Surface cracks Surface treatment Predictive maintenance

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Research on MLD Modeling Method of Robot Aluminum Alloy Pulsed TIG Welding process Based on Visual Sensing

Research on MLD Modeling Method of Robot Aluminum Alloy Puls...

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Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Xiaolan Liu Yan Wang Kunpeng Zhao Rui Hao Liang Zhang Harbin Huade University Harbin Heilongjiang China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This study focuses on the hybrid logic dynamic (MLD) modeling method of robot aluminum alloy pulse TIG (Tungsten Inert Gas) welding process based on vision sensing. Welding is a key technology in modern manufacturing industry, especially in the application of aluminum alloy materials, and its welding quality control is facing complex challenges. This study aims to improve the automation and intelligence level of welding process by combining robot technology and visual sensing technology. A data acquisition system based on high-precision vision sensor and advanced image processing algorithm is designed, which realizes real-time and accurate acquisition of weld and molten pool information. The weld contour is identified by machine learning algorithm, and the geometric parameters and dynamic change characteristics of weld pool are extracted, and the statistical relationship model between welding parameters and geometric characteristics of weld pool is established. On this basis, the MLD model is constructed, which can describe the continuous and discrete dynamics in the welding process. The experimental results show that MLD model can accurately predict the change trend of weld pool geometric parameters, effectively identify the welding state, and realize accurate control of welding process. This study not only improves the stability of welding quality, but also promotes the further development of aluminum alloy welding technology, which has important practical application value and broad application prospects.

关键词： Visualization Accuracy Machine learning algorithms Welding Heuristic algorithms process control Vision sensors Robot sensing systems Aluminum alloys Real-time systems

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Dynamic Assembly Line Supervision Using PLC, SCADA, and IoT Technologies

Dynamic Assembly Line Supervision Using PLC, SCADA, and IoT ...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Fatima Mokhliss El Ayachi Chater Mohammed Hicham Zaggaf Abdelmajid Bybi LASTIMI Lab. EST Salé Mohammed V University in Rabat Morocco LSSDIA Lab. ENSET of Mohammedia Hassan II University Morocco MEAT Team EST Salé Mohammed V University in Rabat Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

In this study, we present the design and implementation of an automated dynamic assembly line to enhance productivity, reduce downtime, and improve the quality of assembly processes by integrating multiple advanced automation technologies. This study aims to demonstrate the feasibility of automating industrial assembly lines, significantly improving operational efficiency and product quality. This setup links the PLC, HMI, SCADA system, and Raspberry Pi to the remote view. The chat among these parts occurs using the OPC UA and MQTT rules employing KepServerEX and Node-RED. In this manner, the plan can ensure easy data sharing, which helps in real-time monitoring and management. A test and check were conducted to confirm how well the system works in collecting information, handling it, and providing remote access to mobile devices.

关键词： Productivity automation SCADA systems process control Real-time systems Product design Quality assessment Security Assembly Remote monitoring

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