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Design and Development of an Intelligent computer Room Management Platform Based on Artificial Intelligence and Big Data Analysis 2

Design and Development of an Intelligent Computer Room Manag...

引用

2nd IEEE international conference on Electrical, automation and computer engineering, ICEACE 2024

作者： Zhou, Wei Huang, Jianxiong Peng, Jian Zhang, Jianyun Xu, Lei State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350368208

The purpose of this paper is to design and develop an intelligent computer room management platform based on artificial intelligence and big data analysis. By integrating AI and big data technologies, the platform is able to realize real-time monitoring, intelligent prediction and alarm, and energy consumption optimization and management of the server room environment. This paper describes the implementation method of the LSTM (Long Short-Term Memory) network-based platform, and verifies the effectiveness of the platform through experiments. Among them, it is shown in the environment monitoring experiments that the mean temperature monitoring error of the platform is 0.12°C with a standard deviation of 0.98°C, and the mean humidity monitoring error is 0.15% with a standard deviation of 1.05%. In the fault response experiment, the average response time of the platform is about 5.88 minutes with a standard deviation of about 3.12 minutes. In the intelligent prediction performance evaluation, the LSTM model has MSE (Mean Squared Error) of 0.103 and MAE (Mean Absolute Error) of 0.256, which is better than ARIMA (Autoregressive Integrated Moving Average), simple RNN (Recurrent Neural Network) and linear regression models. The effectiveness and superiority of the platform in terms of environmental monitoring, fault response and predictive performance is evident from the data conclusions. © 2024 IEEE.

关键词： Artificial Intelligence Big Data Analytics LSTM Prediction Smart Room Management

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Detection of inter row navigation lines in corn fields based on improved CenterNet

Detection of inter row navigation lines in corn fields based...

引用

international conference on computer Vision, Robotics, and automation engineering, CRAE 2024

作者： Zhang, Zhipeng Li, Huiyan Meng, Qingkuan Zhang, Ruitong Zhang, Wenkang School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781510682283

In complex corn orchard environments, traditional machine vision algorithms often struggle to handle changes in light, occlusion between crops, and interference from weeds, which leads to inaccurate navigation line extraction. This article proposes a corn field navigation route detection method based on improved CenterNet to address this issue. Firstly, we replaced the CenterNet backbone network with ResNeSt, which can effectively improve detection performance when facing small targets and complex hierarchical image content. At the same time, we added CBMA attention mechanism in CenterNet to enhance the recognition of target features of corn seedlings and weaken background interference. We use the midpoint of corn seedlings as the navigation positioning base point, use an improved CenterNet model to detect corn seedlings on both sides, use the center point of corn seedlings as the positioning reference point, and then use the least squares method to fit the center points of corn seedlings on both sides. The experimental results show that the improved CenterNet model has a detection accuracy of 76.44% and a detection speed of 42.09 frames per second. Compared with the original CenterNet model, it has improved by 4.28 percentage points and 1.51 frames per second, respectively. From this, it can be seen that the model can accurately identify corn seedlings, and the average processing time for one frame of image is 0.082 seconds, which fully meets the needs of navigation in corn fields. © 2024 SPIE.

关键词： Seed

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Design of a Wearable sEMG Signal Acquisition System for Human-computer Interaction 14

Design of a Wearable sEMG Signal Acquisition System for Huma...

引用

14th IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Fei, Fei Yao, Yike Cao, Qingqing Wu, Changcheng Yang, Ruonan Mao, Lizhi College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Instrument Science and Engineering Harbin Institute of Technology Harbin China School of Aviation Engineering Nanjing Vocational University of Industry Technology Nanjing China

ISBN: (纸本)9798331506056

The method of hand gesture recognition based on surface electromyography (sEMG) signals has garnered increasing attention due to its real-time, convenient, and non-invasive characteristics. This paper presents the design of sEMG signal acquisition system aimed at capturing subtle sEMG signals from the skin surface for applications in human-computer interaction and remote control. Firstly, an analysis of the characteristics of sEMG signals and their sources of interference noise is conducted. Subsequently, a circuit for processing sEMG signals is designed. Additionally, a hardware structure for forearm wearable devices is developed, incorporating a rotating toothed tube to enhance the tightness between the electrodes and the skin surface. Finally, experiments based on the anatomical structure of the forearm muscles are designed to verify the system. © 2024 IEEE.

关键词： Gesture recognition

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Design and Implementation of Cigarette Stamp Authenticity Identification System Based on Machine Vision 5

Design and Implementation of Cigarette Stamp Authenticity Id...

引用

5th international conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Liao, Qiming He, Bingbing Li, Dan Jin, Tiantian Zhang, Yufeng Yunnan University College of Information Science and Engineering Kunming China Yunnan Tobacco Quality Supervision and Testing Station Kunming China

ISBN: (纸本)9798350355253

To swiftly and accurately identify the authenticity of various cigarette types and advance automation in the tobacco industry, a comprehensive cigarette adhesive mark authenticity identification system was designed. This system integrates image acquisition, preprocessing, feature extraction, and authenticity identification. Utilizing hardware platforms such as Raspberry Pi, industrial cameras, and stepper motors, along with custom-developed software for cigarette mark image acquisition, the system automatically captures batch images of cigarette marks to construct a robust image dataset. The transfer learning method was employed to train the authenticity identification model for cigarette rubber marks using the MobileNetV3 network. The model's effectiveness was then validated with independently collected authenticity samples. Subsequently, the trained model was deployed in an embedded system, enabling the complete automation of the cigarette authenticity identification process. The results demonstrated that the MobileNetV3-based cigarette adhesive mark authentication model achieved an accuracy of 94.70%, showcasing strong authentication capabilities with high efficiency and precision. This system provides reliable cigarette authenticity identification services, enhancing the automation and intelligence of the tobacco industry, and significantly supporting the technological advancement of the sector. © 2024 IEEE.

关键词： Stepping motors

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Design and Evaluation of Connectivity Based Home automation System Based on Arduino

Design and Evaluation of Connectivity Based Home Automation ...

引用

2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Kalyan Kumar, G. Mubarak, Suhail Gopila, M. Nalini, N. Abid, Y. Liakath, Javid Ali Rajalakshmi Institute of Technology Department of Computer and Communication Engineering Chennai India Research Institute of Humanities and Social Sciences University of Sharjah Sharjah27272 United Arab Emirates Sona college of Technology Department of Electrical and Electronics Engineering Salem636005 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Department of Vlsi Microelectronics Chennai India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai600119 India

ISBN: (纸本)9798331543617

IoT encompasses interconnected devices that exchange data autonomously, enabling automation. Raspberry Pi, a compact computer, serves as an affordable platform for DIY projects, education, and IoT uses. The CHASA model integrates Arduino Nano and Arduino Mega modules to optimize energy efficiency in home automation networks. The model employs unidirectional communication and AES (Advanced Encryption Standard) encryption to minimize power consumption and enhance security. Experimentation evaluates the model's performance across key metrics, showcasing its superiority over existing protocols. Results reveal CHASA's higher packet delivery ratio, energy efficiency, lower data loss, routing overhead, and average delay. Comparative analysis demonstrates CHASA's effectiveness in enhancing IoT network performance and energy conservation. © 2024 IEEE.

关键词： Advanced Encryption Standard (AES) IoT (Internet of Things)

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Real-Time Edge Computing Services for Internet of Things-based Cloud Networks 15

Real-Time Edge Computing Services for Internet of Things-bas...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Sneha Seth, Jyoti Hannah Jessie Rani, R. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

the emergence of the Internet of Things (IoT) gadgets and cloud-enabled networks has brought about a dramatic growth in the quantity and kinds of programs that require real-time facet computing offerings. Aspect computing is a rising location of allotted computing that methods facts toward the cease person or tool, supplying low-latency responses to operations and analytics. It enables quicker and more dependable facts-extensive applications that can be personalized to consumer alternatives and needs. This paper discusses the design and implementation of real-time edge computing offerings for IoT-primarily based cloud networks, focusing on key components of the device structure, including carrier deployment, aid management, protection, and scalability. Furthermore, the paper describes the usage of area computing for IoT programs, which include video surveillance, domestic automation, and smart town solutions. Subsequently, the paper presents a survey of cutting-edge area computing companies, summarizing their offerings, business models, and services. © 2024 IEEE.

关键词： Analytics Applications automation Business Models Computing Real-Time Services

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Research on data prediction model construction and optimization based on support vector analysis 2

Research on data prediction model construction and optimizat...

引用

2nd IEEE international conference on Electrical, automation and computer engineering, ICEACE 2024

作者： Zhou, Yijing Jiangsu United Vocational and Technical College Nanjing Engineering Branch Nanjing China

ISBN: (纸本)9798350368208

This paper proposes a data prediction model based on support vector analysis, which aims to solve the prediction problem in a complex data environment. Support vector analysis is an algorithm based on statistical learning theory, which has good generalization ability and the advantage of processing high-dimensional data. In the process of model construction, we extracted multi-dimensional features of the data and adjusted the model parameters in combination with the optimization algorithm to improve the accuracy and robustness of the prediction. The simulation experiments on the data set show that the model can effectively achieve high-precision prediction in various scenarios. By comparing with the traditional prediction model, the support vector analysis method shows significant advantages in data processing ability and prediction accuracy, especially in the data set with complex nonlinear relationships, and its prediction error is significantly reduced. This paper not only describes the process of model construction in detail, but also discusses the specific methods of model optimization, such as kernel function selection and hyperparameter tuning. The simulation results show that the average error of the optimized support vector prediction model drops to 3.2%, which is nearly 15% lower than the error of the traditional model. © 2024 IEEE.

关键词： Prediction models

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Application of Model Based Predictive Control for a Rotary Tablet Press 4

Application of Model Based Predictive Control for a Rotary T...

引用

4th international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Nascu, Ioana Nascu, Ioan Nagy, Zoltan K. Technical University of Cluj Napoca Academy of Romanian Scientists Department of Automation Romania Technical University of Cluj Napoca Department of Automation Cluj Napoca Romania Davidson School of Chemical Engineering Purdue University West Lafayette United States

ISBN: (纸本)9798350391183

Recently, the pharmaceutical manufacturing is going through a transition from classic batch manufacturing to continuous manufacturing, that is a faster as well as a more effective approach. The presented work establishes the foundation for cutting-edge advanced Quality-by-Control model-based predictive control strategies applied on a continuous manufacturing rotary tablet press process. Advanced model predictive control strategies are capable in facilitating the switch to Industry 4.0 by assuring a Quality-by-Control approach. To control the continuous manufacturing of solid dosage forms within the pharma industry different advanced model based predictive control strategies including unconstrained as well as constrained MPC are developed and implemented. The model that is used to design the control strategies is calibrated and validated by using actual data taken from a pilot plant. The results present good performances of the developed controllers: increased efficiency of the process as well as flexibility, a decrease in the environmental impact even when accounting for uncertainties in the process, measurement sensor noise as well as disturbances © 2024 IEEE.

关键词： Predictive control systems

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Leveraging Big Data Analysis in Real-Time automation for Improved Operational Efficiency 15

Leveraging Big Data Analysis in Real-Time Automation for Imp...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Puthillath, Beena Wasatkar, N.N. Albattah, Mahmoud M.S. Vijayalakshmi, V.J. Manjunath, C. Verma, Lokesh Department of EEE Department of computer engineering Vishwakarma Institute of Technology Pune India Department of Civil Engineering School of Engineering Jordan University Jordan Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore641021 India Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

This idea has redefined the practices of an organization and revolutionized business decision-making by harnessing extensive data analysis. With industries becoming increasingly complex and data-dependent, the demand for real-time automation has taken an immense upsurge to enhance operational efficiency. Real-time automation uses massive big data analysis to ensure that the continuous monitoring and analyzing of your crucial data takes place in real time, allowing you to make timely decisions based on facts. It is a type of automation that helps an organization recognize and act on issues or opportunities as soon as they occur in real time, shortening downtime due to potential problems while increasing productivity. Automating data collection, processing, and analysis lets organizations better understand their operations and spot trends, patterns, or even outliers that might not be visible, given how cumbersome the manual processes are. In practice, real-time automation simplifies the process to enhance operational efficiency with cost savings and time efficiencies through automated processes that eliminate manual tasks. It also makes organizations adjust quickly to changes in markets or customer requirements, thus making them agile and more competitive. Utilizing real-time automation with the help of extensive data analysis can boost companies' competitive advantage by increasing their operational efficiency and decision-making capabilities. ©2024 IEEE.

关键词： Efficiency

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Renal Cyst Detection using Deep Learning 2

Renal Cyst Detection using Deep Learning

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2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Velusamy, Dhanalakshmi Harish, U. Lingesh, P. Kaviyarasan, P. Cyril Poornaraj, J. Prem, D. Sharan, K. Jeppiaar Institute of Technology Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Bachelor of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350368109

Around the world, kidney diseases are a major open prosperity concern. Environmental components, tall salt levels, and additional minutes are all risk factors. Kidney dissatisfaction can be a critical prosperity issue, and it has gotten to be more transcendent in afterward a long time, with horribleness and mortality extending by 100% over the ultimate decade. Several kidney sicknesses, tallying those caused by renal stones and rankles, can be easily recognized through CT imaging. In separate, manual examination of a CT picture is time-consuming and requires a portion of effort. Deep learning methods, checking 3D CNNs, can be utilized to recognize plans of hurt and make assurance easier. By planning these calculations on clarified datasets, they can distinguish kidney stones and rankles with tall accuracy. Despite its potential to assist inside the early recognizing verification and treatment of ailments, modified revelation has to be utilized with caution and not as an elective to restorative information. © 2024 IEEE.

关键词： Deep learning

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