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Transmission Line Safety Distance Measurement and data Online Fusion processing Technology Based on Multi Physical Quantity Sensing 3

Transmission Line Safety Distance Measurement and Data Onlin...

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3rd international conference on New Energy and Power Engineering, ICNEPE 2023

作者： Chen, Xiaojian Yuan, Song Wu, Hao NanTong Power Supply Company State Grid Jiangsu NanTong China

ISBN: (纸本)9798350393224

To address the real-time monitoring challenges of mechanical collision prevention for power transmission lines, this paper developed a warning device based on multiple sensor sources and proposed a transmission line warning algorithm based on multisensor fusion. The effectiveness of the algorithm was validated in real-world scenarios. The warning device based on multiple sensor sources first employed a visual recognition algorithm based on standard deviation clustering to identify the transmission lines ahead. Subsequently, the millimeter-wave radar in the ranging module was connected to the detection host via a wired interface. The radar data input utilized a millimeter-wave radar ranging algorithm based on improved robust Kalman filtering, providing real-time output of distance and azimuthal information of the power transmission lines at a frequency of 1Hz Experimental results demonstrated that the visual recognition algorithm based on standard deviation clustering effectively identified distances greater than 20 meters with a recognition accuracy of 93% and an output frequency of IHz The millimeter-wave radar ranging algorithm, based on improved robust Kalman filtering, achieved a ranging accuracy of ±0.1m and a ranging error of 2%, operating at an output frequency of IHz These findings confirm that the system adequately meets the overall requirements for real-time monitoring of mechanical collision prevention for power transmission lines. © 2023 IEEE.

关键词： Robust estimation safe distance Transmission line monitoring

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Design of pressure signal acquisition system based on STM32 3

Design of pressure signal acquisition system based on STM32

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2023 3rd international conference on Mechatronics, Automation and Intelligent Control, MAIC 2023

作者： Xu, Wenxian Li, Yingjun Wang, Guicong Meng, Xiangzhou School of Mechanical Engineering University of Jinan Shandong Province Jinan250022 China

In order to realize the function of an automatic target-scoring system for shock wave pressure signal positioning, it is necessary to collect multi-channel shock wave pressure signals. Therefore, this paper designs a sensor signal acquisition and processing system based on STM32. The system can realize the acquisition and processing of 8-channel sensor signals. The hardware module includes the operational amplifier module, the acquisition conversion module, the temperature acquisition module, and the wireless transmission module. In order to solve the positioning coordinates, the program is written based on Keil uVision5 and STM32 firmware library. The PYQT5 is called to design the host computer receiving software. Finally, the acquisition test of the designed acquisition system is carried out. The test results show that the signal reduction degree of the designed acquisition system is more than 90%. The designed signal acquisition system has the advantages of high reliability and small size. It also has a certain universality. © Published under licence by IOP Publishing Ltd.

关键词： Shock waves

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Applying Deep Learning Strategies for Optimizing Real-Time data Analysis in Machine Learning 3

Applying Deep Learning Strategies for Optimizing Real-Time D...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Varun Dev, Sudhanshu Singh, Akhilendra Pratap Krishna, Gautham Geetha, B. Kadam, Rutuja S. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Pune India

ISBN: (纸本)9798350319125

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization. © 2023 IEEE.

关键词： Deep learning

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Designing Robust Deep Learning Models for RealTime data Analysis in Machine Learning 3

Designing Robust Deep Learning Models for RealTime Data Anal...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

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3rd international conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819708918

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM Communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite Image Classification;primitive Roots and Euler Totient Function-Based Progressive Visual Cryptography;approach for Fire Detection Using Image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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A signal processing and Machine Learning Framework for Enhanced TDS Simulation 3

A Signal Processing and Machine Learning Framework for Enhan...

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3rd IEEE international conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shuvo, Shuvendu Pal Pal Shibazee, Shirshendu Khuki, Arifa Akter Das, Chaitee Paul, Goutam Mita, Mitaly Paul Khulna University of Engineering and Technology Dept. of CE Khulna9203 Bangladesh Bangladesh University of Engineering and Technology Dept. of EEE Dhaka1000 Bangladesh Khulna University of Engineering and Technology Dept. of MSE Khulna9203 Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Dept. of Pharmacy Gopalganj8100 Bangladesh Khulna University Dept. of Economics Khulna9208 Bangladesh

ISBN: (纸本)9798331534400

Total dissolved solids (TDS) is one of the most significant indicators of regulated wastewater effluents, and it is critical in assessing and improving the performance of any wastewater treatment facility. The literature review reveals that the pattern of TDS and other water quality parameters generally shows noisy and complicated features;hence, capturing their pattern is tough. This paper proposes a new approach to noise reduction by Ensemble Empirical Mode Decomposition (EEMD), incorporating machine learning algorithms like Artificial Neural Network (ANN) and Support Vector Machine (SVM) to improve the accuracy of TDS prediction. The data on temperature, pH, turbidity, and TDS were collected from the Bangabandhu Water Treatment Plant (BWTP) in Khulna, Bangladesh. In the current work, traditional and EEMD-based approaches were developed, and the performances of these were evaluated based on MSE, RMSE, and MAE. It is presented that the EEMD-based approach reduced the error considerably over the traditional models, while a maximum reduction of 89.46% and 91.04% in MSE are achieved in ANN and SVM, respectively. It further suggested that the proposed approaches could be applied to other wastewater treatment plants. Practical deployment of this model will help the operators make real-time decisions in optimized treatment processes. © 2024 IEEE.

关键词： Empirical mode decomposition

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Enhancing the Long-Term Sustainability of Healthcare data Optimization Through Deep Federated Learning 3

Enhancing the Long-Term Sustainability of Healthcare Data Op...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Kumar, Balraj Evin, Munni Byeon, Haewon Sivaprakash, M. Sheeba, P. Maria Sunil, J. School of Computer Application Lovely Professional University Punjab Phagwara India School of Engineering and Computing American International University Kuwait College of Ai Convergence Inje University Department of AI-Big Data Gimhae50834 Korea Republic of Stella Mary's College of Engineering Department of Mechanical Engineering Tamil Nadu 629202 India St. Mother Theresa Engineering College Department of Electronics and Communication Engineering Thoothukudi India Annai Vailankanni College of Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798350361186

The medical industry regards the prevention and detection of skin diseases as a top priority. The number of people suffering from skin diseases is increasing faster than ever before due to increased levels of pollution and poor diet. Although it's not the major priority, having bad skin makes us less certain. Regularly inspecting your skin is an important step in identifying any degenerative or early changes in your skin that could lead to skin disease. Machine learning algorithms can be utilized to create robust models that can categorize a wide range of skin conditions. First, the skin and non-skin must be distinguished in order to identify skin conditions. In order to determine the unambiguous a group of skin conditions A selection of five distinct machine learning algorithms has been employed to compile a dataset of skin infections. These algorithms include Kernel Self- pattern, Random Forest, Naive Bayes, Logistic Regression, and CNN, among others. A comparable investigation based on training accuracy and skepticism matrix criterion has been carried out and illustrated with graphs. It has been determined that, of all the applicants, CNN offers the highest level of training accuracy for the appropriate assumption of skin disorders. © 2024 IEEE.

关键词： Deep learning

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Complex Event processing Technology for Fresh Food Based on Edge Nodes 3

Complex Event Processing Technology for Fresh Food Based on ...

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3rd international conference on Green Communication, Network, and Internet of Things, CNIoT 2023

作者： Zhao, Guangyue Zhang, Lei Hong, Ruipeng Xiao, Chaoen Zhang, Qingchuan Wan, Xinyuan Beijing Electronic Science and Technology Institute Beijing China National Engineering Laboratory for Agri-product Quality Traceability Beijing Technology and Business University Beijing China

ISBN: (纸本)9781510668867

When tracing data related to fresh food, it is often necessary to handle a substantial volume of real-time data, which grows rapidly and exhibits clear characteristics of Big data. While traceability technology based on IoT terminals can process this data promptly, it still faces limitations in terms of real-time insights and decision-making capabilities. This paper introduces and implements an information collection and fusion scheme utilizing edge nodes. It also presents a complex event processing (CEP) system and method based on edge nodes, along with a hierarchical database model for simplifying complex events. In this approach, when real-time data of fresh food is collected in proximity, complex event processing occurs at edge nodes through IoT terminals. The perceived events from the IoT terminals are categorized into original events, simple events, and complex events, and are processed following predefined rules. This approach effectively enhances the information processing capabilities of edge nodes, ensuring the real-time and efficient collection of fresh food information. © 2023 SPIE.

关键词： Big data

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Wavelet analysis of S&P 500, oil prices, and economic policy uncertainty 3

Wavelet analysis of S&P 500, oil prices, and economic policy...

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3rd international conference on Computer Vision and data Mining, ICCVDM 2022

作者： Shen, Yijun Lai, Zhengyi Zhao, Zhengyang Tao, Zheng Wenzhou-Kean University College of Science and Technology Wenzhou China Wenzhou-Kean University College of Business and Public Management Wenzhou China National University of Singapore Department of Statistics and Data Science Singapore Singapore

ISBN: (纸本)9781510661363

We collect a total of 1830 data from January 2020 to June 2022 and use R for data processing and wavelet analysis. Moreover, we analyze the interactions between the COVID-19 pandemic, the Russian-Ukrainian war, crude oil price, the S&P 500 and economic policy uncertainty within a time-frequency frame work. As a result that the COVID-19 pandemic and the Russian-Ukrainian war has the extraordinary effects on the three indexes and the effect of the Russian- Ukrainian war on the crude oil price and US stock price higher than on the US economic uncertainty. © COPYRIGHT SPIE.

关键词： COVID-19

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A Method of Extracting Outer Eye Corners of Terra Cotta Warriors Based on Point Cloud data 3

A Method of Extracting Outer Eye Corners of Terra Cotta Warr...

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3rd international conference on Image processing and Intelligent Control, IPIC 2023

作者： Cheng, Yue Liu, Xianglei Key Laboratory for Urban Geomatics of National Administration of Surveying Mapping and Geoinformation Engineering Research Center of Representative Building and Architectural Heritage Database Ministry of Education Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9781510668140

The Terracotta Warriors and Horses of the First Qin Emperor are a highly prized relic in China and the world. Some people believe that the Terracotta Warriors are a realistic representation of the Qin dynasty, while others feel that the Terracotta Warriors are the result of artistic re-creation. Therefore, in order to verify the "realism" of the terracotta warriors and horses, the degree of resemblance between the terracotta warriors and real people was quantified. The representative points of the outer corners of the eyes on the head and face of the terracotta warriors are selected. The method of extracting feature points based on the cross-sectional line of approximation is proposed using point cloud data. The method is based on the approximation method of cross-sectional line extraction, which is more accurate and easier to calculate by converting 3D into 2D. The final experimental results show that the curvature of the outer corner points of the terracotta warriors and the curvature pattern of the real eyes basically match. The method verifies that the facial features of terracotta warriors are highly correlated with those of real people. At the same time, the "realistic" nature of the terracotta warriors is demonstrated. © 2023 SPIE.

关键词： data mining

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