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检索条件"主题词=Filtering algorithms"

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Enhanced Cloud Intrusion Detection Using Support Vector Machines

Enhanced Cloud Intrusion Detection Using Support Vector Mach...

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Sustainable Expert Systems (ICSES), International Conference on

作者： R Hari Krishna B Selvapriya Department of CSE Bharath institute of Higher Education and Research India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The broad adoption of cloud computing (CC) as a new technology offering on-demand networked access to shared configurable computer resources has resulted in an increased need for data storage and service management. But at the same time, CC also opens up further vulnerabilities by throwing issues such as insider threats, data breaches and unauthorized access into the mix. The study suggests this mechanism as a cloud-based intrusion detection technique made up of optimization and machine learning algorithms that mitigate these issues. In the beginning, an input noiseless training data undergoes a Gaussian filter to improve the quality of data and remove any background noise. In this step, Random Forest Recursive feature elimination is used to select significant low-dimensional features with the help of correlation-based feature selector. Implemented on the Python platform using the CICIDS-2019 dataset, the proposed method's accuracy is validated and evaluated against various criteria, demonstrating superior performance compared to recent related studies and effectively enhancing intrusion detection in cloud computing.

关键词： Support vector machines Cloud computing Machine learning algorithms Intrusion detection Training data filtering algorithms Data breach Feature extraction Background noise Random forests

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Research on the relay protection method of special vehicle DC power distribution system based on SG filter

Research on the relay protection method of special vehicle D...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Luoyao Ren Zikai Feng Wenyi Li Deshan Kong Yanming Li Northeastern University Shenyang Liaoning China Liaoning Institute of Science and Technology Benxi China China Northern Vehicle Research Institute Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

With the increasing complexity and precision of special vehicle system structure, the efficiency and accuracy of fault diagnosis of on-board DC distribution networks are facing challenges. The complexity and particularity of the DC distribution network make it face many challenges, such as electrical failure, overcurrent, undervoltage, and other problems, which may have a serious impact on the safe and stable operation of special vehicles. Therefore, developing a set of fault identification and protection methods for special vehicle DC distribution network circuits is urgently needed. This paper presents the relay protection method of a special vehicle DC distribution system based on the Savitzky-Golay filter, studies the fault problem of a special vehicle transmission line, constructs the relay protection method of a special vehicle DC transmission system, and realizes the fault identification and protection of the special vehicle DC distribution system.

关键词： Fault diagnosis Accuracy Protective relaying Distribution networks Voltage filtering algorithms Complexity theory Circuit faults Protection DC distribution systems

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Vehicle Number Plate Recognition and Information Access System

Vehicle Number Plate Recognition and Information Access Syst...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Pradeep B. Mane Varsha Kiran Patil Mayur R. Paithankar Mallikarjun M. Kotgire Samveg S. Shah Dept. Electronics and Telecommunication AISSMS Institute of Information Technology Pune India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

Addressing the growing challenges posed by increasing vehicles and stringent traffic violations, many vehicle number plates go undetected while passing through toll booths and traffic lights. Thus it creates the need for a vehicle number plate recognition system. Existing systems employing ANPR algorithms utilize MATLAB and K-Nearest neighbour algorithms for image processing. Drawbacks include limitations in adapting to dynamic environments, Real time recognition, handling multiple vehicles, slow processing speeds, incapability of multiple object detection in single frame. The paper focuses on Automatic Number Plate Recognition (ANPR), employing specialized cameras and advanced image processing and using YOLOv8 for object detection and extracting license plate information from video feeds using EasyOCR. The noisy data generated during character recognition is filtered out using Regex based algorithm. The system integrates with centralized databases for real-time vehicle information access for users. The same database is used to automate e-challan updation, contributing to traffic management by swiftly issuing fines for rule violations. The proposed video-based processing system achieves number plate detection using YOLOv8 and storage of vehicle number plates in a centralized MongoDB database with an accuracy achieved of 96% in license plate detection and reduces noisy data using algorithm. It also enables real-time data retrieval through a user interface using Reactjs as frontend and is also connected to the MongoDB database for precise and user-friendly access in modern traffic systems.

关键词： Image recognition Databases Heuristic algorithms Object detection filtering algorithms Information filters Real-time systems Character recognition Noise measurement License plate recognition

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Feature Extraction and Preprocessing Techniques on Open-Ended Concept Mapping Activity Log Data

Feature Extraction and Preprocessing Techniques on Open-Ende...

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Vocational Education and Electrical Engineering, International Conference on

作者： Didik Dwi Prasetya Muis Muhtadi Hanifah Nur Azizah Azlan Mohd Zain Department of Electrical Engineering and Informatics State University of Malang Malang Indonesia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331505103

ISBN: (纸本)9798331505110

Concept maps are graphic representations of unique ideas widely used in learning. Concept mapping activities are often stored in an activity log which contains a record of data based on a particular time. The high activity of concept mapping can form extensive data collection and result in abundant information. An appropriate initial processing action is needed to process the concept mapping log data properly. This study aims to reduce the concept of map activity log data to obtain a reliable and noise-free dataset. This research begins with the data preprocessing stage, the initial technique in data mining that converts raw data into cleaner information and can be used for further processing. The preprocessing phase could be done using the data reduction method or adding a filter example to observe missing data on an attribute by eliminating missing observation data. The principal component analysis algorithm was used to normalize the data to become stable, neither too high nor too low. This study emphasizes that the extracted data could produce high accuracy and shorter processing time.

关键词： Graphics Electrical engineering Accuracy Data collection filtering algorithms Feature extraction Information filters Data mining Reliability Principal component analysis

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Low Power and High Throughput 12-Tap FIR Filter Using Unfolding Algorithm

Low Power and High Throughput 12-Tap FIR Filter Using Unfold...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Pinninti Kishore Vishlavath Tarun Segi Reddy Venkata Satya Siva Sreemayi Kompella Dept. of ECE VNR VJIET Hyderabad India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

FIR filters (Finite impulse response) are widely used in VLSI due to their characteristics like linear phase and stability. FIR filter finds its key applications in domains like digital signal processing, communication systems, audio and image processing, control systems and sensor signal processing. The existing filter design takes up more area, requiring more memory and time. The design of higher order filters is also complex and requires even more memory and area. Therefore, in VLSI, FIR filters are often implemented using efficient algorithms and architectures to meet the requirements of low power consumption, high speed, and reduced area. This paper is aimed at designing a 12th order FIR filter using various retiming techniques and algorithms to improve the efficiency, speed, and area. The degree of efficiency of a technique is determined based on the application and a tradeoff between various metrics involved in it. The design is implemented in MATLAB Simulink and HDL code is generated for further analysis. The design is modified during the HDL code analysis if required to meet the user specifications of design metrics like area, speed, and power. In this paper we have implemented the $12^{\text{th}}$ order direct form, transposed form, cut-set retiming and unfolded forms of the FIR filter designs in MATLAB Simulink then simulated in the Xilinx design suite tool and implemented on the FPGA board. In comparison to the direct form, transposed form and cut-set retiming form, the 3-unfolded form of FIR filter design have the high area of 24138 LTU's, more power of 107mW and lower delay of 9.451ns as it involves the addition of registers or delay elements. From the results, we can observe that the proposed techniques are proven to be better than the conventional design in terms of performance like speed, delay, power, and area.

关键词： Measurement Finite impulse response filters Software packages Memory management Signal processing algorithms filtering algorithms Very large scale integration

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Random Error Elimination Algorithm of Microelectromechanical Gyroscope Array

Random Error Elimination Algorithm of Microelectromechanical...

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IEEE Conference on Vehicular Technology (VTC)

作者： Siyuan Liang Xuguang Li Guodong Duan School of Communications and Information Engineering Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China Hunan Vanguard Group Co.Ltd Changsha China

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In order to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes, this paper proposes an LSTM-Kalman algorithm that combines a long short-term memory network (LSTM) and a Kalman filter applied to MEMS arrays. The historical MEMS gyroscope data are used to train the LSTM network, the trained network is used to filter the data instead of the state estimation equation in Kalman filtering, and finally the data fusion technique is utilized to obtain a highly accurate output. The experimental results show that the LSTM-Kalman algorithm has a better elimination effect on the random errors of the original data. The zero-bias instability, angular random wandering, and rate slope are improved by 9.99 dB, 8.99 dB, and 11.84 dB, respectively. The standard deviation of roll angle, pitch angle, and yaw angle are improved by 5.81 dB, 6.65 dB, and 1.95 dB, respectively, in dynamic environment, which effectively verifies the feasibility and reliability of the algorithm.

关键词： Micromechanical devices Accuracy Heuristic algorithms filtering algorithms Gyroscopes Kalman filters Arrays

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Optimizing Health Through Nutrition: Machine Learning Insights for Menopausal Women

Optimizing Health Through Nutrition: Machine Learning Insigh...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Logapriya E Surendran R Sundara Rajulu NavaneethaKrishnan Appathurai K Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Dhanalakshmi Srinivasan University Tiruchirappalli India Department of Computer Technology KG College of Arts and Science Coimbatore India

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

This research provides a detailed and sequential framework for constructing a recommendation system focused on women's health, specifically for menopause management and dietary guidance tailored to individuals. These sources encompass medical records, structured questionnaires, and health monitoring programs that facilitate the tracking of essential indicators such as symptom intensity, pulse rate, body temperature, sleep duration, and dietary habits. The management of missing values is executed through forward filling, whereas normalisation of numerical values is performed via standard scaling subsequent to the application of the Standard Scaler. Ultimately, binary and category data are encoded using a one-hot encoder. Feature engineering enhances the predictive capacity of the model by generating new variables from the existing data. Linear regression, logistic regression, support vector machines, and random forest models are constructed and evaluated based on the characteristics of the data utilising performance metrics such as R-Squared (R2) and AUC-ROC, achieving maximum accuracies of 0.92 for linear regression and 0.95 for logistic regression. The system provides women with health and lifestyle advice through the application of collaborative and content-based filtering ensuring that the suggestions are highly accurate and based on information.

关键词： Support vector machines Logistic regression Accuracy Linear regression Pipelines Predictive models filtering algorithms Numerical models Standards Random forests

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An Improved PID Control Method Based on the Kalman Filter Algorithm

An Improved PID Control Method Based on the Kalman Filter Al...

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Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE International Conference on

作者： Jihong Yan Wentao Wang Zhongyi Gou Yizhan Huang Mengxu Ren Zhihao Yan Xuechao Wang Jianping Meng Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350355260

With the increasing demand for high-tech equipment, the need for reliable power supplies has grown, making the stability and accuracy of power supplies particularly critical. In the industrial sector, PID (Proportional-Integral-Derivative) control is the most commonly used method. However, white noise in the power supply and overshoot during system startup can cause instability in traditional PID controllers. To address this issue, this paper proposes a method to enhance the stability and accuracy of power supply output voltage by combining PID controllers with the Kalman filtering algorithm. The Kalman filter is used to process input errors by filtering. Based on the system’s dynamic and measurement models, the Kalman filter provides an optimal estimate of the system state through predictions and updates of measurement data. Simulation results demonstrate that the Kalman filtering algorithm effectively reduces measurement errors and noise, mitigating output instability caused by process noise and significantly improving the performance and stability of the PID controller.

关键词： PI control Accuracy Power supplies Computational modeling filtering algorithms White noise Stability analysis Kalman filters PD control Voltage control

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Fed-MS: Fault Tolerant Federated Edge Learning with Multiple Byzantine Servers

Fed-MS: Fault Tolerant Federated Edge Learning with Multiple...

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International Conference on Distributed Computing Systems

作者： Senmao Qi Hao Ma Yifei Zou Yuan Yuan Peng Li Dongxiao Yu School of Computer Science and Technology Shandong University Qingdao China School of Computer Science and Technology University of Aizu Fukushima Japan

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Due to its decentralized framework and outdoor environments, federated edge learning (FEEL) faces significant vulnerability to malicious attacks within edge networks. Prevailing FEEL approaches typically hinge on a dependable parameter server (PS) to contend with the adversarial updates from Byzantine clients. Recognizing the inherent unreliability of PSs in edge networks, this paper delves into the security challenges of FEEL, specifically addressing Byzantine PSs. We present a Byzantine fault-tolerant FEEL algorithm, named Fed-MS, in which a multi-server technique along with a newly designed trimmed-mean-based model filter is employed. This combination ensures that each client can obtain a feasible global model for its local training, closely approximating a true model aggregated by benign PSs. Furthermore, we propose a sparse uploading strategy in Fed-MS to enhance communication efficiency for model aggregation to multiple PSs. Theoretical analysis demonstrates that, when Byzantine PSs are a minority, Fed-MS achieves an expected convergence speed of $O(1/T)$ with $T$ defined as the number of training rounds, akin to state-of-the-art works under non-Byzantine settings. Extensive experiments are conducted on the CIFAR-10 dataset with MobileNet V2 as the training model. The numerical results show that our Fed-MS can improve the model accuracy from 10% to at least 76% under the malicious attacks from Byzantine PSs. Our code is released at https://***/haoma2772/Fed-MS.

关键词： Training Fault tolerance Federated learning Fault tolerant systems filtering algorithms Fasteners Numerical models

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Comparative Study of PSO and GWO algorithms in Fractional-Order Low Pass Filter Design

Comparative Study of PSO and GWO Algorithms in Fractional-Or...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： A.Konda Babu B.T. Krishna Department of ECE JNTUK Kakinada A.P India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

This work, in an effort to improve the functionality and adaptability of electronic filters, is exploring the design of a Fractional Order Lowpass Filter by means of evolving algorithms. Filters’ fractional order transfer functions provide a more fluid representation of system dynamics than traditional integer order filter. Evolutionary algorithms are employed in the design phase to enhance the fractional-order filter’s parameters. Among the frequency response parameters that are optimized for are cut-off frequency, roll-off, and attenuation. Evolutionary algorithms are a useful tool for efficiently solving the difficult parameter space problems of fractional-order filters by swiftly identifying the optimal solutions. Through the examination of various filter configurations made possible by the algorithmic framework, designs that balance filter complexity and performance are found.

关键词： Technological innovation Fluids System dynamics Heuristic algorithms Low-pass filters Transfer functions filtering algorithms Frequency response Particle swarm optimization Optimization

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