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检索条件"主题词=Software Algorithms"

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Utilizing Decision Tree,SVM And KNN algorithms For Optimal Speed Detection And Control With Superior Accuracy

Utilizing Decision Tree,SVM And KNN Algorithms For Optimal S...

引用

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： N. Ramya K Amrutha H Krishalini K Ramya Department of CSE Sri Sairam Engineering College Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Over-speeding is a major contributor to traffic accidents and fatalities worldwide. AI and ML algorithms like decision tree, SVM and KNN can be used within vehicles to automatically detect and control speed limits, thus improving road safety. This paper explores the potential of AI/ML-based systems for vehicle speed limit detection and control, analyzing their functionalities, challenges and future directions. The paper finds that AI/ML-based systems can effectively detect and control speed limits, leading to a significant reduction in traffic accidents and fatalities. Improving algorithm accuracy and ensuring data reliability are fundamental to unlock the full potential of this technology. The paper also discusses some future directions for AI/ML-based speed limit detection and control, such as the use of more sophisticated algorithms and the development of more comprehensive data sets.

关键词： Support vector machines software algorithms Road safety software Telecommunication computing Safety Decision trees

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Data Analysis and Confidence based on SVM Density Estimation

Data Analysis and Confidence based on SVM Density Estimation

引用

International Joint Conference on Neural Networks (IJCNN)

作者： E.M. Jordaan I. Nischenko Dow Benelux B. V. Terneuzen Netherlands Mathematics for Industry Programme Eindhovan University of Technology Eindhoven Netherlands Department of Medical Statistics Leiden University Leiden Netherlands

Data-driven models are frequently used in industry to predict various characteristics of processes. In order to build robust model, the quality of the data needs to be analysed. These models are also required to associate a level of confidence with their predictions. In a high-dimensional setting it is important to incorporate data density information when analyzing the quality of the data and the determining the confidence in a prediction. The SVM density estimation together with results from the typicalness framework form a powerful tool that is effective for industrial applications.

关键词： Data analysis Support vector machines Predictive models Robustness Information analysis Application software Industrial control software algorithms Neural networks Computer networks

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A Window Switching Algorithm for AVS Audio Coding

A Window Switching Algorithm for AVS Audio Coding

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Shuixian Chen Haojun Ai Ruimin Hu Wuhan University Wuhan Hubei CN

In this paper, we present a window switching algorithm employed in AVS (audio video coding standard of China) generic audio coding. The algorithm - energy and unpredictability measure based window switching decision (ENUPM-WSD) - achieves high performance to complexity ratio by taking the advantages of both the low complexity of energy based decision in time domain and the high accuracy of unpredictability based decision in frequency domain. It significantly improves encoded audio quality, especially those with large transient portions, while preserves low average computational complexity in AVS generic audio encoder. Due to the merits, it is accepted as a recommendation module of the AVS audio standard.

关键词： Audio coding Signal processing algorithms Frequency domain analysis Encoding Signal resolution Communication switching Energy measurement Acoustic noise software algorithms Power engineering and energy

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Application and Development of Neural Network Technology in Mechanical Automation Processing Parameters

Application and Development of Neural Network Technology in ...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Kai Wang Henan College of Industry&Information Technology Jiaozuo Henan China

With the continuous in-depth study of metal cutting mechanism and the development of computer technology, people have established a computer-aided optimization program system for cutting data, which provides new methods and means for selecting the optimal cutting parameters. As a new technology in the field of artificial intelligence research, neural networks have non-linear characteristics and information distribution. When dealing with multiple input and multiple output systems, it eliminates the complicated correlation analysis of various variables required by traditional modeling methods. The purpose of this article is to study the application and development of neural network technology for mechanical automation processing parameters. This article trains the sample set, learns the statistical law of the sample set, and saves the learned information in the weight. When the non-sample set mode is input, the BP network in the ideal neural network is highly nonlinear. The mapping ability is not limited by the number of inputs and outputs. In specific research applications, the original program can be freely modified as needed. This paper uses BP network as a research tool, trains BP network by using a large amount of experimental data, studies and analyzes several influencing factors of error remapping phenomenon, and uses BP network to solve the basic method of remapping problem, and initially established the feasibility of the method. Experimental research shows that this article is the ideal output data (actual data) for network testing and the network output data. From these data, it can be seen that the network training output and the ideal output error are controlled below 5%. It can be seen that the training result of the network is successful.

关键词： Training Wireless communication Analytical models Adaptation models Automation software algorithms Tools

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Machine Learning-Enabled Healthcare Decision Support System: An Advanced Disease Prediction with Patient-Doctor Collaboration

Machine Learning-Enabled Healthcare Decision Support System:...

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Computing and Data Science (ICCDS), International Conference on

作者： V. Ragavi Panneru Kalva Preethi Reddy Vommina Lakshmi Thanmaye Eswari Umaeswari P Computer Science and Business Systems R.M.K Engineering College Chennai Tamil Nadu

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

In this research, a revolutionary healthcare software system that gathers various patient symptoms in order to anticipate and assess diseases is presented. To generate predictions, the system uses five different algorithms: Random Forest, XG Boost, Support Vector Machine, Logistic Regression, and Naive Bayes. The most accurate prediction is chosen as the final result after a comparative study of the forecasts and accuracies from each method. Healthcare practitioners can make better decisions when using a graphical representation. A noteworthy development in proactive healthcare is demonstrated by the system's introduction of a function that recommends appropriate doctors to the patient. This invention supports both individual well-being and more general public health goals.

关键词： Support vector machines Technological innovation Accuracy software algorithms software systems Prediction algorithms Bayes methods

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Experiences Implementing Cognitive Radio Control algorithms

Experiences Implementing Cognitive Radio Control Algorithms

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IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference

作者： Christian Doerr Douglas C. Sicker Dirk Grunwald Department of Computer Science University of Colorado Boulder CO USA

In recent years, several algorithms for controlling cognitive radio platforms have been proposed. In this paper, we review the existing approaches that have been developed for cognitive radio control algorithms and extract the common sensing and control requirements for those algorithms. We also briefly discuss the challenges of defining representative testing scenarios. We then synthesize our experience implementing cognitive radio control algorithms and extract a set of pragmatic issues challenging researchers in cognitive radios. We conclude with a set of open questions and problems and discuss implications for future research.

关键词： Cognitive radio Radio control Chromium Testing software algorithms Hardware Switches Genetic algorithms Game theory Neural networks

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Stabilization of the Platform around Single Axis Using Complementary Filter

Stabilization of the Platform around Single Axis Using Compl...

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International Carpathian Control Conference (ICCC)

作者： Dominik Walica Jaromír Škuta VSB – Technical University of Ostrava Ostrava Czech Republic

The paper deals with the stabilization of a platform rotating around a single axis with the use of an accelerometer and a gyroscope sensor. The paper presents a mechanical model of the platform, which consists of designed 3D printed parts. The electrical part of the model consists of the mentioned sensors, servomotors and a low-cost microcontroller board Arduino. The stabilization algorithm implemented on the Arduino is based on a complementary filter and a feedback controller. The used software application for the operation of the model was developed in the Matlab App designer environment and communicates with the Arduino by a serial interface. The model and obtained results can be used for the development of stabilization algorithms for more complex devices, such as the Stewart platform.

关键词： Accelerometers Three-dimensional displays Microcontrollers software algorithms Signal processing algorithms Filtering algorithms Hardware

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DSP Implementation of Hilbert Transform for Voltage Variation Tracking

DSP Implementation of Hilbert Transform for Voltage Variatio...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Naveen Kumar Buduru Srinivas Bhaskar Karanki Sabarimalai Manikandan School of Electrical Sciences Indian Institute of Technology Bhubaneswar Odisha INDIA

ISBN: (数字)9781728154756

ISBN: (纸本)9781728154763

In this paper, the Hilbert Transform (HT) based detection scheme is presented for the identification of voltage variation events such as sag, swell, and interruption. The distinct feature of the proposed scheme is the utilization of a moving average filter along with the Hilbert transform and fuzzy system. software implementation and hardware prototype were developed for real-time processing of voltage variations on floating-point DSP (TMS320F28379D) board. Experimental and simulation results are presented to validate the proposed scheme. Finally, the performance accuracy of the proposed algorithm validated and achieves promising results on the hardware.

关键词： Signal processing algorithms Transforms Power quality software algorithms Signal processing software Hardware

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An empirical investigation of simulated annealing applied to property-oriented testing

An empirical investigation of simulated annealing applied to...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： O. Abdellatif-Kaddour P. Thevenod-Fosse H. Waeselynck LAAS CNRS Toulouse France

Summary form only given. Property-oriented testing uses the specification of a property to drive the testing process. Our aim is to validate a program with respect to a target property, that is to exercise the program and observe whether the property's violated or not. We define a test strategy for safety properties in cyclic control systems. It consists of the stepwise construction of test scenarios. Each step explores possible continuations of the dangerous scenarios found at the previous step, using black-box sampling techniques. Here, emphasis is put on using a heuristic search technique - namely, simulated annealing - an automatic way to ample over the input pace. Empirical investigation is conducted on a steam boiler case study. The target property is the "non explosion" of the boiler in presence of faults in the physical devices. The experimental results lead us to propose a revised version of the basic simulated annealing algorithm, whose efficiency is promising.

关键词： Simulated annealing software testing System testing Safety Automatic control Control systems Sampling methods Boilers Explosions software algorithms

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Checking Correctness and Compliance of Integrated Process Models

Checking Correctness and Compliance of Integrated Process Mo...

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International Symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Rene Worzberger Thomas Kurpick Thomas Heer Department of Computer Science & Software Engineering RWTH Aachen University Aachen Germany

Models of different kinds are used in the area of business process management. Abstract process knowledge as well as executable process definitions can be visualized and edited in a graphical manner. The same holds true for models of process instances in some process-aware information systems (PAIS), which allow for dynamic modifications in a process instance during process runtime. An appropriate modeling tool must not only provide means to graphically edit different kinds of models on different abstraction layers but also detect or prevent violations of certain model constraints. These constraints comprise a model's internal correctness as well as a model's compliance with general process knowledge expressed in another more abstract model. In this paper we contribute an approach that allows for uniformly specifying correctness as well as compliance checks for diverse graphical process models. This is achieved by means of an integrated meta-model for models of different kinds. The integrated meta-model is referenced by a fixed set of Object Constraint Language (OCL) expressions, which specify correctness and compliance checks. We exemplify some OCL expressions and their application within a prototypical modeling tool.

关键词： Management information systems Prototypes Law Legal factors software algorithms Scientific computing Computer science software engineering Engineering management Visualization

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