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检索条件"主题词=software algorithms"

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Resilience of lattice-based Cryptosystems to Quantum Attacks

Resilience of lattice-based Cryptosystems to Quantum Attacks

引用

IEEE Symposium on Computers and Communications (ISCC)

作者： Tobias Köppl René Zander Nikolay Tcholtchev Fraunhofer Institute (FOKUS) Berlin Germany Fraunhofer Institute (FOKUS) RheinMain University of Applied Sciences

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

In this paper, we present first ideas and concepts for a methodology testing the robustness of public key cryptosystems with respect to quantum attacks. As an example, the key encapsulation mechanism (KEM) described in the NIST report FIPS 203 is considered. This cryptosystem is based on a learning with error problem (LWE problem). For this reason, different quantum algorithms solving LWE problems are analyzed regarding their efficiency and precision. Thereby, we restrict ourselves to computational means i.e. the respective quantum algorithms are implemented using Python based libraries such as Eclipse Qrisp or the software packages provided by D-Wave. In order to be able to test the different algorithms, the key generator described in FIPS 203 is re-implemented, such that keys with reduced lengths can be generated. In particular, it is of great interest to investigate, which key length guarantees a satisfactory security level. Another interesting issue is how the knowledge of side-channel attacks can be used to reduce a given LWE problem. Our test results are considered as training data for neural networks having the key lengths, the type of the quantum solver and further parameters as inputs and a number for the security level of the corresponding key as output. The envisioned neural networks can be used for predicting whether a particular algorithm can be efficiently attacked under the given circumstances and parameters known to the attacker.

关键词： Quantum algorithm software packages Neural networks software algorithms Training data Side-channel attacks NIST Robustness Testing Resilience

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Detection of Defects in PCB Images by Overlaying Images and Machine Learning

Detection of Defects in PCB Images by Overlaying Images and ...

引用

International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Roman Melnyk Ruslan Tushnytskyy Pavlo Viazovskyy Software Department Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

Layering, thinning, and filling algorithms detect short circuits and open defects in PCB images. Areas of excess and insufficient metal are identified. The combination of the defect pattern and the skeleton of the reference image generates features that help identify and measure different types of defects. Indicators of properties inherent in new schematic components are used as input for machine learning training and testing.

关键词： Training Printed circuits software algorithms Metals Machine learning Filling software Skeleton Telecommunications Testing

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Optimal Smart Inverter Volt-Watt and Volt-Var Settings to Maximize Fair Contribution in Voltage Regulation

Optimal Smart Inverter Volt-Watt and Volt-Var Settings to Ma...

引用

IEEE International Conference on Smart Energy Grid Engineering (SEGE)

作者： Zahid Ali Khan Tohid Rahimi Julian L. Cardenas-Barrera Shafait Ahmed Chris Diduch Department of Electrical and Computer Engineering University of New Brunswick Fredricton Canada

ISBN: (数字)9798350377378

ISBN: (纸本)9798350377385

Overvoltage resulting from the high penetration of photovoltaic (PV) sources in distribution networks is a major challenge. Approaches to mitigate such overvoltage conditions include curtailment of PV active power and the injection of reactive power by PV inverters. This paper proposes a centralized control strategy to optimize the Volt-Watt and Volt-Var profiles such that active and reactive power of all PV inverters are controlled to not only to mitigate against overvoltage conditions but to do so in a manner where curtailment of active power is equalized or balanced in a fairly manner across all inverters. The Imperialist Competitive Algorithm (ICA) algorithm is used to optimize the objective functions. MATLAB and Open DSS software are used to simulate the grid model and optimize goals.

关键词： Photovoltaic systems Reactive power software algorithms Distribution networks Linear programming Inverters software Smart grids Voltage control Centralized control

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Intrusion Detection for Predicting Security Attacks Using Hybrid LSTM-GRU Classifiers

Intrusion Detection for Predicting Security Attacks Using Hy...

引用

Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), International Conference on

作者： P S Divya S. Jalaja S Balachandar S Deepak Mohamed Faizal J Department of Electronics and Communication Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354546

Aggressive attacks, computer viruses, and malware routinely affect computer networks. Finding intrusions is one of the most crucial aspects of safeguarding the network and is a proactive defense technique. A novel method has to be provided for locating difficulties in cybersecurity and intrusions in cyber-physical system. This work proposed the hybrid Long Short Term Memory –Gated Recurrent Unit (LSTM-GRU) classifier for efficient intrusion detection system, which minimize modeling's computational cost and choose the best features. Both LSTM and GRU units are integrated into a single network in a hybrid LSTM-GRU design. The architectures of GRU and LSTM are capable of handling sequences of varying lengths. Because of this, hybrid LSTM-GRU models are often used for applications like time series analysis and natural language processing where the length of the input sequences fluctuates. This work is executed in Python software, which obtained a high detection training and validating data accuracy of 96.23% and 95.39% respectively.

关键词： Training Recurrent neural networks Computational modeling Time series analysis software algorithms Intrusion detection Feature extraction

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Optimization of Ethylene Oligomerization Process Based on Genetic algorithms

Optimization of Ethylene Oligomerization Process Based on Ge...

引用

International Russian Automation Conference (RusAutoCon)

作者： Evgenia Antipina Svetlana Mustafina Andrev Antipin Department of Scientific Research and Development Ufa University of Science and Technology Ufa Russia Department of Mathematical Modeling Ufa University of Science and Technology Ufa Russia Department of Applied Informatics and Programming Ufa University of Science and Technology Ufa Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The paper is devoted to the development of a genetic algorithm for finding the optimal values of control parameters of the ethylene oligomerization process. The formulation of the problem of optimal control of the ethylene oligomerization process is given. The control parameters are temperature and reaction duration, the values of which are subject to constraints. It is proposed to search for the solution of the formulated problems using a genetic algorithm with real coding. The principle of operation of genetic algorithms is based on the imitation of the processes of evolution of living nature. It includes cyclic application of selection, crossing, mutation and renewal operations to a population of individuals. For each of the problems under consideration the method of representation of the mathematical analog of the population, on the basis of which the solution is searched, is described. A step-by-step genetic algorithm for solving the formulated optimal control problems is given. A computational experiment is carried out, as a result of which the optimal reaction temperature and process duration, at which the target functional takes the largest value, are calculated.

关键词： software packages Catalysts software algorithms Process control Optimal control Search problems Encoding Temperature control Optimization Genetic algorithms

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Detecting Text Written by ChatBot Based on NLP and Machine Learning Technology

Detecting Text Written by ChatBot Based on NLP and Machine L...

引用

International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Victoria Vysotska Olga Lozynska Oleh Dudak Lyubomyr Chyrun Sofia Chyrun Roman Romanchuk Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine Applied Mathematics Department Ivan Franko National University of Lviv Lviv Ukraine Telecommunication Department Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The paper analyses methods for automatically detecting text generated by artificial intelligence. The subject of our research is the analysis and identification of the characteristics of artificially generated texts. It means that we study various aspects of texts that have been created with the help of artificial intelligence algorithms in detail. Our research focuses on identifying and analysing the specific features of these texts that distinguish them from those written by humans. For these purposes, a software system was developed. Our system is a new approach to training models on datasets to detect artificially generated content. It uses advanced algorithms and technologies to analyse text and determine its origin accurately. This program uses a comprehensive model training approach, including an extensive set of natural language processing, machine learning, and data analysis techniques.

关键词： Training Machine learning algorithms software algorithms Linguistics Chatbots software systems Reliability engineering Market research Telecommunications Safety

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Design and Implementation of an Autonomous Trace-Tracking and Obstacle-Avoidance Mobile Robot Utilizing Multiple Sensors

Design and Implementation of an Autonomous Trace-Tracking an...

引用

Asian Control Conference

作者： Shijin Yan Jialing Yang Jing Jin Rongyao Ling Xianxin Guo College of Mechanical and Electrical Engineering Shaoxing University Shaoxing P. R. China College of Mechanical and Electrical Engineering Shihezi University Shihezi P. R. China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper introduces the design and implementation issues of a wheeled mobile robot capable of autonomously tracking and avoiding obstacles using multiple sensors. The main objective is to design a mobile robot that is both high-performance and relatively low-cost, and by equipping the robot with appropriate sensors, it can efficiently plan paths and achieve accurate line tracking and obstacle avoidance during movement. To achieve these goals, the structure, functional modules, hardware and software, modeling methods, and control algorithms of the robot are designed and elaborated. The completed robot is capable of trajectory tracking, obstacle avoidance, path planning, color recognition, and voice broadcasting functions. In addition, a series of performance debugging and testing methods are proposed to evaluate and adjust various robot functions, thereby enhancing its practical application. Experimental results confirm the effectiveness of the design and implementation methods described in this paper. The robot designed in this study holds potential applications in areas such as warehousing management and smart homes, and provides redundancy to incorporate additional functional modules for expanding its capabilities.

关键词： Trajectory tracking software algorithms Speech recognition Robot sensing systems software Sensors Mobile robots

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Flow measurement device based on self feedback system

Flow measurement device based on self feedback system

引用

Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xuejing Li Shanghai Institute of Measurement And Testing Technology Shanghai China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

The velocity measurement of gas-liquid two-phase flow has a wide range of applications in many industrial production and trade settlements. The complex flow state of two-phase flow makes its measurement difficult. At the same time, China's traceability of two-phase flow is also in its infancy, and there is no effective traceability system, which makes the measurement of two-phase flow velocity very difficult. This article studies the velocity measurement of gas-liquid two-phase flow by building a gas-liquid two-phase flow control system and device with PLC control as the core. The device utilizes self feedback to achieve overall control, data acquisition, and regulation functions of the system process. The diameter of the manifold is DN25, achieving two-phase convergence within a flow range of (0.5-10) m3/h. Pressure tracking is used to ensure consistent pressure during the two-phase control process.

关键词： PI control software design software algorithms Process control Water pumps Time measurement Regulation Real-time systems Velocity measurement PD control

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Research on Internet of Things Tools and the Applications Mini-Project

Research on Internet of Things Tools and the Applications Mi...

引用

Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Yilin Wang Engineering College Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

The Internet of Things (IoT) technology is changing the way we respond to fires. An innovative fire alarm system uses Raspberry Pi, flame sensors, and cameras to detect fires and sends alerts to users through the WeChat Enterprise Platform. The system can capture real-time images of the scene, measure the distance between the flame and the sensor, and classify the danger level. The system’s hardware connection and software implementation include using Python programming and KNN algorithm for data analysis. This intelligent fire alarm system can be installed in factories, homes, or offices, helping to detect fires in a timely manner and take corresponding measures, thereby reducing the risk of fires and protecting life and property safety.

关键词： Social networking (online) software algorithms Fires Alarm systems Sensor systems software Real-time systems Internet of Things Intelligent sensors Python

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Exploring Cutting-Edge Machine Learning and Data Mining Techniques for Enhancing Big Data Management with Advanced Algorithmic Strategies for Optimal Data Processing and Analysis

Exploring Cutting-Edge Machine Learning and Data Mining Tech...

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International Conference on Advances in Information Technology (ICAIT)

作者： M. Anitha V Sheeja Kumari N. Muthuvairavan Pillai P. Jesu Jayarin D. Beulah David Department of Artificial Intelligence & Data Science CARE College of Engineering Trichy Tamil Nadu India Department of Computational Intelligence Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India Department of Computer Science and O. Business Systems R.M.D Engineering College Thiruvallur Tamil Nadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai Tamil Nadu India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This work focuses on diverse machine learning methods for big data analytics, which works to leverage predictive performance. The techniques like data preprocessing, dimensionality reduction, feature selection, model selection, hyperparameter tuning, and model evaluation are a focus. A number of algorithms including Random Forests, SVM (Support Vector Machines) Gradient Boosting Machines (GBM), Neural Networks, and ensemble methods (Bagging and Boosting) have their performance evaluated. Hyperparameter tuning applies the Grid Search and Random Search algorithms while model evaluation is done by the K-fold Cross-validation. Fleiss' kappa provide differing levels of accuracy, precision, recall, and F1-score for different models and techniques. Ensemble methods actually show how they have all the necessary qualities to win and perform better than individual models. repeatedly testing improve the models' reliability and effectiveness for practical use. The study highlights the crucial significance of choices on the methods and algorithms that suit the peculiarities of the dataset and problem domain. Capitalizing on the machine learning techniques and in-depth evaluation methods, organizations can get the actionable insights from the big data that will ensure the headquarters' decision-making process and accomplish the meaningful outcomes. Future research could instead be exploring new algorithms and techniques to improve the prediction and scalability in big data analytics setting.

关键词： Support vector machines Machine learning algorithms Computational modeling Scalability software algorithms Big Data Prediction algorithms Boosting Data models Ensemble learning

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