4,151 research outputs found

    Cooperative Active Learning based Dual Control for Exploration and Exploitation in Autonomous Search

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    In this paper, a multi-estimator based computationally efficient algorithm is developed for autonomous search in an unknown environment with an unknown source. Different from the existing approaches that require massive computational power to support nonlinear Bayesian estimation and complex decision-making process, an efficient cooperative active learning based dual control for exploration and exploitation (COAL-DCEE) is developed for source estimation and path planning. Multiple cooperative estimators are deployed for environment learning process, which is helpful to improving the search performance and robustness against noisy measurements. The number of estimators used in COAL-DCEE is much smaller than that of particles required for Bayesian estimation in information-theoretic approaches. Consequently, the computational load is significantly reduced. As an important feature of this study, the convergence and performance of COAL-DCEE are established in relation to the characteristics of sensor noises and turbulence disturbances. Numerical and experimental studies have been carried out to verify the effectiveness of the proposed framework. Compared with existing approaches, COAL-DCEE not only provides convergence guarantee, but also yields comparable search performance using much less computational power

    1. Helgoland Power and Energy Conference - 24. Dresdener Kreis 2023

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    Der Sammelband "1. Helgoland Power and Energy Conference" beinhaltet neben einem kurzen Bericht zum 24. Treffen des Dresdener Kreises 2023 wissenschaftliche Beiträge von Doktoranden der beteiligten Hochschulinstitute zum Thema Elektroenergieversorgung. Der Dresdener Kreis setzt sich aus der Professur für Elektroenergieversorgung der Technischen Universität Dresden, dem Fachgebiet Elektrische Anlagen und Netze der Universität Duisburg-Essen, dem Fachgebiet Elektrische Energieversorgung der Leibniz Universität Hannover und dem Lehrstuhl Elektrische Netze und Erneuerbare Energie der Otto-von-Guericke Universität Magdeburg zusammen und trifft sich einmal im Jahr zum fachlichen Austausch an einer der beteiligten Universitäten

    Design and experimental implementation of voltage control scheme using the coefficient diagram method based PID controller for two-level boost converter with photovoltaic system

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    Introduction. Currently, in the solar energy systems and a variety of electrical applications, the power converters are essential part. The main challenge for similar systems is controller design. In the literature, the PID controller has proved its effectiveness in many industrial applications, but determining its parameters remains one of the challenges in control theory field. The novelty of the work resides in the design and experimental implementation of a two-level boost DC-DC converter controlled by a PID controller for photovoltaic (PV) maximum power extraction. Purpose. Analysis and control of the two-level boost topology with renewable energy source and design of the PID controller parameters using simple and accurate method. Methods. PID coefficients are optimized using Coefficient Diagram Method (CDM) in the MATLAB environment. Results. A mathematical model of a two-level boost converter with PID controller and PV energy source was developed and analyzed. The model allows to design the controller parameters of the proposed system. Practical value. A prototype steered by the proposed CDM-PID controller was tested using an Arduino embedded board. A comparison between the simulation results and the experimental one is presented. The obtained results illustrate that the experimental results match the simulation closely, and the proposed CDM-PID controller provides a fast and precise results.Вступ. В даний час перетворювачі потужності є невід’ємною частиною сонячних енергетичних систем та різних електричних пристроїв. Основною проблемою для таких систем є проектування контролера. У літературі ПІД-регулятор довів свою ефективність у багатьох промислових застосуваннях, але визначення його параметрів залишається однією з проблем у галузі теорії управління. Новизна роботи полягає у розробці та експериментальній реалізації дворівневого підвищувального перетворювача постійного струму, керованого ПІД-регулятором, для отримання максимальної потужності фотоелектричних пристроїв. Мета. Аналіз та управління дворівневою топологією підвищення з використанням відновлюваного джерела енергії та розрахунок параметрів ПІД-регулятора простим та точним методом. Методи. Коефіцієнти ПІД оптимізуються за допомогою методу діаграми коефіцієнтів (CDM) у середовищі MATLAB. Отримані результати. Розроблено та проаналізовано математичну модель дворівневого підвищувального перетворювача з ПІД-регулятором та фотоелектричним джерелом енергії. Модель дозволяє спроєктувати параметри контролера пропонованої системи. Практична цінність. Прототип, керований пропонованим контролером CDM-PID, протестували з використанням вбудованої плати Arduino. Наведено порівняння результатів моделювання з експериментальними даними. Отримані результати показують, що експериментальні результати близько відповідають моделюванню, а пропонований CDM-ПІД-регулятор забезпечує швидкі та точні результати

    Utilisation of Deep Learning (DL) and Neural Networks (NN) Algorithms for Energy Power Generation: A Social Network and Bibliometric Analysis (2004-2022)

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    The research landscape on the applications of advanced computational tools (ACTs) such as machine/deep learning and neural network algorithms for energy and power generation (EPG) was critically examined through publication trends and bibliometrics data analysis. The Elsevier Scopus database and the PRISMA methodology were employed to identify and screen the published documents, whereas the bibliometric analysis software VOSviewer was used to analyse the co-authorships, citations, and keyword occurrences. The results showed that 152 documents have been published on the topic comprising conference proceedings (58.6%) and articles (41.4%) between 2004 and 2022. Publication trends analysis revealed the number of publications increased from 1 to 31 or by 3,000% over the same period, which was ascribed to the growing scientific interest and research impact of the topic. Stakeholder analysis revealed the top authors/researchers are Anvari M, Ghaderi SF and Saberi M, whereas the most prolific affiliation and nations actively engaged in the topic are the North China Electric Power University, and China, respectively. Conversely, the top funding agency actively backing research on the topic is the National Natural Science Foundation of China (NSFC). Co-authorship analysis revealed high levels of collaboration between researching nations compared to authors and affiliations. Hotspot analysis revealed three major thematic focus areas namely; Energy Grid Forecasting, Power Generation Control, and Intelligent Energy Optimization. In conclusion, the study showed that the application of ACTs in EPG is an active, multidisciplinary, and impact area of research with potential for more impactful contributions to research and society at large

    Synthesis of the neuro-fuzzy regulator with genetic algorithm

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    Real-acting objects are characterized by the presence of various types of random perturbations, which significantly reduce the quality of the control process, which determines the use of modern methods of intellectual technology to solve the problem of synthesis of control systems of structurally complex dynamic objects, allowing to compensate the influence of external factors with the properties of randomness and partial uncertainty. The article considers issues of synthesis of the automatic control system of dynamic objects by applying the theory of intelligent control. In this case, a neural network based on radial-basis functions is used at each discrete interval for neuro-fuzzy approximation of the control system, allowing real-time adjustment of the regulator parameters. The radial basis function is designed to approximate functions defined in the implicit form of pattern sets. The neuro-fuzzy regulator's parameter configuration is accomplished using a genetic algorithm, enabling more efficient computation to determine the regulator's set parameters. The regulator's parameters are represented as a vector, facilitating their application to multidimensional objects. To determine the optimal tuning parameters of the neuro-fuzzy regulator, characterized by high convergence and the possibility of determining global extrema, a genetic algorithm was used. The effectiveness of the neuro-fuzzy regulator is explained by the possibility of providing quality control of the dynamic object under random perturbations and uncertainty of input data

    DTL-IDS: An optimized Intrusion Detection Framework using Deep Transfer Learning and Genetic Algorithm

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    In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks

    Implementing PID Control on Arduino Uno for Air Temperature Optimization

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    This research investigates the precise regulation of liquid filling in tanks, specifically focusing on water storage systems. It employs the Proportional-Integral-Derivative (PID) control method in conjunction with an HC-SR04 ultrasonic sensor and an Arduino Uno microcontroller. Given the paramount importance of water as a resource, accurate management of its storage is of utmost significance. The PID control method, known for its rapid responsiveness, minimal overshoot, and robust stability, effectively facilitates this task. Integrating the ultrasonic sensor and microcontroller further augments the precision of water level regulation. The article expounds upon the foundational principles of the PID control method and elucidates its application in the context of liquid tank filling. It offers a comprehensive insight into the hardware configuration, encompassing pivotal components such as the Arduino Uno microcontroller, HC-SR04 ultrasonic sensor, and the L298 driver responsible for water pump control. The experimental approach is meticulous, presenting results from tests involving the Proportional Controller, Proportional Integral (PI) Controller, and Proportional Integral Derivative (PID) Controller. These tests rigorously analyze the impact of varying Proportional Gain (Kp), Integral Gain (Ki), and Derivative Gain (Kd) parameters on crucial performance metrics such as response time, overshoot, and steady-state error. The findings underscore the critical importance of an optimal parameter configuration, emphasizing the delicate equilibrium between response speed, precision, and error minimization. This research significantly advances PID control implementation in liquid tank filling, offering insights that pave the way for developing more efficient liquid management systems across various sectors. The identified optimal parameter configuration is Kp = 5.0, Ki = 0.3, and Kd = 0.2

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
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