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With the integration of power electronic devices represented by soft open points (SOPs), distribution networks have gradually evolved into interconnected flexible distribution networks (FDNs). Considering the deregulation of electricity market and user privacy, multiple stakeholders have participated in the operation of FDNs. Peer-to-peer (P2P) electricity trading is promising to alleviate operational problems of interconnected FDNs. As multiple regions pursue the maximum profits individually, non-cooperative game methods can be utilized to realize fair profit allocation in P2P trading. In this paper, a non-cooperative game-based P2P trading method is proposed to meet the electricity trading needs of multi-region interconnected FDNs. First, based on non-cooperative games, a two-layer P2P electricity trading framework is established to realize cost reduction and voltage profile improvement of multi-region interconnected FDNs. Then, a P2P trading adjustment mechanism is designed to improve the operational profits of SOP, in which spatial active power trading adjustment, temporal dispatching of energy storage (ES) link and reactive power support are incorporated. Finally, the effectiveness of the proposed method is verified based on a practical distribution network with four-terminal SOP in Tianjin. The results show that the proposed P2P electricity trading method can promote the economic operation performance of interconnected FDNs and improve the operational profit of SOP., Export Date: 12 October 2022; Article; CODEN: IEPSD; Correspondence Address: Ji, H.; Key Laboratory of Smart Grid of Ministry of Education, China; email: jihaoran@tju.edu.cn

This paper investigates solving convex composite optimization on an undirected network, where each node, privately endowed with a smooth component function and a nonsmooth one, is required to minimize the sum of all the component functions throughout the network. To address such a problem, a general Approximate Method of Multipliers (AMM) is developed, which attempts to approximate the Method of Multipliers by virtue of a surrogate function with numerous options. We then design the possibly nonseparable, time-varying surrogate function in various ways, leading to different distributed realizations of AMM. We demonstrate that AMM generalizes more than ten state-of-the-art distributed optimization algorithms, and certain specific designs of its surrogate function result in a variety of new algorithms to the literature. Furthermore, we show that AMM is able to achieve an $O(1/k)$ rate of convergence to optimality, and the convergence rate becomes linear when the problem is locally restricted strongly convex and smooth. Such convergence rates provide new or stronger convergence results to many prior methods that can be viewed as specializations of AMM., QC 20230508

The tremendous complexity of modern distribution systems calls for alternative coordination architectures, supported by smart, self-adaptable, and, to a large degree, environment-agnostic algorithms. In this article, we discuss decentralized and distributed coordination architectures for the operation of active distribution grids aiming at effectively coping with their complexity. We present relevant methods and algorithms under the framework of multiagent systems (MASs) and decentralized decision-making associated with handling different parts of the optimal grid operation. The decision-making models are based on distributed optimization algorithms using consensus/gossip models, bioinspired algorithms from the field of population dynamics, and a method for decomposing the power-flow model. The developed techniques aim at matching production with demand in microgrids, settling the short-term energy imbalances at the distribution grid level, mitigating voltage deviations, and resolving distribution grid congestions in real-time operation. The algorithms are implemented as MAS-based software platforms, able to aggregate diverse DG units and flexible loads. Results are provided from the theoretical simulation-based models and demonstrations of the operational techniques in actual pilot sites. The applied implementations have been performed in a smart grid pilot site, for which MAS platforms have been developed and tested., QC 20230412

In this article, we consider distributed online convex optimization problems, where the distributed system consists of various computing units connected through a time-varying communication graph. In each time step, each computing unit selects a constrained vector, experiences a loss equal to an arbitrary convex function evaluated at this vector, and may communicate to its neighbors in the graph. The objective is to minimize the system-wide loss accumulated over time. We propose a decentralized algorithm with regret and cumulative constraint violation in O(T c,1-c ) and O(T1-c/2), respectively, for any cin (0,1), where T is the time horizon. When the loss functions are strongly convex, we establish improved regret and constraint violation upper bounds in O( (T)) and O(T (T)). These regret scalings match those obtained by state-of-the-art algorithms and fundamental limits in the corresponding centralized online optimization problem (for both convex and strongly convex loss functions). In the case of bandit feedback, the proposed algorithms achieve a regret and constraint violation in O(T c,1-c/3 ) and O(T1-c/2) for any cin (0,1). We numerically illustrate the performance of our algorithms for the particular case of distributed online regularized linear regression problems on synthetic and real data., QC 20220323

This book constitutes the proceedings of the 7th International Workshop on Accelerator Programming Using Directives, WACCPD 2020, which took place on November 20, 2021. The workshop was initially planned to take place in Atlanta, GA, USA, and changed to an online format due to the COVID-19 pandemic. WACCPD is one of the major forums for bringing together users, developers, and the software and tools community to share knowledge and experiences when programming emerging complex parallel computing systems. The 5 papers presented in this volume were carefully reviewed and selected from 7 submissions. They were organized in topical sections named: OpenMP; OpenACC; and Domain-specific Solvers.

This paper focuses on Distributed State Estimation over a peer-to-peer sensor network composed by possible low-computational sensors. We propose a new ℓ-step Neighbourhood Distributed Moving Horizon Estimation technique with fused arrival cost and pre-estimation, improving the accuracy of the estimation, while reducing the computation time compared to other approaches from the literature. Simultaneously, convergence of the estimation error is improved by means of spreading the information amongst neighbourhoods, which comes natural in the sliding window data present in the Moving Horizon Estimation paradigm.

This paper focuses on Distributed State Estimation over a peer-to-peer sensor network composed by possible low-computational sensors. We propose a new ℓ-step Neighbourhood Distributed Moving Horizon Estimation technique with fused arrival cost and pre-estimation, improving the accuracy of the estimation, while reducing the computation time compared to other approaches from the literature. Simultaneously, convergence of the estimation error is improved by means of spreading the information amongst neighbourhoods, which comes natural in the sliding window data present in the Moving Horizon Estimation paradigm.

This paper focuses on Distributed State Estimation over a peer-to-peer sensor network composed by possible low-computational sensors. We propose a new ℓ-step Neighbourhood Distributed Moving Horizon Estimation technique with fused arrival cost and pre-estimation, improving the accuracy of the estimation, while reducing the computation time compared to other approaches from the literature. Simultaneously, convergence of the estimation error is improved by means of spreading the information amongst neighbourhoods, which comes natural in the sliding window data present in the Moving Horizon Estimation paradigm.

Exposing students to international experiences is becoming a common practice to prepare students with global capabilities. One pedagogical activity used to promote global competencies is the participation of students in multinational design projects. This is a problem-based learning approach in an international setting, where students get immersed in the solution of anengineering design task while they work in teams and collaborate with international partners. The main goal of these projects is to foster international collaboration and to offer an opportunity to the students to develop professional skills through international teamwork effort in the solution of a design problem. However, a real challenge of this practice has been to create an effective interaction among the students participating in this type of projects and to maintain the flow of information, and student engagement in the project and in their learning. The main objective of this work is to investigate the sociotechnical interaction of engineering students working in a multinational collaborative project and the role of information technology tools and the nature of the interaction in this experience. For this purpose an assessment tool was developed and used to determine the interaction among the students (frequency, quantity and quality), the value of social interaction in the flow of the interaction, and the impact of the interaction in the development of the project. The main findings of this instrument are presented in this work. © 2015 TEMPUS Publications.

This article presents the first results of using an Augmented Reality (AR) tool, designed to support tutoring sessions in multi-variate calculus. The tool is used either in a face-to-face setting in which the instructor and the students are collocated or in a distance setting, in which the instructor and students are physically in remote places. The tool was used with two groups of students of Differential Calculus. The students had not been exposed to the concept of equations involving 3 variables and the corresponding surfaces in space. The experience explored how students generalized 2D graphics and equations with their 3D surfaces counterparts with the help of the tool.

Visualization of vector fields plays an important role in research activities nowadays. Increasing web applications allow a fast, multi-platform and multi-device access to data. As a result, web applications must be optimized in order to be performed heterogeneously as well as on high-performance as on low capacity devices. This paper presents a hardware-accelerated scheme for integration-based flow visualization techniques, based on a hierarchical integration procedure which reduces the computational effort of the algorithm from linear to logarithmic, compared to serial integration methodologies. The contribution relies on the fact that the optimization is only implemented using the graphics application programming interface (API), instead of requiring additional APIs or plug-ins. This is achieved by using images as data storing elements instead of graphical information matrices. A case study in oceanic currents is implemented.

The advent of the Internet of Things (IoT) paradigm highlighted the importance of core technologies at the root of any distributed systems, as wireless communications, cloud computing and big data analysis. Today, companies and organizations, pursuing the so-called Industry 4.0 (r)evolution, are moving toward smart factories, smart workforce, and intelligent transportation. The use of IoT technologies for connecting smart vehicles is identified by the IoV acronym, i.e. the Internet of Vehicle. In this work, the authors address the characterization of a distributed measurement platform for vehicle fleet management, focusing on time-related performance. In particular, a commercially available solution has been considered and real-world use cases have been identified, showing obtainable performance: with default configuration, an average overall delay less than 11 s has been estimated. © 2021 IEEE., Export Date: 20 September 2021; Conference Paper; CODEN: CRIIE

Дипломна робота: 66 c., 6 табл., 13 рис., 1 додаток, 37 джерел. Необхідність використання систем моніторингу безпеки в комп'ютерних системах і мережах (КСМ) визначається тим, що застосування звичайних засобів і механізмів захисту інформації виявляється недостатнім, тому що вони виконують лише базові функції і не дозволяють контролювати безпеку функціонування інформаційних систем в умовах постійних модифікацій вторгнень, оновлення апаратного та програмного забезпечення КСМ. Попередній аналіз, оперативний контроль і реалізація механізмів реагування на вторгнення в КСМ дозволяють не тільки виявити атаки на комп'ютерні системи, але також сформувати комплекс заходів щодо попередження, локалізації і усунення наслідків несанкціонованих дій в КСМ. Проте, існуючі системи моніторингу безпеки (СМБ) не здатні проводити комплексну оцінку дій зловмисників, будувати послідовність реалізованих вразливостей, визначати кінцеву ціль дій зловмисників і оцінювати ризики реалізації загроз безпеки інформаційних ресурсів, що може призвести до повторних успішних атак на комп'ютерні системи і мережі, а також до фінансових втрат. Для комплексного вирішення даних завдань необхідно збільшити об'єм даних, які обробляються системою моніторингу безпеки, що спричиняє уповільнення реагування на атаки на КСМ. Таким чином, актуальним є створення нових методів, а також модифікованих засобів моніторингу безпеки, що забезпечують високу ймовірність коректного виявлення і своєчасне попередження атак зловмисників. Об'єктом досліджень є процеси аналізу дій легальних користувачів корпоративних мереж в рамках вирішення задач моніторингу безпеки. Предметом досліджень є методи і засоби аналізу ризиків реалізації загроз безпеки інформаційних ресурсів і засоби протидії зловмисникам, що забезпечують підвищення ефективності систем моніторингу безпеки в КСМ. Методи дослідження. В дипломній роботі використовується математичний апарат теорії графів для досліджень і оцінки вразливостей КСМ; лінійної алгебри, теорії ліні, Thesis: 66 p., 6 tabl., 13 fig., 1 appendix, 37 sources. The need to use security monitoring systems in computer systems and networks (CSM) is determined by the fact that the use of conventional means and mechanisms of information protection is insufficient, because they perform only basic functions and do not control the security of information systems in constant modifications of intrusions, update of CSM hardware and software. Preliminary analysis, operational control and implementation of response mechanisms to the intrusion into the CSM allow not only to detect attacks on computer systems, but also to form a set of measures to prevent, localize and eliminate the consequences of unauthorized actions in the CSM. However, existing security monitoring systems (SMS) are not able to comprehensively assess the actions of attackers, build a sequence of implemented vulnerabilities, determine the ultimate goal of attackers and assess the risks of security threats to information resources, which can lead to repeated successful attacks on computer systems and networks , as well as financial losses. To comprehensively address these challenges, it is necessary to increase the amount of data processed by the security monitoring system, which causes a slowdown in response to attacks on KSM. Thus, it is important to create new methods, as well as modified security monitoring tools that provide a high probability of correct detection and timely prevention of attacks by attackers. The object of research is the processes of analysis of actions of legal users of corporate networks in the framework of solving security monitoring problems. The subject of research is the methods and means of risk analysis of the implementation of security threats to information resources and means of counteracting intruders, which increase the effectiveness of security monitoring systems in the CSM. Research methods. The dissertation uses the mathematical apparatus of graph theory for research and ass

This book constitutes the refereed post-conference proceedings of 10 workshops held at the 35th International ISC High Performance 2020 Conference, in Frankfurt, Germany, in June 2020: First Workshop on Compiler-assisted Correctness Checking and Performance Optimization for HPC (C3PO); First International Workshop on the Application of Machine Learning Techniques to Computational Fluid Dynamics Simulations and Analysis (CFDML); HPC I/O in the Data Center Workshop (HPC-IODC); First Workshop \Machine Learning on HPC Systems" (MLHPCS); First International Workshop on Monitoring and Data Analytics (MODA); 15th Workshop on Virtualization in High-Performance Cloud Computing (VHPC). The 25 full papers included in this volume were carefully reviewed and selected. They cover all aspects of research, development, and application of large-scale, high performance experimental and commercial systems. Topics include high-performance computing (HPC), computer architecture and hardware, programming models, system software, performance analysis and modeling, compiler analysis and optimization techniques, software sustainability, scientific applications, deep learning.

This book constitutes the refereed proceedings of the 35th International Conference on High Performance Computing, ISC High Performance 2020, held in Frankfurt/Main, Germany, in June 2020.* The 27 revised full papers presented were carefully reviewed and selected from 87 submissions. The papers cover a broad range of topics such as architectures, networks & infrastructure; artificial intelligence and machine learning; data, storage & visualization; emerging technologies; HPC algorithms; HPC applications; performance modeling & measurement; programming models & systems software. *The conference was held virtually due to the COVID-19 pandemic.

Структура i обсяг роботи: магістерська дисертація викладена на 128 cторiнкаx, складається зі вступу, 3 розділів, висновку, містить 20 рисунків, 4 таблиці, 13 формул, список використаних джерел із 33 найменувань на 5 cторiнкаx. Актуальність роботи. Станом на сьогодні спостерігається поява все більшої кількості комп’ютерних систем які мають гетерогенну структуру, засновану на використанні центрального процесора та акселераторів з відмінною від нього архітектурою. В той же час, для достатнього рівня швидкодії складних програмних комплексів стає не достатньо застосування лише паралельних систем, стандартною практикою для високонавантажених обчислень стає їх розгортка в розподілених комп’ютерних системах, для яких паралельні системи є лише складовими елементами. Для всіх розподілених систем одним з ключових факторів, що визначають ефективність роботи системи, є вміле балансування навантаження на всіх рівнях цієї системи. Вирішенню цієї NP-повної задачі наразі присвячено цілий розділ комп’ютерних наук, але вона досі лишається актуальною проблемою, оскільки абсолютно ідеального рішення за прийнятний час в ній знайти наразі не можливо. А в випадку, коли кінцеві вузли розподіленої системи являють собою гетерогенні комп’ютерні системи, то задача ускладняється ще більше, оскільки необхідно провести балансування обчислень між різнорідними елементами із зазвичай значно відмінними між собою характеристиками продуктивності та комунікабельності. Наразі існує великий набір стандартних методів балансування навантаження в розподілених системах, але більшість із них орієнтовані на конкретний тип задач, лише невелика кількість не прив’язані до конкретних задач. сучасні засоби організації паралельних обчислень в розподілених комп’ютерних системах з акселераторами приймають низький рівень абстракції, що веде до рядку складнощів та обмежень. Вбудовані в них методи балансування навантаження орієнтовані на гомогенні і здебільшого повнозв’язні системи. Мета роботи: підвищення ефективності про, Structure and scope of master's thesis: Master's thesis is presented on 128 pages, consists of introduction, 3 sections, conclusion, contains 20 drawings, 4 tables, 13 formulas, a bibliography of 33 titles on 5 pages. Relevance of work. Today, an increasing number of computer systems have a heterogeneous structure based on the use of central processing unit and accelerators with a different architecture. At the same time, it is not enough to use only parallel systems for a sufficient level of performance of complex software systems; it is standard practice for high-load computations to deploy them in distributed computer systems, in which parallel systems are only constituent elements. For all distributed systems, one of the key characteristics of the system performance is the ability to balance workloads at all levels of the system. A whole section of computer science is currently devoted to solving NP-complete problem, but it still remains an actual problem, since it is not possible to find the perfect solution in an acceptable time for now. And if the end nodes of a distributed system are heterogeneous computer systems, the task becomes even more complicated as it is necessary to balance the computations between heterogeneous elements with typically significantly different performance and communication characteristics. Currently, there is a large set of standard load balancing methods in distributed systems, but most of them are focused on specific types of tasks, and only a small number are not tied to them. Modern parallel computing tools in distributed computer systems with accelerators accept a low level of abstraction, leading to a number of difficulties and limitations. The load balancing techniques built into them are oriented on homogeneous and fully connected systems. Purpose of work: increasing the efficiency of parallel computing in distributed heterogeneous computer systems by optimally distributing and balancing the load on its elements. Research tas