2,530 research outputs found

    Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

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    Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A non-linear SVR-based cascade model for improving prediction accuracy of biomedical data analysis

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    Biomedical data analysis is essential in current diagnosis, treatment, and patient condition monitoring. The large volumes of data that characterize this area require simple but accurate and fast methods of intellectual analysis to improve the level of medical services. Existing machine learning (ML) methods require many resources (time, memory, energy) when processing large datasets. Or they demonstrate a level of accuracy that is insufficient for solving a specific application task. In this paper, we developed a new ensemble model of increased accuracy for solving approximation problems of large biomedical data sets. The model is based on cascading of the ML methods and response surface linearization principles. In addition, we used Ito decomposition as a means of nonlinearly expanding the inputs at each level of the model. As weak learners, Support Vector Regression (SVR) with linear kernel was used due to many significant advantages demonstrated by this method among the existing ones. The training and application procedures of the developed SVR-based cascade model are described, and a flow chart of its implementation is presented. The modeling was carried out on a real-world tabular set of biomedical data of a large volume. The task of predicting the heart rate of individuals was solved, which provides the possibility of determining the level of human stress, and is an essential indicator in various applied fields. The optimal parameters of the SVR-based cascade model operating were selected experimentally. The authors shown that the developed model provides more than 20 times higher accuracy (according to Mean Squared Error (MSE)), as well as a significant reduction in the duration of the training procedure compared to the existing method, which provided the highest accuracy of work among those considered

    Optimal speed trajectory and energy management control for connected and automated vehicles

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    Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations

    When Deep Learning Meets Polyhedral Theory: A Survey

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    In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure \unicode{x2014}such as the typical fully-connected feedforward neural network\unicode{x2014} amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks

    Adaptation pathways to reconcile hydropower generation and aquatic ecosystems restoration

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    The growing demands for water, food and energy, in addition to the need to protect ecosystems, pose significant challenges to water management and the operation of water systems. In hydropower-dominated basins, where reservoirs capture flow variability for energy generation, the modification of the natural flow regime disrupts the natural equilibrium of aquatic ecosystems. Migratory fish species and the associated ecosystem services are particularly vulnerable as the migration and recruitment success relies on the synchronization between the hydrologic flow regime and the reproductive cycle. While there is a consensus on the importance of restoring impacted ecosystems in balance with multiple uses, the current water governance framework lacks a comprehensive understanding of the tradeoffs involved and mechanisms for ensuring the equitable distribution of the adaptation costs among users. The present study brings a contribution to the field by proposing solutions to improve the water governance of river basins, combining the (1) identification of flow-ecological relationships by measuring the response of multiple options of flow regime restoration with a clear ecosystem indicator, (2) incorporation of the flow-ecological relationships and hydroclimatic conditions into the operation decisions of hydropower systems to create dynamic environmental flow solutions (termed Dynamic Adaptive Environmental flows – DAE-flows) with better long-term performance, (3) calculation of the reoperation trade-offs between alternative levels of environmental flow regime restoration and (4) development of mechanisms to share the adaptation costs among stakeholders. The electricity market is proposed as an institutional arrangement and financing mechanism to support the restoration of flow regimes in environmentally sensitive areas. The Upper Paraná River Basin, in Brazil, where consecutive hydropower impoundments have reduced the original floodplain along the last decades, is a recurrent example where reservoirs’ operation need to be reconciled with ecosystem functionality, which makes the basin an important study area. The findings of this dissertation indicate that it is possible to enhance the capacity of water systems to incorporate historically suppressed environmental water demands without imposing a hard constraint to economic uses. The consideration of the long-term effects of operation when designing operating strategies for multiple users leads to improved performance in both hydropower generation and meeting ecosystem demands. So, during severe droughts the water can still be reallocated to hydropower (as it is currently done) but at a lesser cost to the environment.As demandas crescentes por água, alimentos e energia, além da necessidade de proteger os ecossistemas, tornam a gestão dos recursos hídricos, bem como a operação de sistemas hídricos, uma tarefa desafiadora. Em bacias com aproveitamento hidrelétrico, a modificação do regime de vazões decorrente da operação dos reservatórios altera o equilíbrio natural dos ecossistemas aquáticos. Espécies migratórias de peixes e serviços ecossistêmicos associados ficam particularmente vulneráveis, uma vez que o sucesso da migração e recrutamento depende da sincronização entre o regime de vazão e o ciclo reprodutivo. Embora haja consenso sobre a importância de restaurar as demandas ecossistêmicas suprimidas e alcançar um equilíbrio que permita múltiplos usos, o atual quadro de governança carece de uma compreensão abrangente dos trade-offs envolvidos e dos mecanismos para garantir a distribuição equitativa dos custos de adaptação entre os usuários. O presente estudo contribui para o campo, propondo soluções para aprimorar a governança de bacias antropizadas, combinando (1) a identificação das relações vazão-ecológicas por meio da quantificação da resposta de múltiplas opções de restauração do regime de vazão por meio de um indicador de desempenho do ecossistema, (2) a incorporação dessas relações vazão-ecológicas juntamente com condições hidroclimáticas nas decisões operacionais de sistemas hidrelétricos (denominadas Vazões Ambientais Dinâmicas e Adaptativas - DAE-flows) para criar soluções dinâmicas de operação de reservatórios, (3) o cálculo dos trade-offs de reoperação de múltiplos níveis de restauração de regime de vazão ambiental e (4) o desenvolvimento de mecanismos para compartilhar os custos relacionados entre as partes interessadas. Nesse sentido, o mercado de eletricidade é proposto como arranjo institucional e mecanismo de financiamento para apoiar a restauração de regimes de vazão em áreas ambientalmente sensíveis. A Bacia Hidrográfica do Alto Paraná, Brasil, caracterizada como uma das mais represadas da América do Sul, com 65 usinas hidrelétricas integradas ao Sistema Integrado Nacional, é um exemplo recorrente da necessidade de reconciliação entre a geração de energia e a conservação de serviços ecossistêmicos, sendo utilizada como área de estudo. Os resultados indicam que podemos aumentar a capacidade dos sistemas hídricos para incorporar demandas ambientais historicamente suprimidas sem impor uma restrição rígida aos usos econômicos. Ao considerar os efeitos de longo prazo da operação ao projetar estratégias de operação para múltiplos usuários, obtemos um desempenho aprimorado tanto na geração de energia hidrelétrica quanto no atendimento às demandas do ecossistema. Assim, durante períodos de seca severa, a água ainda pode ser realocada para a produção de energia hidrelétrica (como é feito atualmente), porém com menor impacto ambiental

    Mathematical Problems in Rock Mechanics and Rock Engineering

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    With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports
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