This paper addresses optimal allocation and sizing of inverter-based distributed Generation (DG) with the aim of reducing power loss, enhancing voltage profile and preserving power quality of radial distribution netwo...
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AC/ multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of...
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ISBN:
(纸本)9798350386509;9798350386493
AC/ multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint, enabling DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the constructed AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed optimization method.
Phase retrieval is a crucial step in processing data from advanced X-ray diffraction imaging experiments to analyze the 3D structure of biological macromolecules. However, when the 3D volume is large-scale and consist...
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With the continuous advancement of large-scale models and expanding volumes of data, a single acceleration hardware is no longer sufficient to meet the training demands. Simply stacking multiple acceleration hardware ...
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The rapid evolution of the Web has revolutionized communication, enabling individuals to seek advice and share opinions on diverse subjects. However, this freedom has given rise to deceptive practices, such as manipul...
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ISBN:
(纸本)9798331539580
The rapid evolution of the Web has revolutionized communication, enabling individuals to seek advice and share opinions on diverse subjects. However, this freedom has given rise to deceptive practices, such as manipulating product or business ratings through misleading reviews. While recent years have shown significant progress in opinion-based spam detection, the practical deployment of such systems remains a challenge, especially on modern distributed and heterogeneous platforms like the Web. Data distribution plays an essential role, as there is a need to collect as much information as possible from different sources. This paper addresses this gap by exploring the design challenges of distributedsystems tailored for opinion spam detection. We evaluate three datasets, implement an accessible classification service, and test its performance on three distinct distributed system architectures. Our findings indicate the significant influence of certain features on classification performance and demonstrate the advantages of the asynchronous batch processing system over other architectures.
This paper addresses the escalating complexity of smart city environments by proposing the use of Quantum Fuzzy Inference Engine (QFIE) for enhanced control. Smart cities play a pivotal role in optimizing resource uti...
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ISBN:
(纸本)9798350319552;9798350319545
This paper addresses the escalating complexity of smart city environments by proposing the use of Quantum Fuzzy Inference Engine (QFIE) for enhanced control. Smart cities play a pivotal role in optimizing resource utilization and improving overall urban living. However, their intricate and interconnected nature demands advanced control algorithms. QFIE emerges as a promising solution due to its computational power and capability to handle uncertainty. In this research, the suitability of QFIE for the control of smart city environments is assessed for the very first time by the design and test of three different QFIE-based fuzzy rule base systems aiming to solve the problem of computing the average localization error in wireless sensor networks, the prediction of heating demands in buildings, and the control of traffic lights in a junction. In these scenarios, the experimental evaluation of QFIE shows improved control capability compared with classical algorithms.
We present two new assignments in the Peachy Parallel Assignments series of assignments for teaching parallel and distributedcomputing. Submitted assignments must have been successfully used previously and are select...
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ISBN:
(纸本)9798350364613;9798350364606
We present two new assignments in the Peachy Parallel Assignments series of assignments for teaching parallel and distributedcomputing. Submitted assignments must have been successfully used previously and are selected for being easy for other instructors to adopt and for being "cool and inspirational" so that students spend time on them and talk about them with others. The first assignment in this paper familiarizes students with the RAFT library for performing GPU-accelerated computation, pail of the RAPIDS AI ecosystem. Students use this library to accelerate a Radius Nearest Neighbor computation, finding all points within a given distance from a query point. In the second assignment, students parallelize a bird flocking simulation using OpenMP or OpenACC. It is a visual assignment which allows students to readily see the performance improvement.
One of the main challenges of edge computing is to establish efficient mechanisms for computing and offloading jobs in edge systems, such that their computation time and latency are minimized. This paper presents a si...
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ISBN:
(纸本)9798350304367;9798350304374
One of the main challenges of edge computing is to establish efficient mechanisms for computing and offloading jobs in edge systems, such that their computation time and latency are minimized. This paper presents a simulation-based study of job offloading in edge computing scenarios. Our findings indicate the importance of using optimal parameters and offloading strategies to achieve efficient computing in terms of computation times and communication overhead, even with relatively simple probabilistic job offloading schemes. Consequently, job offloading operating with a suboptimal configuration can congest the entire edge network and lead to an increased computation time. Ultimately, such suboptimal configurations can lead to results that are so inferior, that they contradict the rationale of using edge computing for latency critical applications.
Spintronic devices such as the magnetic tunnel junction show significant potential for energy-efficient neuromorphic computing applications. This paper presents a spintronic magnetic tunnel junction neuromorphic devic...
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ISBN:
(纸本)9798350383638;9798350383645
Spintronic devices such as the magnetic tunnel junction show significant potential for energy-efficient neuromorphic computing applications. This paper presents a spintronic magnetic tunnel junction neuromorphic device capable of integration, spike, and self-reset neuron characteristics. The spin-orbit drives the neuron magnetization dynamics, which controls the neuron characteristics. The input pixels are encoded in the amplitude of the current, which controls the spiking frequency of the neuron. We model the neuron characteristics into a compact model to integrate the proposed spiking neuron into a 3-layer SNN and CSNN architecture. We train and test the spiking neuron model to classify the MNIST and FMNIST datasets. The network achieves classification accuracy above 97% on MNIST and 91% on FMNIST. Considering the classification performance, self-resetting functionality, and nanosecond operation range, the proposed device shows a substantial potential for energy-efficient neuromorphic computing.
In this work, our aim is to determine the viability (feasibility) of quantum task mapping algorithms on distributed heterogeneous computingsystems. To quantify viability, we compared the quantum algorithms with nine ...
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