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We consider a task graph mapped on a set of homogeneous processors. We aim at minimizing the energy consumption while enforcing two constraints: a prescribed bound on the execution time (or makespan), and a reliability threshold. Dynamic…
Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high. An effective strategy for reducing such consumption is supply-voltage reduction, but if done too…
The paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes that have a terminal station at a common hub, that allows a portion of (or all) the fleet to be…
In this paper, an operating system scheduling algorithm based on Double DQN (Double Deep Q network) is proposed, and its performance under different task types and system loads is verified by experiments. Compared with the traditional…
Dynamic voltage and frequency scaling (DVFS) and task-to-core allocation are critical for thermal management and balancing energy and performance in embedded systems. Existing approaches either rely on utilization-based heuristics that…
Deep learning-based fault detection methods have achieved significant success. In visual fault detection of freight trains, there exists a large characteristic difference between inter-class components (scale variance) but intra-class on…
Battery Energy Storage Systems (BESS) are essential for peak shaving, balancing power supply and demand while enhancing grid efficiency. This study proposes a cycle-based control strategy for charging and discharging, which optimizes…
This paper introduces an Electric Vehicle Charging Station (EVCS) model that incorporates real-world constraints, such as slot power limitations, contract threshold overruns penalties, or early disconnections of electric vehicles (EVs). We…
Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit…
In this paper, we propose a distributed voltage control in power distribution networks through reactive power compensation. The proposed control can (i) operate in a distributed fashion where each bus makes its decision based on local…
In this paper, a hierarchical distributed method consisting of two iterative procedures is proposed for optimal electric vehicle charging scheduling (EVCS) in the distribution grids. In the proposed method, the distribution system operator…
Semidefinite relaxation techniques have shown great promise for nonconvex optimal power flow problems. However, a number of independent numerical experiments have led to concerns about scalability and robustness of existing SDP solvers. To…
Voltage control plays an important role in the operation of electricity distribution networks, especially with high penetration of distributed energy resources. These resources introduce significant and fast varying uncertainties. In this…
Today, most embedded systems use Dynamic Voltage and Frequency Scaling (DVFS) to minimize energy consumption and maximize performance. The DVFS technique works by regulating the important parameters that govern the amount of energy consumed…
Bus routes are typically updated every 3-5 years to meet constantly changing travel demands. However, identifying deficient bus routes and finding their optimal replacements remain challenging due to the difficulties in analyzing a complex…
In this paper, we propose a two-timescale delay-optimal dynamic clustering and power allocation design for downlink network MIMO systems. The dynamic clustering control is adaptive to the global queue state information (GQSI) only and…
There is widespread interest in emerging technologies, especially resistive crossbars for accelerating Deep Neural Networks (DNNs). Resistive crossbars offer a highly-parallel and efficient matrix-vector-multiplication (MVM) operation. MVM…
The increasing computing demands of autonomous driving applications make energy optimizations critical for reducing battery capacity and vehicle weight. Current energy optimization methods typically target traditional real-time systems with…
The rapidly transforming electric power distribution system due to the integration of distributed energy resources(DERs) specifically roof-top photovoltaic (PV) generation may lead to significant operational challenges. In this paper, we…
The exponential emergence of Field Programmable Gate Array (FPGA) has accelerated the research of hardware implementation of Deep Neural Network (DNN). Among all DNN processors, domain specific architectures, such as, Google's Tensor…