Plug-in electric vehicles (EVs) are widely recognized as being highly flexible electric loads that can be pooled and controlled via aggregators to provide low-cost energy and ancillary services to wholesale electricit...
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Plug-in electric vehicles (EVs) are widely recognized as being highly flexible electric loads that can be pooled and controlled via aggregators to provide low-cost energy and ancillary services to wholesale electricity markets. To participate in these markets, an aggregator must encode the aggregate flexibility of the population of EVs under their command as a single polytope that is compliant with existing market rules. To this end, we investigate the problem of characterizing the aggregate flexibility set of a heterogeneous population of EVs whose individual flexibility sets are given as convex polytopes in half-space representation. As the exact computation of the aggregate flexibility set—the Minkowski sum of the individual flexibility sets—is known to be intractable, we study the problem of computing maximum-volume inner approximations to the aggregate flexibility set by optimizing over affine transformations of a given convex polytope in half-space representation. We show how to conservatively approximate these set containment problems as linear programs that scale polynomially with the number and dimension of the individual flexibility sets. The inner approximation methods provided in this paper generalize and improve upon existing methods from the literature. We illustrate the improvement in approximation accuracy and performance achievable by our methods with numerical experiments. IEEE
The accuracy and timeliness tradeoff prevents Digital Twins (DTs) from realizing their full potential. High accuracy is crucial for decision-making, and timeliness is equally essential for responsiveness. Therefore, t...
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The study presented a novel chipless RFID sensor designed for the detection of cracks in non-metallic materials. The sensor utilized square coplanar split-ring resonators (SRR) and was mounted on the material under te...
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The recent huge advance of Large Language Models (LLMs) is mainly driven by the increase in the number of parameters. This has led to substantial memory capacity requirements, necessitating the use of dozens of GPUs j...
The recent huge advance of Large Language Models (LLMs) is mainly driven by the increase in the number of parameters. This has led to substantial memory capacity requirements, necessitating the use of dozens of GPUs just to meet the capacity. One popular solution to this is storage-offloaded training, which uses host memory and storage as an extended memory hierarchy. However, this obviously comes at the cost of storage bandwidth bottleneck because storage devices have orders of magnitude lower bandwidth compared to that of GPU device memories. Our work, Smart-Infinity, addresses the storage bandwidth bottleneck of storage-offloaded LLM training using near-storage processing devices on a real system. The main component of Smart-Infinity is SmartUpdate, which performs parameter updates on custom near-storage accelerators. We identify that moving parameter updates to the storage side removes most of the storage traffic. In addition, we propose an efficient data transfer handler structure to address the system integration issues for Smart-Infinity. The handler allows overlapping data transfers with fixed memory consumption by reusing the device buffer. Lastly, we propose accelerator-assisted gradient compression/decompression to enhance the scalability of Smart-Infinity. When scaling to multiple near-storage processing devices, the write traffic on the shared channel becomes the bottleneck. To alleviate this, we compress the gradients on the GPU and decompress them on the accelerators. It provides further acceleration from reduced traffic. As a result, Smart-Infinity achieves a significant speedup compared to the baseline. Notably, SmartInfinity is a ready-to-use approach that is fully integrated into PyTorch on a real system. The implementation of Smart-Infinity is available at https://***/AIS-SNU/smart-infinity.
This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsist...
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Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schem...
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The paper presents an innovative approach for predicting breast cancer by employing fine-needle aspiration (FNA), the synthetic minority oversampling method (SMOTE), and a Cubic Support Vector Machine (c-SVM) to gener...
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Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nonde...
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Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.
This article reports two important mixed-signal building blocks using unipolar oxide thin-film transistors (TFTs) on a 30- μ m -thick polymide substrate, which find potential application in communication systems. The...
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This paper investigates the effects of proton radiation on the electrical and optical properties of InGaN light-emitting diodes (LEDs). InGaN LEDs are known for their high brightness and efficiency, making them useful...
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