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Akademický článek

Runtime Tracking-Based Replication of On-Chip Embedded Software Using Transfer Function Learning for Dust Particle Sensing Systems

Autor: Seungmin Lee, Jisu Kwon, Daejin Park

Publikováno v: IEEE Access, Vol 11, Pp 32167-32175 (2023)

A digital twin is a widely used method that uses digitized simulations of the real-world characteristics because it is effective in predicting results at a low cost. In digital twin analysis, the transfer function between the input and output data is

Externí odkaz: https://doaj.org/article/d779b1f0a9da45c49117adb7d2b0be97

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Akademický článek

Efficient Sensor Processing Technique Using Kalman Filter-Based Velocity Prediction in Large-Scale Vehicle IoT Application

Autor: Jisu Kwon, Daejin Park

Publikováno v: IEEE Access, Vol 10, Pp 116735-116746 (2022)

Sensor nodes that operate as edge devices in Internet-of-Things (IoT) networks have various limitations, such as insufficient power supply and small memory size. Therefore, the sensor node must be able to use resources efficiently to achieve the spec

Externí odkaz: https://doaj.org/article/a814b944153146f993ebfcf37acd03ee

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Akademický článek

Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems

Autor: Jisu Kwon, Joonho Kong, Arslan Munir

Publikováno v: IET Computers & Digital Techniques, Vol 16, Iss 1, Pp 29-43 (2022)

Abstract Many recent research efforts have exploited data sparsity for the acceleration of convolutional neural network (CNN) inferences. However, the effects of data transfer between main memory and the CNN accelerator have been largely overlooked.

Externí odkaz: https://doaj.org/article/ace7e778e7cf4e7296bc44054dbbaf99

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Akademický článek

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Autor: Seungmin Lee, Jisu Kwon, Daejin Park

Publikováno v: Sensors, Vol 23, Iss 12, p 5557 (2023)

As the application fields for digital twins have expanded, various studies have been conducted with the objective of optimizing the costs. Among these studies, research on low-power and low-performance embedded devices has been implemented at a low c

Externí odkaz: https://doaj.org/article/ee81e391da3b4c318aeae95b497ea2d6

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Akademický článek

FogSurv: A Fog-Assisted Architecture for Urban Surveillance Using Artificial Intelligence and Data Fusion

Autor: Arslan Munir, Jisu Kwon, Jong Hun Lee, Joonho Kong, Erik Blasch, Alexander J. Aved, Khan Muhammad

Publikováno v: IEEE Access, Vol 9, Pp 111938-111959 (2021)

Urban surveillance, of which airborne urban surveillance is a vital constituent, provides situational awareness (SA) and timely response to emergencies. The significance and scope of urban surveillance has increased manyfold in recent years due to th

Externí odkaz: https://doaj.org/article/cba321746e70404aad2079cf59443609

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Akademický článek

Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices

Autor: Jisu Kwon, Daejin Park

Publikováno v: Applied Sciences, Vol 11, Iss 22, p 11073 (2021)

On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing. Although most edge devices have limited resources, time and energy costs are

Externí odkaz: https://doaj.org/article/937ae8a4f6e948c0a545db9d1d08abf5

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Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Autor: Park, Seungmin Lee, Jisu Kwon, Daejin

Publikováno v: Sensors; Volume 23; Issue 12; Pages: 5557

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=multidiscipl::ea61bf62bbf5d3530f3a17dbec245498

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Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems

Autor: Arslan Munir, Joonho Kong, Jisu Kwon

Publikováno v: IET Computers & Digital Techniques, Vol 16, Iss 1, Pp 29-43 (2022)

Many recent research efforts have exploited data sparsity for the acceleration of convolutional neural network (CNN) inferences. However, the effects of data transfer between main memory and the CNN accelerator have been largely overlooked. In this w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eabfbebb791ca681d710570006f22018
https://doaj.org/article/ace7e778e7cf4e7296bc44054dbbaf99

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