Výsledky vyhledávání - "Xiaorui Shao"

Akademický článek

GAILS: an effective multi-object job shop scheduler based on genetic algorithm and iterative local search

Autor: Xiaorui Shao, Fuladi Shubhendu Kshitij, Chang Soo Kim

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)

Abstract The job shop scheduling problem (JSSP) is critical for building one smart factory regarding resource management, effective production, and intelligent supply. However, it is still very challenging due to the complex production environment. B

Externí odkaz: https://doaj.org/article/5ff6953306ae4ceebd6c6a91244f3e50

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Akademický článek

An Adaptive Job Shop Scheduler Using Multilevel Convolutional Neural Network and Iterative Local Search

Autor: Xiaorui Shao, Chang Soo Kim

Publikováno v: IEEE Access, Vol 10, Pp 88079-88092 (2022)

This manuscript proposed an effective hybrid method based on multi-level convolutional neural network (ML-CNN) and iterative local search (ILS) to solve job shop scheduling problems (JSSP) with small scale training samples and less time. In the propo

Externí odkaz: https://doaj.org/article/93a44596d0ea4467bb2090f80fb2c3ff

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Akademický článek

Highly Accurate Short-Term Gas Consumption and Elapsed Time Forecasting Using Multi-Channel Deep Neural Network

Autor: Yeonjee Choi, Xiaorui Shao, Hyun Suk Hwang

Publikováno v: IEEE Access, Vol 9, Pp 157447-157457 (2021)

Accurate gas consumption and elapsed time forecasting can help decision-makers detect anomaly gas usage and notify users to recognize the facility fault in real-time. However, it is challenging due to its variable and complex factors. This paper prop

Externí odkaz: https://doaj.org/article/2245f4490d44432493268d92ba287031

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Akademický článek

Multi-Step Short-Term Power Consumption Forecasting Using Multi-Channel LSTM With Time Location Considering Customer Behavior

Autor: Xiaorui Shao, Chang Soo Kim

Publikováno v: IEEE Access, Vol 8, Pp 125263-125273 (2020)

Short-Term Load Forecasting (STLF) is one critical assignment regarding the power supply and demand in the smart grid. Multi-step STLF provides strong evidence for decision-making to achieve consistent, quick supply and reduce direct or indirect cost

Externí odkaz: https://doaj.org/article/8c54eef622e44e629a9a1d31429d4b75

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Akademický článek

Domain Fusion CNN-LSTM for Short-Term Power Consumption Forecasting

Autor: Xiaorui Shao, Chen Pu, Yuxin Zhang, Chang Soo Kim

Publikováno v: IEEE Access, Vol 8, Pp 188352-188362 (2020)

Short-term power consumption forecasting plays a critical role in the process of building the smart grid. However, it is very challenging as the power consumption series has strong randomness and volatility. In this paper, the authors proposed a nove

Externí odkaz: https://doaj.org/article/61ebc75eb18743aa8b4b474608355e68

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Akademický článek

A Novel and Effective University Course Scheduler Using Adaptive Parallel Tabu Search and Simulated Annealing.

Autor: Xiaorui Shao, Su Yeon Lee, Chang Soo Kim

Publikováno v: KSII Transactions on Internet & Information Systems; Apr2024, Vol. 18 Issue 4, p843-859, 17p

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Akademický článek

Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing

Autor: Xiaorui Shao, Chang-Soo Kim

Publikováno v: Sensors, Vol 22, Iss 11, p 4156 (2022)

Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance. However, most of the

Externí odkaz: https://doaj.org/article/c5917216b8e44cb4af525b700c07b517

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