• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.165-166
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• 2020

Internationally, manufacturers attempted respectable portion of in-house logistics to satisfy end users and decrease manpower to compete for manufacturing price and quality optimization. Mostly, manufacturers operate variety of facilities such as collaborative robots, conveyor, etc. based on PLC. To achieve it, manufactures shall operate the optimized number of manufacturing processes with logic controlled by computer to reduce human errors. In prior to it, manufacturing industry still own plenty of fields which have not yet been adjusted with automation. For example, we shall put in-house logistics on the issue. This study focuses on manufacturing industry, evaluate efficiency, costs, etc. in all aspects and suggest alternatives by analysis SWAT and OEE, let alone reason of weakness.

• Journal of KIISE:Information Networking
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• v.34 no.6
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• pp.514-524
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• 2007

Recent advances in the speed of multi-rate wireless local area networks (WLANs) and the proliferation of WLAN devices have made rate adaptive, opportunistic scheduling critical for throughput optimization. As WLAN traffic evolves to be more symmetric due to the emerging new applications such as VoWLAN, collaborative download, and peer-to-peer file sharing, opportunistic scheduling at the downlink becomes insufficient for optimized utilization of the single shared wireless channel. However, opportunistic scheduling on the uplink of a WLAN is challenging because wireless channel condition is dynamic and asymmetric. Each transmitting client has to probe the access point to maintain the updated channel conditions at the access point. Moreover, the scheduling decisions must be coordinated at all clients for consistency. This paper presents JUDS, a joint uplink/downlink opportunistic scheduling for WLANs. Through synergistic integration of both the uplink and the downlink scheduling, JUDS maximizes channel diversity at significantly reduced scheduling overhead. It also enforces fair channel sharing between the downlink and uplink traffic. Through extensive QualNet simulations, we show that JUDS improves the overall throughput by up to 127% and achieves close-to-perfect fairness between uplink and downlink traffic.