• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3892-3912
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• 2021

The recent rapid growth of mobile network traffic places multi-access edge computing in an important position to reduce network load and improve network capacity and service quality. Contrasting with traditional mobile cloud computing, multi-access edge computing includes a base station cooperative cache layer and user cooperative cache layer. Selecting the most appropriate cache content according to actual needs and determining the most appropriate location to optimize the cache performance have emerged as serious issues in multi-access edge computing that must be solved urgently. For this reason, a cache placement algorithm based on comprehensive utility in big data multi-access edge computing (CPBCU) is proposed in this work. Firstly, the cache value generated by cache placement is calculated using the cache capacity, data popularity, and node replacement rate. Secondly, the cache placement problem is then modeled according to the cache value, data object acquisition, and replacement cost. The cache placement model is then transformed into a combinatorial optimization problem and the cache objects are placed on the appropriate data nodes using tabu search algorithm. Finally, to verify the feasibility and effectiveness of the algorithm, a multi-access edge computing experimental environment is built. Experimental results show that CPBCU provides a significant improvement in cache service rate, data response time, and replacement number compared with other cache placement algorithms.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.76-86
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• 2023

We are on the verge of paradigm shift for the design and operation of our urban water systems from treatment- and efficiency-based to recirculation- and sustainability-based. One of the most frequently suggested alternatives to embody this paradigm shift is to decentralize the currently highly centralized urban water infrastructure. However, claims for water infrastructure decentralization are often criticized due to poor economic feasibility, unstable performance, and unprofessional operation and maintenance. The current study critically reviews the literature to discuss the technical advancement needs to overcome such challenges. Firstly, decentralized water infrastructure was briefly defined and the rationale for the proposal of its introduction to the next-generation urban water systems was laid down. The main discussion focused on the following water technologies, which require special attention when working with decentralized water infrastructure: i) material collection, storage, and transport; ii) easily scalable water treatment; iii) sensor, information, and communications; and iv) system optimization. The principles, current development status, and challenges were discussed for each of the water technologies. The discussion on the water technologies has enabled the identification of future research needs for their application to the next-generation urban water systems which will be designed following decentralized water infrastructure. This paper will significantly improve the current understanding on water infrastructure decentralization and provides insight on future direction of water technology development.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.147-147
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• 2021

A partition of water distribution network (WDN) into district metered areas (DMAs) brings the efficiency and efficacy for water network operation and management (O&M), especially in monitoring pressure and leakage. Traditionally, the DMA configurations (i.e., number, shape, and size of DMAs) are permanent and cannot be changed occasionally. This leads to changes in water quality and reduced network redundancy lowering network resilience against abnormal conditions such as water demand variability and mechanical failures. This study proposes a framework to automatically divide a WDN into dynamic DMA configurations, in which the DMA layouts can self-adapt in response to abnormal scenarios. To that aim, a complex graph theory is adopted to sectorize a WDN into multiscale DMA layouts. Then, different failure-based scenarios are investigated on the existing DMA layouts. Here, an optimization-based model is proposed to convert existing DMA layouts into dynamic layouts by considering existing valves and possibly placing new valves. The objective is to minimize the alteration of flow paths (i.e., flow direction and velocity in the pipes) while preserving the hydraulic performance of the network. The proposed method is tested on a real complex WDN for demonstration and validation of the approach.

• Journal of Wind Energy
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• v.2 no.1
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• pp.90-96
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• 2011

The purpose of this paper is to describe the control system for optimal performance of 2MW gearless PMSG wind turbine system, and to afford some techniques of the algorithm selection and design optimization of the wind turbine control system through analysis of load calculation and control characteristic. Wind turbine control system is composed of the main control system and remote control and monitoring system. The main control system is industrial PC based controller, and the remote control and monitoring system is a server based computer system. The main control system has a supervisory control of the wind turbine with operation procedures and power-speed control through the torque control by pitch angle. There are some applications to optimize the wind turbine system at the starting mode with increasing of rotor speed, and cut-in operating mode to prevent trundling cut-in and cut-out, a gain scheduling of pitch PID controller, torque scheduling and limitation of generation power by temperature limitation or remote command by remote control and monitoring system. Also, the server operation program of the remote control and monitoring system and the design of graphical display are described in this paper.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1369-1389
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• 2024

In the era of 6G, the rapid increase in communication data volume poses higher demands on traditional channel estimation techniques and those based on deep learning, especially when processing large-scale data as their computational load and real-time performance often fail to meet practical requirements. To overcome this bottleneck, this paper introduces quantum computing techniques, exploring for the first time the application of Quantum Generative Adversarial Networks (QGAN) to broadband channel estimation challenges. Although generative adversarial technology has been applied to channel estimation, obtaining instantaneous channel information remains a significant challenge. To address the issue of instantaneous channel estimation, this paper proposes an innovative QGAN with a dual-module design in the generator. The adversarial loss function and the Mean Squared Error (MSE) loss function are separately applied for the parameter updates of these two modules, facilitating the learning of statistical channel information and the generation of instantaneous channel details. Experimental results demonstrate the efficiency and accuracy of the proposed dual-module QGAN technique in channel estimation on the Pennylane quantum computing simulation platform. This research opens a new direction for physical layer techniques in wireless communication and offers expanded possibilities for the future development of wireless communication technologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1186-1207
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• 2024

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults. Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research. Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis. However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis. Models with interpretability can enhance decision-makers' trust in the output results. However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results. Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper. Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy. By incorporating interpretability constraints into the model, interpretability is effectively ensured. Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.63-67
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• 2024

Recently, artificial intelligence (AI) workloads encompassing various industries such as smart logistics, FinTech, and entertainment are being executed on the cloud. In this paper, we address the scheduling issues of various AI workloads on a multi-tenant cloud system composed of heterogeneous GPU clusters. Traditional scheduling decreases GPU utilization in such environments, degrading system performance significantly. To resolve these issues, we present a new scheduling approach utilizing genetic algorithm-based optimization techniques, implemented within a process-based event simulation framework. Trace driven simulations with diverse AI workload traces collected from Alibaba's MLaaS cluster demonstrate that the proposed scheduling improves GPU utilization compared to conventional scheduling significantly.

• Smart Structures and Systems
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• v.33 no.4
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• pp.253-262
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• 2024

Current-based mechanical fault diagnosis is more convenient and low cost since additional sensors are not required. However, it is still challenging to achieve this goal due to the weak fault information in current signals. In this paper, a dual-loss convolutional neural network (DLCNN) is proposed to implement the intelligent bearing fault diagnosis via current signals. First, a novel similarity loss (SimL) function is developed, which is expected to maximize the intra-class similarity and minimize the inter-class similarity in the model optimization operation. In the loss function, a weight parameter is further introduced to achieve a balance and leverage the performance of SimL function. Second, the DLCNN model is constructed using the presented SimL and the cross-entropy loss. Finally, the two-phase current signals are fused and then fed into the DLCNN to provide more fault information. The proposed DLCNN is tested by experiment data, and the results confirm that the DLCNN achieves higher accuracy compared to the conventional CNN. Meanwhile, the feature visualization presents that the samples of different classes are separated well.