• Journal of Information Processing Systems
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• v.20 no.2
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• pp.226-238
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• 2024

Recently, multi-access edge computing (MEC) has emerged as a promising technology to alleviate the computing burden of vehicular terminals and efficiently facilitate vehicular applications. The vehicle can improve the quality of experience of applications by offloading their tasks to MEC servers. However, channel conditions are time-varying due to channel interference among vehicles, and path loss is time-varying due to the mobility of vehicles. The task arrival of vehicles is also stochastic. Therefore, it is difficult to determine an optimal offloading with resource allocation decision in the dynamic MEC system because offloading is affected by wireless data transmission. In this paper, we study computation offloading with resource allocation in the dynamic MEC system. The objective is to minimize power consumption and maximize throughput while meeting the delay constraints of tasks. Therefore, it allocates resources for local execution and transmission power for offloading. We define the problem as a Markov decision process, and propose an offloading method using deep reinforcement learning named deep deterministic policy gradient. Simulation shows that, compared with existing methods, the proposed method outperforms in terms of throughput and satisfaction of delay constraints.

• KNOM Review
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• v.21 no.2
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• pp.1-9
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• 2018

Network virtualization refers to a technology creating independent virtual network environment on a physical network. Network virtualization technology can share the physical network resources to reduce the cost of establishing the network for each user and enables the network administrator to dynamically change the network configuration according to the purpose. Although the network management can be handled dynamically, the management is manual, and it does not maximize the profit of network virtualization. In this paper, we propose Machine-Learning technology to allow the network to learn by itself and manage its management dynamically. The proposed approach is to dynamically allocate appropriate resources by predicting resource demand of VNF in service function chaining, which is a core and essential problem in virtual network management. Our goal is to predict the resource demand of the VNF and dynamically allocate the appropriate resources to reduce the cost of network operation while preventing service interruption.

• Journal of Korean Library and Information Science Society
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• v.34 no.1
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• pp.379-397
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• 2003

The purpose of this study is to suggest to the reference librarians In the university that how they should perform their roles under the internet environment. For this purpose, first, the environmental factors that the roles of the reference librarians should be changed are examined. Second, such two aspects of the future university library as virtual library and learning resource center are Presupposed. The seven role models for reference librarians based on these are as follows. 1) Contents Analyst, 2) Contents Manager, 3) Learning Resource Supplier, 4) Information Mediators, 5) Virtual Lecture Manager, 6) Instructor for Information Literacy, 7) Reference Resource Developer.

• Knowledge Management Research
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• v.11 no.4
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• pp.41-66
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• 2010

This research examines the effect of the relatedness and the gap between Resources and mechanisms on effectiveness of inter-organizational knowledge transfer. According to the literature, there has been a competing theory between two claims; one is that inter-organizational knowledge transfer will be more effective due to the reduction of the transaction cost as the relatedness increases. And the other is that the mutual complementarity of different organizational characteristics will increase synergy. In total, the relatedness and the gap of the Resource and mechanism makes the inverted U-shaped relationship with the inter-organizational knowledge transfer. As the result of empirical analysis about 109 Korean-based Joint Ventures entered country, it shows that the relatedness of parent company's production Resources, learning mechanisms, and coordination mechanisms made the inverted U-shaped relations with the inter-organizational knowledge transfer and the gap of production Resources and adjustment mechanism formed the same relationship. However, the U-shaped relationship has been established in the relatedness of market Resources, but the gap of market Resources and the learning mechanism was not statistically significant. Through this study, I can draw a best conclusion that the inter-organizational knowledge transfer will be more effective when the relatedness and the gap of management resources and mechanisms is in optimal level. However, when it comes to market Resources, it can be inferred that the result could be the opposite because the partner country's market environment would be different.

• Journal of Industrial Technology
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• v.44 no.1
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• pp.43-49
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• 2024

Smart manufacturing systems play a pivotal role in Industry 4.0, facilitating critical tasks within the manufacturing environment. This study proposes a process-based modeling and simulation (PBM&S) method to support the implementation of such systems. The PBM&S method integrates resource information, functions, and process flows of entities to enable performance analysis through simulation. The PBM&S method consists of four main steps: (1) creating virtual unit models based on resource information; (2) developing encapsulated models by representing entity characteristics and process flows as modules; (3) identifying interrelationships between models and creating process-based models; and (4) performing performance analysis of the generated models using a simulation engine. A case study was conducted to evaluate the PBM&S method within the context of a shipbuilding production line. In conclusion, PBM&S can be integrated with advanced technologies such as cyber-physical systems (CPS), machine learning, and artificial intelligence, contributing significantly to the development of the manufacturing industry.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.10
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• pp.2376-2394
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• 2013

In order to ensure both of the whole system capacity and users QoS requirements in heterogeneous wireless networks, admission control mechanism should be well designed. In this paper, Multi-agent Q-learning based Admission Control Mechanism (MQACM) is proposed to handle new and handoff call access problems appropriately. MQACM obtains the optimal decision policy by using an improved form of single-agent Q-learning method, Multi-agent Q-learning (MQ) method. MQ method is creatively introduced to solve the admission control problem in heterogeneous wireless networks in this paper. In addition, different priorities are allocated to multiple services aiming to make MQACM perform even well in congested network scenarios. It can be observed from both analysis and simulation results that our proposed method not only outperforms existing schemes with enhanced call blocking probability and handoff dropping probability performance, but also has better network universality and stability than other schemes.

• International Journal of Contents
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• v.14 no.2
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• pp.1-6
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• 2018

In a wireless mobile communication system, a pilot signal has been considered to be a necessary signal for estimating a changing channel between a base station and a terminal. All mobile communication systems developed so far have a specification for transmitting pilot signals. However, although the pilot signal transmission is easy to estimate the channel,(Ed: unclear wording: it is easy to use the pilot signal transmission to estimate the channel?) it should be minimized because it uses radio resources for data transmission. In this paper, we propose a pilotless channel estimation scheme (PCE) by introducing the clustering method of unsupervised learning used in our deep learning into channel estimation.(Ed: highlight- unclear) The PCE estimates the channel using only the data symbols without using the pilot signal at all. Also, to apply PCE to a real system, we evaluated the performance of PCE based on the resource block (RB), which is a resource allocation unit used in LTE. According to the results of this study, the PCE always provides a better mean square error (MSE) performance than the least square estimator using pilots, although it does not use the pilot signal at all. The MSE performance of the PCE is affected by the number of data symbols used and the frequency selectivity of the channel. In this paper, we provide simulation results considering various effects(Ed: unclear, clarify).

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.5
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• pp.243-250
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• 2024

This study explores enhancing facial recognition performance in low-light environments using deep learning-based low-light enhancement techniques. Facial recognition technology is widely used in edge devices like smartphones, smart home devices, and security systems, but low-light conditions reduce accuracy due to degraded image quality and increased noise. We reviewed the latest techniques, including Zero-DCE, Zero-DCE++, and SCI (Self-Calibrated Illumination), and applied them as preprocessing steps in facial recognition on edge devices. Using the K-face dataset, experiments on the Qualcomm QRB5165 platform showed significant improvements in F1 SCORE from 0.57 to 0.833 with SCI. Processing times were 0.15ms for SCI, 0.4ms for Zero-DCE, and 0.7ms for Zero-DCE++, all much shorter than the facial recognition model MobileFaceNet's 5ms. These results indicate that these techniques can be effectively used in resource-limited edge devices, enhancing facial recognition in low-light conditions for various applications.