• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.13-28
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• 2021

Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.17-23
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• 2019

IaaS is well known as a very cost effective computing service which enables required infrastructures to be rented on demand without ownership of real hardwares. It is very suitable for price sensitive services due to pay-per-use style. Operators of such services would want to adjust utilization policy quickly by estimating costs for cloud infrastructures as soon as possible. However, swift response is not possible due to that cloud service providers provide a dozen or so hours delayed billing information. Our work proposes a realtime IaaS cost estimation model based on usages monitored by virtual machine instance. We operate PC laboratory service on a public cloud during full semester to validate our suggested model. From that experiment, an averaged disparity between estimation and actual cost is less than 5.2%.

• Journal of Fashion Business
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• v.23 no.1
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• pp.37-48
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• 2019

WKSF (Warp-knitted spacer fabrics) knitted using a double Raschel machine is the three-dimensional knit that has vertically connected separate layers in loop structures. Because of its unique structure, the fabric is light, compressible and breathable. Owing to the high production speed, the use of the fabric is increasing in various areas. The purpose of this study is to establish the design process in the utilization of WKSF program and analyze the difference between WKSF and Neoprene as garment materials.. The study on the design related to WKSF has rarely been carried out because of the complexity of WKSF structure and the difficulties encountered in analyzing the structure and thread. Therefore, checking beforehand the simulation results similar to a final knit using the CAD program for WKSF can only enhance the efficiency of the design for the light knits. The conclusion drawn after designing the light knits using the CAD program and analyzing the pros and cons of WKSF through the various property evaluation techniques is as follows. The tension characteristic analysis results indicated that Neoprene specimen has the elastic transformation and resilience, thus behaving like an elastic product such as rubber. By contrast, in the event that clothing and fashion accessories are designed with WKSF, these products are kept in a boxy style fit so that the fabric can be applied flexibly to a curvy body line. In addition, WKSF is good in forming noticeably around a curvy body, because its resistance shear deformation is lower than that of Neoprene.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.91-98
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• 2022

Currently, new terms are overflowing with the development of technology from VR, AR, XR to Metaverse. Every time a term is generated in this way, society considers it a new technology and tends to use it enthusiastically, but there is confusion in correctly understanding and utilizing the category of the term. He would like to discuss the virtual studio that played an important role in the development of broadcasting CG (Computer Graphics) technology in the 1990s, and talk about the introduction of new terms in the past and how to use them. Therefore, this paper examines the gap between chaos and upright each time a term is generated based on the time when the virtual studio is introduced, and analyzes the utilization of new technology from the past through the introduction machine manufacturing case. By examining the past technological development processes expressed by remediation, this paper argues that the current situation is not a new technology but an expression of a new term, that is, a phenomenon that appears during the gradual development of technology. It is something to do.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.73-85
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• 2022

In the era of the 4th industrial revolution, where automation and connectivity are maximized with artificial intelligence, the importance of data collection and utilization for model update is increasing. In order to create a model using artificial intelligence technology, it is usually necessary to gather data in one place so that it can be updated, but this can infringe users' privacy. In this paper, we introduce federated learning, a distributed machine learning method that can update models in cooperation without directly sharing distributed stored data, and introduce a study to optimize distributed consensus among participants without an existing server. In addition, we propose a pattern and group-based distributed consensus optimization algorithm that uses an algorithm for generating patterns and groups based on the Kirkman Triple System, and performs parallel updates and communication. This algorithm guarantees more privacy than the existing distributed consensus optimization algorithm and reduces the communication time until the model converges.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.933-943
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• 2022

As the Internet develops and the utilization rate of computers increases, the threats posed by malware keep increasing. This leads to the demand for a system to automatically analyzes a large amount of malware. In this paper, an automatic malware analysis technique using a deep learning algorithm is introduced. Our proposed method uses CNN (Convolutional Neural Network) to analyze the malicious features represented as images. To reflect semantic information of malware for detection, our method uses the opcode frequency data of binary for image generation, rather than using bytes of binary. As a result of the experiments using the datasets consisting of 20,000 samples, it was found that the proposed method can detect malicious codes with 91% accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.4
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• pp.159-164
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• 2023

Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.1-7
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• 2009

This paper presents a process routing (PR) algorithm with multiple objectives. PR determines the optimum sequence of operations for transforming a raw material into a completed part within the available machining resources. In any computer aided process planning (CAPP) system, selection of the machining operation sequence is one of the most critical activities for manufacturing a part and for the technical specification in the part drawing. Here, the goal could be to generate the sequence that optimizes production time, production cost, machine utilization or with multiple these criteria. The Pareto Stratum Niche Cubicle (PS NC) GA has been adopted to find the optimum sequence of operations that optimize two conflicting criteria; production cost and production quality. The numerical analysis shows that the proposed PS NC GA is both effective and efficient to the PR problem.

• Journal of the Korean Geotechnical Society
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• v.22 no.7
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• pp.13-21
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• 2006

When enforced earth is used for the retain wall and four walls, the most important thing would be how to maximize the land utilization. Accordingly, in case of enforced earth, we pile up the minimal height of earth ($20{\sim}50\;cm$) and harden the earth using a static dynamic hardening machine. In this paper, we tried to analyze and compare the stress transformation characteristics of reinforced weathered granite soil with geosynthetics when repetitive load is added to the enforced earth structure and when static load is added. The result is that the cohesion component of the strength increased greatly and the friction component decreased slightly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.326-327
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• 2021

Newly, big data utilization has been widely interested in a wide variety of industrial fields. Big data analysis is the process of discovering meaningful new correlations, patterns, and trends in large volumes of data stored in data stores and creating new value. Thus, most big data analysis technology methods include data mining, machine learning, natural language processing, and pattern recognition used in existing statistical computer science. Also, using the R language, a big data tool, we can express analysis results through various visualization functions using pre-processing text data. The data used in this study were analyzed for 29 papers in a specific journal. In the final analysis results, the most frequently mentioned keyword was "Research", which ranked first 743 times. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.