• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.387-388
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• 2020

As the utilization of microservice architectural style in diverse applications are increasing, the microservice deployment cost became a concern for many companies. We propose an approach to reduce the deployment cost by generating an algorithm which minimizes the cost of basic operation of a physical machine and the cost of resources assigned to a physical machine. This algorithm will produce optimal resource allocation and deployment location based on genetic algorithm process.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• IE interfaces
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• v.12 no.4
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• pp.617-624
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• 1999

In order to maximize an availability of machine and utilization of space, the parallel machines scheduling problem with space limit is frequently discussed in the industrial field. In this paper, we consider a scheduling problem for assembly machine in ship engine assembly shop. This paper considers the parallel machine scheduling problem in which n jobs having different release times, due dates and space limits are to be scheduled on m parallel machines. The objective function is to minimize the sum of earliness and tardiness. To solve this problem, a heuristic is developed. The proposed heuristic is divided into three modules hierarchically: job selection, machine selection and job sequencing, solution improvement. To illustrate its effectiveness, a proposed heuristic is evaluated with a large number of randomly generated test problems based on the field situation. Through the computational experiment, we determine the job selection rule that is suitable to the problem situation considered in this paper and show the effectiveness of our heuristic.

• Proceedings of the KIEE Conference
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• 1998.07f
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• pp.1919-1921
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• 1998

In this paper, a control method for field weakening region of induction machine drive which is based on indirect field oriented control was implemented. Also, application method of direct field oriented control in the field weakening region using maximum torque control methods which is adaptable for parking facilities was studied. The implemented method which is based on direct field oriented control method ensures the full utilization of the output torque capability of the machine over the conventional 1/${\omega}_r$ method. And machine drive system can obtain the robustness to motor parameter variation.

• Journal of Power Electronics
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• v.11 no.6
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• pp.846-855
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• 2011

This paper proposes a control mode switching scheme between vector control and constant V/f control for induction machine (IM) drives for maximum torque utilization in a higher speed region. For the constant V/f scheme, a smooth transition method from the linear range of PWM up to the six-step mode is applied, by which the machine flux and torque can be kept constant in a high-speed range. Also, a careful consideration of the initial phase angle of the voltage in the transient state of the control mode change between the vector control and V/f schemes is described. The validity of the proposed strategy is verified by the experiment result for a 3-kW induction motor drives.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.2
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• pp.99-114
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• 1988

The objective of this study is mainly related to design aspects of Multi-aisle S/R machine system (MASS) which can substantially reduce high initial investment cost of Automated Storage/Retrieval System. Firstly, the average travel time of the S/R machine is determined under single and dual commands, from which the average performance of S/R machine is evaluated. Secondly, a design model is developed and the system parameters, such as length and height of the system, and the number of S/R machines, traversers and aisles are determined which provide minimum initial investment and operating costs. Also, through experiments, sensitivity analysis is made for the throughput and storage volume.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1233-1235
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• 2016

It is desirable to place virtual machines for minimizing the number of operational servers in order to save energy in high-density cloud computing environment. However, the compacted servers can incur heat islands. This paper firstly finds out the relationship between the server utilization by the virtual machine placement and the energy consumption of servers and heat from servers. Then, this paper proposes a virtual machine placement algorithm to save energy consumed and avoid heat islands.

• Journal of Integrative Natural Science
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• v.14 no.2
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• pp.50-56
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• 2021

Due to the recent increase in the mobile streaming market, mobile traffic is increasing exponentially. IMT-2020, named as the next generation mobile communication standard by ITU, is called the 5th generation mobile communication (5G), and is a technology that satisfies the data traffic capacity, low latency, high energy efficiency, and economic efficiency compared to the existing LTE (Long Term Evolution) system. 5G implements this technology by utilizing a high frequency band, but there is a problem of path loss due to the use of a high frequency band, which is greatly affected by system performance. In this paper, small cell technology was presented as a solution to the high frequency utilization of 5G mobile communication system, and furthermore, the system performance was improved by applying machine learning technology to macro communication and small cell communication method decision. It was found that the system performance was improved due to the technical application and the application of machine learning techniques.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.268-281
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• 2022

The development of information technology is bringing many changes to everyday life, and machine learning can be used as a technique to solve a wide range of real-world problems. Analysis and utilization of data are essential processes in applying machine learning to real-world problems. As a method of processing data in machine learning, we propose an approach based on applying multiple linear regression models by interlacing data to the task of classifying similar software. Linear regression is widely used in estimation problems to model the relationship between input and output data. In our approach, multiple linear regression models are generated by training on interlaced feature data. A combination of these multiple models is then used as the prediction model for classifying similar software. Experiments are performed to evaluate the proposed approach as compared to conventional linear regression, and the experimental results show that the proposed method classifies similar software more accurately than the conventional model. We anticipate the proposed approach to be applied to various kinds of classification problems to improve the accuracy of conventional linear regression.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.