• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.4
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• pp.531-540
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• 2019

Existing dozer productivity computation models use different input variables, formulas, productivity correction factors, and experimental data source. This paper presents a method that characterizes the productivity outputs obtained by the PLS model and the Caterpillar model that are accepted as industry standards. The method identifies the input variables to be collected from the site, the performance charts to be referenced, and the formulas and implements them in a single computational tool. This study verifies that the PLS model may replace the manual computational process of Caterpillar model by eliminating reliance on graphics manipulation. Replacing the Caterpillar model with the PLS model and implementing the process as a function contributes to assess the productivity of a dozer timely by encouraging to utilize real-time information collected directly from the site. This study allows researchers and practitioners to effectively deal with the values of productivity correction factors collected from the job site and to control the productivity. The practicality and effectiveness of the method have been validated by applying to a project case.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.111-116
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• 2019

Recently, GPU expands application domains from graphic processing to various kinds of parallel workloads. However, current GPU systems focus on the maximization of each workload's parallelism through simplified control rather than considering various workload characteristics. This paper classifies the resource usage characteristics of GPU workloads into computing-bound, memory-bound, and dependency-latency-bound, and quantifies the fine-grained bottleneck for efficient workload allocation. For example, we identify the exact bottleneck resources such as single function unit, double function unit, or special function unit even for the same computing-bound workloads. Our analysis implies that workloads can be allocated together if fine-grained bottleneck resources are different even for the same computing-bound workloads, which can eventually contribute to efficient workload allocation in GPU.

• Journal of Digital Convergence
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• v.17 no.5
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• pp.217-223
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• 2019

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.3
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• pp.31-42
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• 2005

We consider a network storage model whose administrator can not be fully trusted. In this model, we assume that all data stored are encrypted for data confidentiality and one owner distributes the decryption key for each time period to users. In this paper, we propose three privilege management schemes. In the first scheme, called Temporal Privilege Management (TPM), we use a symmetric encryption based on one-way function chains for key encapsulation. In the second scheme, called Asymmetric Temporal Privilege Management (ATPM), anyone can encrypt the data using the public key of owner, but only privileged users can decrypt the encrypted data. Finally, we present a scheme to restrict writers' privilege using ID-based signatures in ATPM. In our schemes, the privilege managements are based on the time and the addition of users is efficient. Specially, applying TPM and ATPM, we can solve the back-issue problem.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.16-22
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• 2020

The characteristics of the LCL filter for grid-connected inverters have been discussed in academia and industry. An online simulation tool was applied to compare and analyze the difference between the LCL filter and L filter. LCL filters were modeled and simulated using a range of professional simulation simulators, and the LCL filters were found to have good filtering effects for high-frequency harmonics. First, this paper summarizes the transfer functions of the LCL filter and provides the Bode plot diagram. The accuracy and validity of the filter attenuation characteristics were confirmed by a fast Fourier transform based on off-line simulation tools, such as PSIM and MATLAB, depending on the given parameters of the LCL filter. Finally, the Typhoon HIL402 real-time simulation was performed for hardware in the loop simulation to verify the actual filtering characteristics of the LCL filter.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.6
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• pp.347-353
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• 2021

In this study, we presented a novel size optimization framework to control the linear buckling temperature and several buckling modes of plates, by optimizing thickness values of composite structures for practical engineering applications. Predicting the buckling temperature and mode shape of structures is a vital research topic in engineering to achieve structural stability. However, optimizing designs of engineering structures through engineering intuition is challenging. To address this limitation, we proposed a method that combines finite element simulation and size optimization. Based on the idea that the structural buckling temperature and mode shape of a plate are affected by the thickness of the structure, the thickness values of the nodes of the target structure were set as the design variables in this optimization method; and the buckling temperature values, and buckling mode shapes were set as the objective functions. This size optimization method enabled the determination of optimal thickness distributions, to induce the desired buckling temperature values and mode shapes. The validity of the proposed method was verified in terms of their buckling temperature values and buckling mode shapes, using several numerical examples of rectangular composite structures.

• Journal of Industrial Convergence
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• v.20 no.11
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• pp.57-63
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• 2022

If ECU data, which is responsible for collecting and processing data such as sensors and signals of automobiles, is manipulated by an attack, it can cause damage to the driver. In this paper, we propose a system that verifies the integrity of automotive ECU data using blockchain. Since the car and the server encrypt data using the session key to transmit and receive data, reliability is ensured in the communication process. The server verifies the integrity of the transmitted data using a hash function, and if there is no problem in the data, it is stored in the blockchain and off-chain distributed storage. The ECU data hash value is stored in the blockchain and cannot be tampered with, and the original ECU data is stored in a distributed storage. Using the verification system, users can verify attacks and tampering with ECU data, and malicious users can access ECU data and perform integrity verification when data is tampered with. It can be used according to the user's needs in situations such as insurance, car repair, trading and sales. For future research, it is necessary to establish an efficient system for real-time data integrity verification.

• Annual Conference of KIPS
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• 2010.04a
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• pp.210-213
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• 2010

최근 사용자의 멀티미디어에 대한 요구의 증가가 VOD (Video-on-Demand) 서비스를 발전시키게 되었다. VOD는 엔터테인먼트나 원격 교육, 광고 및 정보 등 많은 분야에서 사용되고 있다. 이러한 VOD 서비스는 많은 디스크 I/O와 네트워크 I/O를 요구하며 기존 웹 서버 시스템과 비교했을 때 오랜 시간동안 서비스를 해야 하는 특징을 가지고 있다. 또한 VOD 서비스는 많은 네트워크와 디스크의 대역폭을 요구하며, 서비스의 QoS에 민감해서 사용자 응답시간이 길어지면 사용자 요청의 취소율이 높아지게 된다. 따라서 불만족스러운 서비스의 증가로 네트워크 부하만 증가하게 된다. 이러한 기존 웹 서버 환경과는 다른 부하의 패턴이 있는 VOD 서비스 환경에서는 부하를 균형적으로 분배하여 서비스의 QoS를 높이는 것이 매우 중요하다. 본 논문에서는 분산 VOD 시스템 환경에서 부하를 효율적으로 분산하기 위해 계층형 분산 VOD 시스템 모델과 사용자 요청 패턴의 히스토리와 유전 알고리즘을 기반으로 한 스케줄러를 제안한다. 본 논문에서 제안한 계층형 분산 VOD 시스템 모델은 서버들을 지역적으로 분산하고 제어 서버를 지역마다 설치하여 지역에 있는 VOD 서버들을 관리하도록 구성한다. 사용자 요청을 지역 서버군 내에서 분산시키기 위해서 히스토리를 기반으로 한 유전 알고리즘을 사용한다. 이러한 히스토리 정보를 기반으로 유전 알고리즘의 적합도 함수에 적용하여 VOD 시스템을 위한 유전 알고리즘과 유전 연산을 구현한다. 본 논문에서 제안한 부하 분산 알고리즘은 VOD 서비스 환경에서 사용자 요구에 대한 부하를 보다 정확하게 예측하여 부하를 분산할 수 있다. 본 논문에서 제안한 계층형 분산 VOD 시스템의 부하 분산 알고리즘의 성능을 테스트하기 위해 OPNET 기반 시뮬레이터를 구현한다. 라운드로빈(round-robin) 방식과 랜덤(random) 방식과의 비교 실험을 통해 본 논문에서 제안한 부하 분산 알고리즘의 성능을 평가한다. 비교 실험을 통해 본 논문에서 제안한 알고리즘이 보다 안정적인 QoS를 제공하는 것을 보여준다.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.77-87
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• 2023

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.33-39
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• 2023

Recently, reinforcement learning has been used to improve the communication performance of flying ad-hoc networks (FANETs) and to design mobility models. Mobility model is a key factor for predicting and controlling the movement of unmmaned aerial vehicle (UAVs). In this paper, we designed and analyzed the performance of Q-learning with fourier basis function approximation and Deep-Q Network (DQN) models for optimal path finding in a three-dimensional virtual environment where UAVs operate. The experimental results show that the DQN model is more suitable for optimal path finding than the Q-learning model in a three-dimensional virtual environment.