• 한국멀티미디어학회논문지
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• 제19권6호
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• pp.1074-1088
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• 2016

Successful Visual Story Telling(VST) of desire is a crucial key for the success of animation because desire is the leading power of story development of animation. An analysis of the desire of VST using the top 5 successful American feature film animations is carried out. Totally, 147 desire shots are extracted by using the proposed Objective Selection of Desire Shots(OSDS) method based on the theory of Makee's conflict and desire pursuing modeling, Maslow's 20 desire types, Greimas's actant model, and the 17 narrative process classification. In addition to them, the 5 Beat(5B) model of a scene is proposed. Five image specialists have evaluated VST of the selected 147 desire shots. For each shot, the desire type among the 20 desires and the strength are obtained. Among them, the top 3 desires(affiliation, achievement, and nurturance) appearing 51.8% are analyzed. The composition elements of shots affecting the desire type and the strength have found. These can be used for better VST of preproduction and production of animation.

• 한국산학기술학회논문지
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• 제13권6호
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• pp.2441-2451
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• 2012

본 논문은 자동차 윈드실드에 생기는 성에, 얼음, 눈 또는 잔해를 제거하는 워셔액 가열시스템의 성능 개선을 위한 새로운 제어 시스템을 제안한다. 먼저, 워셔액 가열시스템의 모델링 과정을 설명하고 실험결과를 이용하여 모델의 특성파라미터를 추출하고 워셔액 가열시스템 성능에 영향을 미치는 설계변수를 선정한다. 두 번째로 워셔액 가열시스템 가열 성능을 향상시키기 위하여 제어 시스템을 제안하였고 실험을 통하여 검증하였다. 제안된 제어시스템의 특징은 가열 성능을 충족하기 위하여 정의된 목표 값까지 WFHS에 인가되는 부스터 컨버터의 입력 전류를 조절하는 것이다. 목표 전류는 초기온도 조건과 함께 유도된 수학적 모델식을 이용하여 계산하였다. 컴퓨터 시뮬레이션과 실제 실험결과는 제안된 제어시스템이 WFHS의 기 설정한 목표성능을 만족시키면서 가열 동작을 수행 할 수 있다는 것을 보여준다.

• 벤처창업연구
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• 제9권2호
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• pp.23-33
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• 2014

본 연구는 코스닥상장 선도 기술벤처기업들을 대상으로 비즈니스모델의 실행방법을 조사 분석하여 기술벤처 창업기업에 주는 시사점을 도출하는 것이 목적이다. 본 연구는 창업기업의 핵심 성과요인 중에 하나가 창업자의 비즈니스 모델 창출과 실행능력의 확보이고, 코스닥에 상장한 선도 기술벤처기업들의 지속성장 성과 핵심도 비즈니스 모델의 혁신에 두어지는 만큼 양자의 공통요소를 상호 연결하여 창업기업들이 선도 기업들로부터 효과적인 비즈니스 모델 실행방안에 대해 시사점을 확보할 수 있게 했다는 점과 더 나아가, 창업기업 입장에서 비즈니스 모델 실행에 있어 자신이 벤치마킹할 수 있는 롤(Role) 모델을 구체적으로 확보할 수 있다는 점에서 의미가 있다. 선도 기술벤처기업의 비즈니스 모델 실행 연구결과, 본 연구는 크게 세 가지의 시사점을 도출하였다. 첫째, 비즈니스 모델은 단순히 가치창출 구조나 논리 차원을 넘어 CEO 중심으로 한 기업의 사업실행방법론이었다. 둘째, 비즈니스 모델은 CEO주도로 기업의 내부외적인 사업역량과 자산들을 효과적으로 통합하게 하는 입체적인 과정(Process) 이었다. 셋째, 비즈니스 모델은 기업의 재무적 성과를 전제로 한 전략적 접근의 툴(Tool) 이라기보다는 목표고객의 식별과 가치의 창출 및 전달과정의 실행과정으로부터 재무적 결과는 자연스럽게 얻어지는 산물이다. 넷째, 선도 기술벤처기업 CEO들은 비즈니스모델을 활용에 있어 가장 중요하게 생각하는 것이 관련한 현장정보의 수집이었다.

• 대한기계학회논문집B
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• 제34권4호
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• pp.333-339
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• 2010

고장감지 및 진단(FDD) 시스템의 구축의 기초 연구로 정상상태 진단기에 대한 연구를 수행하였다. 정상상태에 대한 진단은 시스템 전체를 관찰하거나 몇몇 필요한 시스템 파라미터를 모니터링 함으로써 가능하다. 최적화된 정상상태 진단기를 이용하면 FDD 시스템에서 필수적인 정상운전 시의 기준모델(no fault reference model)을 자가학습을 통하여 적용할 수 있다. 본 연구에서는 가정용 열펌프가 냉방조건으로 작동할 경우에 대해 이동창을 기반으로 7개의 측정값들에 대한 표준편차를 분석함으로써 정상상태 판정을 내리도록 하였다. 정상상태 진단기의 작동의 여부는 실내부하를 조절함으로써 확인하였다. 본 연구를 통하여 열펌프 등의 증기압축 사이클 시스템에 대하여 이동창을 기반으로 한 정상상태 진단기 개발 방법을 제시하였다.

• 전기학회논문지
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• 제59권8호
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• pp.1456-1463
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• 2010

The safety critical systems in nuclear power plants should be designed to have a high level of fault tolerant capability because those systems are used for protection or mitigation of the postulated accidents of nuclear reactor. Due to increasing of the system complexity of the digital based system in nuclear fields, the reliability of the digital based systems without an auto-test or a self-diagnostic feature is generally lower than those of analog system. To overcome this problem, additional redundant architectures in each redundant channel and self-diagnostic features are commonly integrated into the digital safety systems. The self diagnostic function is a key factor for increasing fault tolerant capabilities in the digital based safety system. This paper presents an availability and safety evaluation model to analyze the effect to the system's fault tolerant capabilities depending on self-diagnostic features when the loss or erroneous behaviors of self-diagnostic function are expected to occur. The analysis result of the proposed model on the several modules of a safety platform shows that the improvement effect on unavailability of each module has generally become smaller than the result of usage of conventional models and the unavailability itself has changed significantly depending on the characteristics of failures or errors of self-diagnostic function.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권5호
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.291-309
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• 2023

This paper addresses the finite element modeling of functionally graded porous (FGP) beams for free vibration and buckling behaviour cases. The formulated finite element is based on simple and efficient higher order shear deformation theory. The key feature of this formulation is that it deals with Euler-Bernoulli beam theory with only three unknowns without requiring any shear correction factor. In fact, the presented two-noded beam element has three degrees of freedom per node, and the discrete model guarantees the interelement continuity by using both C⁰ and C¹ continuities for the displacement field and its first derivative shape functions, respectively. The weak form of the governing equations is obtained from the Hamilton principle of FGP beams to generate the elementary stiffness, geometric, and mass matrices. By deploying the isoparametric coordinate system, the derived elementary matrices are computed using the Gauss quadrature rule. To overcome the shear-locking phenomenon, the reduced integration technique is used for the shear strain energy. Furthermore, the effect of porosity distribution patterns on the free vibration and buckling behaviours of porous functionally graded beams in various parameters is investigated. The obtained results extend and improve those predicted previously by alternative existing theories, in which significant parameters such as material distribution, geometrical configuration, boundary conditions, and porosity distributions are considered and discussed in detailed numerical comparisons. Determining the impacts of these parameters on natural frequencies and critical buckling loads play an essential role in the manufacturing process of such materials and their related mechanical modeling in aerospace, nuclear, civil, and other structures.

• 대한원격탐사학회지
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• 제40권1호
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• 대한전자공학회논문지SD
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• 제45권6호
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• pp.49-59
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• 2008

본 논문에서는 SIFT(Scale Invariant Feature Transform) 알고리즘을 임베디드 환경에서 실시간으로 처리하기 위해 가장 연산량이 많은 특징점 위치 결정 단계를 고정 소수점 모델로 설계 및 분석하고 그에 근거한 하드웨어 구조를 제안한다. SIFT 알고리즘은 객체의 꼭지점이나 모서리와 같이 색상 성분의 차가 심한 구역에서 얻어진 특징점 주위 픽셀의 벡터성분을 추출하는 알고리즘으로, 현재 얼굴인식, 3차원 객체 인식, 파노라마, 3차원 영상 복원 작업의 핵심 알고리즘으로 연구 되고 있다. 본 알고리즘에 대한 최적의 하드웨어 구현을 위해 특징점 위치(Keypoint Localization)와 방향(Orient Assignment)에 대한 정확도, 오차율을 사용하여 고정 소수점 모델에서 각 중요 변수들의 비트 크기를 결정 한다. 얻어진 고정 소수점 모델은 원래의 부동 소수점 모델과 비교했을 때 정확도 93.57%, 오차율 2.72%의 결과를 보이며, 고정 소수점 모델은 부동 소수점 모델과 비교하여 제거된 특징점의 대부분이 두 영상에서 추출된 특징점 끼리의 매칭과정에서 불필요한 객체의 모서리 영역에 몰려있음을 확인했다. 고정 소수점 모델링 결과 ARM 400MHz 환경에서 약 3시간, Pentium Core2Duo 2.13GHz 환경에서 약 15초의 연산시간을 갖는 부동 소수점 모델이 동일한 환경에서 약 1시간과 10초의 연산시간을 가지며, 최적화된 고정 소수점 모델을 하드웨어로 구현 시 $10{\sim}15\;frame/sec$의 성능을 보일 것으로 예상한다.