• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
• /
• pp.319-323
• /
• 1994

In this paper, we discuss a design method of iterative learning control systems for parabolic linear distributed parameter systems(DPSs). First, we discuss some aspects of boundary control of the DPS, and then propose to employ the Karhunen-Loeve procedure to reduce the infinite dimensional problem to a low-order finite dimensional problem. An iterative learning control(ILC) for non-square transfer function matrix is introduced finally for the reduced order system.

• Computers and Concrete
• /
• 제25권5호
• /
• pp.433-445
• /
• 2020

A method is proposed in this paper to estimate the workability of self-compacting concrete (SCC) in different mixing conditions with different mixers and mixing volumes by recording the mixing process based on deep learning (DL). The SCC mixing videos were transformed into a series of image sequences to fit the DL model to predict the SF and VF values of SCC, with four groups in total and approximately thirty thousand image sequence samples. The workability of three groups SCC whose mixing conditions were learned by the DL model, was estimated. One additionally collected group of the SCC whose mixing condition was not learned, was also predicted. The results indicate that whether the SCC mixing condition is included in the training set and learned by the model, the trained model can estimate SCC with different workability effectively at the same time. Our goal to estimate SCC workability in different mixing conditions is achieved.

• Journal of Electrical Engineering and Technology
• /
• 제13권4호
• /
• pp.1715-1723
• /
• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• 정보보호학회논문지
• /
• 제32권4호
• /
• pp.661-671
• /
• 2022

코로나19 팬데믹으로 인해 재택근무와 온라인 교육이 활성화되고 이에 따라 IPsec VPN 사용이 급속히 증가하고 있다. VPN 확산에 따라 VPN 취약점은 공격자들에게 중요 공격 대상이 되고 있으며, 이와 관련된 연구가 활발히 진행되고 있다. IKE v2 분석은 IPsec VPN 시스템 개발과 구축 뿐만 아니라 안전성 분석을 위해서 필요하며, 이를 위해서 Wireshark, Tcpdump 등 네트워크 패킷 분석 도구를 이용한다. Wireshark는 네트워크 분석을 위한 대표적인 도구 중 하나이며, IKE v2 분석을 지원하지만 이를 위해서는 IPsec VPN 시스템 관리자 권한을 알아야하는 등 여러 한계점이 존재한다. 본 논문에서는 Wireshark의 한계점을 분석하고 이를 해결할 수 있는 새로운 분석 기법을 제안한다. 제안하는 분석 기법은 세션키 교환 과정부터 암호화된 모든 IKE v2 메시지를 실시간으로 분석할 수 있다. 이뿐만 아니라 제안하는 분석 기법은 네트워크 패킷 포워딩 기능을 이용하여 패킷을 조작할 수 있기에 퍼징 등과 같은 동적 테스팅에 활용될 수 있을 것으로 기대된다.

• 한국농공학회논문집
• /
• 제61권3호
• /
• pp.55-65
• /
• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2023년도 제68차 하계학술대회논문집 31권2호
• /
• pp.69-71
• /
• 2023

본 논문은 YOLO 기반 모델의 철도 시스템 내 선로 고정장치 탐지 성능을 비교하고 분석한다. 여기서 철도 시스템은 열차가 주행하기 위한 선로, 침목, 패스너 등의 구성요소를 포함한다. 침목은 지반과 직접적으로 연결되며, 선로를 지반 위에 안정적으로 지지하고 궤간을 정확하게 유지하는 역할을 한다. 또한, 패스너는 선로를 침목에 단단히 고정시키는 역할을 한다. 이러한 선로 고정장치의 부재는 인명 사고로 이어질 수 있어 지속적인 관리와 유지 보수가 필수적이다. 본 논문에서는 철도 시스템의 선로 고정장치 탐지를 위해 YOLO V5 및 V8 딥러닝 모델의 적용 가능성을 실험적으로 접근하며, 두 모델의 탐지 성능을 비교한다. 실험 결과, YOLO V8 및 V5 모델은 모두 뛰어난 성능을 보이는데, 특히 YOLO V8 모델이 더욱 우수한 성능을 보인다. 이로써 YOLO 알고리즘은 선로 고정장치 탐지에 적합하다는 것을 증명한다. 그러나 일부 False Positive Sample이 관측되었음을 확인하고, 이로부터 모델 성능의 개선이 필요하다는 결론을 도출하였다.

• International Journal of Computer Science & Network Security
• /
• 제23권12호
• /
• pp.199-203
• /
• 2023

In recent decades, the growth of communication technology has resulted in an explosion of data-related information. Ontology perception is being used as a growing requirement to integrate data and unique functionalities. Ontologies are not only critical for transforming the traditional web into the semantic web but also for the development of intelligent applications that use semantic enrichment and machine learning to transform data into smart data. To address these unclear facts, several researchers have been focused on expanding ontologies and semantic web technologies. Due to the lack of clear-cut limitations, ontologies would not suffice to deliver uncertain information among domain ideas, conceptual formalism supplied by traditional. To deal with this ambiguity, it is suggested that fuzzy ontologies should be used. It employs Ontology to introduce fuzzy logical policies for ambiguous area concepts such as darkness, heat, thickness, creaminess, and so on in a device-readable and compatible format. This survey efforts to provide a brief and conveniently understandable study of the research directions taken in the domain of ontology to deal with fuzzy information; reconcile various definitions observed in scientific literature, and identify some of the domain's future research-challenging scenarios. This work is hoping that this evaluation can be treasured by fuzzy ontology scholars. This paper concludes by the way of reviewing present research and stating research gaps for buddy researchers.

• 대한원격탐사학회지
• /
• 제38권4호
• /
• pp.435-446
• /
• 2022

Identifying ship types is an important process to prevent illegal activities on territorial waters and assess marine traffic of Vessel Traffic Services Officer (VTSO). However, the Terrestrial Automatic Identification System (T-AIS) collected at the ground station has over 50% of vessels that do not contain the ship type information. Therefore, this study proposes a method of identifying ship types through the Random Forest Classifier (RFC) from dynamic and static data of AIS and V-Pass for one year and the Ulsan waters. With the hypothesis that six features, the speed, course, length, breadth, time, and location, enable to estimate of the ship type, four classification models were generated depending on length or breadth information since 81.9% of ships fully contain the two information. The accuracy were average 96.4% and 77.4% in the presence and absence of size information. The result shows that the proposed method is adaptable to identifying ship types.

• International Journal of Internet, Broadcasting and Communication
• /
• 제9권4호
• /
• pp.1-7
• /
• 2017

Clipart is artificial visual contents that are created using various tools such as Illustrator to highlight some information. Here, the style of the clipart plays a critical role in determining how it looks. However, previous studies on clipart are focused only on the object recognition [16], segmentation, and retrieval of clipart images using hand-craft image features. Recently, some clipart classification researches based on the style similarity using CNN have been proposed, however, they have used different CNN-models and experimented with different benchmark dataset so that it is very hard to compare their performances. This paper presents an experimental analysis of the clipart classification based on the style similarity with two well-known CNN-models (Inception Resnet V2 [13] and VGG-16 [14] and transfers learning with the same benchmark dataset (Microsoft Style Dataset 3.6K). From this experiment, we find out that the accuracy of Inception Resnet V2 is better than VGG for clipart style classification because of its deep nature and convolution map with various sizes in parallel. We also find out that the end-to-end training can improve the accuracy more than 20% in both CNN models.

• 방송공학회논문지
• /
• 제24권3호
• /
• pp.430-439
• /
• 2019

CNN (convolutional neural network) 기반의 단일 열화 영상 복원 방법은 우수한 성능을 나타내지만 한가지의 특정 열화를 해결하는 데 맞춤화 되어있다. 본 연구에서는 복합적으로 열화 된 영상 분류 및 복원을 위한 알고리즘을 제시한다. 복합 열화 영상 분류 문제를 해결하기 위해 CNN 기반의 알고리즘인 사전 학습된 Inception-v3 네트워크를 활용하고, 영상 열화 복원을 위해 기존의 CNN 기반의 복원 알고리즘을 사용하여 툴체인을 구성한다. 실험적으로 복합 열화 영상의 복원 순서를 추정하였으며, CNN 기반의 영상 화질 측정 알고리즘의 결과와 비교하였다. 제안하는 알고리즘은 추정된 복원 순서를 바탕으로 구현되어 실험 결과를 통해 복합 열화 문제를 효과적으로 해결할 수 있음을 보인다.