• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.52-55
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• 2004

VMS (virtual manufacturing system) may be defined as a transparent interface/control mechanism to support human decision-making via simulation and monitoring of real operating situation through modeling of all activities in RMS (real manufacturing system). The three main layers in VMS are business process layer, manufacturing execution layer, and facility operation layer, and each layer is represented by a specific software system having its own input modeler module. The current version of these input modelers has been implemented based on its own 'local' framework, and as a result, there are no information sharing mechanism, nor a common user view among them. Proposed in this paper is a unified modeler framework covering the three VMS layers, in which the concept of PPR (product-process-resource) model is employed as a common semantics framework and a 2D graphic network model is used as a syntax framework. For this purpose, abstract class PPRObject and GraphicObject are defined and then a subclass is inherited from the abstract class for each application layer. This feature would make it easier to develop and maintain the individual software systems. For information sharing, XML is used as a common data format.

• Spatial Information Research
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• 제22권2호
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• pp.31-44
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• 2014

응용프로그램은 그 목적에 따라 최적의 데이터 모델을 활용하며, 이러한 응용프로그램을 위한 3차원 모델링 데이터는 선택된 데이터 모델을 기반으로 생성된다. 이러한 이유로, 동일한 공간의 지형지물을 표현하기 위해 다양한 데이터 셋이 존재한다. 그러한 중복된 데이터 셋은 공간정보 산업의 재정적 측면에서 문제를 가져올 뿐만 아니라, 시스템호환성과 데이터 비교가능성에서도 심각한 문제를 야기한다. 이러한 문제를 극복하기 위하여, 본 연구에서는 항목클래스내의 공간객체들 간의 위상적 관계를 이용하여 TRM (Topological Relation Method)이라고 하는 공간데이터융합 방법을 제안한다. TRM은 응용프로그램 수준에서 구현되는 공간데이터 융합방법으로써, 서로 다른 데이터 모델에 의해 생성된 기하데이터들을 응용시스템에서 어떠한 데이터 변환이나 교환 과정을 거치지 않고, 직접적으로 실내공간 위치기반 서비스에 제공하기 위해 사용된다. 이러한 위상관계는 IndoorGML의 기본 개념으로 정의 및 기술된다. TRM의 개념을 기술한 후, 3D GIS상에서 제안된 데이터 융합방법의 실험적 구현을 보여준다. 마지막으로서 본 연구의 한계와 향후 연구에 대해 정리한다.

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2005년도 6th 2005 International Conference on Computers, Communications and System
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• pp.229-232
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• 2005

In this paper, an image-based "approach-align -grasp" visual servo control design is proposed for the problem of object grasping, which is based on the binocular stand-alone system. The basic idea consists of considering a vision system as a specific sensor dedicated a task and included in a control servo loop, and we perform automatic grasping follows the classical approach of splitting the task into preparation and execution stages. During the execution stage, once the image-based control modeling is established, the control task can be performed automatically. The proposed visual servoing control scheme ensures the convergence of the image-features to desired trajectories by using the Jacobian matrix, which is proved by the Lyapunov stability theory. And we also stress the importance of projective invariant object/gripper alignment. The alignment between two solids in 3-D projective space can be represented with view-invariant, more precisely; it can be easily mapped into an image set-point without any knowledge about the camera parameters. The main feature of this method is that the accuracy associated with the task to be performed is not affected by discrepancies between the Euclidean setups at preparation and at task execution stages. Then according to the projective alignment, the set point can be computed. The robot gripper will move to the desired position with the image-based control law. In this paper we adopt a constant Jacobian online. Such method describe herein integrate vision system, robotics and automatic control to achieve its goal, it overcomes disadvantages of discrepancies between the different Euclidean setups and proposes control law in binocular-stand vision case. The experimental simulation shows that such image-based approach is effective in performing the precise alignment between the robot end-effector and the object.

• 정보과학회 논문지
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• 제41권11호
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• pp.921-926
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• 2014

얼굴 검출은 입력 영상에서 얼굴 영역을 추출하는 과정으로, 얼굴 인식 및 인증 과정의 속도와 정확도를 효율적으로 높여주는 작업이며 그 응용분야도 다양하다. 기존에 개발된 얼굴 검출 방법들은 얼굴의 전체 형태를 바탕으로 검출을 수행하기 때문에 착용물 또는 신체 부위로 인해 일부가 가려져 폐색된 얼굴에 대해서는 그 검출 성능이 크게 하락할 수 있다. 이러한 문제를 해결하기 위하여 이 논문에서는 얼굴 영상을 지역적 특징 기술자의 집합으로 표현하고, 이에 대한 통계적 확률 모델을 추정한 뒤 이를 이용하여 입력 영상에서 얼굴 영역을 추출하는 방법을 제안한다. AR 데이터베이스와 Caltech 데이터베이스를 이용한 실험을 통해 제안하는 얼굴 검출 방법이 일부가 폐색된 얼굴 검출에 효과적임을 확인하였다.

• 천문학회보
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• 제39권2호
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• pp.89-89
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• 2014

While the current consensus view holds that galaxy mergers are commonplace, it is sometimes speculated that Globular Clusters (GCs) may also have undergone merging events, possibly resulting in massive objects with a strong metallicity spread such as Omega Centauri. Galaxies are mostly far, unresolved systems whose mergers are most likely wet, resulting in observational as well as modeling difficulties, but GCs are resolved into stars that can be used as discrete dynamical tracers, and their mergers might have been dry, therefore easily simulated with an N-body code. It is however difficult to determine the observational parameters best suited to reveal a history of merging based on the positions and kinematics of GC stars, if evidence of merging is at all observable. To overcome this difficulty, we investigate the applicability of supervised and unsupervised machine learning to the automatic reconstruction of the dynamical history of a stellar system. In particular we test whether statistical clustering methods can classify simulated systems into monolithic versus merger products. We run direct N-body simulations of two identical King-model clusters undergoing a head-on collision resulting in a merged system, and other simulations of isolated King models with the same total number of particles as the merged system. After several relaxation times elapse, we extract a sample of snapshots of the sky-projected positions of particles from each simulation at different dynamical times, and we run a variety of clustering and classification algorithms to classify the snapshots into two subsets in a relevant feature space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권3호
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• pp.755-778
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• 2024

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

• 한국지능시스템학회논문지
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• 제19권4호
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• pp.562-567
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• 2009

얼굴 표정은 사람의 감정을 전달하는 핵심 메커니즘으로 이를 적절하게 활용할 경우 Robotics의 HRI(Human Robot Interface)와 같은 Human Computer Interaction에서 큰 역할을 수행할 수 있다. 이는 HCI(Human Computing Interface)에서 사용자의 감정 상태에 대응되는 다양한 반응을 유도할 수 있으며, 이를 통해 사람의 감정을 통해 로봇과 같은 서비스 에이전트가 사용자에게 제공할 적절한 서비스를 추론할 수 있도록 하는 핵심요소가 된다. 본 논문에서는 얼굴표정에서의 감정표현을 인식하기 위한 방법으로 FACS(Facial Action Coding System)와 AAM(Active Appearance Model)을 이용한 특징 추출과 Bayesian Network 기반 표정 추론 기법이 융합된 얼굴표정 인식 시스템의 개발에 대한 내용을 제시한다.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.149-156
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• 2020

Blast furnace revamping in steel industry is one of the most important work to complete the complicated equipment within a short period of time based on the interfaces of various types of work. P company has planned to build a Smart Tracking System based on the wireless tag system with the aim of complying with the construction period and reducing costs, ahead of the revamping of blast furnace scheduled for construction in February next year. It combines the detailed design data with the wireless recognition technology to grasp the stage status of design, storage and installation. Then, it graphically displays the location information of each member in relation to the plan and the actual status in connection with Building Information Modeling (BIM) 4D Simulation. QR Code is used as a wireless tag in order to check the receiving status of core equipment considering the characteristics of each item. Then, DB in server system is built, status information is input. By implementing BIM 4D Simulation data using DELMIA, the information on location and status is provided. As a feature of the S/W function, a function for confirming the items will be added to the cellular phone screen in order to improve the accuracy of tagging of the items. Accuracy also increases by simultaneous processing of storage and location tagging. The most significant effect of building this system is to minimize errors in construction by preventing erroneous operation of members. This system will be very useful for overall project management because the information about the position and progress of each critical item can be visualized in real time. It could be eventually lead to cost reduction of project management.

• 한국지능시스템학회논문지
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• 제26권6호
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• pp.471-476
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• 2016

본 논문은 깊이 정보를 기반으로 모션 히스토리 영상 및 기울기 방향성 히스토그램과 적출 모델을 사용하여 연속적인 사람 행동들을 인식하는 시스템을 설명하고 연속적인 행동 인식 시스템에서 인식 성능을 개선하기 위해 행동 적출을 수행하는 적출 모델을 제안한다. 본 시스템의 구성은 전처리 과정, 사람 행동 및 적출 모델링 그리고 연속적인 사람 행동 인식으로 이루어져 있다. 전처리 과정에서는 영상 분할과 시공간 템플릿 기반의 특징을 추출하기 위하여 Depth-MHI-HOG 방법을 사용하였으며, 추출된 특징들은 사람 행동 및 적출 모델링 과정을 통해 시퀀스들로 생성된다. 이 생성된 시퀀스들과 은닉 마르코프 모델을 사용하여 정의된 각각의 행동에 적합한 사람 행동 모델과 제안된 적출 모델을 생성한다. 연속적인 사람 행동 인식은 연속적인 행동 시퀀스에서 적출 모델에 의해 의미 있는 행동과 의미 없는 행동을 분할하는 행동 적출과 의미 있는 행동 시퀀스에 대한 모델의 확률 값들을 비교하여 연속적으로 사람 행동들을 인식한다. 실험 결과를 통해 제안된 모델이 연속적인 행동 인식 시스템에서 인식 성능을 효과적으로 개선하는 것을 검증한다.