• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 추계종합학술대회
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• pp.395-400
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• 1999

DNC(Direct Numerical Control) Network을 위한 프로그램을 효율적으로 하기위해 현재 많은 시스템들이 개발되어 사용되고 있다. 그러나 이 시스템들은 원거리 상의 컴퓨터와 머시닝센터간의 상호 연결이 원만하지 않아 작업에 비효율적인 면이 있고, 또 머시닝 센터에서의 데이터 송 수신에서 일어나는 오류 문제에 대한 시스템으로의 적절한 대처를 할 수가 없다는 문제점이 있다. 그래서, 본 논문에서는 DNC Network을 통해 리C(Numerical Control) 선반 제어기에서 컴퓨터의 데이터를 오류 없이 수신 가능한 데이터 원격 제어 시스템을 새로이 구성하였다. 이 데이터 원격 제어 시스템의 주요 장점으로는 머시닝 센터에서 운영자가 쉽게 컴퓨터에 저장된 NC 데이터 호출과 송출이 자유롭고, 컴퓨터와 공작기계간의 상호 대화가 없이도 NC 기계상에서의 원격 제어(Remote Control)가 가능하다.

• 센서학회지
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• 제28권3호
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• Nuclear Engineering and Technology
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• 제53권7호
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• pp.2229-2236
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• 2021

In nuclear-related facilities, such as nuclear power plants, research reactors, accelerators, and nuclear waste storage sites, radiation detection, and mapping are required to prevent radiation overexposure. Sensor network systems consisting of radiation sensor interfaces and wxireless communication units have become promising tools that can be used for data collection of radiation detection that can in turn be used to draw a radiation map. During data collection, malfunctions in some of the sensors can occasionally occur due to radiation effects, physical damage, network defects, sensor loss, or other reasons. This paper proposes a reproduction strategy for radiation maps using a U-net model to compensate for the loss of radiation detection data. To perform machine learning and verification, 1,561 simulations and 417 measured data of a sensor network were performed. The reproduction results show an accuracy of over 90%. The proposed strategy can offer an effective method that can be used to resolve the data loss problem for conventional sensor network systems and will specifically contribute to making initial responses with preserved data and without the high cost of radiation leak accidents at nuclear facilities.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• 한국군사과학기술학회지
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• 제11권1호
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• pp.15-24
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• 2008

The data delivery confirmation method of MIL-STD-188-220C, which is a tactical wireless mobile Ad-Hoc communication protocol, is that a source node requires the end-to-end ack from all destination nodes and the data-link ack from 1-hop neighboring destination nodes and relaying nodes, regardless of the hop distance from a source node to destination nodes. This method has the problem to degrade the whole communication network performance because of excessive data traffic due to the duplicate use of end-to-end ack and data-link ack, and the collision among end-to-end acks on the wireless network in the case of confirming a data delivery within an 1-hop distance. In order to solve this problem, this paper has proposed the method to perform the data delivery confirmation with the improvement of communication network performance through the data traffic reduction by achieving the reliable data delivery confirmation requiring the only data-link ack within an 1-hop distance. The effects of the proposed method are analyzed in the two aspects of the data delivery confirmation delay time and the data delivery confirmation success ratio.

• 한국통신학회논문지
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• 제38B권3호
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• pp.207-215
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• 2013

본 논문은 효율적인 무선 AMI 네트워크를 위한 채널 할당 및 스케줄링 기법에 관한 것이다. AMI 시스템에서, 제안하는 다중채널 클러스터 네트워크는 NC(Network Coordinator)와 CDA(Clustered Data Aggregator) 간의 통신 채널을 네트워크 채널로 정의 하고, CDA와 OMD(Out Meter Display), SMD(Smart Meter Device) 간의 통신채널을 그룹 채널로 정의한다. 네트워크 채널과 그룹채널이 혼합된 다중채널 클러스터 기반의 AMI 네트워크는 물리적/논리적 수용가 채널 클러스터링을 통해서 관리의 효율성을 증대하고, 인접 클러스터간 구별되는 채널 사용을 통한 검침 데이터의 신뢰성을 증대한다. 또한 다중채널 클러스터 기반의 채널할당을 통하여 데이터의 빠른 수집이 가능하며 검침망의 크기를 증가시킨다.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• 한국지리정보학회지
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• 제11권3호
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• pp.80-91
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• 2008

UIS사업의 결과로 나온 공간데이터의 경우 막대한 비용투자에 비하여 활용의 정도는 기대에 미치지 못하고 있으며 공간데이터의 활용확대를 위하여 데이터의 효율적 관리 및 공유체계의 확립을 위한 노력은 반드시 필요하다. 본 연구는 지방자치단체내에서 공간 데이터의 효율적인 관리 및 공유체계확립과 공간데이터의 활용 확대를 위하여 사회연결망 분석 및 계획행동이론을 적용하였으며 부산광역시 공간데이터 사용 공무원을 대상으로 분석결과 공간데이터의 공유에 영향을 미치는 장애요인에는 데이터 사용자들간의 태도 및 주변과의 관계에서 발생하는 비기술적 장애요인의 비중이 높게 나타났다. 또한 데이터의 제공위치에 따라 공간데이터 공유의 장애요인에 대하여 다르게 인식하고 있는 것으로 나타났으며 이러한 사회적 신념이 행동의도에 영향을 미쳐서 또 다른 비기술적 장애요인이 발생하였다. 본 연구는 공간데이터의 활용확대를 위하여 인간의 행동예측을 위한 계획행동이론의 적용 및 공간데이터의 흐름을 파악하기 위한 사회연결망분석을 실시하여 부서에 속한 담당자들의 인식 및 공유장애요인을 규명하고 데이터 활용 확대로 접근할 수 있는 윤곽을 제시하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권2호
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• pp.209-218
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• 2009

In a wireless sensor network(WSN) each sensor node deals with numerous sensing data elements. For the sake of energy efficiency and network lifetime, sensing data must be handled effectively. A technique used for this is data aggregation. Sending/receiving data involves numerous steps such as MAC layer control packet handshakes and route path setup, and these steps consume energy. Because these steps are involved in all data communication, the total cost increases are related to the counts of data sent/received. Therefore, many studies have proposed sending combined data, which is known as data aggregation. Very effective methods to aggregate sensing data have been suggested, but there is no means of deciding how long the sensor node should wait for aggregation. This is a very important issue, because the wait time affects the total communication cost and data reliability. There are two types of data aggregation; the data counting method and the time waiting method. However, each has weaknesses in terms of the delay. A hybrid method can be adopted to alleviate these problems. But, it cannot provide an optimal point of aggregation. In this paper, we suggest a stochastic-based data aggregation scheme, which provides the cost(in terms of communication and delay) optimal aggregation point. We present numerical analysis and results.