• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.190-197
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• 2020

Recently there was an incident that military radars, coastal CCTVs and other surveillance equipment captured a small rubber boat smuggling a group of illegal immigrants into South Korea, but guards on duty failed to notice it until after they reached the shore and fled. After that, the detection of such vessels before it reach to the Korean shore has emerged as an important issue to be solved. In the fields of marine navigation, Automatic Identification System (AIS) is widely equipped in vessels, and the vessels incessantly transmits its position information. In this paper, we propose a method of automatically identifying abnormally behaving vessels with AIS using convolutional autoencoder (CAE). Vessel anomaly detection can be referred to as the process of detecting its trajectory that significantly deviated from the majority of the trajectories. In this method, the normal vessel trajectory is gridded as an image, and CAE are trained with images from historical normal vessel trajectories to reconstruct the input image. Features of normal trajectories are captured into weights in CAE. As a result, images of the trajectories of abnormal behaving vessels are poorly reconstructed and end up with large reconstruction errors. We show how correctly the model detects simulated abnormal trajectories shifted a few pixel from normal trajectories. Since the proposed model identifies abnormally behaving ships using actual AIS data, it is expected to contribute to the strengthening of security level when it is applied to various maritime surveillance systems.

• Smart Structures and Systems
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• v.29 no.1
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• pp.1-16
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• 2022

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.

• Journal of Korean Academy of Nursing
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• v.28 no.2
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• pp.510-518
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• 1998

It was assumed that the maternal identity in primi-gravida is one of the most attribute of the motherhood, that is not biological but cognitive phenomena, appears active process as intelligent human being. The purposes of this study were that the identification the cognitive structure and the influencing factors of the maternal identity in primi-gravida. Theoretical framework in this study, maternal identity in primi-gravida was constructed as a cognitive output, has the cognitive structure of cognitive-perceptual factor, cognitive-behavioral factor, and cognitive-emotional factor. Influencing factors of maternal identity was constructed as a cognitive input, which were pregnancy related perceptions (pregnancy intention, minor discomfort, value of motherhood), interpersonal relationship(relationship with mother, relationship with husband, relationship with social network), preparation to motherhood(maternal knowledge, antenatal self care), and biological factor (gestation period). This study was the descriptive correlational research design, was done from the 3rd January to the 15th March 1996, and the research subjects were selected conviniently 226 the primi-gravida during the gestation period, data collection method was self reported questionnaire cross-sectionally. Descriptive data analysis was done by SAS PC$^{+}$, testing the hypothetical model was done by covariance structural analysis using LISREL 8.03 program. The result of the hypothesis testing, the value of motherhood(y=.650, T=4.26) the maternal knowledge (y=.137, T=2.030), the gestation period( y=.113, T=2.621), showed significant causal effect on the maternal identity in primi-gravida. In conclusion, the maternal identity in primi-gravida had interrelated cognitive structure consist of perceptual, behavioral, and emotional factors. Significant causal factors influencing the maternal identity were value identified. It seems to contribute toward the understanding the characteristics of the maternal identity as a cognitive domains that has been regarded highly abstract concept, so has not been validated empirically.y.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.934-941
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• 2014

In this paper, we present a low profile 2-layered meander built-in monopole antenna for portable RFID reader using FDTD(Finite Difference Time Domain) method. The input impedance, return loss, and VSWR in the frequency domain are calculated by Fourier transforming the time domain results. The double meander 2-layer structure is used to enhance the impedance matching and increase the antenna gain. The measured bandwidth of the antenna is 0.895 GHz ~ 0.930 GHz for a S11 of less than -10dB. The measured peak gain of proposed low profile RFID built-in antenna is 2.3 dBi. And the proposed built-in antenna for portable RFID reader can offers relatively wide-bandwidth and high-gain characteristics, in respectively. Experimental data for the return loss and the gain of the antenna are also presented, and they are relatively in good agreement with the FDTD results. This antenna can be also applied to mobile communication field, energy fields, RFID, and home-network operations, broadcasting, and other low profile mobile systems.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.167-174
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• 2016

Runway and taxiway is base facilities for aircraft take off and landing and runway capacity is one of major factor for airport capacity. Runway occupancy time is affect on the runway capacity. The identification of aircraft using taxiway by analysis of airport ground surveillance data and the measurement of pass time on the points is general method for the confirmation of the runway occupancy time. This study is runway occupancy time analysis of landing airplane using ADS-B message, in this study we surveyed landing aircraft runway occupancy time and analysis of serviced record using taxiway include rapid exit taxiway. The result of analysis is to confirm the different of landing direction and aircraft category on the same runway caused by structure of airport. Also the result of runway occupancy time analyzed data, it is base input data for the air transportation simulation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.2
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• pp.209-218
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• 2002

The Vector Random Decrement technique has been developed as an efficient method for transforming ambient responses into free decays of linear structures. It is shown that the VRD functions nay contain as much information about the modes as the really measured free decay responses. In this paper, the theory of the VRD technique is extended by applying the concept of the mode shape ratio into the Ibrahim Time Domain modal parameter identification algorithm. The VRD function is not shifted in the correction procedures for constant time shifts of the proposed VRD technique. Thus, a number of points equal to the largest of the time shifts used in the vector triggering condition are not deleted. In the VRD functions, any influence of the input to the system is averaged out. The proposed technique is compared with the traditional VRD technique by assessment of the modal parameters. The applicability of the VRD technique has been justified through a simulation study and a study of the response of a laboratory beam model subject to ambient loads.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.35-43
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• 2004

In this paper, we proposed a vehicle recognition and electronic toll collection system with detection and classification of vehicle identification mark and emblem as well as recognition of vehicle license plate to unman toll fee collection system or incoming/outcoming vehicles to an institution. In the proposed algorithm, we first process pre-processing step such as noise reduction and thinning from the rear side input image of vehicle and detect vehicle mark, emblem and license plate region using intensity variation informations, template masking and labeling operation. And then, we classify the detected vehicle features regions into vehicle mark and emblem as well as recognize characters and numbers of vehicle license plate using hybrid and seven segment pattern vector. To show the efficiency of the proposed algorithm, we tested it on real vehicle images of implemented vehicle recognition system in highway toll gate and found that the proposed method shows good feature detection/classification performance regardless of irregular environment conditions as well as noise, size, and location of vehicles. And also, the proposed algorithm may be utilized for catching criminal vehicles, unmanned toll collection system, and unmanned checking incoming/outcoming vehicles to an institution.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1162-1169
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• 2010

The dynamic balancing of a rotor system is needed to alleviate the imbalances originating from various sources encountered during blade manufacturing processes and environmental factors. This work aims at developing a comprehensive analysis system which consists of cumulative module of test D/B and selection of optimal control parameters. This system can be used for the dynamic balancing of helicopter rotors based on tracking results from the whirl tower test. For simplicity of the analysis, a linear relation is assumed between the balancing input parameters and the blade track responses leading to influence coefficients and thereby the rotor system identification is made. In addition, the balancing parameters of the individual blades are sought using the genetic algorithm and the effectiveness of the proposed method is demonstrated in comparison with the test results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.2 no.1
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• pp.17-33
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• 1984

This work represents results of analysis of tidal observations at twelve major ports(Inchon, Kunsan, Mokpo, Daeheuksando, Jeju, Yeosu, Jinhae, Busan, Pohang, Ulsan, Mugho, Sogcho) around Korean coast for the years up to 1979. The reduction of hourly tide gauge sea level records provided by Korean Hydrographic Office was performed in systematic manner resulting digitised hourly observed series, predicted series and residual series. As a first step the application of an extended harmonic method of analyzing the tidal observations leads to the identification of 42 new constituents including 60 orthodox Doodson's constituents at major ports. The sea level statistics including sea level frequency distribution are presented and the tidal emersion curves showing the percentage of time for which different levels are covered by water and exposed are also presented to provide useful design input for coastal development. This study has teen undertaken in association with the programme of sea level research at Korean Hydrographic Office and the programme of adjustment of first order levelling network at National Geographic Institute.