• Current Optics and Photonics
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• v.7 no.4
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• pp.354-361
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• 2023

We present the optical design and experimental verification of resolving performance of a 3× long wavelength infrared (LWIR) zoom camera for drones. The effective focal length of the system varies from 24.5 mm at the wide angle position to 75.1 mm at the telephoto position. The design specifications of the system were derived from ground resolved distance (GRD) to recognize 3 m × 6 m target at a distance of 1 km, at the telephoto position. To satisfy the system requirement, the aperture (f-number) of the system is taken as F/1.6 and the final modulation transfer function (MTF) should be higher than 0.1 (10%). The measured MTF in the laboratory was 0.127 (12.7%), exceeds the system requirement. Outdoor targets were used to verify the comprehensive performance of the system. The system resolved 4-bar targets corresponding to the spatial resolution at the distance of 1 km, 1.4 km and 2 km.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1454-1460
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• 2006

This paper assesses communication performance using a roaming simulator when roaming occurs between onboard and ground wireless communication devices for communication based train control system (CBTC). Generally, CBTC is defined as the system regularly collecting location and speed data from each train, transmitting distance information to a train, and optimizing train speed according to train performance. When a train is moving, roaming is also performed to continuously transmit and receive train control information between the ground controller and the train. To operate CBTC, packet loss rate should be less than 1%, roaming time less than 100ms during roaming. We developed a roaming simulator to check communication performance before installing ground and onboard equipments on actual wireless sections. The roaming simulator to be introduced in this paper is for roaming simulation before conducting CBTC field test, which is the project to develop Urban Rail Signaling System Standards, being conducted in KRRI. The simulation consists of one onboard wireless communication device and three ground wireless communication devices, and the roaming simulator estimate packet loss rate occurring during roaming process of the two devices. Therefore, if you use the roaming simulator before the field test, you can predict various problems to occur in actual environment and reduce time, cost and people necessary to resolve these problems.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.3
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• pp.225-232
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• 2004

The human history has progressed closely related to land. Mankind started land administration as a tool of governance to make land the object of imposing taxation as well as developing the land administration as a concept of securing property rights. People have drawn boundary lines on the ground to form a land parcel according to the usage and/or ownership. Furthermore, the land administration has been developed as a registering system of cadastral records fer the public announcement of fixed boundary instead of changeable ground boundary. Currently the citizens demand the provision of accurate and diverse information on the land which is assessed to has high property value encouraged by the rapid development in the post-industrial society today. However, even though the fact that the Korean cadastral registers produced during the Land Investigation Project are still practically in use causes land-related disputes and promotes public mistrust because of the changed boundaries by parcel mutation, the expansion and contraction of map sheets and the quality deterioration and damage of map paper, but the ultimate resolution is not yet made so far. The distance difference between boundary points are compared and analyzed using TS surveying method in the research as a methodology to resolve the boundary inconsistency, the current problem of cadastral records. Consequently, I'd say that the new surveying method of registering the coordinates of real ground boundary has been regarded as more efficient than considering the matter on the map regardless of urban or rural areas.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.396-399
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• 2009

Stereo vision system is useful method for inferring 3D depth information from two or more images. So it has been the focus of attention in this field for a long time. Stereo matching is the process of finding correspondence points in two or more images. A central problem in a stereo matching is that it is difficult to satisfy both the computation time problem and accuracy at the same time. To resolve this problem, we proposed a new stereo matching technique using weighted cost aggregation. To begin with, we extract the weight in given stereo images based on features. We compute the costs of the pixels in a given window using correlation of weighted color, brightness and distance information. Then, we match pixels in a given window between the reference and target images of a stereo pair. To demonstrate the effectiveness of the algorithm, we provide experimental data from several synthetic and real scenes. The experimental results show the improved accuracy of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.