• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.198-207
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• 2016

Bistatric radar can perform detection and identification for stealth targets that are rarely detected by the conventional monostatic radar. However, high resolution range profile(HRRP) generated from the received signal in the bistatic radar cannot show exact range information of the target because the bistatic geometry lead to the distortions of the bistatic HRRP. In addition, electromagnetic scattering mechanisms of the target are varied depending on the bistatic geometry. Thus, efficient database construction is a crucial factor to achieve successful classification capability in bistatic target identification. In this paper, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic radar, is applied to bistatic target identification. Then, the capability and efficiency of the method is analyzed. Simulation results show that reliable identification performance can be achieved using the database construction based on the flight scenarios when the target is a considerable distance away from the bistatic radar.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1A
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• pp.33-43
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• 2010

Recently, users want to use real-time multimedia services, such as internet, VoIP, etc., using their IEEE 802.11 wireless lan mobile stations. In order to provide such services, a handoff among access points is essential to support the mobility of a node, in such an wide area. However, the legacy handoff methods of IEEE 802.11 technology are easy to lose connections. Also, the recognition of a disconnection and channel re-searching time make the major delay of the next AP to connect. In addition, because IEEE 802.11 decides the selection of an AP depending only on received signal strength, regardless of a node direction, position, etc., it cannot guarantee a stable bandwidth for communication. Therefore, in order to provide a real-time multimedia service, a node must reduce the disconnection time and needs an appropriate algorithm to support a sufficient communication bandwidth. In this paper, we suggest an algorithm which predicts a handoff point of a moving node by using GPS location information, and guarantees a high transmission bandwidth according to the signal strength and the distance. We implemented the suggested algorithm, and confirmed the superiority of our algorithm by reducing around 3.7ms of the layer-2 disconnection time, and guaranteed 24.8% of the communication bandwidth.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.2
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• pp.208-215
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• 2017

When persistent oil, such as crude oil or Bunker C oil, is spilled at sea, viscosity increases through the weathering process. Equipment that can collect this oil when mixed with floating marine debris is very limited. In this study, devices that can be attached to the outside of existing oil skimmers have been applied to the inside of the main body, to develop an unmanned conveyor belt type floating marine debris and high viscosity oil recovery skimmer, which is composed of a conveyor belt, a sweeper with a forced inflow device, and a collection tank equipped with a buoyant body. The resulting skimmer was operated at a speed of 1.2 knots at a distance of 30 m in a sea area test. It was stable when moving laterally in any direction. An oil recovery performance test was conducted using a portable storage tank, and oil was recovered from a minimum of $7.8k{\ell}/h$ to a maximum of $23.3k{\ell}/h$. Moreover, recovery of $7.7k{\ell}/h$ was obtained in a wave water tank test with floating marine debris such as PET bottles and oil mixed. If the equipment developed in this study was used in the field for oil pollution accidents, it could be expected to contribute to improved response capability. We believe our equipment could be used in further studies to improvement the performance of existing portable oil skimmers.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.259-267
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• 2020

For real-time precise positioning, N-RTK (Network Real-Time Kinematic) technology is widely used these days. However, the currently operating N-RTK system has a limitation in terms of the number of users. Therefore, if reference points generate correction messages with no limit on the number of users are developed later, it is determined that an appropriate reference point installation interval is required, so that the accuracy of the N-RTK system according to the baseline distance was analyzed. This experiment utilized receivers with varying performance that estimated the rover position, and RTKLIB, an open-source software, is used for processing data. As a result, the rover position was estimated accurately with a high rate of fixed ambiguity for all the receivers. When the reference station with a baseline length of 40 km was used, the vertical RMSE (Root Mean Squared Error) was quite similar to the short baseline case, but only half of the ambiguity fixing rate was achieved. The outlier in the estimated rover position was not observed for the longer baselines in the case of a high-end receiver. It is necessary to analyze the ambiguity fixing and the accuracy of the kinematic positioning with scientific GNSS processing software.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.397-407
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• 2015

In this paper, we designed 1 chip IC for 3-axis gyroscope and 3-axis accelerometer used for various IoT/M2M mobile devices such as smartphone, wearable device and etc. We especially focused on analysis of gyroscope noise and proposed new architecture for removing various noise generated by gyroscope MEMS and IC. Gyroscope, accelerometer and geo-magnetic sensors are usually used to detect user motion or to estimate moving distance, direction and relative position. It is very important element to designing a low noise IC because very small amount of noise may be accumulated and affect the estimated position or direction. We made a mathematical model of a gyroscope sensor, analyzed the frequency characteristics of MEMS and circuit, designed a low noise, compact and low power 1 chip 6-axis inertial sensor IC including 3-axis gyroscope and 3-axis accelerometer. As a result, designed IC has 0.01dps/${\sqrt{Hz}}$ of gyroscope sensor noise density.

• Journal of Biosystems Engineering
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• v.29 no.4
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.148-154
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• 2020

This study examines room assignments to improve accessibility in a university dormitory depending on the student grade, taking into account frequency of using a certain common space. An integer programming model is presented to minimize the total moving distance from the common space to the students' rooms for accessibility. The model also constrains the maximum capacity of a room, and disallows different grade students to be assigned to the same room. This model is similar to a facility location problem used widely in the supply chain management field. Applying our optimization model to a small group at the dormitory of Unversity A as the case study, our results indicate that lower grade students are assigned rooms closer to the common space due to their higher frequency of using that space to guarantee high accessibility. Moreover, if higher grade students are prioritized to select their rooms, we suggest an objective function that imposes a penalty in cases when lower grade students select rooms with priority. Based on the results obtained, we propose assigning rooms to students in a dormitory by considering their complex requirements and convenience to use the common space.

• Korean Journal of Applied Biomechanics
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• v.13 no.1
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• pp.73-94
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• 2003

The purpose of this investigation was to analyze A kinematic analysis of the Kuzushi-arm motion when performing Morote-Seoinage in judo who was 5 females university representative judokas of light weight category in judo, and filmed on video cameras(60field/s). The data of this study digitizied by KWON3D 2.1 program computed the average and standard deviation calculated individual 5 trials with Programing Lab view 6i. From the data analysis & discussion, the following conclusions were drawn : 1) distance variable of attacking hand arm in kuzushi motion Left right(X direction) displacement variable was all of A, B, C pattern with moving left to right and leaning. Strip of displacement variable was ordo. to C(55.6cm), A(53.3cm), B(43.9cm) pattern, C pattern largely leaned to left Front Rear(Y direction) displacement variable was different A($131.3cm{\pm}3.1cm$), B($128.7{\pm}4.0cm$) and C(111.0cm) on ready position, 3 pattern leaned to rear direction. Strip of displacement was order to B(43.4cm), A(41.1cm) and C pattern(28.3cm). Up down(Z direction) displacement variable was all of A, B, C pattern leaned to up in the Kuzushi-phase and leaned to down in the Kake-phase. Strip of displacement was order to A(83.9cm), B(80.4cm), C pattern(71.9cm). 2) Shoulder joint angle variable Flexion and extension Ready position' angle was A($138.3{\pm}4.9^{\circ}$), B($142.9{\pm}3.7^{\circ}$) and C($164.5^{\circ}$) pattern, strip of flexion extension was order to C($80.9^{\circ}$), A($79.9^{\circ}$) and B($39.0^{\circ}$) pattern, greatly C pattern had largely angle change. Adduction and abduction : B and C pattern's angle change were adduction and abduction in the Kuzushi-phase after adduction in the Kake phase, A pattern's angle change was abduction in the Kuzushi-phase after adduction in the Kake phase. internal and external rotation : 3 pattern were internal rotation in the Tsukuri phase and external rotation in the Kake phase. After B and C pattern were external rotation and A pattern was internal rotation. 3) Elbow joint angle variable Flexion and extension 3 pattern's ready position angle were A($142.0{\pm}4.4^{\circ}$), B($123.5{\pm}5.5^{\circ}$) and C($105.5^{\circ}$) and flexion. Strip of flexion extension were order to A($57.9^{\circ}$), C($34.6^{\circ}$) and B($25.2^{\circ}$) pattern.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.1-8
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• 2016

Automation of inventory management in a steel plate factory was a difficult problem unresolved for a long time. And now, it is also necessary to work diligently in the steel industry on efficient inventory management of thick plates. So far, the environmental characteristics of stacked thick plates means it is not easy to apply advanced technology for their automatic identification. In this paper, we propose a thick-plate robotic scanning system based on radio-frequency identification (RFID) that can provide quick and accurate inventory management by acquiring plate information after the scanning automatically recognizes the RFID tags under difficult load conditions. This system is equipped with a crane to move the plates in a pulled-up operation. It is equipped with a plate-only linear dipole antenna only for scanning the position of the plate tag. Only the linear dipole antenna, while moving the x-axis and y-axis information, automatically identifies the tag information attached to the plate. The tag information acquired by the system is used for stockpiling and is managed by steel plate inventory control software. The effectiveness of the proposed system is verified through field performance evaluation. As a result, the recognition rate of the plate tags is 99.9% at a maximum distance of 320 cm. The developed thick-plate antenna showed excellent performance compared to an existing commercial antenna.

• 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.