• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1251-1260
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• 2008

Diffuse wireless optical communication offers more robust optical links in terms of coverage and shadowing than line-of-sight links. However, traditional diffuse wireless infrared (IR) transceiver systems are more susceptible to multi-path distortion and great power decrease, which results in limiting high-speed performance. Multi-beam is an effective technique to compensate for multi-path distortion in a wireless infrared environment. The goal of this paper is to analyze the transmission characteristics by replacing traditional diffuse system (TDS) which contains single wide angle transmitter and single element receiver by system consisting of three-beam transmitter and non-imaging receiver (TNS) attached with compound parabolic concentrator (CPC). In the simulation, we use the recursive model developed by Barry and Kahn and build the scenario based on 10 different cases which have been listed in Table 1. Moreover, we also check the reliability of the TNS diffuse link channel by BER test on the basis of different receiver positions and room sizes. The simulation results not only show the basic transmission characteristics of TNS diffuse link, but also are references to design more efficient and reliable indoor infrared transmission systems.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.866-878
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• 1999

In this paper, stripline circulator including a ferrite resonator which is consisted of a circular disk and two annuli disks is designed and fabricated. Using RGF(Recursive Green Function) technique, electromagnetic field of port and input impedance is presented. The circulator characteristics are compared according to the ferrite arrangement, bias field intensity and port width. The pass-band frequency of the fabricated circulator using the designed data is from 1.55 GHz to 2.95 GHz, the reflection coefficient $S_{11}$ of input port is -30 dB, and the transmission coefficient $S_{21}$ between input port and isolation port is -28 dB at resonating point 2.38 GHz.

• Korean Journal of Applied Biomechanics
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• v.15 no.4
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• pp.75-83
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• 2005

The purpose of this study was to determine the kinematic characteristics of the ankle joint and RPM(repetition per minutes) during the supra maximal training in cycling. For this study, 8 national representative cyclists, distance cyclists in track and road, were selected. During the super-maximum pedalling, kinematic data were collected using a six-camera(240Hz) Qualisys system. the room coordinate system was right-handed and fixed in the back of a roller for cycle, with right-handed orthogonal segment coordinate systems defined for the leg and foot. Lateral kinematic data were recorded at least for 3 minutes while the participants pedal on a roller. Two-dimensional Cartesian coordinates for each marker were determined at the time of recording using a nonlinear transformation technique. Coordinate data were low-pass filtered using a fourth-order Butterworth recursive filter with cutoff frequency of 15Hz. Variables analyzed in this study were compared using a one factor(time) ANOVA with repeated measures. The results of investigation suggest that the number of rotating pedal was decreased with time phase during the super-maximum pedaling. Maximum angle of the ankle joint showed little in change with time phase compared with minimum angle of that.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.320-324
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• 1996

Digital fetal monitoring system based on the personal computer combined with the digital signal processing board was implemented. The DSP board acquires and digitally processes ultrasound fetal Doppler signal for digital rectification, FIR filtering, autocorrelation function calculation, its peak detection and MEDIAN filtering. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data transmission and on-line analysis of fetal heart rate change including and variability. I used a recursive technique for autocorrelation function computation method and MEDIAN filter which can greatly reduce the amount of calculation and accuracy. I also implemented analysis algorithm of fetal heart rate change based on normal fetal sample data in order to exact diagnosis.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1958-1960
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• 2001

In this paper, we propose the design methodology of target tracking system using the identification of TS fuzzy model based on genetic algorithm(GA) and RLS algorithm. In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. In this paper, to resolve these problems of nonlinear filtering technique, the error of EKF by nonlinearity is compensated by identifying TS fuzzy model. In the proposed method, after composing training datum from the parameters of EKF, by identifying the premise and consequent parameters and the rule numbers of TS fuzzy model using GA, and by tuning finely the consequent parameters of TS fuzzy model using recursive least square(RLS) algorithm, the error of EKF is compensated. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.39-46
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• 2021

To analyze and defend malware codes, reverse engineering is used as identify function location information. However, the stripped binary is not easy to find information such as function location because function symbol information is removed. To solve this problem, there are various binary analysis tools such as BAP and BitBlaze IDA Pro, but they are based on heuristics method, so they do not perform well in general. In this paper, we propose a technique to extract function information using LSTM-based models by applying algorithms of N-byte method that is extracted binaries corresponding to reverse assembling instruments in a recursive descent method. Through experiments, the proposed techniques were superior to the existing techniques in terms of time and accuracy.

• ETRI Journal
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• v.45 no.3
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• pp.448-461
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• 2023

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.149-149
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• 2022

Accurate quantitative evaluation of baseflow contribution to streamflow is imperative to address seasonal drought vulnerability, flood occurrence and groundwater management concerns for efficient and sustainable water resources management in watersheds. Several baseflow separation algorithms using recursive filters, graphical method and tracer or chemical balance have been developed but resulting baseflow outputs always show wide variations, thereby making it hard to determine best separation technique. Therefore, the current global shift towards implementation of artificial intelligence (AI) in water resources is employed to compare the performance of deep learning models with conventional hydrograph separation techniques to quantify baseflow contribution to streamflow of Piney River watershed, Tennessee from 2001-2021. Streamflow values are obtained from the USGS station 03602500 and modeled to generate values of Baseflow Index (BI) using Web-based Hydrograph Analysis (WHAT) model. Annual and seasonal baseflow outputs from the traditional separation techniques are compared with results of Long Short Term Memory (LSTM) and simple Gated Recurrent Unit (GRU) models. The GRU model gave optimal BFI values during the four seasons with average NSE = 0.98, KGE = 0.97, r = 0.89 and future baseflow volumes are predicted. AI offers easier and more accurate approach to groundwater management and surface runoff modeling to create effective water policy frameworks for disaster management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.155-160
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• 2003

This paper presents a multiple face detector based on a robust pupil detection technique. The pupil detector uses active illumination that exploits the retro-reflectivity property of eyes to facilitate detection. The detection range of this method is appropriate for interactive desktop and kiosk applications. Once the location of the pupil candidates are computed, the candidates are filtered and grouped into pairs that correspond to faces using heuristic rules. To demonstrate the robustness of the face detection technique, a dual mode face tracker was developed, which is initialized with the most salient detected face. Recursive estimators are used to guarantee the stability of the process and combine the measurements from the multi-face detector and a feature correlation tracker. The estimated position of the face is used to control a pan-tilt servo mechanism in real-time, that moves the camera to keep the tracked face always centered in the image.