• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• The Journal of the Acoustical Society of Korea
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• v.43 no.5
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• pp.529-535
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• 2024

This paper presents our research on automatic audio panning for media content production. Previously, tracking an audio was done manually. With the advent of the immersive audio era, the need for an automatic audio panning system has increased, yet no substantial research has been progressed to date. Therefore, we propose a computer vision-based human tracking and depth feature processing system which processes depth feature through using 2-dimensional coordinates and models 3-dimensional view transformation for automatic audio panning to ensure audiovisual congruence. Also, this system applies stage size estimation model which gets input as an image and extrapolates stage width and depth as meter unit. Since our system estimates stage sizes and directly applies them to view transformation, no additional depth data training is required. To validate the proposed system, we also conducted a pilot test with Unity based sample video. Our team expects that our system will enable automated audio panning, assisting many audio engineers.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.429-434
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• 2008

The objective of this paper is to introduce a project on developing Finish Drawing Automation System. The system aims to improve efficiency of the BIM-based estimation, which is realized by automatic derivation of three-dimensional geometry models of the finish details. First, overall workload differences between the drawing-based estimation methods and the BIM-based methods are analyzed. Second, an automated finish detail design method is proposed as a time-saving measure for the BIM-based estimation, as manual modeling accounts for the most time spent in the model-based estimation process. Finally, the proposed system is evaluated using a case of washboard design.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.29-35
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• 2013

Recently, various Best Management Practices (BMPs) have been applied at a field to reduce soil erosion. Hourly Runoff and Sediment Model for Best Management Practices (HRSM4BMP) model could be used to evaluate soil erosion reduction for various agricultural BMPs at fields. Runoff and sediment yield from source areas have to be predicted with greater accuracies to evaluate sediment reduction efficiently with BMPs. To achieve this, the best parameters related with runoff and sediment modules of the HRSM4BMP model should be identified with proper calibration processes. Although manual calibration is often utilized in calibrating runoff and sediment using the HRSM4BMP, objective calibration method would be recommended. The purpose of the study was to develop an automatic calibration tool of the HRSM4BMP model with PARASOL method. This automatic calibration tool was applied to Bangdongri, Chuncheon-si to evaluate its calibration performance. The $R^2$, NSE and RMSE value for runoff estimation were 0.92, 0.92, $0.3m^3$, and for sediment yield estimation were 0.94, 0.94, 0.0027 kg. As shown in this result, automatic calibration tool of HRSM4BMP model would be used to determine the best parameters and can be used to simulate runoff and sediment yield with acceptable accuracies.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.1-10
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• 2001

Neural networks we increasingly being seen as an addition to the statistics toolkit which should be considered alongside both classical and modern statistical methods. Neural networks are usually useful for classification and function estimation. In this paper we concentrate on function estimation using neural networks with weight decay factor The use of weight decay seems both to help the optimization process and to avoid overfitting. In this type of neural networks, the problem to decide the number of hidden nodes, weight decay parameter and iteration number of learning is very important. It is called the optimization of weight decay neural networks. In this paper we propose a automatic optimization based on genetic algorithms. Moreover, we compare the weight decay neural network automatically learned according to automatic optimization with ordinary neural network, projection pursuit regression and support vector machines.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.40-44
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• 2002

Precision correction is the process of geometrically aligning images to a reference coordinate system using GCPs(Ground Control Points). Many applications of remote sensing data, such as change detection, mapping and environmental monitoring, rely on the accuracy of precision correction. However it is a very time consuming and laborious process. It requires GCP collection, the identification of image points and their corresponding reference coordinates. At typical satellite ground stations, GCP collection requires most of man-powers in processing satellite images. A method of automatic registration of satellite images is demanding. In this paper, we propose a new algorithm for automatic precision correction by GCP chips and RANSAC(Random Sample Consensus). The algorithm is divided into two major steps. The first one is the automated generation of ground control points. An automated stereo matching based on normalized cross correlation will be used. We have improved the accuracy of stereo matching by determining the size and shape of match windows according to incidence angle and scene orientation from ancillary data. The second one is the robust estimation of mapping function from control points. We used the RANSAC algorithm for this step and effectively removed the outliers of matching results. We carried out experiments with SPOT images over three test sites which were taken at different time and look-angle with each other. Left image was used to select UP chipsets and right image to match against GCP chipsets and perform automatic registration. In result, we could show that our approach of automated matching and robust estimation worked well for automated registration.

• Journal of Software Engineering Society
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• v.26 no.4
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• pp.77-92
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• 2013

To plan a software project, the manager (1)make a work breakdown structure (WBS), (2) estimate efforts for each task, (3) assign employee to each task, and (4) estimate overall schedule. When software project becomes complicated, the possible combination of WBS, effort, and employee assignments dramatically becomes larger. Software planning tool can help software project managers to deal with this complexity. In this research, we discuss with a group of experts who work in software industry, to elicit practical requirements that should be considered in the software planning technique. Considering these requirements, we develop a software project planning tool APP (Automatic Project Planner) which provide effort estimation based on historical knowledge data and automatic human resource allocation. Our technique can be the basis of reasonable and practical software project planing.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.59-61
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• 2004

For automatic landing of small VTOL UAV, it is necessary to calculate the distance from the UAV and the ground. The distance can be generally measured by a ultra-sonic sensor, but the ultra-sonic sensor has errors according to velocity of a sensor board. To compensate these errors, we proposed a sensor fusion method using a Kalman filter.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1077-1080
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• 1991

It has been recognized as important subject by users that PID-Controllers widely used in industrial processes must be well-tuned, In this paper, We present an automatic tuning method for PID-Controllers which is based on discrete parameter estimation and application of conventional tuning-rules. The method is easy to implement on microprocessor because critical values are obtained by the mathematical computation. Also, it permits quick on-line tuning. Simulation results show that most processes are well tuned by the suggested tuning method in this paper.

• Structural Engineering and Mechanics
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• v.15 no.5
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• pp.579-608
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• 2003

This paper deals with adaptive finite element analysis of linearly elastic structures using different error estimators based on flux projection (or best guess stress values) and residual methods. Presentations are given on a typical h-type adaptive analysis, a mesh refinement scheme and the coupling of adaptive finite element analysis with automatic mesh generation. Details about different error estimators are provided and their performance, reliability and convergence are studied using six node quadratic triangular elements. Several examples are presented to demonstrate the reliability of different error estimators.