• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.131-142
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• 2005

The low rate speech coders under 4 kbit/s are based on sinusoidal transform coding (STC) or multiband excitation (MBE). Since the harmonic coders are not efficient to reconstruct the transient segments of speech signals such as onsets, offsets, non-periodic signals, etc, the coders do not provide a natural speech quality. This paper proposes method of a efficient transient model :d a multi-mode low rate coder at 2.4 kbit/s that uses harmonic model for the voiced speech, stochastic model for the unvoiced speech and a model using aperiodic pulse location tracking (APPT) for the transient segments, respectively. The APPT utilizes the harmonic model. The proposed method uses different models depending on the characteristics of LPC residual signals. In addition, it can combine synthesized excitation in CELP coding at time domain with that in harmonic coding at frequency domain efficiently. The proposed coder shows a better speech quality than 2.4 kbit/s version of the mixed excitation linear prediction (MELP) coder that is a U.S. Federal Standard for speech coder.

• Journal of the Korea Computer Graphics Society
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• v.27 no.1
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• pp.9-17
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• 2021

In this paper, we present a simple and fast supervised learning framework based on model predictive control so as to learn motion controllers for a physic-based character to track given example motions. The proposed framework is composed of two components: training data generation and offline learning. Given an example motion, the former component stochastically controls the character motion with an optimal controller while repeatedly updating the controller for tracking the example motion through model predictive control over a time window from the current state of the character to a near future state. The repeated update of the optimal controller and the stochastic control make it possible to effectively explore various states that the character may have while mimicking the example motion and collect useful training data for supervised learning. Once all the training data is generated, the latter component normalizes the data to remove the disparity for magnitude and units inherent in the data and trains an artificial neural network with a simple architecture for a controller. The experimental results for walking and running motions demonstrate how effectively and fast the proposed framework produces physics-based motion controllers.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.53-69
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• 2020

The increase of the impermeable area due to industrialization and urban development distorts the hydrological circulation system and cause serious stream drying phenomena. In order to manage this, it is necessary to develop a technology for impact assessment of stream drying phenomena, which enables quantitative evaluation and prediction. In this study, the cause of streamflow reduction was assessed for dam and weir watersheds in the five major river basins of South Korea by using distributed hydrological model DrySAT-WFT (Drying Stream Assessment Tool and Water Flow Tracking) and GIS time series data. For the modeling, the 5 influencing factors of stream drying phenomena (soil erosion, forest growth, road-river disconnection, groundwater use, urban development) were selected and prepared as GIS-based time series spatial data from 1976 to 2015. The DrySAT-WFT was calibrated and validated from 2005 to 2015 at 8 multipurpose dam watershed (Chungju, Soyang, Andong, Imha, Hapcheon, Seomjin river, Juam, and Yongdam) and 4 gauging stations (Osucheon, Mihocheon, Maruek, and Chogang) respectively. The calibration results showed that the coefficient of determination (R²) was 0.76 in average (0.66 to 0.84) and the Nash-Sutcliffe model efficiency was 0.62 in average (0.52 to 0.72). Based on the 2010s (2006~2015) weather condition for the whole period, the streamflow impact was estimated by applying GIS data for each decade (1980s: 1976~1985, 1990s: 1986~1995, 2000s: 1996~2005, 2010s: 2006~2015). The results showed that the 2010s averaged-wet streamflow (Q95) showed decrease of 4.1~6.3%, the 2010s averaged-normal streamflow (Q185) showed decreased of 6.7~9.1% and the 2010s averaged-drought streamflow (Q355) showed decrease of 8.4~10.4% compared to 1980s streamflows respectively on the whole. During 1975~2015, the increase of groundwater use covered 40.5% contribution and the next was forest growth with 29.0% contribution among the 5 influencing factors.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.113-127
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• 2012

Face detection methods using image processing have been proposed in many different ways. Generally, the most widely used method for face detection is an Adaboost that is proposed by Viola and Jones. This method uses Haar-like feature for image learning, and the detection performance depends on the learned images. It is well performed to detect face images within a certain distance range, but if the image is far away from the camera, face images become so small that may not detect them with the pre-learned Haar-like feature of the face image. In this paper, we propose the far distance face detection method that combine the Aadaboost of Viola-Jones with a saliency map and user's attention information. Saliency Map is used to select the candidate face images in the input image, face images are finally detected among the candidated regions using the Adaboost with Haar-like feature learned in advance. And the user's eye-tracking information is used to select the interest regions. When a subject is so far away from the camera that it is difficult to detect the face image, we expand the small eye gaze spot region using linear interpolation method and reuse that as input image and can increase the face image detection performance. We confirmed the proposed model has better results than the conventional Adaboost in terms of face image detection performance and computational time.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.4
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• pp.284-291
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• 2017

There is no doubt that advances in UAV technology have improved military performance. However, these advances require humans to adapt to new and complex operational systems. UAV has been rapidly expanding to a variety of fields such as reconnaissance, transportation, communication and aerial photographing recently. Also, with the development of UAV automation technology, one operator is able to supervisory-control multiple-UAVs. However, as the number of assigned UAV increases, the amount of information increases and this results in the workload of the operator increasing and deterioration in controlling performance. Accordingly, there is a need for a model to determine the level of overload an operator may encounter with regard to multiple-UAV but nationally this kind of research is currently lacking. Therefore, this paper provides an experimental platform for evaluating workload index effectiveness integrating multiple-UAV operational environments, GCS, and eye-tracking system followed by a limited survey of domestic and international studies of multi-UAV overload studies.

• Journal of Soil and Groundwater Environment
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• v.11 no.1
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• pp.37-44
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• 2006

This study summarizes advantages and disadvantages of numerical methods and compares ELLAM and LEZOOMPC to develop an efficient numerical modeling technique on contaminant transport. Eulerian-Lagrangian method and Eulerian method are commonly used numerical techniques. However Eulerian-Lagrangian method does not conserve mass globally and fails to treat boundary in a straightforward manner. Also, Eulerian method has restrictions on the size of Courant number and mesh Peclet number because of time truncation error. ELLAM (Eulerian Lagrangian Localized Adjoint Method) which has been popularly used for past 10 years in numerical modeling, is known for overcoming these numerical problems of Eulerian-Lagrangian method and Eulerian method. However, this study investigates advantages and disadvantages of ELLAM and suggests a change for the better. To figure out the disadvantages of ELLAM, the results of ELLAM, LEZOOMPC (Lagrangian-Eulerian ZOOMing Peak and valley Capturing), and visual MODFLOW are compared for four examples having different mesh Peclet numbers. The result of ELLAM generates numerical oscillation at infinite of mesh Peclet number, but that of LEZOOMPC yields accurate simulations. The simulation results suggest that the numerical error of ELLAM could be alleviated by adopting some schemes in LEZOOMPC. In other words, the numerical model which combines ELLAM with backward particle tracking, forward particle tracking, adaptively local zooming, and peak/valley capturing of LEZOOMPC can be developed for not only overcoming the numerical error of ELLAM, but also keeping the numerical advantage of ELLAM.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3037-3047
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• 2000

This paper describes a method of tracking a close leading vehicle by color image processing using the pairs of tail and brake lights. which emit red light and are housed on the rear of the vehicle in stop-and-go traffic condition. In the color image converted as an HSV color model. candidate regions of rear lights are identified using the color features of a pair of lights. Then. the pair of tailor brake lights are detected by means of the geometrical features and location features for the pattern of the tail and brake lights. The location of the leading vehicle can be estimated by the location of the detected lights and the vehicle can be tracked continuously. It is also possible to detect the braking status of the leading vehicle by measuring the change in HSV color components of the pair of lights detected. In the experiment. this method tracked a leading vehicle successfully from urban road images and was more useful at night than in the daylight. The KAV-Ill (Korea Autonomous Vehicle- Ill) equipped with a color vision system implementing this algorithm was able to follow a leading vehicle autonomously at speeds of up to 15km!h on a paved road at night. This method might be useful for developing an LSA (Low Speed Automation) system that can relieve driver's stress in the stop-and-go traffic conditions encountered on urban roads.

• International Journal of Highway Engineering
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• v.6 no.2 s.20
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• pp.1-13
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• 2004

This study dealt with size effect of specimen in measuring deformation strength and estimating rut resistance of asphalt concretes under static loading using Kim test. Two aggregates, a normal asphalt (pen 60-80) and 6 polymer-modified asphalt (PMA) binders were used for preparation of 14 dense-graded mixtures. Mixtures were prepared based on optimum asphalt content by Marshall compactor (S= 10cm) and gyratory compactor (S= 15cm) for Kim test and for wheel tracking test. In statistical analysis by general linear model (GLM) procedure of SAS, the diameter of specimen was found not to be a significant factor that affect the Kim test result. Therefore, it was found that either loom-diameter or 15cm-diameter of specimen gave no significant difference in deformation strength ($K_D$) values in Kim test for any aggregate mixture. However, the thickness of specimen was found to be a significant factor in determining $K_D$. It is estimated that $K_D$ is a function of y, vertical deformation, and y has something to do with thickness of specimen. Therefore, it is suggested that the thickness of specimen should not be higher than 6.6cm, and the correction factor depending on the thickness value should be developed in the future study.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1167-1176
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• 2004

Along the advent of Internet technology, the computing environment has been considerably changed in many application domains. Especially, a lot of researches for e-Logistics have been done for the last 3 years. The e-Logistics means the virtual business activity and service architecture among the logistics companies based on the Internet technology. To construct effectively the e-Logistics framework, researches on the development of the Moving Object Technology(MOT) including GPS and GIS with spatiotemporal databases technique so far has been done The Moving Object Technology stands for the efficient management for the spatiotemporal objects such as vehicles, airplanes, and vessels which change continuously their spatial location along with time flows. However, most systems manage just only the location information detected lately by many reasons so that the uncertainty processing for the past and future location of the moving objects is still very hard. In this paper, we propose the moving object uncertainty model and system design for e-Logistics applications. The MOMS architecture in e-Logistics is suggested and the detailed explain of sub-systems including the uncertainty processor of moving objects is described. We also explain the comprehensive examples of MOMS and uncertainty processing in Delivery Parcel Application that is one of major application of e-Logistics domain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.1C
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• pp.26-35
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• 2006

In this paper, an automatic mobile robot system for a intelligent path planning using the detection scheme of the spatial coordinates based on stereo camera is proposed. In the proposed system, face area of a moving person is detected from a left image among the stereo image pairs by using the YCbCr color model and its center coordinates are computed by using the centroid method and then using these data, the stereo camera embedded on the mobile robot can be controlled for tracking the moving target in real-time. Moreover, using the disparity map obtained from the left and right images captured by the tracking-controlled stereo camera system and the perspective transformation between a 3-D scene and an image plane, depth information can be detected. Finally, based-on the analysis of these calculated coordinates, a mobile robot system is derived as a intelligent path planning and a estimation. From some experiments on robot driving with 240 frames of the stereo images, it is analyzed that error ratio between the calculated and measured values of the distance between the mobile robot and the objects, and relative distance between the other objects is found to be very low value of $2.19\%$ and $1.52\%$ on average, respectably.