• Journal of the korean society of oceanography
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• v.38 no.2
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• pp.52-59
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• 2003

In this paper, a brief overview on data assimilation is provided in the context of oceanographic application. The ocean data assimilation needs to ingest various types of data such as satellites and floats, thus essentially requires dynamically-consistent assimilation methods. For such purpose, sequential and variational approaches are discussed and compared. The major advantage of the Kalman filter (KF) is that it can forecast error covariances at each time step. However, for models with very large dimension of state vector, the KF Is exceedingly expensive and computationally less efficient than four-dimensional variational assimilation (4D-Var). For operational application, simplified 4D-Var schemes as well as ensemble KF may be considered.

• Korean Journal of Remote Sensing
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• v.10 no.2
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• pp.63-82
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• 1994

Multitemporal analysis with remotely sensed data is complicated by numerous intervening factors, including atmospheric attenuation and occurrence of clouds that obscure the relationship between ground and satellite observed spectral measurements. A reconstruction system was developed to increase the discrimination capability for imagery that has been modified by residual dffects resulting from imperfect sensing of the target and by atmospheric attenuation of the signal. Utilizing temporal information based on an adaptive timporal filter, it recovers missing measurements resulting from cloud cover and sensor noise and enhances the imagery. The temporal filter effectively tracks a systematic trend in remote sensing data by using a polynomial model. The reconstruction system were applied to the AVHRR data collected over Korean Peninsula. The results show that missing measurements are typically recovered successfully and the temporal trend in vegetation change is exposed clearly in the reconstructed series.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.30 no.2
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• pp.87-98
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• 2020

Objective: The purpose of this study was to propose a sequential procedure for the simultaneous analysis of soluble and insoluble metal compounds. Methods: Methods for sampling and analyzing metal compounds such as ISO standards, NIOSH methods, HSE methods, and OSHA methods were reviewed. Results: Some metals have different OELs depending on the solubility of the compound. Therefore, we should take into account these characteristics and perform an exposure assessment. Soluble metal compounds are first extracted from the filter, and then the filter is digested by acids to analyze residual insoluble components. The extraction of soluble compounds can be completed by agitation for about 60 minutes with a leach solution (water) in a water bath at 37℃. For the analysis of insoluble compounds, the sample filter and the filtration filter remaining after the extraction of the soluble compounds are analyzed. This allows simultaneous determination of soluble and insoluble metal compounds. For hexavalent chromium compounds, soluble hexavalent chromium can first be extracted from the filter by using sulfate buffer. The insoluble hexavalent chromium remaining in the filter can then be extracted using carbonate buffer. Conclusion: Workers are often exposed to many hazardous substances with different exposure limits at industrial sites. The OELs for compounds of the same metal can be set differently depending on solubility. This study can help evaluate a worker's exposure to metal compounds by suggesting methods for the simultaneous determination of soluble and insoluble metal compounds.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.7
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• pp.536-542
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• 2016

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

• Journal of Korea Game Society
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• v.9 no.5
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• pp.137-144
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• 2009

This paper predicts 6 facial regions that may have sharply different scattering properties, rendering the face more realistically based on their diffusion profiles. The scattering properties are acquired in the form of high dynamic range by photographing the pattern formed around an unit ray incident on facial skin. The acquired data are fitted to a 'linear combination of Gaussian functions', which well approximates the original diffusion profile of skin and has good characteristics as the filter. During the process, to prevent its solutions from converging into local minima, we take advantage of the genetic algorithm to set up the initial value. Each Gaussian term is applied to the irradiance map as a filter, expressing subsurface scattering effect. In this paper, to efficiently handle the maximum 12 Gaussian filterings, we make use of the parallel capacity of CUDA.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.40-49
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• 2007

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be 'distracted' causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7B
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• pp.664-672
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• 2002

This paper analyzes a sequential estimator with flexible structure for rapid acquisition of the m-sequence signals. If the received PN chips stored in the estimator's internal registers as initial loading values include one error, this chip with error can be corrected through multiple local PN code generators to achieve high-speed acquisition performance. Hamming distance between regenerated local PN codes from the proposed system and received PN code is compared with given threshold to choose a possible correct path and to declare success of the code acquisition. Using signal flow graph, average acquisition time that depends on detection and false alarm probability is calculated. By modifying generally used matched filter structure for PN code acquisition, the proposed system provides flexible structure and rapid acquisition process.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.411-420
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• 2019

Camera model identification has been a subject of steady study in the field of digital forensics. Among the increasingly sophisticated crimes, crimes such as illegal filming are taking up a high number of crimes because they are hard to detect as cameras become smaller. Therefore, technology that can specify which camera a particular image was taken on could be used as evidence to prove a criminal's suspicion when a criminal denies his or her criminal behavior. This paper proposes a deep learning model to identify the camera model used to acquire the image. The proposed model consists of four convolution layers and two fully connection layers, and a high pass filter is used as a filter for data pre-processing. To verify the performance of the proposed model, Dresden Image Database was used and the dataset was generated by applying the sequential partition method. To show the performance of the proposed model, it is compared with existing studies using 3 layers model or model with GLCM. The proposed model achieves 98% accuracy which is similar to that of the latest technology.

• Journal of Soil and Groundwater Environment
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• v.10 no.6
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• pp.68-74
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• 2005

This study was carried out to evaluate the feasibility of field-scale sequential soil washing process for remediation on Kyongsangnamdo D mine soils which was heavily contaminated by arsonic. Arsenic concentration of untreated soils was $321\pm32mg/kg$. By applying the basic operating condition which was proposed from several pilot-scale experiments, arsenic concentration of treated soils was reduced 2.04 mg/kg ($99\%$ removal efficiency). We optimized the basic operating condition (mainly on washing solution concentration, cut-off size, and mixing ratio) to improve efficiently and economically the field-scale sequential soil washing process. The resulting optimized conditions were that solution concentration is 0.2M HCl, 1.0M HCl, 1.0M NaOH, that the cut-off size is 0.15mm (seive $\sharp$100), and that the mixing ratio is 1 3. Also, the optimized pH value for soil washing effluent treatment was 6 (33 ppb), in which the precipitation disruption caused by supersaturation of the floe did not occur. Results of TCLP tests showed that arsenic concentration from the washed gravels was 1.043 mg/L, that from soils ND (not detected), and that from filter cakes 0.066 mg/L. Also, the water content as a percentage of dewatered sludges was low $(48\%)$ and so the dewatered sludges can be disposed by landfilling. Through these results, we can concluded that tile field-scale sequential soil washing process developed in this study is adopted for remediation of arsenic-contaminated soils.