• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.740-751
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• 2020

In the aquaculture industry, few studies are analyzing big data for intrinsic meaning. Fishcare Laboratory (www.fishcare.kr) diagnostic data from 2016-2018 was analyzed for scuticociliatosis (caused by Miamiensis avidus) outbreak patterns in cultured olive flounder Paralichthys olivaceus in Jeju, Korea. The scuticociliatosis monthly occurrence ratio is reported in the summary table after preparing and filtering the basic dataset model. Nonparametric test results suggest differences in the water temperature, body length, and weight between groups with and without scuticociliatosis. Data distribution visualization revealed that shorter body length and lighter weight increased the occurrence of scuticociliatosis. The association rule mining technique was applied to determine the primary clinical signs of mixed scuticociliatosis and bacterial infections. Venn diagrams were used to report clinical signs and suggest commonalities. These results may help diagnose and treat fish and provide a decision-making reference.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.92-96
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• 2000

In this paper, we propose a call admission control(CAC) scheme fer the mixed voice/data DS-CDMA systems and analyze the Er-lang capacity under the proposed CAC scheme. Voice and data traffics require different system resources based oil their Quality of Service(QoS) requirements. In the proposed CAC scheme, some system resources are reserved exclusively for handoff calls to have high priority Over new calls. Additionally the queueing of both new and handoff data traffics that are not sensitive to delay is allowed. Ar a performance measure for the suggested CAC scheme. Erlang capacity is utilized. For the performance analysis, a four-dimensional Markov chain model is developed. Erlang capacity of a practical IS-95B type system depicts, and optimum values of system parameters such as the number of reservation channels and queue lengths are found with respect to Erlang capacity. Finally, it is observed that Erlang capacity is improved more than two times by properly selecting the system parameters with the proposed CAC scheme.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.417-424
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• 2012

In this paper, we propose an automatic system that recognizes dynamic human gestures activity, including Arabic numbers from 0 to 9. We assume the gesture trajectory is almost in a plane that called principal gesture plane, then the Least Squares Method is used to estimate the plane and project the 3-D trajectory model onto the principal. An improved FNN model combined with HMM is proposed for dynamic gesture recognition, which combines ability of HMM model for temporal data modeling with that of fuzzy neural network. The proposed algorithm shows that satisfactory performance and high recognition rate.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1396-1403
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• 2019

The radar wind profiler data contaminates with various non-atmospheric components that produce errors in moments and wind velocity estimations. This study implemented an adaptive Gaussian model to detect and remove the clutter from the radar return. This model includes DC filtering, ground clutter recognition, Gaussian fitting, and cost function to mitigate the clutter component. The adaptive model tested for the various types of clutter components and found that it is effective in clutter removal process. It is also applied for the both time series and spectrum datasets. The moments estimated using this method are compared with those derived using conventional DC-filtering clutter removal method. The comparisons show that the proposed method effectively removes the clutter and produce reliable moments.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1122-1137
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• 2021

PM_2.5 concentration in Seoul could be predicted by deep neural network model. In this paper, the contribution of input factors to the model's prediction results is analyzed using the LRP(Layer-wise Relevance Propagation) technique. LRP analysis is performed by dividing the input data by time and PM concentration, respectively. As a result of the analysis by time, the contribution of the measurement factors is high in the forecast for the day, and those of the forecast factors are high in the forecast for the tomorrow and the day after tomorrow. In the case of the PM concentration analysis, the contribution of the weather factors is high in the low-concentration pattern, and that of the air quality factors is high in the high-concentration pattern. In addition, the date and the temperature factors contribute significantly regardless of time and concentration.

• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.14-22
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• 2006

The multimedia visualizational spreadsheet environment is shown to be extremely effective in supporting the organized visualization of multi-dimensional data sets. In this paper, we designed the visualization model that consists of the configurational 2D arrangement of spreadsheet elements at run time and each spreadsheet element has a novel framestack. As the feature, it supports 3D data structure of each element on the proposed model. It enables the visualization spreadsheet 1) to effectively manage, organize, and compactly encapsulate multi-dimensional data sets, 2) to reconfigure cell-structures dynamically according to client request, and 3) to rapidly process interactive user interface. Using several experiments with scientific users, the model has been demonstrated to be a highly interactive visual browsing tool for 2D and 3D graphics and rendering in each frame.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.82-91
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• 2022

In this paper, time series prediction method of photovoltaic power is introduced using seasonal autoregressive integrated moving average (SARIMA). In order to obtain the best fitting model by a time series method in the absence of an environmental sensor, this research was used data below 50% of cloud cover. Three samples were extracted by time intervals from the raw data. After that, the best fitting models were derived from mean absolute percentage error (MAPE) with the minimum akaike information criterion (AIC) or beysian information criterion (BIC). They are SARIMA (1,0,0)(0,2,2)14, SARIMA (1,0,0)(0,2,2)28, SARIMA (2,0,3)(1,2,2)55. Generally parameter of model derived from BIC was lower than AIC. SARIMA (2,0,3)(1,2,2)55, unlike other models, was drawn by AIC. And the performance of models obtained by SARIMA was compared. MAPE value was affected by the seasonal period of the sample. It is estimated that long seasonal period samples include atmosphere irregularity. Consequently using 1 hour or 30 minutes interval sample is able to be helpful for prediction accuracy improvement.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2B
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• pp.44-52
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• 2005

This paper proposes a neural network prediction model to improve the valid packet transmission rate for the QoS(Quality of Service) of multimedia transmission. The Round Trip Time(RTT) and Packet Loss Rate(PLR) are predicted using a neural network and then the transmission rate is decided based on the predicted RTT and the PLR. The suggested method will improve the transmission rate since it uses the rate control factors corresponding to time of data is being transmitted, while the conventional one uses the transmission rate determined based on the past informations. An experimental set-up has been established using a Linux PC system, and the multimedia data are transmitted using UDP protocol in real time. The valid transmitted packets are about 5% higher than the one in the conventional TCP-Friendly congestion control method when the suggested algorithm was applied.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1385-1395
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• 2019

In this paper, we propose a method to fabricate a patient-specific breast implant using MRI images and 3D scan data. Existing breast implants for breast reconstruction surgery are primarily fabricated products for shaping, and among the limited types of implants, products similar to the patient's breast have been used. In fact, the larger the difference between the shape of the breast and the implant, the more frequent the postoperative side effects and the lower the satisfaction. Previous researches on the fabrication of patient-specific breast implants have used limited information based on only MRI images or on only 3D scan data. In this paper, we propose an algorithm for the fabrication of patient-specific breast implants that combines MRI images with 3D scan data, considering anatomical suitability for external shape, volume, and pectoral muscle. Experimental results show that we can produce precise breast implants using the proposed algorithm.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.716-725
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• 2007

In this paper we describe a motion tracking algorithm for 3D human animation using stereo vision system. This allows us to extract the motion data of the end effectors of human body by following the movement through segmentation process in HIS or RGB color model, and then blob analysis is used to detect robust shape. When two hands or two foots are crossed at any position and become disjointed, an adaptive algorithm is presented to recognize whether it is left or right one. And the real motion is the 3-D coordinate motion. A mono image data is a data of 2D coordinate. This data doesn't acquire distance from a camera. By stereo vision like human vision, we can acquire a data of 3D motion such as left, right motion from bottom and distance of objects from camera. This requests a depth value including x axis and y axis coordinate in mono image for transforming 3D coordinate. This depth value(z axis) is calculated by disparity of stereo vision by using only end-effectors of images. The position of the inner joints is calculated and 3D character can be visualized using inverse kinematics.