• Proceedings of the Korea Society of Environmental Toocicology Conference
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• 2002.10a
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• pp.107-116
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• 2002

GLP regulations were initially “promulgated to address assuring the validity of data in the wake of investigations by EPA and FDA during the mid -1970's which revealed that some studies submitted to the agencies had not been conducted in accordance with acceptable laboratory practices.” [1] In the early 1970s, results of an investigation by the FDA in about 40 laboratories revealed many cases of badly managed studies, poor training of personnel and some cases of deliberate fraud. The general findings were that there were poorly trained study directors and study personnel, poorly designed protocols, protocols not followed, procedures not conducted as described, raw data badly collected, data not correctly identified, data without traceability, data not verified and approved by responsible persons, lack of standardised procedures, poor animal husbandry, inadequate characterisation of test items and test systems, inadequate resources, equipment poorly calibrated or otherwise qualified, reports not sufficiently verified, not an accurate account of the actual study, not a proper reflection of raw data and inadequate archiving of data. These problems are not just past history, since they resurface time and time again, even in quite recent times as the experience of GLP inspectors shows [1]. The GLPs specify minimum practices and procedures in order to ensure the quality and integrity of data submitted in accordance with a regulatory requirement

• Environmental Engineering Research
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• v.25 no.4
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• pp.515-521
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• 2020

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1($\frac{{\mu}g}{l}$), however, the four-layer NNs proved superior in terms of R² and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

• Journal of International Academy of Physical Therapy Research
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• v.5 no.2
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• pp.772-780
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• 2014

The purpose of this study is to identify the practical knowledge about osteoporosis and health promoting behavior possessed by male and female university students in their twenties. Next, the study seeks to analyze the difference in the degree of knowledge and practice of health promoting behavior depending on the students' area of study (health-related or non-health-related major) and previous education about osteoporosis. A survey was given to 300 male and female university students in Jeju Island from November 18 to December 6, 2013. Regarding knowledge about osteoporosis, the accuracy rate of health science major participants was 16.8 % higher than that of those of non-health science, and the accuracy rate of participants with previous education about osteoporosis was 12.9 % higher than those who had not. Health promoting behavior showed a higher degree of practice among students in health-related majors and those with previous applicable education. There were significant differences between the knowledge of osteoporosis and major and the presence and absence of prior education. Regarding the degree of health promoting behavior and major, the presence or absence of prior education showed significant differences. Among male and female students in their twenties, the recognition of knowledge about osteoporosis is very low. There is a need to develop various programs that focus on osteoporosis prevention rather than treatment, to improve the quality of education and training content according to the individual, and to lower the target age for osteoporosis education.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.44-47
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• 1991

There exists a transform trellis code that is optimal for stationary Gaussian sources and the squared-error distortion measure at all rates. In this paper, we train an asymptotically optimal version of such a code to obtain one which is matched better to the statistics of real world data. The training algorithm uses the M-algorithm to search the trellis codebook and the LBG-algorithm to update the trellis codebook. To adapt the codebook for the varying input data. we use two gain-adaptive methods. The gain-adaptive scheme 1, which normalizes input block data by its gain factor, is applied to images at rate 0.5 bits/pixel. When each block is encoded at the same rate, the nonstationarity among the block variances leads to a variation in the resulting distortion from one block to another. To alleviate the non-uniformity among the encoded image, we design four clusters from the block power, in which each cluster has its own trellis codebook and different rates. The rate of each cluster is assigned through requiring a constant distortion per-letter. This gain-adaptive scheme 2 produces good visual and measurable quality at low rates.

• Journal for History of Mathematics
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• v.25 no.2
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• pp.97-111
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• 2012

This paper aims to identify the area of knowledge required for constructing test items by comparing the consulting contents with the area of MKT(Mathematical Knowledge for Teaching). This paper collects the consulting results by the consulting group of A provincial office of education from 2007 to 2009, and analyzes and categorizes the results. The knowledge for constructing test items are known by the analysis on the consulting contents. Training and consulting for teachers are necessary to enable them to recognize the importance of these categories and improve the quality of test items.

• Journal of Hospice and Palliative Care
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• v.15 no.4
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• pp.183-187
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• 2012

The importance of palliative care for terminal cancer patients has been emphasized globally. Korea has formulated and implemented its policy for cancer control as it drew up a 10-year plan for cancer patient care. We examined Korea's National Cancer Act and the second 10-year plan for cancer patient care, which are legal grounds for palliative care projects for terminal cancer patients, to check the current status of Korea's efforts to establish a hospice and palliative care system. Institutionalization of hospice and palliative care has been developed within a framework of the national cancer project. Efforts such as expansion of hospice units, experts training and quality improvement should continue after the reimbursement of hospice and palliative care begins in 2013.

• Journal of Biosystems Engineering
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• v.43 no.3
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• pp.229-236
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• 2018

Purpose: Makgeolli is a traditional alcoholic beverage made from rice with a fermentation starter called "nuruk." The concentration of alcohol in makgeolli depends on the temperature of the fermentation tank. It is important to monitor the alcohol concentration to manage the makgeolli production process. Methods: Data were collected from 84 makgeolli fermentation tanks over a year period. Independent variables included the temperatures of the tanks and the room where the tanks were located, as well as the quantity, acidity, and water concentration of the source. Software for the multilayer perceptron model (MLP) was written in Python using the Scikit-learn library. Results: Many models were created for which the optimization converged within 100 iterations, and their coefficients of determination $R^2$ were considerably high. The coefficient of determination $R^2$ of the best model with the training set and the test set were 0.94 and 0.93, respectively. The fact that the difference between them was very small indicated that the model was not overfitted. The maximum and minimum error was approximately 2% and the total MSE was 0.078%. Conclusions: The MLP model could help predict the alcohol concentration and to control the production process of makgeolli. In future research, the optimization of the production process will be studied based on the model.

• Horticultural Science & Technology
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• v.17 no.6
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• pp.753-754
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• 1999

Application of compost to vineyards reduced nitrogen absorption into vine roots, increased soil nitrogen content, soil pH, soil organic matter, and fine roots density. Long pruning reduced the growth of 'Campbell Early' of Wakeman's training system. Maintaining proper vine shape was very difficult because long pruning decreased the vine growth. In conclusion, the application of compost to 'Campbell Early', 'Kyoho', 'Sheridan' vineyard decreased vine growth, increased soil organic matter, soil pH and favored the rhizosphere condition and the growth of fine roots increased.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.29-36
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• 2019

When dealing with outdoor images in a variety of computer vision applications, the presence of shadow degrades performance. In order to understand the information occluded by shadow, it is essential to remove the shadow. To solve this problem, in many studies, involves a two-step process of shadow detection and removal. However, the field of shadow detection based on CNN has greatly improved, but the field of shadow removal has been difficult because it needs to be restored after removing the shadow. In this paper, it is assumed that shadow is detected, and shadow-less image is generated by using original image and shadow mask. In previous methods, based on CGAN, the image created by the generator was learned from only the aspect of the image patch in the adversarial learning through the discriminator. In the contrast, we propose a novel method using a discriminator that judges both the whole image and the local patch at the same time. We not only use the residual generator to produce high quality images, but we also use joint loss, which combines reconstruction loss and GAN loss for training stability. To evaluate our approach, we used an ISTD datasets consisting of a single image. The images generated by our approach show sharp and restored detailed information compared to previous methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3873-3887
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• 2018

In this paper, we propose a novel method to recover images corrupted by impulse noise. The proposed method uses two stages: noise detection and filtering. In the first stage, we use pixel values, rank-ordered logarithmic difference values, and median values to train a neural-network-based impulse noise detector. After training, we apply the network to detect noisy pixels in images. In the next stage, we use group-based weighted couple sparse representation to filter the noisy pixels. During this second stage, conventional methods generally use only clean pixels to recover corrupted pixels, which can yield unsuccessful dictionary learning if the noise density is high and the number of useful clean pixels is inadequate. Therefore, we use reconstructed pixels to balance the deficiency. Experimental results show that the proposed noise detector has better performance than the conventional noise detectors. Also, with the information of noisy pixel location, the proposed impulse-noise removal method performs better than the conventional methods, through the recovered images resulting in better quality.