• Asia pacific journal of information systems
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• v.17 no.2
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• pp.83-95
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• 2007

We are motivated by how offline and online firms compete. The Internet made many conventional offline firms build a dynamic online business as another sales channel using their advantages such as brand equity, an existing customer base with comprehensive purchasing data, integrated marketing, economies of scale, and longtime experience with the logistics of order fulfillment and customer service. Even though the hybrid selling using both offline and online channel seems to have advantages over a pure online retailer, all the conventional offline firms are not seen to create an online business. Many conventional offline firms began to launch online business since the Internet era, however, just being online business is not likely to guarantee success. According to Bizate.com's report whether the hybrid channel strategy is successful is still under investigation. For example, consider the classic case of Barnes and Noble versus Amazon.com, Barnes and Noble was already the largest chain of bookstores in the U,S., when Amazon.com was established in 1995, BarnesandNoble.com followed suit in 1997, After suffering losses in its initial years, Amazon finally turned profitable in 2003. In 2004, Amazon's net income was $588 million on revenues of $6.92 billion, while Barnes and Noble earned $143 million on revenues of $4.87 billion, which included BarnesandNoble.com's loss of $21 million on revenues of $420 million. While these examples serve to motivate our thinking, it does not explain when offline firms should venture online. It also does not provide an analytical framework that can generalized to other competitive online-offline situations. We attempt to do this in this paper and analyze a hybrid channel model where a conventional offline firm competes against online firms using its own direct online channels. We are particularly interested in an optimal channel strategy when a conventional offline firm sells its products through its own direct online channel to compete with other rival online firms. We consider two situations where its direct online channel and other online firms are symmetric and asymmetric in the brand effect. The analysis of this paper presents several findings. In the symmetric model where a hybrid firm's online channel is not differentiated from a pure online firm, (i) a conventional offline firm will not launch its online business. In the asymmetric model where a hybrid firm's online channel is differentiated from a pure online firm, (ii) a conventional offline firm can launch its online business if its brand effect is greater than a certain threshold. (iii) there is a positive relationship between its brand effect and online customer costs showing that a conventional offline firm needs more brand effect in order to launch online business as online customer costs decrease. (iv) there is a negative relationship between its brand effect and the number of customers with access to the Internet showing that a conventional offline firm tends to launch its online business when customers with access to the Internet increases.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.615-624
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• 2020

An effective sampling method is necessary to monitor potato tuber moths (Phthorimaea operculella) because they are the biggest concern in potato-cultivating areas. In this study, a sequential sampling method was developed based on the results of field surveys of potato tuber moths in South Korea. Potato tuber moths were collected in fields cultivating potatoes at six sites, and their spatial distribution was investigated using the Taylor power law. The optimal sampling size and cumulative number of potato tuber moths in traps to stop sampling were determined based on the spatial distribution pattern and mean density of the collected potato tuber moths. Finally, the developed sampling method was applied to propose a control action, and its sampling efficiency was compared with that of the traditional sampling method using a binomial distribution. The potato tuber moths tended to aggregate; the optimal number was approximately 5 - 16 traps for sampling, and the number varied with the mean density of potato tuber moths according to the sampling sites. In addition, one, two, and three sites might require the following actions: Continued sampling, control, and no control, respectively. Sampling with the binomial distribution showed the minimum sample size was 12 when considering the economic threshold level. Here, we propose an effective sampling method that can be applied for future monitoring and field surveys of potato tuber moths in South Korea.

• Journal of Bio-Environment Control
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• v.26 no.1
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• pp.13-18
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• 2017

This study was conducted to investigate optimal temperature of garlic and develop bulb weight model in harvest time. Day and night temperature in chambers was set to $11/7^{\circ}C$, $14/10^{\circ}C$, $17/12^{\circ}C$, $20/15^{\circ}C$, $23/18^{\circ}C$, $28/23^{\circ}C$(16/8h). Bulb fresh and dry weight was heaviest on $20/15^{\circ}C$. In $11/7^{\circ}C$ and $14/10^{\circ}C$, leaf number and total leaf area increased slowly. But in the harvest, leaf number and total leaf area were not significant, except $28/23^{\circ}C$. Models were developed with fresh bulb weight. As a result of analyzing the model, $18{\sim}20^{\circ}C$ certified optimal mean temperature. And the growing degree day base temperature estimated $7.1^{\circ}C$, upper temperature threshold estimated $31.7^{\circ}C$. To verify the model, mean temperature on temperature gradient tunnel applied to the growth rate model. Lineal function model, quadric model, and logistic distribution model showed 79.0~95.0%, 77.2~92.3% and 85.0~95.8% accuracy, respectively. Logistic distribution model has the highest accuracy and good for explaining moderate temperature, growing degree day base temperature and upper temperature threshold.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.989-998
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• 2021

In the utilization of optical satellite imagery, which is greatly affected by clouds, periodic composite technique is a useful method to minimize the influence of clouds. Recently, a technique for selecting the optimal pixel that is least affected by the cloud and shadow during a certain period by directly inputting cloud and cloud shadow information during period compositing has been proposed. Accurate extraction of clouds and cloud shadowsis essential in order to derive optimal composite results. Also, in the case of an surface targets where spectral information is important, such as crops, the loss of spectral information should be minimized during cloud-free compositing. In thisstudy, clouds using two spectral indicators (Haze Optimized Tranformation and MeanVis) were used to derive a detection technique with low loss ofspectral information while maintaining high detection accuracy of clouds and cloud shadowsfor cabbage fieldsin the highlands of Gangwon-do. These detection results were compared and analyzed with cloud and cloud shadow information provided by Sentinel-2A/B. As a result of analyzing data from 2019 to 2021, cloud information from Sentinel-2A/B satellites showed detection accuracy with an F1 value of 0.91, but bright artifacts were falsely detected as clouds. On the other hand, the cloud detection result obtained by applying the threshold (=0.05) to the HOT showed relatively low detection accuracy (F1=0.72), but the loss ofspectral information was minimized due to the small number of false positives. In the case of cloud shadows, only minimal shadows were detected in the Sentinel-2A/B additional layer, but when a threshold (= 0.015) was applied to MeanVis, cloud shadowsthat could be distinguished from the topographically generated shadows could be detected. By inputting spectral indicators-based cloud and shadow information,stable monthly cloud-free composited vegetation index results were obtained, and in the future, high-accuracy cloud information of Sentinel-2A/B will be input to periodic cloud-free composite for comparison.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.637-654
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• 2023

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.431-438
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• 2020

In this paper, the method to extract lip contour by multiple threshold is proposed. Spyridonos et. el. proposed a method to extract lip contour. First step is get Q image from transform of RGB into YIQ. Second step is to find lip corner points by change point detection and split Q image into upper and lower part by corner points. The candidate lip contour can be obtained by apply threshold to Q image. From the candidate contour, feature variance is calculated and the contour with maximum variance is adopted as final contour. The feature variance 'D' is based on the absolute difference near the contour points. The conventional method has 3 problems. The first one is related to lip corner point. Calculation of variance depends on much skin pixels and therefore the accuracy decreases and have effect on the split for Q image. Second, there is no analysis for color systems except YIQ. YIQ is a good however, other color systems such as HVS, CIELUV, YCrCb would be considered. Final problem is related to selection of optimal contour. In selection process, they used maximum of average feature variance for the pixels near the contour points. The maximum of variance causes reduction of extracted contour compared to ground contours. To solve the first problem, the proposed method excludes some of skin pixels and got 30% performance increase. For the second problem, HSV, CIELUV, YCrCb coordinate systems are tested and found there is no relation between the conventional method and dependency to color systems. For the final problem, maximum of total sum for the feature variance is adopted rather than the maximum of average feature variance and got 46% performance increase. By combine all the solutions, the proposed method gives 2 times in accuracy and stability than conventional method.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• Journal of Ginseng Research
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• v.33 no.2
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• pp.104-110
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• 2009

It is well known that the saponin of Korean red ginseng (KRG) has an anti-oxidant effect and could suppress the accumulation of lipid peroxidation. The aim of the present study was to observe the inhibitory effect of KRG on mice with noise-induced hearing loss, and to determine its optimal dose. BALB/c mice with a normal hearing level and normal Preyer's reflexes were used in the study. The mice in the permanent-threshold-shift (PTS) group were exposed to noise (120-dB SPL, white noise band) in a noise booth for 3 h a day, for three consecutive days. The mice in the experimental group were given heat-processed red-ginseng extract (50 mg/kg, 100 mg/kg, and 200 mg/kg), and those in the control group were given normal saline alone during their noise exposure. The mice in the temporary-threshold-shift (TTS) group were exposed to noise (120 dBSPL, white noise band) in a noise booth for 3 h. The mice in the experimental group were given heat-processed red-ginseng extract (50 mg/kg, 100 mg/kg, and 200 mg/kg), and those in the control group were given normal saline alone before their noise exposure. The hearing levels of the mice were measured through auditory brainstem response (ABR) immediately and I, 3, 5, 7, and 14 days after their noise exposure. Cochleae were removed from the mice 14 days after their noise exposure. lmmunochemical and immunofluorescent staining were performed to observe the expression of 8-oxoG in cochlea. In the PTS group, the hearing function of the mice in all the groups was not recovered after their noise exposure. In the TTS group, however, the hearing function of the mice in all the groups was recovered within 14 days. Reduced hearing impairment and early recovery were observed in the mice that were given 200 mg/kg KRG, and early recovery was observed in the mice that were given 100 mg/kg KRG The immunopositive staining of 8-oxoG was detected in the stria vascularis in the control group but was diminished in the mice that were given 200 mg/kg KRG The ingestion of more than 100 mg/kg KRG demonstrated a protection and recovery effect on the noiseinduced-TTS group. Since KRG has been reported to be a safe compound even up to hundreds of mg/kg, a higher concentration of it may effectively protect and recover TTS.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.148-157
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• 2014

Various techniques have been developed for efficient compression of stereoscopic 3D videos. Mixed-resolution based approach is one representative bit-rate saving method based on the characteristics of human visual system that the mixed-resolution stereoscopic videos are perceived close to the higher resolution. However, when the difference between the left and right image resolutions is bigger than a certain threshold level, it causes the perceived quality degradation of the 3D images. Subsequently, several researches tried to find the correlation between the difference in resolution and the level of the perceived quality degradation, but they conducted the experiments just considering the difference in resolution without considering the viewing distances, so thereby different results were retrieved from test to test. In this work, we calculated the optimal viewing distance based on the human visual system, and conducted the subjective tests with the calculated viewing distance. With the results, we demonstrate that the fixed and mobile hybrid 3DTV, which is based on mixed-resolution stereoscopic images, can provide the high quality 3D services.