• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.224-236
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• 2002

A long-term growth simulation was performed at 496 land units in the western coastal plains (WCP) of North Korea to test the potential adaptability of each land unit for growing South Korean rice cultivars. The land units for rice cultivation (CZU), each of them represented by a geographically referenced 5 by 5 km grid tell, were identified by analyzing satellite remote sensing data. Surfaces of monthly climatic normals for daily maximum and minimum temperature, precipitation number of rain days and solar radiation were generated at a 1 by 1 km interval by spatial statistical methods using observed data at 51 synoptic weather stations in North and South Korea during 1981-2000. Grid cells felling within a same CZU and, at the same time, corresponding to the satellite data- identified rice growing pixels were extracted and aggregated to make a spatially explicit climatic normals relevant to the rice growing area of the CZU. Daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CERES-rice model suitable for 11 major South Korean cultivars were derived from long-term field observations. Eight treatments comprised of 2 transplanting dates $\times$ 2 cropping systems $\times$ 2 irrigation methods were assigned to each cultivar. Each treatment was simulated with the randomly generated 30 years' daily weather data (from planting to physiological maturity) for 496 land units in WCP to simulate the growth and yield responses to the interannual climate variation. The same model was run with the input data from the 3 major crop experiment stations in South Korea to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for comparison. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific South Korean cultivar. The results may be utilized as decision aids for agrotechnology transfer to North Korea, for example, germplasm evaluation, resource allocation and crop calendar preparation.

• Korea Journal of Hospital Management
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• v.8 no.1
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• pp.112-134
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• 2003

The purpose of this study is to analyze the consumer's expectation before the health care service and the consumer's satisfaction after it. The participants of the study are inpatients in a general hospital located in Seoul. The resources were collected from the self-administration questionnaire survey run parallel with face to face interview. In order to measure the degree of the consumer's expectation, 349 samples were collected from the first questionnaire survey on the date of admission to the hospital. The second questionnaire survey was carried out on the date of discharge to the hospital with the participants responding to the first questionnaire survey. There are 154 samples collected from this survey. The results from the analysis of these resources are as follow. First, the survey shows that one of the highest consumers' expectations was about the generosity, kindliness and sincerity from the staff at the hospital, specially from doctors. Second, according to the analysis of the factors affecting the expectations of the consumers, with regard to path of admission to a hospital relating to patient's features, outpatient who gets into a hospital expected good medical care much more than the other patients. In regard of doctor's features, patients usually and highly expect good medical care from doctors who have good carrier and much experience. Third, according to the second questionnaire survey, what patients are satisfied most with is about the generosity and sincerity from staff at a hospital, especially from doctors and their gem attitudes. The results from survey show that the differences among the degree of consumers' satisfaction are very variable, depending on surrounding environments and facilities. The only fact that expectation didn't meet with satisfaction appeared to the case about technology and skill of medical care and the case about updated medical skills and equipments. Fourth, comparing the degree of expectation with the degree of satisfaction of consumers, correlative analysis was concerned significantly and specifically about the part of overall cleanliness relating to facilities and surrounding environments, the items about medical examination and test plan procedure relating to skill of medical care, professional specialties and convenience for procedure, and the items about satisfying explanations and concern about patients from doctors relating to staff's generosity and sincerity. Fifth, the analysis of the factors affecting the degree of how much patients are satisfied with shows that relating to sociodemographical features, patients are not satisfied with the case when the time and process of medical treatment are getting longer. It is surveyed that consumer were satisfied with the motivation to visit a hospital and the insurance type in patient's feature and so were the medical department and the factor of the degree of the expectation in disease's feature. Sixth, according to analysis based on the survey, patients would join again a hospital when they get satisfaction from the medical care and also they want to come again regarding to doctor's capability. For example, when doctors are old, have a good carrier and much experience, patients would come again. As seen from the above, consumers are usually satisfied with the medical treatment more than that they expected before. They would intend to use again when they get satisfaction from the medical care provided at a hospital. Patients and consumers highly expect good attitude as well as capacity from medical doctors and they are also generally satisfied with those things. Therefore, in order to increase the degree of consumer's satisfaction and their intention to come again, the hospital staff would have to commit themselves to achieve high quality service continuously and would have to make an effort to offer the finest quality service.

• Journal of Life Science
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• v.20 no.2
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• pp.190-196
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• 2010

Nowadays many people use antibiotics to protect processed foods from many pathogenic bacteria. The abuse of antibiotics, however, can run the risk of creating resistant forms of bacterium. Our study focus is on making new substances that can not only replace antibiotics but also be friendly to the environment. In our experiments, we used fermented citrus fruit, soil microbes and coenzyme Q10 as probiotics and prebiotics. Chickens in the experimental group were fed these substances via oral route while those in the control group were not. After specific time periods, blood and feces samples were collected to test for Salmonella spp.. It is interesting that fermented citrus fruit was the most effective in suppressing this bacterium. Furthermore, dissection of the experiment group chickens shows that their livers did not change to a yellow color, in contrast to the control group. The results confirmed our proposal that the chickens fed with these materials can be protected from infection by Salmonella and other pathogens. These probiotics and prebiotics are highly practical because they are natural substances that can be easily recycled in the environment. It can also be used as an animal feed ingredient because of its safety.

• Journal of Life Science
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• v.24 no.9
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• pp.959-966
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• 2014

This study was carried out to develop a functional healthy drink using 60% ethanol of dried Acanthopanax senticosus stem extract (ASE). The preparation, physical activity, anti-oxidant activity, and sensory properties of ASE were investigated. The moisture, crude protein, crude lipid, and ash contents of dried ASE were $6.50{\pm}0.12%$, $5.89{\pm}0.16%$, $1.18{\pm}0.11%$, and $3.03{\pm}0.40%$ respectively. The 1,1-Diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity was $87.42{\pm}1.63%$ at 1/10 folds diluted ASE. In total, 40 male ICR mice were divided into five groups including the control (PBS), positive control (Red ginseng 200 mg/kg/day), and ASE-treated groups at doses of 35, 70, and 140 mg/kg/day for five weeks, respectively. ASE was administrated orally one time per day for five weeks before treadmill exercises, and normal and positive controls were fed PBS and red ginseng extract. In the treadmill test, ASE-treated mice (140 mg/kg/day) could run 1.4 times longer than the control mice. Healthy drinks were prepared with the addition of ASE at levels of 0.97% or 0.49% (A, B, and C type). Among the healthy drinks, the B type (ASE, 0.97%) was revealed to have the highest level of taste and overall acceptability through a sensory evaluation. The brix and pH of the ASE health drink (B type) were 14.9 and 4.51, respectively. These results indicated that the dried stem of Acanthopanax senticosus could be used as a functional material in the health drink industry.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.