• Journal of agriculture & life science
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• v.45 no.1
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• pp.101-107
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• 2011

A Total of 48,101 performance records of sows for Yorkshire and Landrace breeds were collected from swine breeding farms in Korea from 2001 to 2008. A general ingredient analysis included the fixed effects of breed, parity, year, season, and farm. For the number of heads per 1st parity analysis by each growth traits, the data of 48,101 heads was used to analyze growth traits group. In the general ingredient analysis, the results showed high significance except for lean percentage by season (p<0.05). Average daily gain of Landrace breed ($640.48{\pm}0.749g$) was better than that of Yorkshire breed ($624.22{\pm}0.608g$), and the backfat thickness of Yorkshire breed ($13.44{\pm}0.030mm$) was thicker than that of Landrace breed ($12.50{\pm}0.037mm$). For the number of born alive and number of stillborn by growth traits for each breed, number of born decreased after test end day of 161 to 165 day, and average daily gain of 620 g to 640 g and the highest number of born appeared at the backfat thickness of 13 mm to 14 mm for yorkshire breed. In case of Landrace breed, number of born was the highest, and the number of stillborn increased together with average daily gain. The number of born was high when backfat thickness was less than 11 mm. The number of born trended to decrease when backfat thickness increased.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.25-58
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• 2021

This study investigated the corporate growth with more emphasis on longitudinal characteristics, not the results of companies with relatively more emphasis on cross-sectional, in the 21st-century entrepreneurial context. As of the end of 2019, sampled 479 global unicorn companies, and 333 high-growth companies with revenue of more than $100 million among 5,000 private companies in the U.S. with a compound annual growth rate (CAGR) exceeding 15% for the past three years. They were examined with 3 perspectives in terms of corporate growth that 1) the growth of enterprise value, 2) the pace of growth, and 3) the effectiveness of growth. As a result of our study, the corporate growth of the perspective of creating enterprise value had a relatively higher relationship with the characteristics of industries and markets. The pace of growth was more fully explained by the characteristics of the industry and the market environment and the choice of strategies that make up a valid combination. In addition, growth in terms of the effectiveness of corporate performance was influenced by the choice of strategy, the characteristics of the industry and market environment, and its business age, the proxy variable of resource accumulation, comprehensively. This study through a sample based on companies with an enterprise value of more than $1 billion and annual revenue of more than $100 million can be a valid reference in terms of creating milestones and roadmaps for scale-up of early-stage startups, particularly in terms of practitioners' point of view. It also provides a critical reference for overcoming the limitations of mainstream theories of the 20th century and developing the theory of corporate growth that fits the 21st-century entrepreneurial context.

• Journal of the Korean Society of Radiology
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• v.15 no.6
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• pp.781-788
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• 2021

Due to the high sensitivity to radiation, excessive exposure needs to be prevented by accurately measuring the dose irradiated to the skin during radiation therapy. Although clinical trials use dosimeters such as film, OSLD, TLD, glass dosimeter, etc. to measure skin dose, these dosimeters have difficulty in accurate dosimetry on skin curves. In this study, to solve these problems, we developed a skin dosimeter that can be attached according to human flexion and evaluated its response characteristics. For the manufacture of the dosimeter, lead oxide (PbO) with high atomic number (Z_Pb: 82, Z_O: 8) and density (9.53 g/cm³) and silicon binders that can bend according to human flexion were used. In the case of a dosimeter made of PbO material, the performance degradation has been prevented by using parylene and others due to the presence of degradation due to oxidation, but the previously used parylene is affected by bending, so a new form of passive layer was produced and applied to the skin dosimeter. The characteristic evaluation of the skin dosimeter was evaluated by analyzing SEM, reproducibility, and linearity. Through SEM analysis, bending was evaluated, reproducibility and linearity at 6 MeV energy were evaluated, and applicability was assessed with a skin dosimeter. As a result of observing the dosimeter surface through SEM analysis, the parylene passive layer PbO dosimeter with the positive layer raised to the parylene produced cracks on the surface when bent. On the other hand, no crack was observed in the silicon passive layer PbO dosimeter, which was raised to silicon passive layer. In the reproducibility measurement results, the RSD of the silicon passive layer PbO dosimeter was 1.47% which satisfied the evaluation criteria RSD 1.5% and the linearity evaluation results showed the R² value of 0.9990, which satisfied the evaluation criteria R² 9990. The silicon passive layer PbO dosimeter was evaluated to be applicable to skin dosimeters by demonstrating high signal stability, precision, and accuracy in reproducibility and linearity, without cracking due to bending.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.383-392
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• 2021

It has been known that oyster mushrooms cultivated in facilities with thermal insulation have been strongly affected by inner environments. Forced air-circulation fans exert much direct influence on disturbing air inside the facility so the matter is of particular interest. This study is carried out to investigate the measured levels of air uniformity in a cultivation facility for oyster mushroom in the various cases that reversibly controlled air-circulation fans which drove the flow in the upward and reverse direction by turn and unidirectional fans by which the wind blew upwards only were operated from July 1 to 10. The actual survey for the selection of ongoing operation cases presented that farmers, even though there were some discrepancies, have made use of fans in a way that it paused for 5-30min after running for 5-15min by turn. The level of air uniformity in the case of adopting reversible fans revealed a slight difference of 1.4-1.8℃ (Temp.) and 7.8-8.7% (R.H.) under the condition of not using a cooler during the investigation period. By contrast, unidirectional fans showed a noticeable difference of 3.2-3.7℃ and 14.0-15.4%, which meant that air uniformity driven by reversible fans much more increased compared to that for unidirectional fans. Among the twenty operational applications considered for reversible fans, the circumstance that the wind blew upwards for 10-15min and ceased for 5-10min and blew again in the reverse direction for 10-15min in succession gave minor improvements at the level of air uniformity, but at present there was somewhat difficult to make decision on which cases were optimally best. It seems necessary that the effects of reversible fans on air uniformity as well as qualities of oyster mushrooms have to be appraised in the cultivation period and the flow visualization needs to be done to ascertain the performance of air mixture.

• Journal of Korean Philosophical Society
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• v.147
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• pp.117-146
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• 2018

This paper discusses, whether religion is possible even in the age of artificial intelligence, and whether humans alone are the subject of religious faith or ultra intelligent machines with human minds can be also subjects of faith. In order for ultra intelligent machines to be subjects of faith in the same conditions as humans, they must be able to have unique characteristics such as emotion, will, and self-consciousness. With the advent of ultra intelligent machines with the same level of cognitive and emotional abilities as human beings, the religious actions of artificial intelligence will be inevitable. The ultra intelligent machines after 'singularity' will go beyond the subject of religious belief and reign as God who can rule humans, nature and the world. This is also the common view of Morabeck, Kurzweil and Harari. Leonhart also reminds us that technological advances should make us used to the fact that we are now 'gods'. But we fear we may face distopia despite the general affluence of the 'Star Trec' economy. For this reason, even if a man says he has learned the religious truth, one can't help but wonder if it is true. Kant and Bloch are thinkers who critically reflected on our religious ideals and highest concept in different world-view premises. Kant's concept of God as 'idea of pure reason' and 'postulate of practical reason', can seem like a 'god of gap' as Jesse Bering said earlier. Kant recognized the need for religious faith only on a strict basis of moral necessity. The subjects of religious faith should always strive to do the moral good, but such efforts themselves were not enough to reach perfection and so postulated immortality of the soul. But if an ultra intelligent machines that has emerged above a singularity is given a new status in an intellectual explosion, it can reach its morality by blocking evil tendencies and by the infinite evolution of super intelligence. So it will no longer need Kant's 'Postulate for continuous progress towards greater goodness', 'Postulate for divine grace' and 'Postulate for infinite expansion of the kingdom of God on earth.' Artificial intelligence robots would not necessarily consider religious performance in the Kant's meaning, and therefore religion will also have to be abolished. Ernst Bloch transforms Kant's postulate to be Persian dualism. Therefore, in Bloch, even though the ultra intelligent machines is a divine being, one must critically ask whether it is a wicked or a good God. Artificial intelligence experts warn that ultra intellectual machine as Pandora's gift will bring disaster to mankind. In the Kant's Matrix, a ultra intelligent machines, which is the completion of morality and God itself, may fall into a bad god in Bloch's Matrix. Therefore, despite the myth of singularity, we still believe that ultra intelligent machines, whether as God leads us to the completion of one of our only religious beliefs, or as bad god to the collapse of mankind through complete denial of existence.

• Korean Journal of Heritage: History & Science
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• v.49 no.3
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• pp.270-285
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• 2016

Various opinions and discussions have been actively in progress which are connected with cultural heritage since 'Sustainable Development Goals, SDGs' was announced by UN Sustainable Development Summit 2015 as Post-2015 Development Agenda. Apart from SDGs, conservation of cultural heritage itself stands on the basis of sustainability that originality, characteristic, diversity of cultural heritage should be permanently preserved. From that point of view, it is necessary to understand practical ODA for cultural heritage, far from theoretical approaches and policies. This paper is intended to look into the domestic and overseas situation related to ODA of Asian cultural heritage and the mentioned problems, future plans and challenges. First, the background and concepts about ODA were described and then ODA projects which have been carried out by Japan and China as typical ODA countries for Southeast Asia were introduced. ODA of cultural heritage in Korea has relatively recently started for restoration work for historic sites of Laos and Cambodia and its scale and performance do not come to much yet. Therefore, to develop ODA of cultural heritage, there are suggestions as in the followings. First, it is necessary to have a long-term master plan of ODA projects for sustainability of cultural heritage. Second, based on the view from the long-term perspective, the selection and focus for ODA partner countries should be considered, avoiding short-term projects aiming at a number of countries. Not widespread existing projects by other countries, but the model of Korean ODA for cultural heritage only Korea can conduct should be prepared. The next thing is connection with sustainability, and ultimately the conservation of cultural heritage should result in benefit to the natives by giving an impetus to economy as well as fostering tourism of local areas. To accomplish that connection, educational training and building capacity are suggested as the most suitable alternatives. Cultural heritage of each country reflects its indigenous originality and characteristics, therefore, the restoration work should be conducted by people in each country as the best way. From this point of view, ACPCS held by National Research Institute of Cultural Heritage will take a role of a specialized training program in Korean way. Lastly, establishment of a control tower for ODA in Korea is necessary. JCIC(Japan Consortium for International Cooperation in Cultural Heritage), which was set up in Japan for sharing information, establishment of cooperation system and prevention of overlapped projects will be an example we can take into consideration.

• Journal of Food Hygiene and Safety
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• v.34 no.2
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• pp.148-157
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• 2019

An optimized analytical method for sodium iron chloriphyllin in foods was established and verified by using high performance liquid chromatography with attached diode array detection. An Inertsil ODS-2 column and methanol-water (80:20 containing 1% acetate) as a mobile phase were employed. The limit of detection and quantitation of sodium iron chloriphyllin were 0.1 and 0.3 mg/kg, respectively, and the linearity of calibration curve was excellent ($R^2=0.9999$). The accuracy and precision were 93.9~104.95% and 2.0~7.7% in both inter-day and intra-day tests. Recoveries for candy and salad dressing were ranged between 93 and 104% (relative standard deviation, (RSD) 0.3~4.3%), and between 83 and 115% (RSD 1.2~2.0%), respectively. Liquid chromatography mass spectrometry was used to verify the main components of sodium iron chlorophyllin which were Fe-isochlorin e4 and Fe-chlorin e4.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.201-210
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• 2019

Recently, due to the efforts to mitigate traffic congestion and expansion of space efficiency, the construction of underground roads has been increased in big-scale cities. Since tunnels in the city have a higher chance for a fire leading to a great tragedy during a severe traffic jam than mountain tunnels, it is highly likely that it will be constructed as a tunnel, having a small cross section, for small vehicles. However, if they are constructed as such small-vehicle tunnels, it would be possible to reduce the design fire intensity while the concentration of harmful gases would increase due to a reduction in the small cross sectional area, led by a decrease in the tunnel height. In this study, behaviors of fire smoke by the installation interval and format of large-scale exhaust-gas ports were examined and compared in the analysis of temperatures and CO concentrations of a tunnel and its results were as the following. Although there were no significant differences in the smoke spreading distance between installation intervals, but in this study, 100 m was found to be the most effective installation interval. The smoke exhaustion performance was found to be excellent in the order of $4m{\times}3m$, $6m{\times}2m$, and $3m{\times}2m$ (2 lane) of the smoke spreading distance. Although there was no significant difference in the smoke spreading distance between formats of large-scale exhaust-gas ports, it was found that the smoke spreading distance was larger than other cases when it was $3m{\times}2m$ in the fire growing process. The analysis of smoke spreading distances by the aspect ratio showed that a smoke spreading distance was shorted when its the smoke spreading distance was found to be shorter when its traverse distance was relatively longer than its longitudinal distance.