• Journal of the Korean Geotechnical Society
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• v.24 no.4
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• pp.79-88
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• 2008

Recently, the high speed railway comes into the spotlight as the important and convenient traffic infrastructure. In Korea, Kyung-Bu high speed train service began in bout 400 km section in 2004, and the Ho-Nam high speed railway will be constructed by 2017. The high speed train will run with a design maximum speed of 300-350 km/hr. Since the trains are operated at high speed, the differential settlement of subgrade under the rail is able to cause a fatal disaster. Therefore, the differential settlement of the embankment must be controlled with the greatest care. Furthermore, the characteristics and causes of settlements which occurred under construction and post-construction should be investigated. A considerable number of studies have been conducted on the settlement of the natural ground over the past several decades. But little attention has been given to the compression settlement of the embankment. The long-term settlement of compacted fills embankments is greatly influenced by the post-construction wetting. This is called 'hydro collapse' or 'wetting collapse'. In spite of little study for this wetting collapse problem, it has been recognized that the compressibility of compacted sands, gravels and rockfills exhibit low compressibility at low pressures, but there can be significant compression at high pressures due to grain crushing (Marachi et al. 1969, Nobari and Duncan 1972, Noorany et al. 1994, Houston et al. 1993, Wu 2004). The characteristics of compression of fill materials depend on a number of factors such as soil/rock type, as-compacted moisture, density, stress level and wetting condition. Because of the complexity of these factors, it is not easy to predict quantitatively the amount of compression without extensive tests. Therefore, in this research I carried out the wetting collapse tests, focusing on various soil/rock type, stress levels, wetting condition more closely.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.120-129
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• 2023

To evaluate the vertical loads on railway bridges, conventional load tests are typically conducted. However, these tests often entail significant costs and procedural challenges. Railway conditions involve nearly identical load profiles due to standardized rail systems, which may appear straightforward in terms of load conditions. Nevertheless, this study aims to validate load tests conducted under operational train conditions by comparing the results with those obtained from conventional load tests. Additionally, static and dynamic structural behaviors are extracted from the measurement data for evaluation. To ensure the reliability of load testing, this research demonstrates feasibility through comparisons of existing measurement data with sensor attachment locations, train speeds, responses between different rail lines, tendency analysis, selection of impact coefficients, and analysis of natural frequencies. This study applies to the Dongho Railway Bridge and verifies the applicability of the proposed method. Ten operational trains and 44 sensors were deployed on the bridge to measure deformations and deflections during load test intervals, which were then compared with theoretical values. The analysis results indicate good symmetry and overlap of loads, as well as a favorable comparison between static and dynamic load test results. The maximum measured impact coefficient (0.092) was found to be lower than the theoretical impact coefficient (0.327), and the impact influence from live loads was deemed acceptable. The measured natural frequencies approximated the theoretical values, with an average of 2.393Hz compared to the calculated value of 2.415Hz. Based on these results, this paper demonstrates that for evaluating vertical loads, it is possible to measure deformations and deflections of truss railway bridges through load tests under operational train conditions without traffic control, enabling the calculation of response factors for stress adjustments.

• The Korean Journal of Quaternary Research
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• v.33 no.1_2
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• pp.1-23
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• 2021

In response to the increase in atmospheric carbon dioxide and greenhouse gases, the global mean temperature is rising rapidly. In particular, the warming of the Arctic is two to three times faster than the rest. Associated with the rapid Arctic warming, the sea ice shows decreasing trends in all seasons. The faster Arctic warming is due to ice-albedo feedback by the presence of snow and ice in polar regions, which have higher reflectivity than the ocean, the bare land, or vegetation, higher long-wave heat loss to space than lower latitudes by lower surface temperature in the Arctic than lower latitudes, different stability of atmosphere between the Arctic and lower latitudes, where low stability leads to larger heat losses to atmosphere from surface by larger latent heat fluxes than the Arctic, where high stability, especially in winter, prohibits losing heat to atmosphere, increase in clouds and water vapor in the Arctic atmosphere that subsequently act as green house gases, and finally due to the increase in sensible heat fluxes from low latitudes to the Arctic via lower troposphere. In contrast to the rapid Arctic warming, in midlatitudes, especially in eastern Asia and eastern North America, cold air outbreaks occur more frequently and last longer in recent decades. Two pathways have been suggested to link the Arctic warming to cold air outbreaks over midlatitudes. The first is through troposphere in synoptic-scales by enhancing the Siberian high via a development of Rossby wave trains initiated from the Arctic, especially the Barents-Kara Seas. The second is via stratosphere by activating planetary waves to stratosphere and beyond, that leads to warming in the Arctic stratosphere and increase in geopotential height that subsequently weakens the polar vortex and results in cold air outbreaks in midlatitudes for several months. There exists lags between the Arctic warming and cold events in midlatitudes. Thus, understanding chain reactions from the Arctic warming to midlatitude cooling could help improve a predictability of seasonal winter weather in midlatitudes. This study reviews the results on the Arctic warming and its connection to midlatitudes and examines the trends in surface temperature and the Arctic sea ice.

• Journal of Nutrition and Health
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• v.24 no.3
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• pp.230-242
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• 1991

This research was conducted on the 10 take-out boxed-lunches commercially prepared in the department stores. chain stores. and the public railroad trains in Korea. Sampling was conducted from February 1990 to March 1990. Nutritive values and sodium contents of the 10 boxed-lunch samples are summarized as follows : 1) The average weight(percentage) of the cooked rice and the side dishes were 304.6g(49.4) and 312.4(506%), respectively. The weight of these samples were significantly heavier than that of Japanese style boxed-lunches. 2) The average number of the side dishes was 12. The average numbers of food items classified by the five food groups were 6.1 in protein food group, 0.3 in calcium food group. 6.0 in vitamin and mineral food group. 1.5 in carbohydrate food group, and 1.5 in oil and fat food group. 3) They contained on the average 840.7kcal of energy, 38.9g of protein, 22.7g of fat, 120.4g of carbohydrate. 300.8mg of calcium. 410.8mg of phosphours, 6.61 mg of iron. 219.8 R.E. of vitamin A, 0.46mg of thiamin, 0.67mg of riboflavin, 10.5mg of niacin, 27.5mg of ascorbic acid. Thus. except vitamin t the content of all the nutrients were higher than the value of 1/3 of the RDA for adults. 4) The high priced group(group 2) had more protein, calcuim. iron and niacin contents than the cheaper group(group 1). Probably, it's because the group 2 had more animal foods than the group 1. 5) The average energy content per unit price(100 won) was 37.3kcal and the average protein content per unit price(100 won) was 1.64g. Korena style boxed-lunches had higher energy and protein contents per unit price than Japanese style, and the group 1 higher than the group 2. 6) The average energy Proportions of Protein, carbohydrate. and fat were 18.3%, 57.4%, and 24.3%, respectively. These proportions are good enough. 7) Frequency of cooking methods for the side dishes were found in the decreasing order : pan-frying, frying, braising, seasoning, kimchi, grilling, pickling, stir-frying, steaming and fermenting. Generally simple cooking methods were used, thus the menus were lack or varieties. 8) Frequency of colors for the side dishes were found in the decreasing order : red, brown. yellow, green, black, white. Too much red pepper was used. 9) The average capacity of the containers for the staples and the side dishes were 468.1ml and 590.6ml, respectively. And the containers could not keep the food items well seperated. 10) The average contensts of sodium and salt were 2.287mg and 5.76g, in the range of 1, 398mg to 3, 489mg and 3.53g to 8.80g, respectively. These are much higher values than the recommended amount of salt.

• Journal of the Korean Society of School Health
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• v.7 no.1
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• pp.57-71
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• 1994

I investigated actual conditions of smoking of teenagers who were randomly chosen middle and high school students. 1. Juvenile smoking 1) Parents' opinions of juvenile smoking Most parents do not want their children to smoke after growth : 88.6% of fathers (middle school students: 88.9%, high school students: 88.4%) and 95.1% of mothers (middle school students: 93.4%, high school students :95.5%). 2) Teenagers' opinions of smoking after growth The rate of students who will smoke after growth is 10.8% (middle school students: 12.0%, high school students: 9.9%): students in agricultural areas show the higher rate than those in cities. 3) Parents' opinions of their children's smoking now 1.5% of fathers want their children to smoke now (middle school students: 1.3%, high school students: 1.6%) and 1.1% of mothers do (middle school students: 0.6%, high school students: 1.5%). This shows that most parents do not want their children to smoke now. 4) Students' opinions of their friends' smoking now Students who want their friends smoke now cover 7.8% (middle school students: 7.1%, high school students: 8.4%). This rates are higher than those of parents shown in (3). And more high school students and more girl students gave the positive reponse than middle school boy and girl students, respectively. 5) Students' views of smoking "Look like an adult" covers the rate of 4.0% (boy: 7.8%, girl:3.6%) 6.7% of middle school students have this view, while 3.7% of high school students have. 16.1% of students had an experience of smoking during the last one year (boy: 29.9%, girl: 8.6%): this shows that the rate of the boy students is more than 3 times greater than that of the girl students and high students who experienced smoking last year covers 20.2%, while middle school students shows 10.9%. 6) Actual conditions of students' smoking The present rate of students' smoking is 22.4% (boy:38.3%, girl:13.8%): the rate of boy students is greater than that of girl students. Students who smoke more than pack of cigarettes a day cover 8.2% (boy: 17.5%, girl: 3.2%): 5.2% of middle school students (boy:11.4%, girl: 2.1%) smoke more than one pack while 10.7% of high school students do (boy:21.5%, girl: 4.2%). This shows that the rate of boy students' smoking is greater than that of girl students' smoking. 7) The rate of smoking of students' parents 75.4% of fathers (city: 74.5%, agricultural area:75.9%) smoke: and more than a half (62.4%) smoke more than a pack cigarettes a day. On the other hand, the rate of smoking mothers is 5.2%(city: 4.3%, agricultural area: 7.3%): the rate is higher in agricultural areas. 8) Opinions of smoking population in the future 61.4% of students answered that smoking population will increase, while 27.0% have the opinion that smoking population will decrease. 2. Opinions of the effects of smoking on health 1) Have you heard that smokers are likely to suffer from tuberclosis? 78.3% of students said yes (boy: 80.8%, girl: 76.4%): it is shown that the rate of boys is greater than that of girls. 2) Have you heard that smokers are likely to get out of endurance? 76.6% of students (boy: 69.3%, girl: 49.7%) answered yes: it is shown that the rate of boys is greater than that of girls. 3) Have you heard that heart-beats get fast when one smokes? 32.5% of students (boy: 35.5%, girl: 30.9%) answered yes: 32.2% in cities(boy: 33.0%, girl: 31.8%) and 33.5% in agricultural areas(boy: 41.8%, girl: 28.8%): and 28.7% middle students and 35.5% of high school students answered yes. 4) Have you heard that smokers are likely to have heart-diseases? 35.1% of students (boy: 34.0%, girl: 34.1%) answered yes: 35.3% in cities (boy: 37.2%, girl: 34.2%) and 36.7% in agricultural areas (boy: 39.0%, girl: 33.9%): 34.8% of middle school students and 35.4% of high school students. 5) Have you heard that smokers are likely to have a lung cancer? 91.4% of students (boy: 93.2%, girl: 89.9%) answered yes: 90.35% in cities and 94.2% in agricultural areas. 6) Have you heard that the life of smokers gets shorter? 94.3% of students (boy:94.6%, girl: 92.2%) answered yes. 7) Have you heard that pregnant smokers will deliver a baby with low birth weight? 29.6% of students (boy: 29.8%, girl: 29.4%) answered yes: the rates of boys and girls almost the same. 8) Have you heard that one feels calm when one smokes? 80.1% of students (boy: 81.8%, girl: 79.2%) answered yes: boys and girls showed almost the same rate. 3. Preventive measures Smoking people continued to increase all over the world because smoking not only mitigated emotional uneasiness such as loneliness, nervousness and so on, but also could be very helpful from the social perspective. This was so because they did not consider harmful effects of smoking on health, and victims. However, because any -one can have physical disorders caused by smoking, people should always keep in mind the following preventive measures. 1) Doctors or teachers should set an example of giving up smoking. Informing patients or students of harmful effects of smoking to persuade their family and relatives not to smoke. 2) Through mass media like newspapers, periodicals or broadcasting, to make people know harmful effects of smoking and not smoke. 3) To prohibit selling teenagers cigarette by law. 4) To prohibit smoking in public places like work places, offices, lecture rooms, recreation rooms, buses, trains and so on. 5) To decrease the rate of life insurance for non-smokers as in foreign countries and to give a warming of the harmful effects on cigarette packets or ads.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.107-127
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• 2022

Global challenges such as the corona pandemic, climate change and the war-on-tech ensure that the demand who the technologies of the future develops and monitors prominently for will be on the agenda. Development of, and applications in, agrifood, biotech, high-tech, medtech, quantum, AI and photonics are the basis of the future earning capacity of the Netherlands and contribute to solving societal challenges, close to home and worldwide. To be like the Netherlands and Europe a strategic position in the to obtain knowledge and innovation chain, and with it our autonomy in relation to from China and the United States insurance, clear choices are needed. Brainport Eindhoven: Building on Philips' knowledge base, there is create an innovative ecosystem where more than 7,000 companies in the High-tech Systems & Materials (HTSM) collaborate on new technologies, future earning potential and international value chains. Nearly 20,000 private R&D employees work in 5 regional high-end campuses and for companies such as ASML, NXP, DAF, Prodrive Technologies, Lightyear and many others. Brainport Eindhoven has a internationally leading position in the field of system engineering, semicon, micro and nanoelectronics, AI, integrated photonics and additive manufacturing. What is being developed in Brainport leads to the growth of the manufacturing industry far beyond the region thanks to chain cooperation between large companies and SMEs. South-Holland: The South Holland ecosystem includes companies as KPN, Shell, DSM and Janssen Pharmaceutical, large and innovative SMEs and leading educational and knowledge institutions that have more than Invest €3.3 billion in R&D. Bearing Cores are formed by the top campuses of Leiden and Delft, good for more than 40,000 innovative jobs, the port-industrial complex (logistics & energy), the manufacturing industry cluster on maritime and aerospace and the horticultural cluster in the Westland. South Holland trains thematically key technologies such as biotech, quantum technology and AI. Twente: The green, technological top region of Twente has a long tradition of collaboration in triple helix bandage. Technological innovations from Twente offer worldwide solutions for the large social issues. Work is in progress to key technologies such as AI, photonics, robotics and nanotechnology. New technology is applied in sectors such as medtech, the manufacturing industry, agriculture and circular value chains, such as textiles and construction. Being for Twente start-ups and SMEs of great importance to the jobs of tomorrow. Connect these companies technology from Twente with knowledge regions and OEMs, at home and abroad. Wageningen in FoodValley: Wageningen Campus is a global agri-food magnet for startups and corporates by the national accelerator StartLife and student incubator StartHub. FoodvalleyNL also connects with an ambitious 2030 programme, the versatile ecosystem regional, national and international - including through the WEF European food innovation hub. The campus offers guests and the 3,000 private R&D put in an interesting programming science, innovation and social dialogue around the challenges in agro production, food processing, biobased/circular, climate and biodiversity. The Netherlands succeeded in industrializing in logistics countries, but it is striving for sustainable growth by creating an innovative ecosystem through a regional industry-academic research model. In particular, the Brainport Cluster, centered on the high-tech industry, pursues regional innovation and is opening a new horizon for existing industry-academic models. Brainport is a state-of-the-art forward base that leads the innovation ecosystem of Dutch manufacturing. The history of ports in the Netherlands is transforming from a logistics-oriented port symbolized by Rotterdam into a "port of digital knowledge" centered on Brainport. On the basis of this, it can be seen that the industry-academic cluster model linking the central government's vision to create an innovative ecosystem and the specialized industry in the region serves as the biggest stepping stone. The Netherlands' innovation policy is expected to be more faithful to its role as Europe's "digital gateway" through regional development centered on the innovation cluster ecosystem and investment in job creation and new industries.