• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Radiation Oncology Journal
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• v.19 no.3
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• pp.224-229
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• 2001

Purpose : The aim of this study is to analyze the treatment failure patterns and the risk factors for locoregional or distant failure of uterine cervical carcinoma treated with radiation therapy. Materials and methods . A retrospective analysis was undertaken of 154 patients treated with curative radiation therapy in Gyeongsang National University Hospital from April 1989 through December 1997. According to FIGO classification, 12 patients were stage IB, 24 were IIA, 98 were IIB, 1 were IIIA, 17 were IIIB, 2 were IVA. Results : Overall treatment failure rate was $42.1\%$ (65/154), and that of complete responder was $31.5\%$ (41/130). Among 65 failures, 25 failed locoregionally, another 25 failed distantly, and 15 failed locoregionally and distantly. Multivariate analysis confirmed tumor size (>4 cm) as risk factor for locoregional failure, and tumor size (>4 cm), pelvic lymph node involvement as risk factors for distant failure. Conclusion : On the basis of results of our study and recent published data of prospective randomized study for locally advanced uterine cervical carcinoma, we concluded that uterine cervical carcinoma with size more than 4 cm or pelvic lymph node involvement should be treated with concurrent chemoradiation.

• Korean Journal of Food Science and Technology
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• v.45 no.1
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• pp.1-7
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• 2013

Niphon spinosus, Epinephelus bruneus, and Epinephelus septemfasciatus are involved in the Perciformes Order and Serranidae Family. When E. bruneus and E. septemfasciatus are fully grown, the striped pattern on the body gradually disappears. Therefore, morphological classification of adult fishes is quite difficult to identify the differences to N. spinosus. In this study, we investigate the method to differentiate those using PCR. To design the primers, 16S rRNA region of N. spinosus, E. bruneus, and E. septemfasciatus registered in the GeneBank (www.ncbi.nlm.nih.gov) have been used and for the analysis, Bio Edit ver. 7.0.9.0 was used. As a result, it was design NS-003-F/NS-005-R (136 bp), EB-001-F/EB-002-R (181 bp), and ES-001-F/ES-001-R (123 bp) primers for the differentiation of each 3 different fishes. Therefore, the species-specific primer sets would be a useful tool for scientific and speedy differentiation against the illegal distribution for consumer protection.

• Journal of Food Hygiene and Safety
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• v.17 no.2
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• pp.61-70
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• 2002

In order to investigate the classification and antibiotic resistance of Salmonella species,718 isolates were isolated from patient in Seoul from 1996 to 2001. The two hundred and ninety eight isolates (41.5%) were identified as Sal. Enteritidis, followed by Sal. Typhi 218 isolates (30.4%), and Sal. Typhimurium 87 isolates (12.1%). The identified Salmonella species were most resistant to tetracycline (32.7%), followed by streptomycin (28.0%), ticarcillin (18.1%) and ampicillin (12.4%). Among isolates,34.7% of Sal. Enteritidis were resistant to tetracycline, 32.3% to streptomycin,23.2% to ticarcillin,13.5% to ampicillin, respectively. 13.8% of Sal. Typhi were resistant to streptomycin,10.6% to tetracycline, respectively.66.7% of Sal. Typhimurium were resistant to tetracycline, 42.5% to streptomycin, 28.7% to ticarcillin, 26.4% to ampicillin and 17.2% to chloramphenicol, respectively. Of 718 isolates, 324 isolates (45.1%) were resistant to 1 or more drugs and 64 isolates (19.8%) were resistant to 1 drug, 132 isolates (40.7%) were resistant to 2 drugs,50 isolates (15.4%) were resistant to 3 drugs, 27 isolates (8.3%) to 4 drugs,27 isolates (8.3%) to 5 drugs,22 Isolates (6.8%) to 6 drugs. The most prevalent multiple resistant pattern was tetracycline-kanamycin (35.5%), followed by tetracycline-kanamycin-ticarcillin (8.3%), and tetracycline-kanamycin-ticarcillin-ampicillin (7.4%) . Antibiotic resistant rate of Sal. Typhimurium was 73.6%,1311owe4 by Sal. Enteritidis 53.7% and Sal. Typhi 19.3%. Most Sal. Enteritidis was resistant to 1 drug o.2 drugs, whereas Sal. Typhi. and Sal.. Typhunurium were more .resistant to 5 (16.7%) or 6 drugs (26.6%). The old generation antibiotics such as ampicillin, tetracycline, and streptomycin were annually more resistant than the new generation antibiotics such as ceftriaxone, ciprofloxacin or cefoxitin.

• Journal of the Korean Arthroscopy Society
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• v.8 no.2
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• pp.119-123
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• 2004

Purpose: The purpose of this study is to check the range of motion of shoulder and inverstigate the frequencies and patterns of partial thickness rotator cuff tear in SLAP lesions. Materials and Methods: Forty-six patients, forty-seven cases who had SLAP lesions at shoulder arthroscopy were analyzed spectively using the medical records, intra-operative arthroscopic photo & video for SLAP lesions and rotator cuff articular side partial tear. Under the interscalene anesthesia, the range of notion of foreward elevation, internal rotation and external rotation was measured on fixed scapula and 90 degree abduction of the shoulder. Results: The rang of Motion are 150 degree on foreward elevation, 65.5 degree on external rotation, 61.7 degree on internal rotation. By Snyder's classification, type ll SLAP lesion is noted in 24 cases (five cases in type 1, one case in type IV). Rotator cuff articular side partial tear is noted in 24 cases ( one case in type I, 22 cases in type II, one case in type IV SLAP). All the rotator cuff articular side partial thickness tear were located in the anterior part of the supraspinatus. Conclusion: The rotator cuff partial thickness tear is mostly noted on the articular side and frequently found in the relatively more unstable type of SLAP lesions. So we consider that SLAP lesion may be a one of the causes for partial tear of the rotator cuff articular side.

• Korean Journal of Heritage: History & Science
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• v.38
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• pp.129-156
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• 2005

Increasing discovery cases of underground storage facilities made of earth, wood, or stone are being reported from the recent excavation survey of the Baekje relics. Accordingly, the purpose of this study is to examine the structure and function of the underground storage facilities of Baekje following a classification made by the type and building method as follows: plask shape, wooden box shape, and stone box shape. The plask shape storage is the most representative underground storage of Baekje that has been found in numerous relics more than 600 sets around Hangang(Han River) and Geumgang(Geum River) from the Hansung period to Sabi period in Baekje Dynasty. It is a historical artefact as a part of the unique storage culture of Baekje around Hangang and Geumgang from the 3rd to 7th Century. Considering its structure and the example of Chinese one, it might had been used for a long-term storage of grains and various other items including earth wares. The storage facility in wooden box shape and stone box shape are found mostly in the relics Of Sabi period. Thus it might had taken some functions of the storage in traditional pouch shape which had decreased after the 6th Century. In particular, the wooden box shape and stone box shape storage required enormous labor force to build owing to their structure and building method. Thus, they were considered to had been used for official purposes in province fortress and citadel artefact. The wooden box shape storage facility is classified into flat rectangular type and square type based on the structure, and into Gagu type(架構式) and Juheol type(柱穴式) based on the building method. It might had been decided according to the geography and geological feature of the place where the storage was to be built. Considering the examples of Gwanbuk-ri relics and Weolpyong-dong relics, the wooden box shape storage facility might had been used for various items depending on the needs, including foods such as fruits and essential provisions at the military base. Considering the long-term food storage, the examples in Japan, and the functional characteristics of the underground storage facility, there is a possibility that the wooden and stone box shape storage facilities had been built so as to safely store important items in case of fire. This study is only a rudimentary examination for the storage facility in Baekje. Thus further studies are to be made specifically and comprehensively on the comparison with other regions, distribution pattern, discovered relics and artefacts, and functions.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.5
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• pp.431-449
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• 2022

As many tunnels generally have been constructed, various experiences and techniques have been accumulated for tunnel design as well as tunnel construction. Hence, there are not a few cases that, for some usual tunnel design works, it is sufficient to perform the design by only modifying or supplementing previous similar design cases unless a tunnel has a unique structure or in geological conditions. In particular, for a tunnel blast design, it is reasonable to refer to previous similar design cases because the blast design in the stage of design is a preliminary design, considering that it is general to perform additional blast design through test blasts prior to the start of tunnel excavation. Meanwhile, entering the industry 4.0 era, artificial intelligence (AI) of which availability is surging across whole industry sector is broadly utilized to tunnel and blasting. For a drill and blast tunnel, AI is mainly applied for the estimation of blast vibration and rock mass classification, etc. however, there are few cases where it is applied to blast pattern design. Thus, this study attempts to automate tunnel blast design by means of machine learning, a branch of artificial intelligence. For this, the data related to a blast design was collected from 25 tunnel design reports for learning as well as 2 additional reports for the test, and from which 4 design parameters, i.e., rock mass class, road type and cross sectional area of upper section as well as bench section as input data as well as16 design elements, i.e., blast cut type, specific charge, the number of drill holes, and spacing and burden for each blast hole group, etc. as output. Based on this design data, three machine learning models, i.e., XGBoost, ANN, SVM, were tested and XGBoost was chosen as the best model and the results show a generally similar trend to an actual design when assumed design parameters were input. It is not enough yet to perform the whole blast design using the results from this study, however, it is planned that additional studies will be carried out to make it possible to put it to practical use after collecting more sufficient blast design data and supplementing detailed machine learning processes.

• Journal of the Korean Society of International Agriculture
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• v.31 no.4
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• pp.378-383
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• 2019

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate papaya at metabolic level. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and 1,100-950 cm^-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700-1,500 cm^-1), phosphodiester groups from nucleic acid and phospholipid (1,500-1,300 cm^-1) and carbohydrate compounds (1,100-950 cm^-1). The result of PCA analysis showed that papaya leaves could be separated into clusters depending on different growth temperature. In this case, showed discrimination confirmed according to metabolite content of growth condition from papaya. And PLS-DA analysis also showed more clear discrimination pattern than PCA result. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful papaya cultivars.

• Korean Journal of Heritage: History & Science
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• v.56 no.3
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• pp.62-74
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• 2023

Much attention has been focused on the Baekpoman area due to the archaeological achievements of the past, but studies on prehistoric times when villages began to form is insufficient, and the Bronze Age village landscape was examined in order to supplement this. In the area of Baekpo Bay, the natural geographical limit connected to the inland was culturally confirmed by the distribution density of dolmens, and the generality of the Bronze Age settlement was confirmed with the Hwangsan-ri settlement. Bunto Village in Hwangsan-ri represents a farming-based village in the Baekpo Bay area, and the residential group and the tomb group are located on the same hill, and it is composed of three individual residential groups, and the village landscape had attached buildings used as warehouses and storage facilities. In the area of Baekpo Bay, it spread in the Tamjin River basin and the Yeongsan River basin where Songgukri culture and dolmen culture were integrated, and the density distribution of the villages was considered to correspond to the distribution density of dolmens. In order to examine the landscape of village distribution, the classification of Sochon-Jungchon-Daechon was applied, and it was classified as Sochon, a sub-unit constituting the village, in that the number of settlements constituting the village in the Bronze Age was mostly less than five. There are numerical differences between Jungchon and Daechon, and the distribution pattern does not necessarily coincide with the hierarchy. The three individual residential groups of Bunto Village in Hwangsan-ri are Jungchon composed of complex communities of blood relatives with each family community, and a stabilized village landscape was created in the Gusancheon area. In the area of Baekpo Bay, Bronze Age villages formed a landscape in which small villages were scattered around the rivers and formed a single-layered relationship. Dolmens (tombs) were formed between the villages and villages, and seem to have coexisted. Sochondeul is a family community based on agriculture, and it is believed that self-sufficient stabilized rural villages that live by acquiring various wild resources in rivers, mountains, and the sea formed a landscape.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.