• Journal of the Korean Society for Marine Environment & Energy
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• v.8 no.4
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• pp.227-235
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• 2005

This study aimed at suggesting management policy directions for the uninhabited islands of Korea which are national land resources with economic potential for tourism and development and strategic value for boundary delineation of territorial waters and exclusive economic zone as well as their unique ecological status. Review of existing management arrangements related to the uninhabited islands revealed six management issues to be addressed: insufficient data and their low reliability, lack of management policy directions, increase in ecosystem deterioration and perturbation by human activities, lack of policy measures for meeting utilization and development demands, weak management base with insufficient personnel and budget, and legal measures not taking Into account their unique ecological and socioeconomic characteristics. The management policy directions to improve the management of the uninhabited islands of Korea include management directions and strategies, and suggestions for legal improvement. Considering the unique ecological value of the uninhabited islands, management directions suggested are anti-degradation in which current and future demands for their utilization and development do not degrade the ecological potential of the uninhabited islands and integration in which land and sea areas are managed as an integrated management unit. Four strategies proposed to follow the management directions are enhancement of the knowledge base through a comprehensive survey, development and legislation of guidelines for the rational management of utilization and development demands, establishment of the comprehensive island debris collection and disposal system, and enhancement of management capacity. Legal improvement for the effective implementation of the management policy directions should include comprehensive uninhabited islands survey, legal utilization restraints and management guidelines based on classification of the islands, management boundary, and improvement of regulations on designated islands.

• 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.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.61-83
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• 2012

A knowledge map describes the network of related knowledge into the form of a diagram, and therefore underpins the structure of knowledge categorizing and archiving by defining the relationship of the referential navigation between knowledge. The referential navigation between knowledge means the relationship of cross-referencing exhibited when a piece of knowledge is utilized by a user. To understand the contents of the knowledge, a user usually requires additionally information or knowledge related with each other in the relation of cause and effect. This relation can be expanded as the effective connection between knowledge increases, and finally forms the network of knowledge. A network display of knowledge using nodes and links to arrange and to represent the relationship between concepts can provide a more complex knowledge structure than a hierarchical display. Moreover, it can facilitate a user to infer through the links shown on the network. For this reason, building a knowledge map based on the ontology technology has been emphasized to formally as well as objectively describe the knowledge and its relationships. As the necessity to build a knowledge map based on the structure of the ontology has been emphasized, not a few researches have been proposed to fulfill the needs. However, most of those researches to apply the ontology to build the knowledge map just focused on formally expressing knowledge and its relationships with other knowledge to promote the possibility of knowledge reuse. Although many types of knowledge maps based on the structure of the ontology were proposed, no researches have tried to design and implement the referential navigation-enabled knowledge map. This paper addresses a methodology to build the ontology-based knowledge map enabling the referential navigation between knowledge. The ontology-based knowledge map resulted from the proposed methodology can not only express the referential navigation between knowledge but also infer additional relationships among knowledge based on the referential relationships. The most highlighted benefits that can be delivered by applying the ontology technology to the knowledge map include; formal expression about knowledge and its relationships with others, automatic identification of the knowledge network based on the function of self-inference on the referential relationships, and automatic expansion of the knowledge-base designed to categorize and store knowledge according to the network between knowledge. To enable the referential navigation between knowledge included in the knowledge map, and therefore to form the knowledge map in the format of a network, the ontology must describe knowledge according to the relation with the process and task. A process is composed of component tasks, while a task is activated after any required knowledge is inputted. Since the relation of cause and effect between knowledge can be inherently determined by the sequence of tasks, the referential relationship between knowledge can be circuitously implemented if the knowledge is modeled to be one of input or output of each task. To describe the knowledge with respect to related process and task, the Protege-OWL, an editor that enables users to build ontologies for the Semantic Web, is used. An OWL ontology-based knowledge map includes descriptions of classes (process, task, and knowledge), properties (relationships between process and task, task and knowledge), and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. Therefore a knowledge network can be automatically formulated based on the defined relationships, and the referential navigation between knowledge is enabled. To verify the validity of the proposed concepts, two real business process-oriented knowledge maps are exemplified: the knowledge map of the process of 'Business Trip Application' and 'Purchase Management'. By applying the 'DL-Query' provided by the Protege-OWL as a plug-in module, the performance of the implemented ontology-based knowledge map has been examined. Two kinds of queries to check whether the knowledge is networked with respect to the referential relations as well as the ontology-based knowledge network can infer further facts that are not literally described were tested. The test results show that not only the referential navigation between knowledge has been correctly realized, but also the additional inference has been accurately performed.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.55-75
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• 2010

Sequential pattern mining aims to discover interesting sequential patterns in a sequence database, and it is one of the essential data mining tasks widely used in various application fields such as Web access pattern analysis, customer purchase pattern analysis, and DNA sequence analysis. In general sequential pattern mining, only the generation order of data element in a sequence is considered, so that it can easily find simple sequential patterns, but has a limit to find more interesting sequential patterns being widely used in real world applications. One of the essential research topics to compensate the limit is a topic of weighted sequential pattern mining. In weighted sequential pattern mining, not only the generation order of data element but also its weight is considered to get more interesting sequential patterns. In recent, data has been increasingly taking the form of continuous data streams rather than finite stored data sets in various application fields, the database research community has begun focusing its attention on processing over data streams. The data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. In data stream processing, each data element should be examined at most once to analyze the data stream, and the memory usage for data stream analysis should be restricted finitely although new data elements are continuously generated in a data stream. Moreover, newly generated data elements should be processed as fast as possible to produce the up-to-date analysis result of a data stream, so that it can be instantly utilized upon request. To satisfy these requirements, data stream processing sacrifices the correctness of its analysis result by allowing some error. Considering the changes in the form of data generated in real world application fields, many researches have been actively performed to find various kinds of knowledge embedded in data streams. They mainly focus on efficient mining of frequent itemsets and sequential patterns over data streams, which have been proven to be useful in conventional data mining for a finite data set. In addition, mining algorithms have also been proposed to efficiently reflect the changes of data streams over time into their mining results. However, they have been targeting on finding naively interesting patterns such as frequent patterns and simple sequential patterns, which are found intuitively, taking no interest in mining novel interesting patterns that express the characteristics of target data streams better. Therefore, it can be a valuable research topic in the field of mining data streams to define novel interesting patterns and develop a mining method finding the novel patterns, which will be effectively used to analyze recent data streams. This paper proposes a gap-based weighting approach for a sequential pattern and amining method of weighted sequential patterns over sequence data streams via the weighting approach. A gap-based weight of a sequential pattern can be computed from the gaps of data elements in the sequential pattern without any pre-defined weight information. That is, in the approach, the gaps of data elements in each sequential pattern as well as their generation orders are used to get the weight of the sequential pattern, therefore it can help to get more interesting and useful sequential patterns. Recently most of computer application fields generate data as a form of data streams rather than a finite data set. Considering the change of data, the proposed method is mainly focus on sequence data streams.

• Journal of Navigation and Port Research
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• v.26 no.3
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• pp.323-328
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• 2002

Soil-steel structures have been used for the underpass, or drainage systems in the road embankment. This type of structures sustain external load using the correlations with the steel wall and engineered backfill materials. Buried flexible conduits made of corrugated steel plates for the coastal road was tested under vehicle loading to investigate the effects of live load. Testing conduits was a circular structure with a diameter of 6.25m. Live-load tests were conducted on two sections, one of which an attempt was made to reinforce the soil cover with the two layers of geo-gird. Hoop fiber strains of corrugated plate, normal earth pressures exerted outside the structure, and deformations of structure were instrumented during the tests. This paper describes the measured static and dynamic load responses of structure. Wall thrust by vehicle loads increased mainly at the crown and shoulder part of the conduit. However additional bending moment by vehicle loads was neglectable. The effectiveness of geogrid-reinforced soil cover on reducing hoop thrust is also discussed based on the measurements in two sections of the structure. The maximum thrusts at the section with geogrid-reinforced soil cover was 85-92% of those with un-reinforced soil cover in the static load tests of the circular structure; this confirms the beneficial effect of soil cover reinforcement on reducing the hoop thrust. However, it was revealed that the two layers of geogrid had no effect on reducing the overburden pressure at the crown level of structure. The obtained values of DLA decrease approximately in proportion to the increase in soil cover from 0.9m to 1.5m. These values are about 1.2-1.4 times higher than those specified in CHBDC.

• Journal of radiological science and technology
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• v.29 no.2
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• pp.57-62
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• 2006

Purpose : This study was to understand about the measurement of shoulder motion fraction and motion ratio. We proposed the radiological criterior of glenohumeral and scapulothoracic movement ratio. Materials and Methods : We measured the motion fraction of the glenohumeral and scapulothoracic movement using CR(computed radiological system) of arm elevation at neutral, 90 degree, full elevation. Central ray was $15^{\circ},\;19^{\circ},\;22^{\circ}$ to the cephald for the parallel scapular spine, and the tilting of torso was external oblique $40^{\circ},\;36^{\circ},\;22^{\circ}$ for perpendicular to glenohumeral surface. Healthful donor of 100 was divided 5 groups by age(20, 30, 40, 50, 60). The angle of glenohumeral motion and scapulothoracic motion could be taken from gross arm angle and radiological arm angle. We acquired 3 images at neutral, $90^{\circ}$ and full elevation position and measured radiographic angle of glenoheumeral, scapulothoracic movement respectively. Results : While the arm elevation was $90^{\circ}$, the shoulder motion fraction was 1.22(M), 1.70(W) in right arm and 1.31, 1.54 in left. In full elevation, Right arm fraction was 1.63, 1.84, and left was 1.57, 1.32. In right dominant arm(78%), $90^{\circ} and Full motion fraction was 1.58, 1.43, in left(22%) 1.82, 1.94. In generation 20, $90^{\circ} and Full motion fraction was 1.56, 1.52, 30' was 1.82, 1.43, 40' was 1.23, 1.16, 50' was 1.80, 1.28, 60' was 1.24, 1.75. There was not significantly by gender, dominant arm and age. Conclusion : The criterior of motion fraction was useful reference for clinical dignosis the shoulder instability.

• Journal of radiological science and technology
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• v.35 no.2
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• pp.93-102
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• 2012

Thanks to the rapid increase of the interest in the quality control of the General X-ray systems, this research proposes the direction of the quality control through comparing and inspecting the actual condition of the respective quality control in the Clinic, the educational institution and the hospital. The subjects of the investigation are diagnostic radiation equipment's in the clinic, the educational institution and the hospital around the capital. A test of kVp, mR/mAs out put test and reproducibility of the exposure dose, half value layer, an accordance between the light field and the beam alignment test, and lastly reproducibility of the exposure time. Then the mean difference of the percentage, the CV (Coefficient of Variation, CV) and the attenuated curve which are respectively resulted from the above tests are computed. After that we have evaluated the values according to the regulations on the Diagnostic Radiation Equipment Safety Administration regulations. In the case of the clinic and the educational institution, there were 22 general X-ray devices. And 18.2% of the kVp test, 13.6% of the reproducibility of exposure dose test, 9.1% of the mR/mAs out put test, and 13.6% of the HVL (Half Value Layer) test appeared to be improper. In the case of the hospital, however, there were 28 devices. And 7.1% of the reproducibility of exposure dose, 7.1% of the difference in the light field/ beam alignment, and 7.1% of the reproducibility of the exposure time appeared to be improper. According to the investigation, the hospital's quality control condition is better than the condition in the clinic and the educational institution. The quality control condition of the general X-ray devices in the clinic is unsatisfactory compared to the hospital. Thus, it is considered that realizing the importance of the quality control is necessary.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.597-605
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• 2014

The continuous observations for atmospheric aerosols were conducted during 3 years (2009 to 2011) by using Gwangju Institute of Science and Technology (GIST) multi-wavelength Raman lidar at Gwangju, Korea ($35.10^{\circ}N$, $126.53^{\circ}E$). The aerosol depolarization ratios calculated from lidar data were used to identify the Asian dust layer. The optical properties of Asian dust layer were different according to its vertical distribution. In order to investigate the difference between the optical properties of each individual dust layers, the transport pathway and the transport altitude of Asian dust were analyzed by Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We consider that the variation of optical properties were influenced not only their transport pathway but also their transport height when it passed over anthropogenic pollution source regions in China. The lower particle depolarization ratio values of $0.12{\pm}0.01$, higher lidar ratio of $67{\pm}9sr$ and $68{\pm}9sr$ at 355 nm and 532 nm, respectively, and higher ${\AA}ngstr\ddot{o}m$ exponent of $1.05{\pm}0.57$ which are considered as the optical properties of pollution were found. In contrast with this, the higher particle depolarization ratio values of $0.21{\pm}0.09$, lower lidar ratio of $48{\pm}5sr$ and $46{\pm}4sr$ at 355 nm and 532 nm, respectively, and lower ${\AA}ngstr\ddot{o}m$ exponent of $0.57{\pm}0.24$ which are considered as the optical properties of dust were found. We found that the degree of mixing of anthropogenic pollutant aerosols in mixed Asian dust govern the variation of optical properties of Asian dust and it depends on their altitude when it passed over the polluted regions over China.

• Korean Security Journal
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• no.43
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• pp.205-230
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• 2015

This study aims to compare the differences of crucial factors that are associated with probation warning tickets between sex offenders and non-sex offenders in South Korea. Serious high-profile cases have occurred in recent years which resulted in public and political conners for successful sex offender management and monitoring strategy through community corrections. The official response has been to initiate a series of legislative probation and parole measures by using GPS electronic monitoring system, chemical castration, and sex offender registry and notification. In this context, the current study is designed to explore the major factors that could affect the failure of probation by comparing the differences between sex offenders and non-sex offenders in terms of their major factors which are related to the failure of probation. The failure of probation is measured by the number of warning tickets which would be issued when there is the violation of probation conditions. The data is obtained from Seoul Probation office from January, 29, 2014 to February, 28, 2014. The sample number of sex offenders is 144 and the number of non-sex offenders is 1,460. The data includes the information regarding the offenders who completed their probation order after they were assigned to Seoul Probation in 2013. Furthermore, this study uses the chi-square and logistic regression analysis by using SPSS statistical package program. The result demonstrated that only prior criminal history was statistically significant factor that was related to the number of warning tickets in the sex offender group when other variables were controlled($X^2=25.15$, p<0.05, Nagelkerke $R^2=0.23$)(b=0.19, SE=0.08, p<0.05). By contrast, there were various factors that were associated with the number of warning tickets in non-sex offender group. Specifically, the logistic regression analysis for the non-sex offenders showed that demographic variable(marital status and employment type), offender-victim relationships, alcohol addiction, violent behavior, prior criminal history, community service order, and attendance order were statistically significant factors that were associated with the odds of warning tickets. Further policy implication will be discussed.