• The Journal of Bigdata
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• v.4 no.2
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• pp.159-173
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• 2019

Last-Mile delivery optimization plays a key role in the urban supply chain operation, which is the most expensive and time-consuming and most complicated part of the whole delivery process. The urban consolidation center (UCC) is regarded as a significant asset for supporting customer demand in the last-mile delivery service. It is the key benefit of UCC to improve the load balance of vehicles and to reduce the total traveling distance by finding the better route with the well-organized multi-leg vehicle journey in the urban area. This paper presents the model using multiple scenario analysis integrated with mathematical optimization techniques using Geographic Information System (GIS). The model aims to find the best solution for the distribution network consisted of DC and UCC, which is applied to the case of Ulaanbaatar Mongolia. The proposed methodology integrates two sub-models, location-allocation model and vehicle routing problem. The multiple scenarios devised by selecting locations of UCC are compared considering the general performance and delivery patterns together. It has been adopted to make better decisions the quantitative metrics such as the economic value of capital cost, operating cost, and balance of using available resources. The result of this research may help the manager or public authorities who should design the distribution network for the last mile delivery service optimization using UCC within the urban area.

• Journal of Environmental Impact Assessment
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• v.29 no.2
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• pp.122-131
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• 2020

Marine Spatial Planning is an emerging strategy that promoting sustainable development at coastal and marine areas based on the concept of ecosystem services. Regarding its methodology, usage rate of resources and its impact should be considered in the process of spatial planning. Particularly, considering the rapid increase of coastal tourism, visitation pattern is required to be identified across coastal areas. However, actions to quantify visitation pattern have been limited due to its required high cost and labor for conducting extensive field-study. In this regard, this study aimed to pose the usage of social big data in Marine Spatial Planning to identify spatial visitation density and critical management zone throughout coastal areas. We suggested the usage of GPS information from Flickr and Twitter, and evaluated the critical management zone by applying spatial statistics and density analysis. This study's results clearly showed the coastal areas having relatively high visitors in the southern sea of South Korea. Applied Flickr and Twitter information showed high correlation with field data, when proxy excluding over-estimation was applied and appropriate grid-scale was identified in assessment approach. Overall, this study offers insights to use social big data in Marine Spatial Planning for reflecting size and usage rate of coastal tourism, which can be used to designate conservation area and critical zones forintensive management to promote constant supply of cultural services.

• Journal of Technology Innovation
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• v.19 no.2
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• pp.99-128
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• 2011

University-to-industry technology transfer has become an increasingly important issue in recent years. Studies on technology transfer and commercialization evolved to diverse knowledge transfer channels. Among them, university spin-off is not only known as the most direct and tangible method but also suitable for effectively transferring tacit knowledge. Much of the studies on university spin-offs are mostly focused on macro-level but studies using the individual professors as their unit of analysis need better understanding as well. This paper investigates why the speed of university spin-off formations differ among individual professors drawing on the resource-based view. Utilizing data of 149 professors in 25 universities who formed spin-offs, Cox regression results suggest that professors' technological research capabilities, academic research capabilities and financial resources promote university spin-off formations.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.409-418
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• 2021

The land cover map is a very important data that is used as a basis for decision-making for land policy and environmental policy. The land cover map is mapped using remote sensing data, and the classification results may vary depending on the acquisition time of the data used even for the same area. In this study, to overcome the classification accuracy limit of single-period data, multi-series satellite images were used to learn the difference in the spectral reflectance characteristics of the land surface according to seasons on a U-Net model, one of the deep learning algorithms, to improve classification accuracy. In addition, the degree of improvement in classification accuracy is compared by comparing the accuracy of single-period data. Seoul, which consists of various land covers including 30% of green space and the Han River within the area, was set as the research target and quarterly Sentinel-2 satellite images for 2020 were aquired. The U-Net model was trained using the sub-class land cover map mapped by the Korean Ministry of Environment. As a result of learning and classifying the model into single-period, double-series, triple-series, and quadruple-series through the learned U-Net model, it showed an accuracy of 81%, 82% and 79%, which exceeds the standard for securing land cover classification accuracy of 75%, except for a single-period. Through this, it was confirmed that classification accuracy can be improved through multi-series classification.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Korean Journal of Environment and Ecology
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• v.36 no.2
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• pp.202-216
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• 2022

Animals exhibit certain behaviors and movement patterns as they react to their internal needs, external stimuli, and surrounding environments. They have a bounded range in which they mostly spend their time, and it is referred to as a home range. Based on the fact that the home range is a critical area for the survival and preservation of species, there has been a growing body of research on developing more precise home range estimation methods to use the estimated ranges as a ground for establishing an effective conservation policy since the early 1940s. Recent rapid advancements in telemetry technology that resulted in the presence of autocorrelation between locations with short time intervals revealed the limitations of the existing estimators. Many novel estimators have been developed to compensate for it by incorporating autocorrelation in calculating home ranges. However, studies on the animal home range are still in their early stage in Korea, and newly developed methodologies have not yet been adopted. Therefore, this study aims to introduce the foreign home range estimation methods and foster domestic research activities on home ranges. Firstly, we compared and contemplated seven estimators by categorizing them into geometrical and statistical methodologies and then divided them into estimators that assume independent observations and those that consider autocorrelation in each category. After that, the home ranges of black-tailed gulls (Larus crassirostris) were calculated using GPS tracking data for the month of June and derived home range estimators by applying the methodology introduced in this study. We analyzed and compared the results to discuss the strengths and weaknesses of each method. Lastly, we proposed a guideline that can help researchers choose an appropriate estimator for home range calculation based on the animal location data characteristics and analysis purpose.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.21-41
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• 2019

Thanks to the rapid development of information technologies, the data available on Internet have grown rapidly. In this era of big data, many studies have attempted to offer insights and express the effects of data analysis. In the tourism and hospitality industry, many firms and studies in the era of big data have paid attention to online reviews on social media because of their large influence over customers. As tourism is an information-intensive industry, the effect of these information networks on social media platforms is more remarkable compared to any other types of media. However, there are some limitations to the improvements in service quality that can be made based on opinions on social media platforms. Users on social media platforms represent their opinions as text, images, and so on. Raw data sets from these reviews are unstructured. Moreover, these data sets are too big to extract new information and hidden knowledge by human competences. To use them for business intelligence and analytics applications, proper big data techniques like Natural Language Processing and data mining techniques are needed. This study suggests an analytical approach to directly yield insights from these reviews to improve the service quality of hotels. Our proposed approach consists of topic mining to extract topics contained in the reviews and the decision tree modeling to explain the relationship between topics and ratings. Topic mining refers to a method for finding a group of words from a collection of documents that represents a document. Among several topic mining methods, we adopted the Latent Dirichlet Allocation algorithm, which is considered as the most universal algorithm. However, LDA is not enough to find insights that can improve service quality because it cannot find the relationship between topics and ratings. To overcome this limitation, we also use the Classification and Regression Tree method, which is a kind of decision tree technique. Through the CART method, we can find what topics are related to positive or negative ratings of a hotel and visualize the results. Therefore, this study aims to investigate the representation of an analytical approach for the improvement of hotel service quality from unstructured review data sets. Through experiments for four hotels in Hong Kong, we can find the strengths and weaknesses of services for each hotel and suggest improvements to aid in customer satisfaction. Especially from positive reviews, we find what these hotels should maintain for service quality. For example, compared with the other hotels, a hotel has a good location and room condition which are extracted from positive reviews for it. In contrast, we also find what they should modify in their services from negative reviews. For example, a hotel should improve room condition related to soundproof. These results mean that our approach is useful in finding some insights for the service quality of hotels. That is, from the enormous size of review data, our approach can provide practical suggestions for hotel managers to improve their service quality. In the past, studies for improving service quality relied on surveys or interviews of customers. However, these methods are often costly and time consuming and the results may be biased by biased sampling or untrustworthy answers. The proposed approach directly obtains honest feedback from customers' online reviews and draws some insights through a type of big data analysis. So it will be a more useful tool to overcome the limitations of surveys or interviews. Moreover, our approach easily obtains the service quality information of other hotels or services in the tourism industry because it needs only open online reviews and ratings as input data. Furthermore, the performance of our approach will be better if other structured and unstructured data sources are added.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Korean Journal of Construction Engineering and Management
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• v.13 no.4
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• pp.152-163
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• 2012

At the conceptual phase of a construction project, estimated construction cost is very important as it significantly influences the owner's decision-making. Accurate cost estimating, in the early stage of a public construction project, serves as a critical factor because initial decision-making effects the final cost of a construction project. However in the cases of Korean public apartment projects, excluding a few of the public owners, there is a problem in properly estimating construction cost due to the lack of construction cost estimating system. Thus, this research developed a public apartment cost estimating system using case-based reasoning that was suggested by a previous research with 66 cases of Korean public apartment projects. Based on the system experiments involving 19 public officers and 10 cases of Korean public apartment projects, the effectiveness of the system in terms of estimation accuracy and user-friendly was confirmed. As a result, the developed system has an error range of 1.47% to 13.74% and mean of 6.15%. In addition, the system was evaluated that it could greatly improve the current estimation task of public officers. Consequently, the results of this research can be used as a foundation for a technological advance in estimating construction cost and improving the accuracy and consistency of construction cost estimation.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.68-79
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• 2010

With the social infrastructure being deteriorated, there is a growing need to introduce the asset management to social infrastructure management in order to increase their value and save budget. Social infrastructure asset management is a new concept of facility management in response to these demands. It is defined as a procedure for collecting and analysing facility maintenance data and for making and practicing an economically optimized management plan. Detailed survey work of asset management business is analyzed in order to derive a strategy for asset management information. The contents of IIMM of New Zealand and the asset management definition of the FHWA of the United States, and representative facility management systems of Korea are analysed. The role between organizations and the relationship between business and organization were analyzed. Information required for asset management and for existing facility management systems is compared with business of asset management. In this thesis, three development strategies are suggested. The first one is to develop core business of asset management while excluding duplicated development. The second one is to divide system's structure into three layers. And the last one is to share information by interfacing asset management systems with existing facility management systems.