• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Korean Journal of Weed Science
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• v.15 no.2
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• pp.105-114
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• 1995

Differences in critical period of weed competition were investigated among five types of rice(Oryza sativa L.) cultivation. Increase in weed-free period resulted in 1-2 days delay of heading date in machine transplanting and direct-seeding as compared with complete weed-free plot. When weedy period increased, there was no difference in heading date in transplanting cultivations. In direct-seeding, however, weedy period of 7-10 weeks after seeding(WAS) resulted in 4-7 days delay of heading date, whereas further increase in the weedy period caused rather 5-6 days advance in heading date. Weed-free period did not significantly affect yield components in conventional hand transplanting. In machine transplanting with 30-day-old seedling decreases in percent ripening and 1,000-grain weight were caused by weeds emerged within 4 weeks after transplanting(WAT). All yield components were decreased due to weedy period in machine transplanting with 10-day-old seedling. In direct-seeding weedy periods caused to decrease in number of panicles, number of spikelets, percent ripening, and 1,000-grain weight were 8-9, 4-5, 3-4, and 8-10 WAS, respectively. The critical periods of weed competition were determined as the following. In conventional hand transplanting weed-free must be maintained for either 4 weeks after transplanting or the rest period after 8 WAT. In machine transplanting with 30-day-old seedling weed-free must keep for either 5 weeks after transplanting or the rest period after 8 WAT. In machine transplanting with 10-day-old seedling weeds must be removed for either 5 weeks after transplanting or the rest period after 7 WAT. Weed-free must be kept between 5 and 7 WAS in flood direct-seeded rice and between 6 and 9 WAS in dry direct-seeded rice.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.423-433
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• 2018

Particulate matter air pollution is a serious problem affecting human health and visibility. The variations in $PM_{10}$ concentrations are influenced by not only local emission sources, but also atmospheric circulation conditions. In this study, we investigate the temporal features of $PM_{10}$ concentrations in South Korea and the atmospheric circulation patterns associated with high concentration episodes of $PM_{10}$ during winter (December-January-February) 2001-2016. Based on those analyses, a Korea Particulate matter Index (KPI) is developed to represent the large-scale atmospheric pattern associated with high concentration episodes of $PM_{10}$. The atmospheric patterns are characterized by persistent high-pressure anomalies, weakened lower-level north-westerly anomalies, and northward shift of the upper-level meridional wind anomalies near the Korean Peninsula. To evaluate the change in occurrence of high concentration episodes of $PM_{10}$ under a possible future warmer climate, we apply KPI analysis to CMIP5 climate simulations. Here, historical and two representative concentration pathway (RCP) scenarios (RCP 4.5 and RCP 8.5) are used. It is found that the occurrence of atmospheric conditions favorable for high $PM_{10}$ concentration episodes tends to increase over South Korea in response to climate change. This suggests that large-scale atmospheric circulation changes under future warmer climate can contribute to increasing high $PM_{10}$ concentration episodes in South Korea.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.1
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• pp.21-28
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• 2021

Purpose Brown fat, or brown adipose tissue (BAT), is involved in non-shivering thermogenesis and creates heat through glucose metabolism. BAT activation occurs stochastically by internal factors such as age, sex, and body mass index (BMI) and external factors such as temperature and environment. In this study, as a retrospective, electronic medical record (EMR) observation study, statistical analysis is conducted to confirm BAT activation and various factors. Materials and Methods From January 2018 to December 2019, EMR of patients who underwent PET/CT scan at the National Cancer Center for two years were collected, a total of 9155 patients were extracted, and 13442 case data including duplicate scan were targeted. After performing a univariable logistic regression analysis to determine whether BAT activation is affected by the environment (outdoor temperature) and the patient's condition (BMI, cancer type, sex, and age), A multivariable regression model that affects BAT activation was finally analyzed by selecting univariable factors with P<0.1. Results BAT activation occurred in 93 cases (0.7%). According to the results of univariable logistic regression analysis, the likelihood of BAT activation was increased in patients under 50 years old (P<0.001), in females (P<0.001), in lower outdoor temperature below 14.5℃ (P<0.001), in lower BMI (P<0.001) and in patients who had a injection before 12:30 PM (P<0.001). It decreased in higher BMI (P<0.001) and in patients diagnosed with lung cancer (P<0.05) In multivariable results, BAT activation was significantly increased in patients under 50 years (P<0.001), in females (P<0.001) and in lower outdoor temperature below 14.5℃ (P<0.001). It was significantly decreased in higher BMI (P<0.05). Conclusion A retrospective study of factors affecting BAT activation in patients who underwent PET/CT scan for 2 years at the National Cancer Center was conducted. The results confirmed that BAT was significantly activated in normal-weight women under 50 years old who underwent PET/CT scan in weather with an outdoor temperature of less than 14.5℃. Based on this result, the patient applied to the factor can be identified in advance, and it is thought that it will help to reduce BAT activation through several studies in the future.

• The Korean Journal of Archival Studies
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• no.78
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• pp.153-195
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• 2023

RDBMS is a widely used database system worldwide, and the term dataset refers to the vast amount of data produced in administrative information systems using RDBMS. Unlike business systems that mainly produce administrative documents, administrative information systems generate records centered around the unique tasks of organizations. These records differ from traditional approval documents and metadata, making it challenging to seamlessly transfer them to standard record management systems. With the 2022 revision of the 'Public Records Act Enforcement Decree,' dataset was included in the types of records for which only management authority is transferred. The core aspect of this revision is the need to manage the lifecycle of records within administrative information systems. However, there has been little exploration into how to manage dataset within administrative information systems. As a result, this research aims to design a database for a record management module that needs to be integrated into administrative information systems to manage the lifecycle of records. By modifying and supplementing ISO 16175-1:2020, we are designing an "human resource management system" and identifying and evaluating personnel management dataset. Through this, we aim to provide a concrete example of record management within administrative information systems. It's worth noting that the prototype system designed in this research has limitations in terms of data volume compared to systems currently in use within organizations, and it has not yet been validated by record researchers and IT developers in the field. However, this endeavor has allowed us to understand the nature of dataset and how they should be managed within administrative information systems. It has also affirmed the need for a record management module's database within administrative information systems. In the future, once a complete record management module is developed and standards are established by the National Archives, it is expected to become a necessary module for organizations to manage dataset effectively.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.