• Journal of Korea Game Society
• /
• v.10 no.3
• /
• pp.61-71
• /
• 2010

This study looks into the usage pattern in online games based on genres and socio-demographic characteristics. Compared to the prior studies that adopted survey as their main research method, this study has analyzed the actual data of game login records and adopted parametric modeling and mathematical approach. In terms of the socio-demographic characteristics, the following facts were confirmed: men > women by gender, students > white-collars > housewives > blue-collars > self-employed > jobless(etc.) by occupation, college graduates > K-12 students > high-school graduates > undergrads & grads by academic background, 3∼5 million > 1∼3 million > over 5 million > less than 1 million by income levels, and not married > married by marital status. In terms of genres, the population of the players is in the order of web board games, RPG, action/racing/shooting, and sports. The RPG game is confirmed to have a higher level of MCR (Max Concurrent User Ratio) than any other genres. On the other hand, the hypothesis on the difference in Repeated Use Ratio according to genres is rejected. This study has also confirmed that interactions exist between gender and age; genre and gender; genre and age among online game users, and conducted post-hoc analysis about those interactions.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.4
• /
• pp.34-42
• /
• 2017

Since existing nonintrusive appliance load monitoring (NIALM) studies assume that voltage fluctuations are negligible for load identification, and do not affect the identification results, the power factor or harmonic signals associated with voltage are generally not considered parameters for load identification, which limits the application of NIALM in the Smart Home sector. Experiments in this paper indicate that the parameters related to voltage and the characteristics of harmonics should be used to improve the accuracy and reliability of the load monitoring system. Therefore, in this paper, we propose an improved NIALM method that can efficiently analyze the types of household appliances and electrical energy usage in a home network environment. The proposed method is able to analyze the energy usage pattern by analyzing operation characteristics inherent to household appliances using harmonic characteristics of some household appliances as recognition parameters. Through the proposed method, we expect to be able to provide services to the smart grid electric power demand management market and increase the energy efficiency of home appliances actually operating in a home network.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.3
• /
• pp.122-138
• /
• 2023

Today, fishing villages are on the verge of extinction due to severe aging within the population and outward migration. Recent projects and studies targeting fishing villages viewed the fishing villages from an outsider-centered perspective, without a local-centered understanding of the village as a daily living space. Therefore, to understand the settlement environment of fishing villages, this study analyzed empirical data on the usage behavior of fishing village residents to gain insight into the characteristics of the outdoor space uses of residents in fishing villages. In this regard, a face-to-face survey was conducted among residents, and a village map drawn by the villagers showedthe spatial perception of the villagers. Empirical data on the behavior of fishing villagers using the village space was collected and analyzed through GPS. The study results suggested that residents of fishing villages tend to focus on productive activities, such as fishing, leading to a lack of awareness of other leisure activities and spaces. This monotonous pattern of space utilization within the village appears to stem from an absolute lack of available facilities within the target area. Therefore, in future village regeneration projects aimed at improving the quality of life for residents in fishing villages, it is essential to consider the residents' perception and utilization of space as a priority. The results of this study can be considered valuable foundational data for understanding the utilization of spaces within fishing villages and can be effectively utilized in planning initiatives to enhance quality of life.

• Journal of the Korean Society of Food Culture
• /
• v.22 no.5
• /
• pp.633-644
• /
• 2007

The study results regarding the ingredient differences, sensory characteristics, purchasing type, usage and improvement direction for home-made traditional doenjang and factory produced commercial doenjang are as follows. The L-value indicates that home-made traditional doenjang has a higher value in average than the factory produce done, and the a-value indicates the opposite. Home-made traditional doenjang had higher water content than commercial doenjang ; however the pH values of commercial doenjang and home-made were 5.34 and 5.32 respectively, which was very similar. Factory produced commercial doenjang showed higher protein content than the home-made traditional doenjang. Regarding the correlation between ingredients, there was a significantly negative relationship between the L-value and a-value but a significantly positive relationship between the L-value and b-value. There were no significant relationship with water content, pH and protein content. For the color and taste, which are the sensory characteristics, commercial doenjang showed higher value than the traditional doenjang, but for smell, the values were similar. Regarding grittiness, the factory produced commercial doenjang had bigger particles than the traditional doenjang. Preference was a bit higher in the traditional doenjang. Of the 380 study subjects, most were from 40 to 49 years old (65.5%), and the most family type were nuclear families which was a total of 400 people (69%). Moreover, the most residential type was apartment which was 355people (61.2%), and for the monthly income, more than 2,510,000won was 48.3%. For the educational background, college education was 304 people (52.4%), and high school education was 199 people, 34.3%. In the usage, most of the people eat doenjang more than once a week, and usually their parents make the doenjang. People used both commercial doenjang and home-made traditional doenjang >home-made only >factory produced commercial doenjang only in that order. The reasons for using the home-made traditional doenjang aredelicate taste and flavor>more nutritious> anti-cancer ingredients in that order. The reason they use the factory produced commercial doenjang is because they don't know how to make it at home. The things that needed to be improved in the home-made traditional doenjang are bad smell> entire quality> flavor> color in order, indicating that studies for reducing bad smell are required. The things that needed to be improved in the factory produced commercial doenjang are taste & flavor> entire quality>bad smell> color in that order, indicating that people are more concern about it tasting like home-made than the smell. From the above results, we can see that better functional doenjang should be developed for family health and to increase the consumption of the doenjang, which has good functional psychological activities, also more various types of foods that use doenjang and scientific studies to reduce the home-made doenjang smell should be continuously studied. Moreover, studies on how to make the factory produced commercial doenjang taste more like traditional doenjang should be performed.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.2
• /
• pp.13-20
• /
• 2020

One of the major issues surrounding big data is the availability of massive time-based or telemetry data. Now, the appearance of low cost capture and storage devices has become possible to get very detailed time data to be used for further analysis. Thus, we can use these time data to get more knowledge about the underlying system or to predict future events with higher accuracy. In particular, it is very important to define custom tailored contract offers for many households and businesses having smart meter records and predict the future electricity usage to protect the electricity companies from power shortage or power surplus. It is required to identify a few groups with common electricity behavior to make it worth the creation of customized contract offers. This study suggests big data transformation as a side effect and clustering technique to understand the electricity usage pattern by using the open data related to smart meter and KNIME which is an open source platform for data analytics, providing a user-friendly graphical workbench for the entire analysis process. While the big data components are not open source, they are also available for a trial if required. After importing, cleaning and transforming the smart meter big data, it is possible to interpret each meter data in terms of electricity usage behavior through a dynamic time warping method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.4
• /
• pp.407-413
• /
• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Information Systems Review
• /
• v.21 no.2
• /
• pp.1-27
• /
• 2019

It is possible to provide Smart Tourism Service through the development of information technology. It is necessary for the tourism industry to understand and utilize Big Data that has tourists' consumption patterns and service usage patterns in order to continuously create a new business model by converging with other industries. This study suggests to activate Jeju Smart Tourism by analyzing Big Data based on credit card usage records and location of tourists in Jeju. The results of the study show that First, the percentage of Chinese tourists visiting Jeju has decreased because of the effect of THAAD. Second, Consumption pattern of Chinese tourists is mostly occurring in the northern areas where airports and duty-free shops are located, while one in other regions is very low. The regional economy of Jeju City and Seogwipo City shows a overall stagnation, without changes in policy, existing consumption trends and growth rates will continue in line with regional characteristics. Third, we need a policy that young people flow into by building Jeju Multi-complex Mall where they can eat, drink, and go shopping at once because the number of young tourists and the price they spend are increasing. Furthermore, it is necessary to provide services for life-support related to weather, shopping, traffic, and facilities etc. through analyzing Wi-Fi usage location. Based on the results, we suggests the marketing strategies and public policies for understanding Jeju tourists' patterns and stimulating Jeju tourism industry.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.3
• /
• pp.89-101
• /
• 2014

Recently, as domestic air traffic dramatically increases, the need of ATC(air traffic control) systems has grown for safe and efficient ATM(air traffic management). Especially, for smooth ATC, it is far more important that performance of display system which should show all air traffic situation in FIR(Flight Information Region) without additional latency is guaranteed. In this paper, we design a ASTERIX(All purpose STructured Eurocontrol suRveillance Information eXchange) parsing module to promote stable ATC by minimizing system loads, which is connected with reducing overheads arisen when we parse ASTERIX message. Our ASTERIX parsing module based on pattern matching creates patterns by analyzing received ASTERIX data, and handles following received ASTERIX data using pre-defined procedure through patterns. This module minimizes display errors by rapidly extracting only necessary information for display different from existing parsing module containing unnecessary parsing procedure. Therefore, this designed module is to enable controllers to operate stable ATC. The comparison with existing general bit level ASTERIX parsing module shows that ASTERIX parsing module based on pattern matching has shorter processing delay, higher throughput, and lower CPU usage.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.4
• /
• pp.456-462
• /
• 2008

The classification is that a new data is classified into one of given classes and is one of the most generally used data mining techniques. Memory-Based Reasoning (MBR) is a reasoning method for classification problem. MBR simply keeps many patterns which are represented by original vector form of features in memory without rules for reasoning, and uses a distance function to classify a test pattern. If training patterns grows in MBR, as well as size of memory great the calculation amount for reasoning much have. NGE, FPA, and RPA methods are well-known MBR algorithms, which are proven to show satisfactory performance, but those have serious problems for memory usage and lengthy computation. In this paper, we propose DPA (Dynamic Partition Averaging) algorithm. it chooses partition points by calculating GINI-Index in the entire pattern space, and partitions the entire pattern space dynamically. If classes that are included to a partition are unique, it generates a representative pattern from partition, unless partitions relevant partitions repeatedly by same method. The proposed method has been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory and FPA, and RPA.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.3B
• /
• pp.185-192
• /
• 2012

Drought vulnerability index was developed by selecting drought-related indicators with trend test. Study areas were determined by considering the weir locations from the four major rivers restoration project in Nakdong and Geum river watersheds. Ten indicators were selected and they were categorized into three groups, water resources, precipitation pattern, and social aspects. Annual average surface water level, annual minimum surface water level, annual average groundwater level, and annual minimum groundwater level data sets were collected for water resources aspects. The number of non-rainy days, rainfall concentration ratio, and rainfall deviation were considered for precipitation pattern category. The amount of water available per capita, financial soundness for water resources, and water usage equity were related to social aspects. Mann-Kendall, Hotelling-Pabst, and Sen trend tests were performed for the ten indicator data sets and the results were scored for the drought vulnerability index. The results shows Gumi, Sangjoo, and Hapcheon weirs are relatively vulnerable to drought. The indices were relatively low for the regions in Geum river watershed compared to those in Nakdong river watershed.