• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.93-100
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• 2022

In this paper, we propose a machine learning-based method for supporting resource management of IoT software platforms in a multi-modal sensing scenario. We assume that an IoT device installed with a oneM2M-compatible software platform is connected with various sensors such as PIR, sound, dust, ambient light, ultrasonic, accelerometer, through different embedded system interfaces such as general purpose input output (GPIO), I2C, SPI, USB. Based on a collected dataset including CPU usage and user-defined priority, a machine learning model is trained to estimate the level of nice value required to adjust according to the resource usage patterns. The proposed method is validated by comparing with a rule-based control strategy, showing its practical capability in a multi-modal sensing scenario of IoT devices.

• Journal of The Korea Institute of Healthcare Architecture
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• v.30 no.1
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• pp.27-36
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• 2024

Purpose: The role and facilities of public health centers responsible for local health are becoming increasingly important due to recurring infectious diseases such as COVID-19. With sudden outbreaks of infectious diseases, the infrastructure of public health center facilities like screening clinics are constructed varies depending on local conditions. resulting in discrepancies between intended usage and actual usage. Establishing guidelines for infectious disease response facilities that can be efficiently used within local communities is necessary. Methods: Field surveys are conducted at 6 public health centers to gather insights into the essential rooms, circulation patterns, and key considerations for space planning in screening clinics. Results: Ten design considerations emerge from the data, including spatial requirements, circulation guidelines, and considerations for accommodating diverse user needs and local conditions. Implications: Further research is needed to translate these guidelines into prototypes of temporary facilities.

• Journal of the Korean housing association
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• v.16 no.6
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• pp.21-27
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• 2005

The purpose of this study was to explore behavioral patterns on residential spaces such as the patterns of residential space usage and perception on residential spaces. A questionnaire survey and interview were conducted with 426 residents living in apartment with 3 bedrooms, stairway access, and 3-bay style in the City of Seoul, Busan, and Gwangju, during the month of September in 2004. The data was analyzed with frequency, factor analysis by using the SPSS 10.0 for windows, and with proc iml by using SAS. The major findings of this study were that: 1) The most typical activities in the Anbang are $\ulcorner$sleeping and getting dressed$\lrcorner$, $\ulcorner$family communication and T.V. watching$\lrcorner$, and $\ulcorner$private affairs$\lrcorner$ ; Anbang(master bedroom) has been perceived and used as couple's private area. 2) Living room was used as a multi-purpose room carrying out various activities such as $\ulcorner$family interaction$\lrcorner$, $\ulcorner$entertaining guest with meals$\lrcorner$, $\ulcorner$children's private affairs$\lrcorner$, $\ulcorner$private affairs$\lrcorner$, $\ulcorner$hobby activities$\lrcorner$, $\ulcorner$clothes management$\lrcorner$, $\ulcorner$couple interaction$\lrcorner$, and $\ulcorner$occasions$\lrcorner$. 3) The representative patterns of activities in dining-kitchen was food $\ulcorner$preparation$\lrcorner$, and the need for social interaction in DK has been growing. The patterns of residential space usages can be used to develop and evaluate the unit plan of Korean middle-size apartment.

• The Research Journal of the Costume Culture
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• v.32 no.2
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• pp.199-214
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• 2024

Until now, research on consumers' purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers' self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

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

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.95-106
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• 2016

The purpose of this study was to examine how college students use Facebook and the ways in which they feel of commitment while using Facebook. The Big Five Personality Model has been considerably used in the psychology fields, and the researchers have started to explore the role of characteristic factors in influencing an individual's use of social media, such as Facebook which has become one of the most popular social networking site in the world. Therefore, the current study aims to specify the links between The Big Five Personality Model and usage patterns as well as commitment of Facebook. Two hundreds thirty five college students participated in a survey and the results are as follows: First, participants who were high in extraversion and agreeableness were more likely to do information sharing activities such as sharing posts to their friends, writing comments on the other's posts. In addition, participants who were high in openness to experience, conscientiousness, and neuroticism were more likely to do information producing activities including offering events, group, or public pages to meet people both on and offline. Second, in terms of the relationship between personality traits and commitment to the Facebook, the study found that extraversion and neuroticism were related to users' commitment to Facebook. These findings are consistent with the existing literature regarding extraversion and neuroticism were representative personality factors when it comes to commitment of media. Specifically, the study found that those who were high in neuroticism were more likely to produce information such as posting photos repeatedly or tagging their friends on posts, and also more likely to feel commitment on Facebook. These findings confirm that personality is a highly relevant factor in determining individual's behavior and the degree of commitment on Facebook. Based on these findings implications and limitations of the study are discussed.

• Information Systems Review
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• v.21 no.2
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• pp.1-27
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• 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 Internet Computing and Services
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• v.15 no.3
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• pp.101-107
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• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of Energy Engineering
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• v.21 no.4
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• pp.435-443
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• 2012

The combination of space shortage and the high population density concentrated in urban areas of South Korea has resulted in the growth of large-scale high-rise residential complexes, naturally affecting water and hot water usage patterns as well. But the current designs for water and hot water supply in South Korea rely mostly on international design standards and data calculated on site due to the severe shortage of basic data in relation to actual use, which result in the frequent problem of the under-or over-design of water and hot water supply. The following study measures the hot water supplier's conditions and the user's heat usage to realize the amount of time required for hot water supply load generation and the pattern of actual use in order to create basic data for effective hot water supply facility design and maintenance.

• Journal of Life Science
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• v.16 no.4
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• pp.579-583
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• 2006

The influence of disinfectant on bacterial concentration and carbon usage patterns by Biolog GN plates were investigated for biofilm on carbon steel pipe. Heterotrophic bacterial concentrations were not different among non-, monochloramine- (1.0, 1.5 mg/l) and free chlorine- (0.5, 1.0 mg/l) treated samples (P = 0.56, ANOVA). However treatment of 1.5 mg/l free chlorine and 2.0 mg/l monochloraime showed significantly lower densities than control (P < 0.01, ANOVA). By the stepwise increasement of disinfectant concentration, the carbon usage activities of biofilm microflora were decreased after increase without the decrease of bacterial concentration, following reduction of cell density. Carbon usage patterns were qualitatively and quantitatively different with similar bacterial concentrations. Principal component analysis suggested that type and concentration of disinfectant were main factors on the usage of carbons. Our result suggest that the differences of bacterial communities were different among the samples and the need of monochloramine for the reduction of biofilm in drinking water.