• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.205-232
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• 2022

This study analyzes the effect of the 2^nd pilot test of Tiime of Use (TOU) pricing for Korean households using a two-level electricity demand model. The test, implemented from May to September 2021, was conducted to compare the effects of two TOU pricing rates and the standard rates for households living in apartment and detached house in 7 provinces of Korea. Based on the data on electricity consumption during the test period and during the same period last year of the 1,292 participants and their socio-economic characteristics, this study analyzes (1) whether the relative demand across periods has changed in response to hourly price changes and (2) whether the price responsiveness of daily consumption has changed after the introduction of TOU pricing. The results show that both types of TOU pricing affect neither the relative demand across periods nor the price responsiveness of daily consumption. The reason behind the results could be related to the level of TOU pricing rates and the periodical classification, which were not sufficient to induce changes in the participants' electricity demand patterns.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.294-304
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• 2022

This study carried out from March 2021 to October 2021 in the upper part (St. 1) and middle part (St. 2) section of Yongsu stream, a branch of the Geum river, using PIT telemetry to understand the movement patterns and habitat characteristics of Odontobutis interrupta, a Korean endemic species. O. interrupta collection was used kick net (5×5 mm) and fish trap (5×5 mm). After collecting fish, PIT tag insertion was performed immediately in the site. Reader (HPR Plus Reader, biomark, USA) and portable Antenna (BP Plus Portable Antenna, biomark, USA) were used for detection of fish to monitoring the tagged O. interrupta. As a result of PIT telemetry applied to 70 individuals, mean movement distance was 36.5 (SE, ±6.6) m. There was a significant difference between total length and movement distance (P≤0.05). O. interrupta was mainly identified in average water depth, 36.2±1.9 cm, average water velocity, 0.03±0.07 m s^-1 and average distance from watershed, 4.4±0.3 m. Extent of rock used for habitat was varied from 32 to 4,000 cm². There was no statistical difference between the area of the first selected rock and the area of the after selected rock (P>0.05). but there was significant difference between total length and the area of the rock except for detection before 24 hours (P<0.01). Therefore, to restore the habitat, it is considered necessary to create various substrate structures by providing various habitat environments (water depth, flow rate, stone, etc.) for each individual size.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of the Korean Geotechnical Society
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• v.22 no.8
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• pp.107-118
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels, however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results is clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the first part are used to prepare a set of data for learning process. Tunnel behavior, especially the displacements of the lining, has been exclusively investigated for the back analysis.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.241-249
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• 2009

Recently, a number of researches related to video annotation and representation have been proposed to analyze video for searching and abstraction. In this paper, we have presented a method to provide the picture elements of conversational participants in video and the enhanced representation of the characters using those elements, collectively called Character-net. Because conversational participants are decided as characters detected in a script holding time, the previous Character-net suffers serious limitation that some listeners could not be detected as the participants. The participants who complete the story in video are very important factor to understand the context of the conversation. The picture elements for detecting the conversational participants consist of six elements as follows: subtitle, scene, the order of appearance, characters' eyes, patterns, and lip motion. In this paper, we present how to use those elements for detecting conversational participants and how to improve the representation of the Character-net. We can detect the conversational participants accurately when the proposed elements combine together and satisfy the special conditions. The experimental evaluation shows that the proposed method brings significant advantages in terms of both improving the detection of the conversational participants and enhancing the representation of Character-net.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.119-125
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• 2023

IT infrastructure operation has advanced, and the methods for managing systems have become widely adopted. Recently, research has focused on improving system management using Syslog. However, utilizing log data collected through these methods presents challenges, as logs are extracted in various formats that require expert analysis. This paper proposes a system that utilizes edge computing to distribute the collection of Syslog data and preprocesses duplicate data before storing it in a central database. Additionally, the system constructs a data dictionary to classify and count data in real-time, with restrictions on transmitting registered data to the central database. This approach ensures the maintenance of predefined patterns in the data dictionary, controls duplicate data and temporal duplicates, and enables the storage of refined data in the central database, thereby securing fundamental data for big data analysis. The proposed algorithms and procedures are demonstrated through simulations and examples. Real syslog data, including extracted examples, is used to accurately extract necessary information from log data and verify the successful execution of the classification and storage processes. This system can serve as an efficient solution for collecting and managing log data in edge environments, offering potential benefits in terms of technology diffusion.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.4
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• pp.183-196
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• 2018

Studies that approach from the point of view that human thoughts or minds are dominated by behavior as well as that human behavior is dominated by thoughts or minds, have begun to attract attention from the late 2000s. The physical experience is reminiscent of a metaphorically connected abstract concept, which ultimately affects the judgment or evaluation of a particular object. However, studies that have been carried out so far have been limited to studies on the difference in perception and judgment depending on the objects to be viewed, the objects to be touched, and the objects to which they are carried. In this study, we tried to find out that the physical movement of the body in the upward or downward direction affects the psychological judgment differently. In the first experiment, a pair of words that were considered to be connected metaphorically was tested. In the second experiment, the subjects tried to solve the complicated calculation problem in a short time, and then they watched the video related to the upward movement or downward movement, and then proceeded to measure the psychological judgment. As a result, it was found that 'downward movement' of the body has a metaphorical connection with 'closure', while 'upward movement' is related to 'progress'. In the case of downward-experienced group compared to upward-experienced group, the reverse intentions of their own decision were low, and the confidences in their own decision and the expectations for performance were high.

• The Korean Journal of Franchise Management
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• v.1 no.1
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• pp.19-42
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• 2010

Since circulation market whole surface opening, traditional market is real condition that is looked away more gradually to consumer as reasons of international retail firms and domestic enterprise firms to enter distribution industry, internet mail order rapid increase by information-oriented society, the pursuit of upgradation and normalization by elevation of income level and consumption pattern change that consideration convenience with young consumers as the central figure. Therefore, the purpose of this study is to analyze stagnation cause of traditional market and problem within a change of new distribution environment, and to develop new approaches for dealing with domestic traditional market relationship prompting competition through activation example analysis of foreign traditional market and domestic traditional market. The result of the study indicated that there are a lot of cases that are begun by a few's merchant with leadership that has been will which is strong in activation in beginning in market's occasion that succeed in activation. In particular, software side such as operational efficiency or marketing expertise strengthening of management is that effect is high relatively than hardware side market activation. Also essential to the settlement of credit transactions using credit cards is important for expanding the effort, for the expansion of credit card merchant credit card advantage and raise awareness among traders about the expected effects is needed. Though these study finding submits plan that create market ecosystem so that many consumers may become place that could visit naturally and create pleasure and convenience, and time, monetary, psychological value of shopping to traditional market, there is sense.

• The Journal of Bigdata
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• v.8 no.1
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• pp.131-156
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• 2023

The traditional retail industry significantly changed over the past decade due to the mobile and online technologies. This change has been accompanied by a shift in consumer behavior regarding purchasing patterns. Despite the rise of online shopping, there are still specific categories of products, such as "Processed food" in Mongolia, for which traditional shopping remains the preferred purchase method. To prepare for the inevitable future of retail businesses, firms need to closely analyze the performance of their offline stores to plan their further actions in a new multi-channel environment. Retailers must integrate diverse channels into their operations to stay relevant and adjust to the shifting market. In this research, we have analyzed the performance data such as sales, profit, and amount of sales of offline stores by using clustering approach. From the clustering, we have found the several distinct insights by comparing the circumstances and performance of retail stores. For the certain retail stores, we have proposed three different strategies: a fulfillment hub store between online and offline channels, an experience store to elongate customers' time on the premises, and a merge between two non-related channels that could complement each other to increase traffic based on the store characteristics. With the proposed strategies, it may enhance the user experience and profit at the same time.