• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.95-103
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• 2007

Existing human resources dispatch systems face various limits for managing increasing information derived from the work-place as it required much time managing the basic data created at the work-place and the data input methods are complicated. This study focuses on how to solve the above mentioned problems by utilizing the cellular phone system, which provides vital connection between the organizations using the dispatched human resources and the resources. The study offers building of a necessary work history data base and its management through development of a mobile human resources dispatch system. In order to optimally place the given resources, the system utilizes deductive analytical process. Utilizing the intelligent, deductive analytical process in properly planning the placing of the right human resources to do the job will result satisfaction in human resources dispatch and management.

• Journal of the Korean Society for Railway
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• v.15 no.5
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• pp.459-466
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• 2012

Due to the issues such as omission of data, document based management, maintenance based on measurement data and wire-based network, it is difficult existing maintenance system for railway bridges to act to diverse characteristics of site and environmental changes in real time. With these reasons, there are many constraints in establishing active maintenance strategies for railway bridges. To solve these issues, this study suggests an integrated maintenance business model based on practical utilization and information management based on BIM technology to build a smart maintenance operation system based on ubiquitous computing for railway bridges. To secure its development and practical applications, a quantitative evaluation by questionnaire analysis was performed. Therefore, it is expected that the suggested model will be utilized as a framework model in order to build the smart maintenance operation system from collection of maintenance data to action for railway bridges.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.60-64
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• 2010

To discover association rules from nontransactional data, there have been many studies on discretization of attribute values. These studies do not reflect the change of discovered rules' confidence according to the change of the ranges of the discretized attributes, and perform the discretization stage and the rule discovery stage independently. This causes the ranges of attributes not properly discretized, thereby making the rules having high confidence excluded in the result set. To solve this problem, we propose a novel method that performs the discretization and rule discovery stages simultaneously in order to discretize ranges of attributes in such a way that the rules having high confidence are discovered well. To the end, we perform hierarchical clustering on the attributes in the right hand side of rules, then do characterization on every cluster thus obtained. The experimental result demonstrates that our method discovers the rules having high confidence better than existing methods.

• Smart Media Journal
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• v.8 no.2
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• pp.86-93
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• 2019

The purpose of this study is to develop walk score application GUI in a smart device for improving user convenience. This study uses the walk score dataset of Seoul City developed in previous studies. Application GUI consists of five parts: address input window and search button, detail information (address, latitude, longitude, walk score), window switching and input window for a number of data, data input window, and menu button. For verifying application GUI, this study uses 12 locations (sets of address, latitude, longitude, and walk score) near Gangnam Station and Myungdong Station in Seoul in several scenarios. First, this study checks if the application has been implemented with address and keyword search options. Then, new data insertion and file output operations are evaluated. The application system developed in this study operated properly in all scenarios. This walk score application can be a useful device in our daily lives.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.131-146
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• 2023

Smart card data is widely used in the public transportation field. Despite the inevitability of various errors occur during the data collection and storage; however, smart card data errors have not been extensively studied. This paper investigates inherent errors in boarding and alighting station information in smart card data. A comparison smart card data and bus boarding and alighting survey data for the same time frame shows that boarding station names differ by 6.2% between the two data sets. This indicates that the error rate of smart card data is 6.2% in terms of boarding station information, given that bus boarding and alighting survey data can be considered as ground truth. This paper propose 6-step algorithm for correcting errors in smart card boarding station information, linking them to corresponding information in Bus Information System(BIS) Data. Comparing BIS data and bus boarding and alighting survey data for the same time frame reveals that boarding station names correspond by 98.3% between the two data sets, indicating that BIS data can be used as reliable reference for ground truth. To evaluate its performance, applying the 6-step algorithm proposed in this paper to smart card data set shows that the error rate of boarding station information is reduced from 6.2% to 1.0%, resulting in a 5.2%p improvement in the accuracy of smart card data. It is expected that the proposed algorithm will enhance the process of adjusting bus routes and making decisions related to public transportation infrastructure investments.

• Geophysics and Geophysical Exploration
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• v.20 no.2
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• pp.78-87
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• 2017

For efficient data processing, trace interpolation and regularization techniques should be antecedently applied to the seismic data which were irregularly sampled with missing traces. Among many interpolation techniques, MWNI (Minimum Weighted Norm Interpolation) technique is one of the most versatile techniques and widely used to regularize seismic data because of easy extension to the high-order module and low computational cost. However, since it is difficult to interpolate spatially aliased data using this technique, model-constrained MWNI was suggested to compensate for this problem. In this paper, conventional MWNI and model-constrained MWNI modules have been developed in order to analyze their performance using synthetic data and validate the applicability to the field data. The result by using model-constrained MWNI was better in spatially aliased data. In order to verify the applicability to the field data, interpolation and regularization were performed for two field data sets, respectively. Firstly, the seismic data acquired in Ulleung Basin gas hydrate field was interpolated. Even though the data has very chaotic feature and complex structure due to the chimney, the developed module showed fairly good interpolation result. Secondly, very irregularly sampled and widely missing seismic data was regularized and the connectivity of events was quite improved. According to these experiments, we can confirm that the developed module can successfully interpolate and regularize the irregularly sampled field data.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.325-331
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• 2022

As IoT devices are used in various ways in an edge network environment, multiple studies are being conducted that utilizes the information collected from IoT devices in various applications. However, it is not easy to apply accurate IoT data immediately as IoT data collected according to network environment (interference, interference, etc.) are frequently missed or error occurs. In order to minimize mistakes in IoT data collected in an edge network environment, this paper proposes a management technique that ensures the reliability of IoT data by randomly generating signature values of IoT data and allocating only Security Information (SI) values to IoT data in bit form. The proposed technique binds IoT data into a blockchain by applying multiple hash chains to asymmetrically link and process data collected from IoT devices. In this case, the blockchainized IoT data uses a probability function to which a weight is applied according to a correlation index based on deep learning. In addition, the proposed technique can expand and operate grouped IoT data into an n-layer structure to lower the integrity and processing cost of IoT data.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.361-367
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• 2008

When constructing a synthetic seismogram in the earthquake study or in seismic data interpretation by using a ray-tracing technique, the most troublesome and error-prone task is to define a suite of ray codes for the corresponding rays to trace in advance. An infinite number of rays exist for any arbitrarily located source and receiver in a medium. Missing certain important rays or an inappropriate selection of ray codes in tracing rays may result in wrong interpretation of the earthquake record or seismogram. Automatic ray code generation could be able to eliminate those problems. In this study we have developed an efficient algorithm with which one can generate systematically all the ray codes for the source(s) and receiver(s) arbitrarily located in a model. The result of this work could be used not only in analysing multiples in seismic data processing and interpretation, but also in coda wave study, study on the amplification effects in a basin and phase identification of the waves multiply reflected/refracted in earthquake study.

• Korean Security Journal
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• no.41
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• pp.263-292
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• 2014

This study is an attempt to introduce an effective human resource management way by analyzing the relationship of personality type of Security Agents and coping strategy for job stress and the job satisfaction. To achieve this purpose, this study surveyed users of the Gyeonggi and Chungnam in based on cluster sampling method. A total of 283 samples were used for this study, except 17 erroneous samples dropped. For the data process of the questionnaire, each answer content was coded and an element analysis, credibility analysis, frequency analysis, co-relationship analysis and regression analysis were performed using the SPSS version 18.0 of Angel for Windows. Through the data analysis following the research methods above, the conclusion was acquired as follows: First, the nature of the security personnel Factors affecting job stress. Second, the nature of the security personnel Factors affect coping behavior.