• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1702-1721
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• 2019

A workflow process (or business process) management system helps to define, execute, monitor and manage workflow models deployed on a workflow-supported enterprise, and the system is compartmentalized into a modeling subsystem and an enacting subsystem, in general. The modeling subsystem's functionality is to discover and analyze workflow models via a theoretical modeling methodology like ICN, to graphically define them via a graphical representation notation like BPMN, and to systematically deploy those graphically defined models onto the enacting subsystem by transforming into their textual models represented by a standardized workflow process definition language like XPDL. Before deploying those defined workflow models, it is very important to inspect its syntactical correctness as well as its structural properness to minimize the loss of effectiveness and the depreciation of efficiency in managing the corresponding workflow models. In this paper, we are particularly interested in verifying very large-scale and massively parallel workflow models, and so we need a sophisticated analyzer to automatically analyze those specialized and complex styles of workflow models. One of the sophisticated analyzers devised in this paper is able to analyze not only the structural complexity but also the data-sequence complexity, especially. The structural complexity is based upon combinational usages of those control-structure constructs such as subprocesses, exclusive-OR, parallel-AND and iterative-LOOP primitives with preserving matched pairing and proper nesting properties, whereas the data-sequence complexity is based upon combinational usages of those relevant data repositories such as data definition sequences and data use sequences. Through the devised and implemented analyzer in this paper, we are able eventually to achieve the systematic verifications of the syntactical correctness as well as the effective validation of the structural properness on those complicate and large-scale styles of workflow models. As an experimental study, we apply the implemented analyzer to an exemplary large-scale and massively parallel workflow process model, the Large Bank Transaction Workflow Process Model, and show the structural complexity analysis results via a series of operational screens captured from the implemented analyzer.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.333-336
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• 2005

The advance of wireless telecommunication and observation technologies leads developing sensor and sensor network for serving the context information continuously. Besides, in order to understand and cope with the context awareness based on the sensor network, it is becoming important issue to deal with plentiful data transmitted from various sensors. Therefore, we propose a context awareness system to deal with the plentiful sensor data in a vast area such as the prevention of a forest fire, the warning system for detecting environmental pollution, and the analysis of the traffic information, etc. The proposed system consists of the context acquisition to collect and store various sensor data, the knowledge base to keep context information and context log, the rule manager to process context information depending on user defined rules, and the situation information manager to analysis and recognize the context, etc. The proposed system is implemented for managing renewable energy data management transmitted from a large scale area.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.51-69
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• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Journal of Distribution Science
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• v.12 no.2
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• pp.73-79
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• 2014

Purpose - The Korean government has revised the distribution industry development law to regulate large-scale retailer operations to protecting medium- and small-scale retailers and traditional markets. According to the revised law, large-scale retailers must follow regulations on operating hours and compulsory store closures two days per month. Based on the revised distribution industry development law, most local governments regulate operation hours and they have adopted compulsory closure programs for large-scale retail stores. However, it is argued that fresh food producers suffer from a decrease in sales based on the compulsory closure of stores operated by large-scale retailers. Large-scale retailers reduce their fresh food orders from agricultural and fishery producers because of the compulsory store closures. Fresh food producers also suffer from a decrease in prices because reduced orders lead to a decrease in auction prices based on the availability of excess goods in wholesale markets. This paper investigates the effects of operation regulations for large-scale retailers on agricultural producers by surveying agricultural and fishery producer organizations. Research design, data, methodology - A survey was conducted on 117 producer organizations of fruits and vegetables, cereals, fisheries, and livestock products from September 10 to October 4, 2012. Survey items are annual sales, shares of sales accounted for by large-scale retailers, reduction of orders and prices from large-scale retailers, methods to deal with the sales reduction, unfair trade practices of large-scale retailers, opinion of the large-scale retailer regulations, and so on. The average sales of the sampled producer organizations are 13.7 billion won and the average share of sales accounted for by large-scale retailers is 35.4%. Results - Survey results show that the sample producer organizations' sales decreased 10.1% because of the compulsory closures of stores operated by large-scale retailers. It is estimated that the total sales of producer organizations decreased 371.2 billion won because of the regulations on the operation of large-scale retailers. In addition to the direct effect of a sales decrease due to order reduction, agricultural and fishery producer organizations suffered from the secondary effect of price reduction in wholesale markets. When orders from large-scale retailers decreased, most agricultural and fishery producer organizations shipped redundant products to wholesale markets, decreasing auction prices. It was estimated that the price received decreased 21.9% when sold in other marketing channels. As producer organization sales decreased, it was reported that the labor force employed by producer organizations also decreased by 15.1%. Therefore, we can conclude that the regulations for large-scale retailer operations resulted in negative impacts on agricultural producers. Conclusions - Although the sales reduction due to the regulations for large-scale retailer operations are not great, the cumulative effects due to the continued compulsory closure of stores operated by large-scale retailers could be great. This paper suggests governmental programs that could help agricultural producer organizations to find new and effective marketing channels such as direct marketing, farmers' markets, exports, Internet shopping, and so on.

• Journal of Fashion Business
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• v.8 no.2
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• pp.42-55
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• 2004

The purpose of this study was to classify the contents of department stores and large-scale discount stores of consumer on information source, shopping orientation and store image in an effort to determine which variable gave a crucial impact on cosmetics department stores and large-scale discount stores selection behavior. The subjects of this study were 557 adult women visited department store and large-scale discount store in Busan. The data were analyzed by using Factor analysis, Frequency analysis, Correlation analysis, Cronabach $\alpha$ and Regression analysis. The results were as follows; 1. There was a difference in the demographical characteristics on department stores and large-scale discount stores of consumers. 2. Shopping Orientation of consumers were categorized into 5 types, and information source of consumers did 3 types, and store image of consumers did 5 types. 3. Leisure utilization, store & brand loyalty, store information, personal information, massmedia information, product & operate on, store atmosphere & salesperson and shopping convenience had positive correlations with cosmetics department stores selection beavior. but convenient location, rationality & economy and sales promotion had negative correlations with cosmetics department stores selection beavior. 4. Rationality & economy and sales promotion had positive correlations with cosmetics large-scale discount stores selection beavior. but convenient location, leisure utilization, store & brand loyalty, massmedia information, product & operate on, store atmosphere & salesperson and shopping convenience had negative correlations with cosmetics large-scale discount stores selection beavior. 5. Age, income, business(-), convenient location(-), rationality & economy(-), leisure utilization, store & brand loyalty, store information, personal information, massmedia information, store atmosphere & salesperson, shopping convenience and sales promotion(-) had a direct effect on cosmetics department stores selection beavior. Age, income, marriage, education had an indirect effect on department stores selection beavior through information source and store image, and information source did through store image, and shopping orientation did through store image. 6. Rationality & economy, convenient location(-), leisure utilization(-), store & brand loyalty(-), buying independence(-), personal information, massmedia information(-), product & operate on(-), shopping convenience(-) and sales promotion had a direct effect on cosmetics large-scale discount stores selection beavior. Age, income, marriage, education had an indirect effect on large-scale discount stores selection beavior through information source, shopping orientation and store image, and information source did through store image, and shopping orientation did through store image.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.3
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• pp.343-354
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• 2009

This study examined the factors(intrinsic cues, extrinsic cues, private brands familarity, store loyalty) influencing clothing PB preference of large-scale discount stores, and investigated the differences according to the consumer's decision types. Questionaries were collected from 316 female customers in front of the discount stores and the data were analyzed by SPSS 12.0 using T-test, correlation, regression analysis and cluster analysis. The results were as follows. First of all, According to the correlation and regression analysis, the private brand preference were influenced by store loyalty and PB familiarity. Secondly, Differences are found according to the consumer's decision types. That is the price-conscions consumers regard store loyalty, PB familiarity, extrinsic cues, and PB preference more importantly than the value-conscious consumers.

• Asia Pacific Journal of Corpus Research
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• v.2 no.2
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• pp.31-41
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• 2021

This paper makes a quantitative analysis of the diachronic evolution of ancient Chinese vocabulary by constructing and counting a large-scale rough annotated corpus. The texts from Si Ku Quan Shu (a collection of Chinese ancient books) are automatically segmented to obtain ancient Chinese vocabulary with time information, which is used to the statistics on word frequency, standardized type/token ratio and proportion of monosyllabic words and dissyllabic words. Through data analysis, this study has the following four findings. Firstly, the high-frequency words in ancient Chinese are stable to a certain extent. Secondly, there is no obvious dissyllabic trend in ancient Chinese vocabulary. Moreover, the Northern and Southern Dynasties (420-589 AD) and Yuan Dynasty (1271-1368 AD) are probably the two periods with the most abundant vocabulary in ancient Chinese. Finally, the unique words with high frequency in each dynasty are mainly official titles with real power. These findings break away from qualitative methods used in traditional researches on Chinese language history and instead uses quantitative methods to draw macroscopic conclusions from large-scale corpus.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.111-114
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• 2018

Traffic accident analysis is important to reduce the occurrence of the accidents. In this paper, we analyze the traffic accident with Apriori algorithm to find out an association rule of traffic accident in Korea. We first design the traffic accident analysis model, and then collect the traffic accidents data. We preprocessed the collected data and derived some new variables and attributes for analyzing. Next, we analyze based on statistical method and Apriori algorithm. The result shows that many large-scale accident has occurred by vans in daytime. Medium-scale accident has occurred more in day than nighttime, and by cars more than vans. Small-scale accident has occurred more in night time than day time, however, the numbers were similar. Also, car-human accident is more occurred than car-car accident in small-scale accident.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.389-395
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• 2004

We consider the interface between the parallel distributed memory multifrontal solver and the finite element method. We give in detail the requirement and the data structure of parallel FEM interface which includes the element data and the node array. The full procedures of solving a large scale structural problem are assumed to have pre-post processors, of which algorithm is not considered in this paper. The main advantage of implementing the parallel FEM interface is shown up in the case that we use a distributed memory system with a large number of processors to solve a very large scale problem. The memory efficiency and the performance effect are examined by analyzing some examples on the Pegasus cluster system.