• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.1-9
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• 2019

Recently, as the importance of warehouses has increased, much efforts are being made to improve the picking process in order to cope with a small amount of high frequency and fast delivery. This study proposes an algorithm to assign orders to pickers in the situation where Single Order Picking policy is used. This algorithm utilizes five attributes related to picking such as picking processing time, elapsed time after receipt of order, inspection/packing workstation situation, picker error, customer importance. A measure of urgency is introduced so that the units of measure for each attribute are the same. The higher the urgency, the higher the allocation priority. In the proposed algorithm, the allocation policy can be flexibly adjusted according to the operational goal of the picking system by changing the weight of each attribute. Simulation experiments were performed on a hypothetical small logistics warehouse. The results showed excellent performance in terms of system throughput and flow time.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.145-153
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• 2008

Existing shortest-path algorithms mainly consider a single attribute. But traveler actually chooses a route considering not single attribute but various attributes which are synthesized travel time, route length, personal preference, etc. Therefore, to search the optimal path, these attributes are considered synthetically. In this study route searching algorithm which selects the maximum utility route using discrete choice model has developed in order to consider various attributes. Six elements which affect route choice are chosen for the route choice model and parameters of the models are estimated using survey data. A multinomial logit models are developed to design the function of route choice model. As a result, the model which has route length, delay time, the number of turning as parameter is selected based on the significance test. We use existing shortest path algorithm, which can reflect urban transportation network such as u-turn or p-turn, and apply it to the real network.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.2
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• pp.162-169
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• 2011

Land information is the real-time spatial thing which must be considered with spatial and time factors. This study aims to apply and implement an appropriate spatiotemporal model for land information by exploring spatiotemporal data models which have been suggested in the previous studies. The implemented spatiotemporal model in this study is characterized by time and attribute. In the time aspect, it is divided by valid time and transaction time, and in the attribute aspect, includes the related information such as area and ownership. At the spatial point of view, the model has a spaghetti information structure as reducing information overlapped by managing the spatial information coordinates. The spatiotemporal land information model in this study facilitates representing the quality of attribute, spatial and time information.

• Journal of KIISE:Databases
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• v.35 no.4
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• pp.319-332
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• 2008

Data cube, which is multi-dimensional data model, have been successfully applied in many cases of multi-dimensional data analysis, and is still being researched to be applied in data stream analysis. Data stream is being generated in real-time, incessant, immense, and volatile manner. The distribution characteristics of data arc changing rapidly due to those characteristics, so the primary rule of handling data stream is to check once and dispose it. For those characteristics, users are more interested in high support attribute values observed rather than the entire attribute values over data streams. This paper propose dynamic data cube for applying data cube to data stream environment. Dynamic data cube specify user's interested area by the support ratio of attribute value, and dynamically manage the attribute values by grouping each other. By doing this it reduce the memory usage and process time. And it can efficiently shows or emphasize user's interested area by increasing the granularity for attributes that have higher support. We perform experiments to verify how efficiently dynamic data cube works in limited memory usage.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.260-266
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• 2018

The concept of customer utility, which affects customer's adoption, is important to understand the process of technology diffusion and substitution regarding multiple generation technology. This research defined the concept of attribute-based customer utility and developed a model for measuring attribute-based customer utility. Based on the literature review and modeling, we provided the definition and a model regarding customer utility and the accuracy of the model is verified through a case study of the semiconductor industry. Customer utility for a multiple generation technology needs to consider changes by generation, or time within the same generation, and is defined as the summation of both technological and economic utilities. In addition, we can model the measurement of customer utility after converting technological and economical attributes into utilities. This research is valuable in understanding not only customer utility as a driver of customer adoption, but also for establishing technological strategy after forecasting diffusion and substitution paths based on customer utility.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.113-130
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• 2021

Recently, ground-penetrating radar (GPR) surveys have been actively employed to obtain a large amount of data on occurrences such as ground subsidence and road safety. However, considering the cost and time efficiency, more intuitive and accurate interpretation methods are required, as interpreting a whole survey data set is a cost-intensive process. For this purpose, GPR data can be subjected to attribute analysis, which allows quantitative interpretation. Among the seismic attributes that have been widely used in the field of exploration, complex trace analysis and similarity are the most suitable methods for analyzing GPR data. Further, recently proposed attributes such as edge detecting and texture attributes are also effective for GPR data analysis because of the advances in image processing. In this paper, as a reference for research on the attribute analysis of GPR data, we introduce the useful attributes for GPR data and describe their concepts. Further, we present an analysis of the interpretation methods based on the attribute analysis and past cases.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.647-653
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• 2022

Data mining is a technique that explores knowledge such as relationships and patterns between data by exploring and analyzing data. Data that occurs in the real world includes a temporal attribute. Temporal data mining research to find useful knowledge from data with temporal properties can be effectively utilized for predictive judgment that can predict the future. In this paper, we propose an algorithm using time-unit grouping to classify the database into regular time period units and discover frequent pattern itemsets in time units. The proposed algorithm organizes the transaction and items included in the time unit into a matrix, and discovers frequent items in the time unit through grouping. In the experimental results for the performance evaluation, it was found that the execution time was 1.2 times that of the existing algorithm, but more than twice the frequent pattern itemsets were discovered.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.1-7
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• 2016

As new information and communication technologies evolve, security threats are also becoming increasingly intelligent and advanced. In this paper, we analyze the time series data continuously entered through a series of periods from the network device or lightweight IoT (Internet of Things) devices by using the statistical technique and propose a system to detect abnormal behaviors of the device or abnormality based on the analysis results. The proposed system performs the first level abnormal detection by using previously entered data set, thereafter performs the second level anomaly detection according to the trust bound configured by using stored time series data based on time attribute or group attribute. Multi-level analysis is able to improve reliability and to reduce false positives as well through a variety of decision data set.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.201-211
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• 2010

The aim of this study is to investigate the method to enhance user cognition for "STM information(Short-term Memorable Information)" that is relatively accessible to information in a short period of time in information design types. What stands out from this study is the design attributes and expression method of information in a broad perspective. By 4 visualization attributes of function variable; 'Operations', 'Events', 'Methods' and 'Use cases', STM information should be satisfied by the attribute of 'Understandable' and 'Accessibility' from the point of view of visual representation and by the attribute of 'Errorless' and 'Timeliness' from the point of view of user operation. As the expression method of each perspectives, I suggested "Attribution theory", "Cognitive model", "Maximization of Proactivity", "Minimization of surplus information" and "Using dual-code" in the point of view of visual representation, and "Context effect", "Using memory code" and "Two methods of information scanning" in the point of view of user operation. I assured that above-mentioned methods are efficient and cognitive pattern of user for STM information is found out by survey and interview.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.17-24
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• 2020

The traveling salesman problem is an algorithmic problem tasked with finding the shortest route that a salesman visits, visiting each city and returning to the started city. Due to the exponential time complexity of TSP, it's hard to implement on cases like amusement park or delivery. Also, TSP is hard to meet user's demand that is associated with multi-dimensional attributes like travel time, interests, waiting time because it uses only one attribute - distance between nodes. This paper proposed Top-n Skyline-Multi Dimension TSP to resolve formerly adverted problems. The proposed algorithm finds the shortest route faster than the existing method by decreasing the number of operations, selecting multi-dimensional nodes according to the dominance of skyline. In the simulation, we compared computation time of dynamic programming algorithm to the proposed a TS-MDT algorithm, and it showed that TS-MDT was faster than dynamic programming algorithm.