• IE interfaces
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• v.17 no.1
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• pp.1-12
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• 2004

In a modern semiconductor device manufacturing industry, statistical bin limits on wafer level test bin data are used for minimizing value added to defective product as well as protecting end customers from potential quality and reliability excursion. Most wafer level test bin data show skewed distributions. By Monte Carlo simulation, this paper evaluates methods and sample size effect regarding determination of statistical bin limits. In the simulation, it is assumed that wafer level test bin data follow the Poisson distribution. Hence, typical shapes of the data distribution can be specified in terms of the distribution's parameter. This study examines three different methods; 1) percentile based methodology; 2) data transformation; and 3) Poisson model fitting. The mean square error is adopted as a performance measure for each simulation scenario. Then, a case study is presented. Results show that the percentile and transformation based methods give more stable statistical bin limits associated with the real dataset. However, with highly skewed distributions, the transformation based method should be used with caution in determining statistical bin limits. When the data are well fitted to a certain probability distribution, the model fitting approach can be used in the determination. As for the sample size effect, the mean square error seems to reduce exponentially according to the sample size.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.209-213
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• 2021

Data mining is the application of specific algorithms for extracting patterns from data and KDD is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories or data streams. Data mining can be used for decision making in educational system. But educational institution does not use any knowledge discovery process approach on these data; this knowledge can be used to increase the quality of education. The problem was happening in the educational management system, but to make education system more flexible and discover knowledge from it huge data, we will use data mining techniques to solve problem.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.9
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• pp.463-471
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• 2017

According to the Korea industrial standard of air conditioning systems (KS C 9306), cooling and heating loads for buildings can be calculated by using maximum and minimum temperature in BIN data. Cooling and heating loads can be determined by building set temperature and ambient temperature. Cooling and heating system capacity of buildings can be normally designed according to determined heating and cooling loads. Cooling and heating system capacity can be reduced by updated BIN data, applying TAC (Technical Advisory Committee) values. In this study, updated BIN data have been analyzed using ambient temperature of 19 areas in Korea for the last 10 years (2005~2014) provided by KMA (Korea Meteorological Administration). Building cooling and heating loads have been calculated following TAC based BIN data. As a result, designed system capacity decreased depending on applying TAC. Those were reduced as 7.1% ($100m^2$ building), 8.7% ($1,000m^2$ building) in cooling capacity, 11.7% in heating capacity when TAC 2.5% applied. And also, it is expected system initial and operating cost by decreasing system capacity.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.103-111
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• 2021

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.17 no.1
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• pp.28-43
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• 1988

The purpose of this research is to produce bin weather data for Seoul from Standard Weather Data. The intended use of these data is for input to recently developed models for simplified energy calculations and for generating variable-base degree-day information. The data produced under this study include $3^{\circ}C$ bin data covering the full range of dry-bulb temperatures with mean coincident wet-bulb and daytime coincident solar radiation, wet-bulb bins down to freezing temperature, wind speed bins with prevailing directions, and heating and cooling degree hours to nine different temperature bases. All of these data are tabulated in six separate time periods and total daily categories for monthly and annual periods.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1165-1174
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• 2011

In data broadcasting systems, servers continuously disseminate data items through broadcast channels, and mobile client only needs to wait for the data of interest to present on a broadcast channel. However, because broadcast channels are shared by a large set of data items, the expected delay of receiving a desired data item may increase. This paper explores the issue of designing proper data allocation on multiple broadcast channels to minimize the average expected delay time of all data items, and proposes a new data allocation scheme named two level bin-packing(TLBP). This paper first introduces the theoretical lower-bound of the average expected delay, and determines the bin capacity based on this value. TLBP partitions all data items into a number of groups using bin-packing algorithm and allocates each group of data items on an individual channel. By employing bin-packing algorithm in two step, TLBP can reflect a variation of access probabilities among data items allocated on the same channel to the broadcast schedule, and thus enhance the performance. Simulation is performed to compare the performance of TLBP with three existing approaches. The simulation results show that TLBP outperforms others in terms of the average expected delay time at a reasonable execution overhead.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• Journal of Biosystems Engineering
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• v.13 no.2
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• pp.28-37
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• 1988

This study was conducted to construct the soil bin system and associated microcomputer-based data acquisition system which is to be used for the effective evaluation of wheel performance. The soil bin system consists of four main parts; soil bin, carriage drive system, test carriage and soil processing carriage. The test carriage was constructed to measure the five performance parameters of testing wheels; pulling forte, motion resistance, sinkage and rotational speed of test wheel, and speed of test carriage. The test wheel is powered by a hydraulic system up to 8 ps. Soil processing carriage was designed to provide uniform test soil condition across the toil bin, and reproduction of soil conditions found satisfiable. The data acquisition system consists of APPLE II PLUS microcomputer, strain amplifier, I/O interface, A/D converter, digital counter and various transducers. It takes about 0.86 seconds to measure a set of performance parameters and store on the floppy disk simultaneously. Series of experiment showed that this system can be used effectively for evaluating the wheel performance associated with soil.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.197-204
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• 2022

Sorting is an important data structure in many applications in the real world. Several sorting algorithms are currently in use for searching and other operations. Sorting algorithms rearrange the elements of an array or list based on the elements' comparison operators. The comparison operator is used in the accurate data structure to establish the new order of elements. This report analyzes and compares the time complexity and running time theoretically and experimentally of insertion, merge, and heap sort algorithms. Java language is used by the NetBeans tool to implement the code of the algorithms. The results show that when dealing with sorted elements, insertion sort has a faster running time than merge and heap algorithms. When it comes to dealing with a large number of elements, it is better to use the merge sort. For the number of comparisons for each algorithm, the insertion sort has the highest number of comparisons.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.298-311
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• 2015

Vector data represents a map by its coordinate and Raster data represents a map by its pixel. Since these data types have very large data size, data compression procedure is a compulsory process. This paper compare the results from three different methodologies; GIS (Geographic Information System) vector map data compression using DP(Douglas-Peucker) Simplification algorithm, vector data compression based on Bin classification and the combination between two previous methods. The results shows that the combination between the two methods have the best performance among the three tested methods. The proposed method can achieve 4-9% compression ratio while the other methods show a lower performance.