• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1304-1319
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• 2004

We can significantly reduce the cost o# program testing by automating the process of test data generation. Test data generation usually concerns identifying input values on which a selected path is executed. Although lots of research has been done so far, there still remains a lot of issues to be addressed. One of the issues is the shape problem. The shape problem refers to the problem of figuring out a shape of the input data structure required to cause the traversal of a given path. In this paper, we introduce a new method for the shape problem. The method converts the selected path into static single assignment (SSA) form without pointer dereferences. This allows us to consider each statement in the selected path as a constraint involving equality or inequality. We solve the constraints to get a solution which will be represented in terms of the points-to relations for each input variable. Simple, but illustrative examples are given to explain the proposed method.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.3
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• pp.404-416
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• 2015

Small-area, low-power coarse and fine frequency detectors (FDs) are proposed for an adaptive bandwidth referenceless CDR with a wide range of input data rate. The coarse FD implemented with two flip-flops eliminates harmonic locking as long as the initial frequency of the CDR is lower than the target frequency. The fine FD samples the incoming input data by using half-rate four phase clocks, while the conventional rotational FD samples the full-rate clock signal by the incoming input data. The fine FD uses only a half number of flip-flops compared to the rotational FD by sharing the sampling and retiming circuitry with PLL. The proposed CDR chip in a 65-nm CMOS process satisfies the jitter tolerance specifications of both USB 3.0 and USB 3.1. The proposed CDR works in the range of input data rate; 2 Gb/s ~ 8 Gb/s at 1.2 V, 4 Gb/s ~ 11 Gb/s at 1.5 V. It consumes 26 mW at 5 Gb/s and 1.2 V, and 41 mW at 10 Gb/s and 1.5 V. The measured phase noise was -97.76 dBc/Hz at the 1 MHz frequency offset from the center frequency of 2.5 GHz. The measured rms jitter was 5.0 ps at 5 Gb/s and 4.5 ps at 10 Gb/s.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.384-392
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• 2012

Recently, much research on blind estimation of the interleaver parameters has been performed by using Gauss-Jordan elimination to find the linearity of the block channel code. When using Gauss-Jordan elimination, the input data to be calculated needs to run as long as the square multiple of the number of the interleaver period. Thus, it has a limit in estimating the interleaver parameters with insufficient input data. In this paper, we introduce and analyze an estimation algorithm which can estimate interleaver parameters by using only 15 percent of the input data length required in the above algorithm. The shorter length of input data to be calculated makes it possible to estimate the interleaver parameters even when limited data is received. In addition, a 80 percent reduction in the number of the interleaver period candidates increases the efficiency of analysis. It is also feasible to estimate both the type and size of the interleaver and the type of channel coding.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.3
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• pp.284-299
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• 2008

Emission input modules was developed to produce emission input data and change some profiles for Sparse Matrix Operator Kernel Emissions (SMOKE) using Clean Air Policy Support System (CAPSS)'s activities and previous studies. Specially, this study was focused to improve chemical speciation and temporal allocation profiles of SMOKE. At first, SCC cord mapping was done. 579 SCC cords of CAPSS were matched with EPA's one. Temporal allocation profiles were changed using CAPSS monthly activities. And Chemical speciation profiles were substituted using Kang et al. (2000) and Lee et al. (2005) studies and Kim et al. (2005) study. Simulation in Seoul Metropolitan Area (Seoul, Incheon, Gyeonggi) using MM5, SMOKE and CMAQ modeling system was done for effect analysis of changed input modules of SMOKE. Emission model results adjusted with new input modules were slightly changed as compared to using EPA's default modules. SMOKE outputs shows that aldehyde emissions were decreased 4.78% after changing chemical profiles, increased 0.85% after implementing new temporal profiles. Toluene emissions were decreased 18.56% by changing chemical speciation profiles, increased 0.67% by replacing temporal profiles as well. Simulated results of air quality were also slightly elevated by using new input modules. Continuous accumulation of domestic data and studies to develop input system for air quality modeling would produce more improved results of air quality prediction.

• Journal of Sport and Applied Science
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• v.2 no.2
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• pp.1-10
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• 2018

Prior literature has posited that the sport industry has been effective method to drive the economic growth. Given the rationale, this study sets China as a research object with a quarterly data from the first quarter of 2003 to the fourth quarter of 2017 to explore how the sport industry affects economic growth. This study employed Johansen cointegration test and dynamic ordinary least squares as methods for an empirical analysis. The input of sport industry, the labor input, the capital input, and the economic growth are used as research variables. The results show that there is a long-run relationship among them. Johansen cointegration test's estimation indicated that 1% increase in the input of sport industry will lead to 0.064% increase in economic growth. Dynamic ordinary least squares' estimation showed that whenever in the one lead, in the one lag and in the present period, the input of sport industry always poses a positive effect on economic growth. Labor input also has a positive effect on economic growth. The capital input has a negative effect on economic growth. Finally, even though the input of sport industry has a positive effect on economic growth, its impact on economic growth is relative weak.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8370-8377
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• 2015

In spatial data stream management systems, it is needed appropriate load shedding algorithm because real-time input spatial data streams could exceed the limitation of main memory. However previous researches, lack regard for input ratio and spatial utilization rates of spatial data streams, or the characteristics of data source which generates data streams with spatial information efficiently, can lead to decrease the performance and accuracy of spatial data stream query. Therefore, multi-level load shedding scheme for spatial data stream management systems is proposed to increase the spatial query performance and accuracy. This proposed scheme limits overloads in relation to the input rate and the characteristics of data source first, and then, if needed, query data representing low query participation probability based on spatial utilizations are dropped relatively. Our experiments show that the proposed method could decrease load shedding frequency for previous researches by more than 11% despite query results accuracy and query performance are superior at 0.04% and 3%.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.221-224
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• 2007

MMS(Mobile Mapping System) generates a efficient image data for mapping and facility management. However, this image data of MMS has many difficulties in a practical use because of huge data volume. Therefore the important information likes road sign post must be extracted from huge MMS image data. In Korea, there is the HMS(Highway Management System) to manage a national road that acquire the line and condition of road from the MMS images. In the HMS each road sign information is manually inputted by the keyboard from moving MMS. This manually passive input way generate the error like inaccurate position, mistaking input in this research we developed the automatic road sign identifying technique using the image processing and the direct geo-referencing by GPS/INS data. This development brings not only good flexibility for field operations, also efficient data processing in HMS.

• International Journal of Contents
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• v.14 no.1
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• pp.28-33
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• 2018

In this paper, we design and implement a distributed, moving objects management system for processing locations and sensor data from smart black boxes. The proposed system is designed and implemented based on Apache Kafka, Apache Spark & Spark Streaming, Hbase, HDFS. Apache Kafka is used to collect the data from smart black boxes and queries from users. Received location data from smart black boxes and queries from users becomes input of Apache Spark Streaming. Apache Spark Streaming preprocesses the input data for indexing. Recent location data and indexes are stored in-memory managed by Apache Spark. Old data and indexes are flushed into HBase later. We perform experiments to show the throughput of the index manager. Finally, we describe the implementation detail in Scala function level.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.915-926
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• 2020

Outlier detection methods help one to identify unusual instances in data that may correspond to erroneous, exceptional, or surprising events or behaviors. This work studies conditional outlier detection, a special instance of the outlier detection problem, in the context of incorrect data label identification. Unlike conventional (unconditional) outlier detection methods that seek abnormalities across all data attributes, conditional outlier detection assumes data are given in pairs of input (condition) and output (response or label). Accordingly, the goal of conditional outlier detection is to identify incorrect or unusual output assignments considering their input as condition. As a solution to conditional outlier detection, this paper proposes the ratio-based outlier scoring (ROS) approach and its variant. The propose solutions work by adopting conventional outlier scores and are able to apply them to identify conditional outliers in data. Experiments on synthetic and real-world image datasets are conducted to demonstrate the benefits and advantages of the proposed approaches.