• Agribusiness and Information Management
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• v.1 no.2
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• pp.43-60
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• 2009

To improve the performance of wide-issue superscalar processors, it is essential to increase the width of instruction fetch and the issue rate. Removal of control hazard has been put forward as a significant new source of instruction-level parallelism for superscalar processors and the conditional branch prediction is an important technique for improving processor performance. Branch mispredictions, however, waste a large number of cycles, inhibit out-of-order execution, and waste electric power on mis-speculated instructions. Hence, the branch predictor with higher accuracy is necessary for good processor performance. In global-history-based predictors like gshare and GAg, many mispredictions come from commit update of the branch history. Some works on this subject have discussed the need for speculative update of the history and recovery mechanisms for branch mispredictions. In this paper, we present a new mechanism for recovering the branch history after a misprediction. The proposed mechanism adds an age_counter to the original predictor and doubles the size of the branch history register. The age_counter counts the number of outstanding branches and uses it to recover the branch history register. Simulation results on the SimpleScalar 3.0/PISA tool set and the SPECINT95 benchmarks show that gshare and GAg with the proposed recovery mechanism improved the average prediction accuracy by 2.14% and 9.21%, respectively and the average IPC by 8.75% and 18.08%, respectively over the original predictor.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.103-108
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• 2007

This paper presents the conceptual framework for estimating and predicting system's susceptibility to failure as function of condition parameter value which is representing the current status of performance measure using on-line performance reliability. The performance of such system depends on one parameter with a probability distribution that degrades with time gracefully. Performance reliability represents the probability that physical performance will remain satisfactory over a finite period of time or usage cycles in the future. An empirical physical performance function is constructed to incorporate explanatory variables (operating and environmental conditions) over a time or usage dimension. This function enables one to model device performance and the associated classical reliability measures simultaneously, in the performance domain and time domain. The conditional performance reliability structure developed represents a tool to predict system performance over time or usage for next usage period. By enabling such a framework, it can bring us more efficient planning and execution in system's operation control as well as maintenance to reduce costs and/or increase profits.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.127-133
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• 2019

Since frequent item extraction so far requires searching for patterns and traversal for the FP-Tree, it is more likely to store the mining data in a tree and thus CPU time is required for its searching. In order to overcome these drawbacks, in this paper, we provide each item with its location identification of transaction data without relying on conditional FP-Tree and convert transaction data into 2-dimensional position information look-up table, resulting in the facilitation of time and spatial accessibility. We propose an algorithm that considers the mapping scheme between the location of items and items that guarantees the linear time complexity. Experimental results show that the proposed method can reduce many execution time and memory usage based on the data set obtained from the FIMI repository website.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.3
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• pp.54-68
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• 2011

As the prediction accuracy of conditional branch instruction is increased highly, the importance of prediction accuracy for unconditional branch instruction is also increased accordingly. Except the case of RAS(Return Address Stack) overflow, the prediction accuracy of function return address should be 100% theoretically. However, there exist some possibilities of miss-predictions for RAS return addresses, when miss-speculative execution paths are invalidated, in case of modern speculative microprocessor environments. In this paper, we propose the RAS rename technique to prevent RAS pollution, results in the reduction of RAS miss-prediction. We divide a RAS stack into a soft-stack and a hard-stack and we handle the instructions for speculative execution in the soft-stack. When some overwrites happen in the soft-stack, we move the soft-stack data into the hard-stack. In addition, we propose an enhanced version of RAS rename scheme. In simulation results, our solution provide 1/90 reduction of miss-prediction of function return address, results in up to 6.85% IPC improvement, compared to normal RAS method. Furthermore, it reduce miss-prediction ratio as 1/9, compared to previous technique.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.905-912
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• 2012

We are dealing with the Bayes' rule education tool with Excel Macro and its usage example. When an event occurs, we are interested in whether it does under certain conditions or not. In this case, we use the Bayes' rule to calculate the probability. Bayes' rule is very useful in making decision based on newly obtained statistical information. We introduce an efficient self-teaching educational tool developed to help the learners understand the Bayes' rule through intermediate steps and descriptions. The concept and examples of intermediate steps such as conditional probability, multiplication rule, law of total probability, prior probability and posterior probability could be acquired through step-by-step learning. All the processes leading to result are given with diagrams and detailed descriptions. By just clicking the execution button, users could get the results in one screen.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.814-818
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• 2004

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic and timing information besides has various handwriting, extreme overlap of the consonant and vowel and many error image of stroke. Consequently off-line handwritten character recognition needs to study about preprocessing of various methods such as binarization and thinning. This paper considers running time of watershed algorithm and the quality of resulting image as preprocessing For off-line handwritten Korean character recognition. So it proposes application of effective watershed algorithm for segmentation of character region and background region in gray level character image and segmentation function for binarization image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods which effectively extracts skeleton through conditional test mask considering running time and quality. of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Watershed image conversion uses prewitt operator for gradient image conversion, extracts local minima considering 8-neighborhood pixel. And methods by using difference of mean value is used in region merging step, Converted watershed image by means of this methods separates effectively character region and background region applying to segmentation function. Average execution time on the previous method was 2.16 second and on this paper method was 1.72 second. We prove that this paper's method removed noise effectively with overlap stroke as compared with the previous method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.537-544
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• 2020

Advances in detection techniques, such as mutation and obfuscation, are being advanced with the development of malware technology. In the malware detection technology, unknown malware detection technology is important, and a method for Malware Authorship Attribution that detects an unknown malicious code by identifying the author through distributed malware is being studied. In this paper, we try to extract the compiler information affecting the binary-based author identification method and to investigate the sensitivity of feature selection, probability and non-probability models, and optimization to classification efficiency between studies. In the experiment, the feature selection method through information gain and the support vector machine, which is a non-probability model, showed high efficiency. Among the optimization studies, high classification accuracy was obtained through feature selection and model optimization through the proposed framework, and resulted in 48% feature reduction and 53 faster execution speed. Through this study, we can confirm the sensitivity of feature selection, model, and optimization methods to classification efficiency.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.155-176
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• 2016

This study aims to develope a scholarly metadata information system based on conceptual elements of text structure of Korean studies research articles and to identify the applicability of text structure based metadata as compared with the existing similar system. For the study, we constructed a database(Korean Studies Metadata Database, KMD) with text structure based on metadata of Korean Studies journal articles selected from the Korea Citation Index(KCI). Then we verified differences between KCI system and KMD system through search results using same keywords. As a result, KMD system shows the search results which meet the users' intention of searching more efficiently in comparison with the KCI system. In other words, even if keyword combinations and conditional expressions of searching execution are same, KMD system can directly present the content of research purposes, research data, and spatial-temporal contexts of research et cetera as search results through the search procedure.

• The Journal of Natural Sciences
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• v.5 no.2
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• pp.39-43
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• 1992

Detection of parallelism by a compiler is very desirable from a user's point of view. However, even the most sophisticated techniques to detect parallelism trip on trivial impediments, such as conditionals, function calls, and input/output statements, fail to detect most of the parallelism present in a program. Some parallelizing compilers provide feedback to the user when they have difficulty in deciding about parallel execution. Under these circumstances, a programmer has to restructure the source code to aid the detection of parallelism. But, functional and declarative languages can be said to offer many advantages in this context. Functional programs are easier to reason about because their output is determinate, that is, independent of the order of evaluation. However, functional languages traditionally have lacked good facilities for manipulating arrays and matrices. In this paper, a declarative language called Id has been proposed as a solution to some of these problems.

• Journal of KIISE
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• v.43 no.2
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• pp.212-221
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• 2016

Android apps suffer from intent vulnerabilities in that they abnormally stop execution when Android components such as, activity, service, and broadcast receiver, take malformed intents. This paper proposes a method to prevent intent vulnerabilities by allowing programmers to write a specification on intents that a component expects to have, and by checking intents against the specification in runtime. By declaring intent specifications, we can solve the problem that one may miss writing conditional statements, which check the validity of intents, or one may mix those statements with another regular code, so making it difficult to maintain them. We perform an experiment by applying the proposed method to 7 Android apps, and confirm that many of abnormal termination of the apps because of malformed intents can be avoided by the intent specification based runtime assertion.