• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2933-2940
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• 2022

Approximations such as the delete-term approximation, rare event approximation, and minimal cutset upper bound (MCUB) need to be prudently applied for the quantification of a seismic probabilistic safety assessment (PSA) model. Important characteristics of seismic PSA models indicate that preserving the success branches in a primary seismic event tree is necessary. Based on the authors' experience in modeling and quantifying plant-level seismic PSA models, the effects of applying negate-down to the success branches in primary seismic event trees on the quantification results are summarized along with the following three insights gained: (1) there are two competing effects on the MCUB-based quantification results: one tending to increase and the other tending to decrease; (2) the binary decision diagram does not always provide exact quantification results; and (3) it is identified when the exact results will be obtained, and which combination provides more conservative results compared to the others. Complicated interactions occur in Boolean variable manipulation, approximation, and the quantification of a seismic PSA model. The insights presented herein can assist PSA analysts to better understand the important theoretical principles associated with the quantification of seismic PSA models.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.795-801
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• 2013

Recently, support vector machine has been widely used in various application fields due to its superiority of classification performance comparing with decision tree and neural network. Since support vector machine is basically designed for the binary classification problem, output coding method to analyze the classification result of multiclass binary classifier is used for the application of support vector machine into the multiclass problem. However, previous feature selection method for output coding based support vector machine found the features to improve the overall classification accuracy instead of improving each classification accuracy of each classifier. In this paper, we propose the novel feature selection method to find the features for maximizing the classification accuracy of each binary classifier in output coding based support vector machine. Experimental result showed that proposed method significantly improved the classification accuracy comparing with previous feature selection method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.11-18
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• 1998

A binary line code, called MB46d, is designed by use of the BUDA(Binary Unit DSV and ASV) cell concept to retain the property of being runlength limited, DC tree, and with a power spectral null at the Nyquist frequency. This new code is a stateless line code with a simple encoding and a decoding rule and enables efficient error monitoring. The power spectrum and the eye pattern of the new line code are simulated for a minimum-bandwidth digital transmission system where the sinc function is used as a basic pulse. The obtained power null at the Nyquist frequency is wide enough to enable easy band-limiting as well as secure insertion of a clock pilot where necessary. The eye is also substantially wide to tolerate a fair amount of timing jitter in the receiver.

• Convergence Security Journal
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• v.15 no.7
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• pp.75-84
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• 2015

In an "NTFS Index Record Data Hiding" method messages are hidden by using file names. Windows NTFS file naming convention has some forbidden ASCII characters for a file name. When inputting Hangul with the Roman alphabet, if the forbidden characters for the file name and binary data are used, the codes are convert to a designated unicode point to avoid a file creation error due to unsuitable characters. In this paper, the problem of a file creation error due to non-admittable characters for the file name is fixed, which is used in the index record data hiding method. Using Hangul with Roman alphabet the characters cause a file creation error are converted to an arbitrary unicode point except Hangul and Roman alphabet area. When it comes to binary data, all 256 codes are converted to designated unicode area except an extended unicode(surrogate pairs) and ASCII code area. The results of the two cases, i.e. the Hangul with Roman alphabet case and the binary case, show the applicability of the proposed method.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.119-132
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• 2007

In molecular biology, approximate subsequence search is one of the most important operations. In this paper, we propose an accurate and efficient method for approximate subsequence search in large DNA databases. The proposed method basically adopts a binary trie as its primary structure and stores all the window subsequences extracted from a DNA sequence. For approximate subsequence search, it traverses the binary trie in a breadth-first fashion and retrieves all the matched subsequences from the traversed path within the trie by a dynamic programming technique. However, the proposed method stores only window subsequences of the pre-determined length, and thus suffers from large post-processing time in case of long query sequences. To overcome this problem, we divide a query sequence into shorter pieces, perform searching for those subsequences, and then merge their results. To verify the superiority of the proposed method, we conducted performance evaluation via a series of experiments. The results reveal that the proposed method, which requires smaller storage space, achieves 4 to 17 times improvement in performance over the suffix tree based method. Even when the length of a query sequence is large, our method is more than an order of magnitude faster than the suffix tree based method and the Smith-Waterman algorithm.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1395-1405
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• 2017

In this paper, we analyses heart beat data to identify subjects stress state (binary) using heart rate variability (HRV) features extracted from heart beat data of the subjects and implement supervised machine learning techniques to create the mental stress classifier. There are four steps need to be done: data acquisition, data processing (HRV analysis), features selection, and machine learning, before doing performance measurement. There are 56 features generated from the HRV Analysis module with several of them are selected (using own algorithm) after computing the Pearson Correlation Matrix (p-values). The results of the list of selected features compared with all features data are compared by its model error after training using several machine learning techniques: support vector machine, decision tree, and discriminant analysis. SVM model and decision tree model with using selected features shows close results compared to using all recording by only 1% difference. Meanwhile, the discriminant analysis differs about 5%. All the machine learning method used in this works have 90% maximum average accuracy.

• ETRI Journal
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• v.40 no.4
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• pp.471-482
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• 2018

This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization-based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly-exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach.

• The KIPS Transactions:PartA
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• v.11A no.3
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• pp.213-216
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• 2004

Proirity queues(PQ) can be used in applications such as scheduling or sorting. The data structures for PQ can be constructed with or without pointers. The implicit representation without pointers uses less memory space than pointer-based representation. It if shown that a 2-heap, a traditional Implicit PQ based on a binary tree, is slower than an f-heap based on a 8-ary tree. This is because 8-heap utilizes cache memory more efficiently This paper presents a novel fast implicit heap called 8-heap＊ which is easier to implement. Experimental results show that the 8-heap＊ is faster than 8-heap as well as 2-heap.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.781-790
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• 1996

processors must be allocated to incoming tasks in a way that will maximize the processor utilization and minimize the system fragmentation. Thus, an efficient method of allocating processors in a hypercube is a key to system performance. In order to achieve this goal, it is necessary to detect the availability of a subcube of required size and merge the released small cubes to form a larger ones. This paper presents the tree-exchange algorithm which detemines the levels and partners of the binary tree representation of a hypercube, and an efficient allocation strategy using the algorithm. The complexity for search time of the algorithm is $O\ulcorner$n/2$\lrcorner$$\times$2n)and it shows good performance in comparison with other strategies.