• Journal of Information Technology Services
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• v.16 no.1
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• pp.73-82
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• 2017

Recently in-memory data stream processing has been actively applied to various subjects such as query processing, OLAP, data mining, i.e., frequent item sets, association rules, clustering. However, finding regular periodic patterns of events in an infinite data stream gets less attention. Most researches about finding periods use autocorrelation functions to find certain changes in periodic patterns, not period itself. And they usually find periodic patterns in time-series databases, not in data streams. Literally a period means the length or era of time that some phenomenon recur in a certain time interval. However in real applications a data set indeed evolves with tiny differences as time elapses. This kind of a period is called as a pseudo-period. This paper proposes a new scheme called FPMH (Finding Periods using Multiple Hash functions) algorithm to find such a set of pseudo-periods over a data stream based on multiple hash functions. According to the type of pseudo period, this paper categorizes FPMH into three, FPMH-E, FPMH-PC, FPMH-PP. To maximize the performance of the algorithm in the data stream environment and to keep most recent periodic patterns in memory, we applied decay mechanism to FPMH algorithms. FPMH algorithm minimizes the usage of memory as well as processing time with acceptable accuracy.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.261-273
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• 2022

Over the last decade, the scientific community has been actively developing technologies for automated software bug fixes called Automated Program Repair (APR). Several APR techniques have recently been proposed to effectively address multiple classroom programming errors. However, little attention has been paid to the advances in effective APR techniques for software bugs that are widely occurring during the software life cycle maintenance phase. To further enhance the concept of software testing and debugging, we recommend an optimized automated software repair approach based on hybrid technology (OAPR-HOML'1). The first contribution of the proposed OAPR-HOML'1 technique is to introduce an improved grasshopper optimization (IGO) algorithm for fault location identification in the given test projects. Then, we illustrate an opposition learning based artificial neural network (OL-ANN) technique to select AST node-level transformation schemas to create the sketches which provide automated program repair for those faulty projects. Finally, the OAPR-HOML'1 is evaluated using Defects4J benchmark and the performance is compared with the modern technologies number of bugs fixed, accuracy, precession, recall and F-measure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3868-3888
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• 2022

A widely used social networking service like Twitter has the ability to disseminate information to large groups of people even during a pandemic. At the same time, it is a convenient medium to share irrelevant and unverified information online and poses a potential threat to society. In this research, conventional machine learning algorithms are analyzed to classify the data as either non-rumor data or rumor data. Machine learning techniques have limited tuning capability and make decisions based on their learning. To tackle this problem the authors propose a deep learning-based Rumor Detection Neural Network model to predict the rumor tweet in real-world events. This model comprises three layers, AttCNN layer is used to extract local and position invariant features from the data, AttBi-LSTM layer to extract important semantic or contextual information and HPOOL to combine the down sampling patches of the input feature maps from the average and maximum pooling layers. A dataset from Kaggle and ground dataset #gaja are used to train the proposed Rumor Detection Neural Network to determine the veracity of the rumor. The experimental results of the RDNN Classifier demonstrate an accuracy of 93.24% and 95.41% in identifying rumor tweets in real-time events.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.8-13
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• 2023

High bandwidth memory a core technology of the future memory semiconductor industry, is attracting attention. Temporary bonding and debonding process technology, which plays an important role in high bandwidth memory process technology, is also being studied. In this process, total thickness variation is a major factor determining wafer performance. In this study, the reliability of the equipment measuring total thickness variation is identified, and the servo motor settling, and wafer total thickness variation measurement accuracy are analyzed. As for the experimental variables, vacuum, acceleration time, and speed are changed to find the most efficient value by comparing the stabilization time. The smaller the vacuum and the larger the radius, the longer the settling time. If the radius is small, high-speed rotation performance is good, and if the radius is large, low-speed rotation performance is good. In the future, we plan to conduct an experiment to measure the entire of the wafer.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.221-228
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• 2003

This paper presents a novel method that can identify the individual's haplotype from the given genotypes. Because of the limitation of the conventional single-locus analysis, haplotypes have gained increasing attention in the mapping of complex-disease genes. Conventionally there are two approaches which resolve the individual's haplotypes. One is the molecular haplotypings which have many potential limitations in cost and convenience. The other is the in-silico haplotypings which phase the haplotypes from the diploid genotyped populations, and are cost effective and high-throughput method. In-silico haplotyping is divided into two sub-categories - statistical and computational method. The former computes the frequencies of the common haplotypes, and then resolves the individual's haplotypes. The latter directly resolves the individual's haplotypes using the perfect phylogeny model first proposed by Dan Gusfield [7]. Our method combines two approaches in order to increase the accuracy and the running time. The individuals' haplotypes are resolved by considering the MLE (Maximum Likelihood Estimation) in the process of computing the frequencies of the common haplotypes.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.448-452
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• 2007

Many industrial operations require continuous or nearly-continuous operation of machines, which if interrupted can result in significant financial loss. The condition monitoring of these machines has received considerable attention recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is the development of smart sensor using which can on-line perform condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This sensor can receive data in real-time or periodic time from MEMS accelerometer. Furthermore, this system is capable for signal preprocessing task (High Pass Filter, Low Pass Filter and Gain Amplifier) and analog to digital converter (A/D) which is controlled by CPU. A/D converter that converts 10bit digital data is used. This sensor communicates with a remote site PC using TCP/IP protocols. Wireless LAN contain IEEE 802.11i-PSK or WPA (PSK, TKIP) encryption. Developed sensor executes performance tests for data acquisition accuracy estimations.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.511-516
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• 2001

Recently small punch creep testing (or miniature disc bend creep test) has received much attention through European collaborative research projects. This method was considered as a substitute for the conventional creep rupture testing by which the residual creep life is measured from the specimen taken out from serviced components of high temperature plants. It would be beneficial if the material creep properties such as power law creep constants as well as the creep rupture life can be measured from the small punch creep test. In this paper a method of assessing creep constants from the small punch creep testing is proposed. Finite element analyses were performed to investigate evolution of stress and strain rate at the weakest locations of the small punch creep specimen. Elastic-plastic-secondary creep analyses were carried out. The estimation equations for creep constants by the small punch creep testing are proposed based on the finite analysis results. Small punch creep tests were also performed with 9Cr steel and the accuracy of the proposed equation was verified by the experimental results.

• The Korean Journal of Emergency Medical Services
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• v.24 no.2
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• pp.111-121
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• 2020

Purpose: The purpose of this study was to compare visual blood loss estimation and subjective emergency according to clothing color. Methods: This is a quasi-experimental study which involved the use of mock bleeding patients wearing different colors tops. Results: Differences in visual estimates according to clothing color were significant in both paramedic students (F=6.69, p=.002) and the general department students (F=20.92, p=.000). When looking specifically at the accuracy of visual estimates, the paramedic students group tended to underestimate (50% white, 62.5% black, 32.5% yellow) the actual blood volume in all experimental conditions. On the other hand, the general department group tended to overestimation (45% white, 40% black, 67.5% yellow). The subjective emergency was also found to differ between paramedic students (F=13.58, p=.000) and general department students (F=9.67, p=.000). Conclusion: Paramedics treating bleeding patients at pre-hospital stages need to pay attention to blood loss estimations depending on clothing color, a factor not to be neglected or underestimated.

• Anxiety and mood
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• v.19 no.1
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• pp.19-26
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• 2023

Objective : This study aimed to investigate the effect of a 5-minute short mindfulness intervention on emotional regulation and cognitive improvement compared to self-awareness intervention. Methods : A total of 40 participants were randomly assigned and divided into a mindfulness group and a self-aware group. Participants responded to Korean Version of Positive Affect and Negative Affect Schedule (K-PANAS), and Korean version of Toronto Mindfulness Scale (K-TMS) to confirm prior homogeneity. Both groups performed processing according to each group after completing sentences related to themselves for self-focusing. Afterwards, the participants performed the Emotional Attentional Blink (EAB) task as a behavioral measure, and responded to K-PANAS and K-TMS post hoc. Results : The mindfulness group showed lower negative emotions in the K-PANAS than the self-awareness group. The mindfulness group showed higher accuracy than the self-awareness group in negative stimuli presented in the 200 ms condition and neutral stimuli presented in the 800 ms condition. However, there was no difference between groups in K-TMS. Conclusion : The study suggests that mindfulness and self-awareness have different emotion regulation strategies in negative stimuli. Additionally, 5-minute mindfulness intervention was relatively beneficial to improve cognitive function.