• Journal of Digital Convergence
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• v.19 no.1
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• pp.249-260
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• 2021

This study is a meta-analysis study conducted to integrate and analyze the results of flip-learning studies for Korean nursing students. We searched PubMed, EMBASE, Cochrane, CINAHL, and Korean databases. Randomized controlled trials (RCTs) and Non-Randomized controlled trials (Non-RCTs) evaluating the effects of flipped learning for Korean nursing students were included. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated using a random-effects meta-analysis. The entire effect size in flipped learning was big in effect size (SMD = 1.21; 95% CI = 0.84 to 1.63; I2 = 93.9; n = 23) compared to the control groups. The analysis results of subgroups according to the classification of Bloom showed that flipped learning was found to have a significant effect on psychomotor domain, cognitive domain, and affective domain. A total of 10 literature analyses, this meta-analysis showed that flipped learning on Korean nursing students is effective in psychomotor, cognitive, and affective domain than the traditional teaching method. The flip learning can be integrated into theoretical and practical nursing education to improve the academic performance of nursing students.

• Korean Journal of Food Science and Technology
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• v.52 no.6
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• pp.580-586
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• 2020

Fishery resources have been widely consumed as protein- and vitamin-rich food sources in the Korean diet. However, information regarding their vitamin levels is extremely limited. In this study, trienzyme-Lactobacillus casei method was validated and used to determine the folate contents in fishery foods. The trienzyme-L. casei method for folate analysis showed excellent accuracy (85.2 to 95.3% recovery) and precision (repeatability 1.4% RSD and reproducibility 2.4% RSD). Folate contents of 20 fish foods (4 fish, 3 crustaceans, 3 sea algae, 3 cephalopods, 4 shellfish, and 3 others) ranged from 1.75 to 97.98 ㎍/100 g. Furthermore, we found that the folate content in seaweed fusiforme was the highest, followed by gulfweed (69.73 ㎍/100 g). Folate analysis using the trienzyme-L. casei method was determined excellent based on the z-score of -0.3 in the Food Analysis Performance Assessment Scheme test. Analytical and method validation data generated in this study could be used to update the national food composition table on vitamin B9 in Korean fishery resources.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.61-75
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• 2011

Recently, there is significant increasing of massive attacks, which try to infect PCs that visit websites containing pre-implanted malicious code. When visiting the websites, these hidden malicious codes can gain monetary profit or can send various cyber attacks such as BOTNET for DDoS attacks, personal information theft and, etc. Also, this kind of malicious activities is continuously increasing, and their evasion techniques become professional and intellectual. So far, the current signature-based detection to detect websites, which contain malicious codes has a limitation to prevent internet users from being exposed to malicious codes. Since, it is impossible to detect with only blacklist when an attacker changes the string in the malicious codes proactively. In this paper, we propose a novel approach that can detect unknown malicious code, which is not well detected by a signature-based detection. Our method can detect new malicious codes even though the codes' signatures are not in the pattern database of Anti-Virus program. Moreover, our method can overcome various obfuscation techniques such as the frequent change of the included redirection URL in the malicious codes. Finally, we confirm that our proposed system shows better detection performance rather than MC-Finder, which adopts pattern matching, Google's crawling based malware site detection, and McAfee.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Journal of IKEEE
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• v.25 no.2
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• pp.273-279
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• 2021

In this paper, we propose an algorithm that shortens the learning time by performing individual learning using partitioning the deep learning structure. The proposed algorithm consists of four processes: network classification origin setting process, feature vector extraction process, feature noise removal process, and class classification process. First, in the process of setting the network classification starting point, the division starting point of the network structure for effective feature vector extraction is set. Second, in the feature vector extraction process, feature vectors are extracted without additional learning using the weights previously learned. Third, in the feature noise removal process, the extracted feature vector is received and the output value of each class is learned to remove noise from the data. Fourth, in the class classification process, the noise-removed feature vector is input to the multi-layer perceptron structure, and the result is output and learned. To evaluate the performance of the proposed algorithm, we experimented with the Extended Yale B face database. As a result of the experiment, in the case of the time required for one-time learning, the proposed algorithm reduced 40.7% based on the existing algorithm. In addition, the number of learning up to the target recognition rate was shortened compared with the existing algorithm. Through the experimental results, it was confirmed that the one-time learning time and the total learning time were reduced and improved over the existing algorithm.

• Phonetics and Speech Sciences
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• v.12 no.4
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• pp.81-90
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• 2020

This paper describes a method of building Korean conversational speech data in the emergency medical domain and proposes an annotation method for the collected data in order to improve speech recognition performance. To suggest future research directions, baseline speech recognition experiments were conducted by using partial data that were collected and annotated. All voices were recorded at 16-bit resolution at 16 kHz sampling rate. A total of 166 conversations were collected, amounting to 8 hours and 35 minutes. Various information was manually transcribed such as orthography, pronunciation, dialect, noise, and medical information using Praat. Baseline speech recognition experiments were used to depict problems related to speech recognition in the emergency medical domain. The Korean conversational speech data presented in this paper are first-stage data in the emergency medical domain and are expected to be used as training data for developing conversational systems for emergency medical applications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.38-50
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• 2022

Purpose: The goal of this research is to create a numerical analytic database that may assist fire prevention managers and building officials in prioritizing items that need to be addressed in order to improve evacuation safety performance while working within a constrained budget and time frame. Method: It was carried out utilizing the CFD Tool, a quantitative evaluation approach, to assess evacuation safety. One direct staircase-type apartment houses and one corridor-type apartment were chosen to make it. Result: In the fire compartment category, Apartment A's evacuation time was around 130 percent longer than that of sprinkler facilities. Conclusion: Fire prevention managers and building officials feel that starting with a single level and implementing "dwelling unit separations" will increase evacuation safety, and that maintaining fire compartments and sprinkler systems at all times will be effective. Because of the limited characteristics of smoke propagation in corridor-type apartments compared to direct staircase-type flats, it is thought that fire extinguishing equipment should be addressed.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1643-1652
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• 2022

Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.266-274
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• 2021

Ship officers are subject to excessive job stress, which has negative physical and psychological impacts and may adversely affect the smooth supply and demand of human resources. In this study, a mobile web application was developed as a tool for systematic job stress measurement and management of officers and verified through quality evaluation. Requirement analysis was performed by ship officers and staff in charge of human resources of shipping companies, and the results were reflected in the application configuration step. The application was designed according to the waterfall model, which is a traditional software development method, and functions were implemented using JSP and Spring Framework. Performance evaluation on the user interface, confirmed that proper input and output results were implemented, and the respondent results and the database were configured in the administrator interface. The results of evaluation questionnaires for quality evaluation of the interface based on ISO/IEC 9126-2 metric were significant 4.60 for the user interface and 4.65 for the administrator interface in a 5-point scale. In the future, it is necessary to conduct follow-up research on the development of data analysis system through utilization of the collected big-data sets.