• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.60-64
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• 2022

In the automotive industry, the development of automobiles to meet safety requirements is becoming increasingly complex. This is because quality evaluation agencies in each country are continually strengthening new safety standards for vehicles. Among these various requirements, collision safety must be satisfied by controlling airbags, seat belts, etc., and can be defined as post-crash safety. Apart from this safety system, the Advanced Driver Assistance Systems (ADAS) use advanced detection sensors, GPS, communication, and video equipment to detect the hazard and notify driver before the collision. However, research to improve passenger safety in case of an accident by using the sensor of active safety represented by ADAS in the existing passive safety is limited to the level that utilizes the sudden braking level of the FCA (Forward Collision-avoidance Assist) system. Therefore, this study aims to develop logic that can improve passenger protection in case of an accident by using ADAS information and driving information secured before a collision. The proposed logic was constructed based on LSTM deep learning techniques and trained using crash test data.

• Iberoamérica
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• v.23 no.2
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• pp.209-241
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• 2021

Over the last few years, rural areas in northeastern Mexico have present significant changes in social, economic, and territorial aspects linked to the New Rurality. In this context, winemaking has become one of the most dynamic and growing activities in the regional economy. This emerging development has prompted different forms of appropriation and use of this space, but it also highlights the lack of access to knowledge for wine production due to the lack of formal educational centers. As a result, learning communities enable the development of skills and competencies through non-formal educational practices. The objective of this paper is to analyze the role of learning communities in non-formal educational environments, taking as a case study: a collective of small-scale wine producers in Parras de la Fuente, Coahuila. This research focuses on two perspectives of learning: appropriation and technology transfer, and promotion of Mexican wine culture. The main finding was to demonstrate the importance of including educational processes that respond to the context and needs of the community.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1075-1080
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• 2022

Recently, the effort is continuously applied in machine learning and deep learning algorithm which is represented as artificial intelligence algorithm in the varies field such as prediction, classification and clustering. In this paper, we propose detection algorithm for un-peeling status of PCB protection film by using Dectron2. We use 42 images of data as training and 19 images of data as testing based on 61 images which was taken under the condition of a critical reflection angel of 42.8°. As a result, we get 16 images that was detected and 3 images that was not detected among 19 images of testing data.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.32-44
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• 2023

Information security breach is a major risk in e-learning. This study presents the potential information security disruptions in Learning Management Systems (LMS) from the perspective of users. We use the Technology Acceptance Model approach as a user perception model of information security, and the results of a questionnaire comprising 44 questions for instructors and students across Indonesia to verify the model. The results of the data analysis and model testing reveals that lecturers and students perceive the level of information security in the LMS differently. In general, the information security aspects of LMSs affect the perceptions of trust of student users, whereas such a correlation is not found among lecturers. In addition, lecturers perceive information security aspect on Moodle is and Google Classroom differently. Based on this finding, we recommend that institutions make more intense efforts to increase awareness of information security and to run different information security programs.

• Journal of Korean Physical Therapy Science
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• v.29 no.4
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• pp.54-64
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• 2022

Background: This study aims to investigate the perception and actual situation of online classes of college physical therapy students after COVID-19. In addition, it is necessary to conduct a fact-finding survey on how physical therapy students who have experienced online classes think about the online class method, what are the overall problems, and to what extent they are satisfied. Methods: The subjects of this study were 102 students in the 3rd year of physical therapy at University A, and the survey period was from June 10 to June 23, 2020, and the perception and use of online classes and self-regulated learning ability were investigated. Design: Cross-section study. Results: The perception of online lectures, it was found that the perception of online classes after actual online classes was better than the perceptions before watching (utilization), and satisfaction with online classes was generally high. There was a significant difference according to the grades in self regulated learning. The data values measured in this study were analyzed using SPSS (Statistical Package for Social Science) Windows version 12.0 statistical program. Conclusion: These results suggest that in future research, it is necessary to study the perceptions and actual conditions of each class compared to online classes and face-to-face classes.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.53-61
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• 2010

Through the use of Internet, we can now exchange large amount of information in quick seconds, and provide learners abundant amount of information at the same time. However, the amount of information is so large and limitless that learners cannot learn and process everyone of them. This study focuses on the Customizing learning system for personalized needs, which aims to reduce the amount of "unnecessary" information, to collect educational information of learners' needs, and also to provide useful information individually to each learners. Customizing system is designed to recognize learners' needs individually, and to analyze and collect any information based on the learning assessment. Through this system, being given only the necessary information, learners can increase their efficiency in studying and reach their study goal at the same time.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3985-3995
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• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.17-24
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• 2009

Increasing use of information system has led to larger amount of developing expenses and demands on software. Until recent days, the model using regression analysis based on statistical algorithm has been used. However, Machine learning is more investigated now. This paper estimates the software cost using SVR(Support Vector Regression). a sort of machine learning technique. Also, it finds the best set of parameters applying immune algorithm. In this paper, software cost estimation is performed by SVR based on immune algorithm while changing populations, memory cells, and number of allele. Finally, this paper analyzes and compares the result with existing other machine learning methods.

• Asia Pacific Journal of Corpus Research
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• v.4 no.2
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• pp.41-55
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• 2023

In data-driven learning (DDL), learners explore a corpus to understand vocabulary and grammar. Although many studies have emphasized the role of DDL in second language (L2) acquisition, L2 light verbs have been largely under-explored. To bridge this gap, this study focused on the learning outcomes of L2 light verbs among 29 intermediate-level Japanese university students. The research zeroed in on six prevalent light verbs in English: "make," "do," "take," "have," "give," and "get." Over nine weeks, the participants engaged with verb-noun collocations using worksheets that juxtaposed Japanese translations of the target collocations with their English equivalents, with the verbs omitted. With the aid of Wordbanks Online, they filled in the blanks and constructed accurate sentences. Before this activity, a 20-minute tutorial was given to the participants on how to interpret the concordance lines. The effectiveness of the DDL method was evaluated using pre-tests, immediate post-tests, and delayed post-tests. The results showed that DDL significantly improved the participants' knowledge of the target collocations of light verbs and nouns; the post-test and delayed post-test scores were significantly higher than the pre-test scores. The results showed that, overall, DDL contributed to memorizing the collocations of light verbs and nouns; however, DDL had different effects on the memorization of collocations across different light verbs. The extent of work on the worksheet is not the only factor in its retention, and observing concordance lines may promote learners' memorization of light-verb collocations.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.945-963
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• 2022

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information-both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.