• The Journal of Industrial Distribution & Business
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• v.14 no.1
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• pp.57-65
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• 2023

Purpose: The purpose of this research paper, therefore, is to explore the role of lifelong learning in improving HRM policies in a company. This research begins with a literature review of existing research on the topic, followed by a discussion of the findings and their implications for practitioners. Research design, data and methodology: The present author of this research collected textual dataset based on the numerous literature which has been investigated thoroughly in terms of the HRM policy and lifelong learning. For this reason, the author could obtain adequate prior studies, checking their validity and reliability. Results: The present research figured out that demonstrating that physical activity and exercise can enhance life expectancy, improve physical and mental health, and improve functional ability, and Examining the broad topic of socialization and interaction's function in raising elderly adults' living standards is necessary. Also, this research found that the social change and social isolation of older individuals in relation to the impact of digital technology. Conclusions: This research suggests that companies should also ensure that their HRM policies are designed in such a way that they allow employees to pursue further learning and development opportunities without having to sacrifice their current job responsibilities.

• Journal of Korean society of Dental Hygiene
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• v.23 no.6
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• pp.501-509
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• 2023

Objectives: This study aimed to analyze the relation between factors related to the clinical performance competency of dental hygiene students and their clinical learning environment. Methods: The study conducted a survey of dental hygiene students from October 18 to 30, 2023. The data were analyzed using one way analysis of variance, t-test, and stepwise multiple regression. Results: The total practicum lasted ≤10 weeks, 11-15 weeks, and ≥16 weeks for 41.7%, 33.5%, and 24.8% of the students, respectively. Half of them had experience at only one clinical institution. Clinical learning environment had an average score of 3.46 points, whereas the average clinical performance competency of the participants was 3.60 points. The major influencing factors on clinical performance competency were identified as preceptor' s guidance (β=0.277), work participation opportunities (β=0.213), and perceived importance of clinical practice (β=0.136). Conclusions: Efforts are required to provide students with prior education on the importance of clinical practice, improve the clinical learning environment with a focus on preceptor's guidance and work participation opportunities. And standardize various elements to resolve differences in the practice of clinical institutions across regions.

• Current Optics and Photonics
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• v.8 no.2
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• pp.127-137
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• 2024

The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags' coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the three-dimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.

• Geomechanics and Engineering
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• v.36 no.4
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• pp.381-390
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• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.683-688
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• 2018

In the 21stcentury, teachers must welcome new technology to ensure the best learning in virtual classrooms, aside from the physical classroom. Google Classroom provides a vital chance to promote blended learning and professional development. The purpose of this study is to specify the procedures in problem design when employing blended problem-based learning (BPBL) and to design problems for learning the contents of the subject. The design of problems is crucial for effective BPBL. The underlying theory of BPBL is that learning is most effectively initiated and facilitated by posing and solving real-life problems that interest the learner, because working on such problems makes learning meaningful and motivates students. Ineffective problem-based learning (PBL) could affect students when acquiring sufficient domain knowledge, activating appropriate prior knowledge, and properly directing their own learning. The procedures for designing good problems are composed of the selection of educational content, figuring out the learner's characteristics, finding problems, setting up roles and situations, and writing down problems. Using these procedures, we designed five integration problems covering the contents of management subjects. Planned management subjects based on BPBL in a Mongolian university focuses on the process of designing problems.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.229-238
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• 2021

The purpose of this study is to understand in-depth the effect of self-directed practice in for core fundamental nursing skills applying reciprocal peer tutoring on the learning of nursing college students. The study participants were 15 students in the 4th grade of the department of nursing, and data were collected through in-depth interviews. The results of analysis resulted in four themes: 'motivation of learning', 'self-directed learning', 'improving achievement', and 'insufficient learning requirements'. The core fundamental nursing skills applying reciprocal peer tutoring has a positive experience of inducing motivation of learning for participants through a comfortable environment and improving self-confidence, and gaining opportunities to lead prior learning and learn in the learner's language. However, there have been experiences where additional knowledge expansion is difficult and feelings of insecurity is felt due to insufficient learning requirements. Therefore, when applying the reciprocal peer tutoring method to self-directed practice, it is considered to be effective if the professor applies a method that can sufficiently satisfy the learner's learning needs.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.3
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• pp.55-65
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• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• Journal of Information Science Theory and Practice
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• v.8 no.4
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• pp.55-66
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• 2020

The purpose of this study is to examine the relationship between collaborative information seeking and users' learning style preferences and their experience of information systems. The study investigates the role of four different factors including learning style, task complexity, and user experience in collaborative information seeking in digital environments. Sixty participants (30 pairs) were randomly chosen from volunteer graduate students of Kharazmi University (Iran). Participants completed Kolb's learning style questionnaire and a user experience questionnaire and then performed two information seeking tasks (one simple and one difficult) in a lab setting. They could exchange information with their partners or a librarian using Skype. The sessions were recorded using Camtasia. The results showed that with an increase in task difficulty, collaborative information seeking activities increased and more interactions with partners and the librarian occurred. The number of executive help-seeking requests was higher than the number of instrumental help-seeking requests. This research confirms that learning style is related to the way users interact with the digital library and help seeking. The research showed that in difficult tasks, the differences among users with different learning styles become more evident, and that generally interactions increase in more difficult tasks. Among the learning styles, the accommodating style had the highest number of relationships with collaborative information seeking variables. Most of the statistically significant relationships between users' prior computer experience and collaborative information seeking variables were related to the time variable.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.273-284
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• 2022

The goal of this paper is to propose the use of machine learning platforms in education to train learners to recognize data biases. Learners can cultivate the ability to recognize when learners deal with AI data and systems when they want to prevent damage caused by data bias. Specifically, this paper presents a method of data bias education using MachineLearningforKids, focusing on the case of AI baseball referee. Learners take the steps of selecting a specific topic, reviewing prior research, inputting biased/unbiased data on a machine learning platform, composing test data, comparing the results of machine learning, and present implications. Learners can learn that AI data bias should be minimized and the impact of data collection and selection on society. This learning method has the significance of promoting the ease of problem-based self-directed learning, the possibility of combining with coding education, and the combination of humanities and social topics with artificial intelligence literacy.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.417-426
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• 2024

In general, the implementation of machine learning requires prior knowledge and experience with deep learning models, and substantial computational resources and time are necessary for data processing. As a result, machine learning encounters several limitations when deployed on embedded processors. To address these challenges, this paper introduces a novel approach where a genetic algorithm is applied to the convolution operation within the machine learning process, specifically for performing a selective convolution operation.In the selective convolution operation, the convolution is executed exclusively on pixels identified by a genetic algorithm. This method selects and computes pixels based on a ratio determined by the genetic algorithm, effectively reducing the computational workload by the specified ratio. The paper thoroughly explores the integration of genetic algorithms into machine learning computations, monitoring the fitness of each generation to ascertain if it reaches the target value. This approach is then compared with the computational requirements of existing methods.The learning process involves iteratively training generations to ensure that the fitness adequately converges.