• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.211-219
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• 2023

The structural health of a pipeline is usually assessed by visual inspection. In addition to the fact that this method is expensive and time consuming, inspection of the whole structure is not possible due to limited access to some points. Therefore, adopting a damage detection method without the mentioned limitations is important in order to increase the safety of the structure. In recent years, vibration-based methods have been used to detect damage. These methods detect structural defects based on the fact that the dynamic responses of the structure will change due to damage existence. Therefore, the location and extent of damage, before and after the damage, are determined. In this study, fuzzy genetic algorithm has been used to monitor the structural health of the pipeline to create a fuzzy automated system and all kinds of possible failure scenarios that can occur for the structure. For this purpose, the results of an experimental model have been used. Its numerical model is generated in ABAQUS software and the results of the analysis are used in the fuzzy genetic algorithm. Results show that the system is more accurate in detecting high-intensity damages, and the use of higher frequency modes helps to increase accuracy. Moreover, the system considers the damage in symmetric regions with the same degree of membership. To deal with the uncertainties, some error values are added, which are observed to be negligible up to 10% of the error.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.109-112
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• 2022

메타버스란 가상, 초월을 뜻하는 Meta와 우주, 세계를 뜻하는 Universe의 합성어로 가상과 현실이 상호작용 하면서 사회·경제·문화 활동이 이루어지며 가치를 창출하는 세상을 말한다. 이러한 메타버스는 5G와 그래픽 기술의 발전과 함께 코로나 사태로 인한 비대면 생활이 계속되면서 디지털 네이티브 세대인 Z세대가 메타버스 플랫폼을 소통의 장으로 삼기 시작하면서 성장하고 있다. 메타버스 안에서 콘서트나 사인회를 하고, 가상 아이템을 판매하며 마케팅을 벌이는 등 다양한 산업에서 메타버스에 뛰어들고 있다. 점점 진화하고 있는 메타버스는 메타버스 내에서 유저들 간에 맺는 상호관계 활동이 큰 장점이지만, 이로 인해 개인정보 보호 문제나, 위치정보, 금융정보 문제 등의 다양한 문제가 발생할 수 있는 문제점을 가지고 있다. 이에 본 논문에서는 메타버스에서 발생할 수 있는 문제점과 그 대응 방안을 제시하였다.

• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Educational Technology International
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• v.10 no.1
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• pp.1-23
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• 2009

Learners are often posited in a paradoxical situation where they are not fully involved in decision making processes on how to learn, in designing their tools. Cognitive artifacts in e-learning are supposed to effectively support learner-centered e-learning. The purpose of the study is to analyze cases of cognitive artifacts and to inquire those design principles for facilitating the learner-centered e-learning. Four research questions are suggested: First, it will be analyzed the characteristics of learners with respect to design of cognitive artifacts for supporting the learner-centered e-learning. Second, characteristics of four cases to design cognitive artifacts in learner-centered e-learning environment are analyzed. Third, it will be suggested the appropriate design principles of cognitive artifacts to facilitating learner-centered learning in e-learning environment. Four cases of cognitive artifacts design in learner-centered e-learning was identified as follows: Wiki software as cognitive artifacts in computer-supported collaborative learning; 'Play Around Network (PAN)' as cognitive artifact to monitor learning activities in knowledge community; Knowledge Forum System (KFS) as a cognitive artifact in knowledge building; cognitive artifacts in Courses-as-seeds applied meta-design. Five design principles are concluded as follows: Promoting externalization of cognitive artifacts to private media; Helping learners to initiate their learning processes; Encouraging learners to make connections with other learners' knowledge building and their cognitive artifacts; Promoting monitoring of participants' contributions in collaborative knowledge building; Supporting learners to design their cognitive artifacts.

• ETRI Journal
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• v.45 no.5
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• pp.768-780
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• 2023

The application of unmanned aerial vehicles (UAVs) has recently attracted considerable interest in various areas. A three-dimensional multiple-input multiple-output concentric two-hemisphere model is proposed to characterize the scattering environment around a vehicle in an urban UAV-to-vehicle communication scenario. Multipath components of the model consisted of lineof-sight and single-bounced components. This study focused on the key parameters that determine the scatterer distribution. A time-variant process was used to analyze the nonstationarity of the proposed model. Vital statistical properties, such as the space-time-frequency correlation function, Doppler power spectral density, level-crossing rate, average fade duration, and channel capacity, were derived and analyzed. The results indicated that with an increase in the maximum scatter radius, the time correlation and level-crossing rate decreased, the frequency correlation function had a faster downward trend, and average fade duration increased. In addition, with the increase of concentration parameter, the time correlation, space correlation, and level-crossing rate increased, average fade duration decreased, and Doppler power spectral density became flatter. The proposed model was compared with current geometry-based stochastic models (GBSMs) and showed good consistency. In addition, we verified the nonstationarity in the temporal and spatial domains of the proposed model. These conclusions can be used as references in the design of more reasonable communication systems.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.327-353
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• 2022

With the development of technology and media and the pursuit of non-face-to-face due to the corona pandemic, the influence of live commerce, a real-time streaming shopping channel, is growing. Starting from China, the popularity of live commerce is growing all over the world, and it has become an interesting topic among many practitioners and researchers. However, compared to its popularity, there are few studies on live commerce. Therefore, we build a theoretical model in terms of IT affordance such as visibility, guidance shopping, trading, and meta-voicing and investigate how live commerce affects engagement with customers. We empirically measure 428 individuals who have used live commerce using survey data. In addition, we conduct four types of scenario experiments on whether social cues on exposures of other consumers, influence customer engagement. Our results show that trading affordance has the most significant effect. This shows that the live commerce platform may want to devise a program that helps make payment easier for users who prefer a quick and simple process. Our study contributes to the literature by presenting the importance of IT affordance for live commerce.

• Journal of Information Technology Applications and Management
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• v.30 no.4
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• pp.29-38
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• 2023

A single-learner model which integrates the user's positive and negative perceptions is proposed by augmenting counterfactual data to the interaction data between users and items, which are mainly used in collaborative filtering in this study. The proposed CausRec showed superior performance compared to the existing NCF model in terms of F1 value and AUC in experiments using three published datasets: MovieLens 100K, Amazon Gift Card, and Amazon Magazine. Compared to the existing NCF model, the F1 and AUC values of CausRec showed 1.2% and 2.6% performance improvement in MovieLens 100K data, and 2.2% and 10% improvement in Amazon Gift Card data, respectively. In particular, in experiments using Amazon Magazine data, F1 and AUC values were improved by 11.7% and 21.9%, respectively, showing a significant performance improvement effect. The performance of CausRec is improved because both positive and negative perceptions of the item were reflected in the recommendation at the same time. It is judged that the proposed method was able to improve the performance of the collaborative filtering because it can simultaneously alleviate the sparsity and imbalance problems of the interaction data.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.21-28
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• 2024

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

• The Journal of Industrial Distribution & Business
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• v.15 no.7
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• pp.11-18
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• 2024

Purpose: The current research has investigated the question of how wellness tourism can be optimized to serve the diverse needs of its participants better while ensuring sustainable and inclusive growth. It will help design and implement better health and tourism policies to improve health and tourism policies to improve societies and economies. Research design, data and methodology: This research adopts a systematic literature review approach in collecting and synthesizing previous research works contained in the study to develop the result discussed in the next section. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Results: The findings of this research have indicated total of four brief suggestions to answer the research question, such as (1) Integration of Health and Wellness Programs in Mainstream Tourism, (2) Economic Revitalization of Rural and Underdeveloped Areas, (3) Enhancement of Public Health through Preventive Wellness, (4) Promotion of Sustainable Tourism Practices. Conclusions: Finally, this research concludes that incorporating health and wellness programs into mainstream tourism is a strategic focal area in hospitality practice. By raising the stakes in, for example, physical activities, beauty treatments, healthy meals, and mental health sessions, old-fashioned hotels or resorts can broaden their client base.

• Advances in Computational Design
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• v.9 no.2
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• pp.115-136
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• 2024

Stochastic optimization methods have been extensively studied for structural optimization in recent decades. In this study, a novel algorithm named the CA-SA method, is proposed for topology optimization of steel double-layer grid structures. The CA-SA method is a hybridized algorithm combining the Simulated Annealing (SA) algorithm and the Cellular Automata (CA) method. In the CA-SA method, during the initial iterations of the SA algorithm, some of the preliminary designs obtained by SA are placed in the cells of the CA. In each successive iteration, a cell is randomly chosen from the CA. Then, the "local leader" (LL) is determined by selecting the best design from the chosen cell and its neighboring ones. This LL then serves as the leader for modifying the SA algorithm. To evaluate the performance of the proposed CA-SA algorithm, two square-on-square steel double-layer grid structures are considered, with discrete cross-sectional areas. These numerical examples demonstrate the superiority of the CA-SA method over SA, and other meta-heuristic algorithms reported in the literature in the topology optimization of large-scale skeletal structures.