• Journal of Biosystems Engineering
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• v.28 no.2
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• pp.117-126
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• 2003

This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

• Journal of The Korean Association For Science Education
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• v.14 no.2
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• pp.184-191
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• 1994

In this study, a part of high school science instructional materials is evaluated by Instructional Quality Profile(IQP) based on the Merrill's Component Display Theory(CDT). The CDT is based on the Gagne assumption of different conditions of learning for different outcomes. The IQP enables the user to check both the consistency and adequacy of existing cognitive instruction. The IQP can be used to predict student performance, and also to design and develop new insturctional materials. The instructional components are classified according to 5 task levels; An Use-Generalities on Newly Encountered Examples(UGeg), A Remember-Paraphrased -Generalities (RpG), A Remember-Verbatim-Generalities (RvG), A Remember-Paraphrased -Examples (Rpeg), A Remember-Verbatim-Examples(Rveg). And the instructional presentations are classified according to 4 levels: Explain Generalities(EG), Explain examples(Eeg), Inquiry Generalities(IG), Inquiry examples(Ieg). The instructional presentations are determined by instructional components of a related test item, and indexes of the presentation adequacy are calculated by the instructional presentations. The indexes of this study(0.17 - 0.44) were very low and it indicates that the instructional presentations were not adequate to the instructional components of the related text item.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.4
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• pp.1-18
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• 2015

This paper proposes relational capability as a main driver of constructing inter-organizational collaboration networks. Based on social network theory and relational view literature, three components of relational capability are constructed and implemented by an agent-based model. The components include organizational capability, structural capability, and trust between a partner and a focal firm. These three components are updated by two micro mechanisms: structural mechanism and relational mechanism. Structural mechanism is a feedback loop in which the relational capability increases structural capability and vice versa. Relational mechanism is a learning-by-doing process in which a focal firm experiences success or failure of collaboration and the experience increases or decreases cumulative trust in a partner firm. Result of agent-based simulation shows that a collaboration network emerges through interactions of firm's relational capabilities and the characteristics of emerged networks vary with the contribution of structural capability and trust to relational capability. Specifically, in case structural capability contributes more to relational capability, the average degree centrality and collaboration proportion increases as time passes and enters into an equilibrium state. In that case, almost every firms participated in the network collaborates each other so that the emerged network becomes highly cohesive. In case trust contributes more to relational capability, the results are reversed. In an equilibrium state, the balance of contribution between structural capability and trust makes an emerged network larger and maximizes average degree centrality of the network.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.195-200
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• 2021

In recent years, Vietnam's economic growth rate has been attributed to the growth of many well-managed industries within Southeast Asia. Among them is the civil construction industry. Construction projects typically take a long time to complete and require a huge budget. Many socio-economic variables and factors affect total construction project costs due to market fluctuations. In recent years, crucial socioeconomic development indicators of construction reached a fairly high growth rate. Also, most infrastructure and construction projects have a high degree of complexity and uncertainty. This makes it challenging to predict the accurate project price. These challenges raise the need to recognize significant factors that influence the construction price index of civil buildings in Vietnam, both micro and macro. Therefore, this paper presents critical factors that affect the construction price index using the fuzzy extent analysis process in an uncertain environment. This proposed quantitative model is expected to reflect the uncertainty in the process of evaluating and ranking the influencing factors of the construction price index in Vietnam. The research results would also allow project stakeholders to be more informed of the factors affecting the construction price index in the context of Vietnam's civil construction industry. They also enable construction contractors to estimate project costs and bid rates better, enhancing their project and risk management performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.289-306
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• 2021

This paper aims to investigate how and to what extent FDI impacts the relative demand for skilled labor within firms in the case of developing countries. The analysis uses a sizeable micro-level dataset for Malaysian manufacturing industries using the System-GMM estimators to control the estimations' endogeneity problems. For this purpose, the study uses foreign equity share at the firm level to investigate foreign ownership effects at the firm level and the Horizontal FDI index by Smarzynska Javorcik (2004) to analyze FDI intra-industry linkages influence on the structure of labor demand for Malaysian domestic firms. Our findings indicate that foreign ownership increases the skilled demand within Malaysian manufacturing through the learning process, exclusively for small- and medium-sized firms (SMEs). Conversely for foreign-owned firms, changes in their skilled-labor share do not associate with changes in firm-level foreign equity share. We conclude that foreign ownership per se is not the major contributing factor for skill upgrading in Malaysian manufacturing firms. Furthermore, the competitive pressures caused by foreign firms' presence within the same industry - namely horizontal FDI - has a significant negative spillover effect on the level of skilled-labor share for domestic firms in the Malaysian manufacturing sector within periods of the understudies.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• Clinical and Experimental Reproductive Medicine
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• v.49 no.4
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• pp.225-238
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• 2022

The ultimate goal of human assisted reproductive technology is to achieve a healthy pregnancy and birth, ideally from the selection and transfer of a single competent embryo. Recently, techniques for efficiently evaluating the state and quality of preimplantation embryos using time-lapse imaging systems have been applied. Artificial intelligence programs based on deep learning technology and big data analysis of time-lapse monitoring system during in vitro culture of preimplantation embryos have also been rapidly developed. In addition, several molecular markers of the secretome have been successfully analyzed in spent embryo culture media, which could easily be obtained during in vitro embryo culture. It is also possible to analyze small amounts of cell-free nucleic acids, mitochondrial nucleic acids, miRNA, and long non-coding RNA derived from embryos using real-time polymerase chain reaction (PCR) or digital PCR, as well as next-generation sequencing. Various efforts are being made to use non-invasive evaluation of embryo quality (NiEEQ) to select the embryo with the best developmental competence. However, each NiEEQ method has some limitations that should be evaluated case by case. Therefore, an integrated analysis strategy fusing several NiEEQ methods should be urgently developed and confirmed by proper clinical trials.

• Journal of Distribution Science
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• v.20 no.4
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• pp.85-94
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• 2022

Purpose: Previous research has found that organizational performance pressures and barriers have an effect on the long-term viability of Micro, Small, and Medium Enterprises (MSMEs). Furthermore, MSMEs' intellectual capital and strategic orientation, according to recent research findings, have an impact on this. The goal of this study is to see how intellectual capital and strategic orientation affect MSMEs' performance. Research design, data and methodology: The performance of MSMEs is the dependent variable, with intellectual capital, market orientation, learning orientation, and technical orientation as independent factors. With a sample size of 113 respondents, this research focused on information technology-based MSMEs in Indonesia's Solo Raya area. Data was analyzed use Confirmatory Factor Analysis for the reliability test and path analysis SEM. Results: The interaction between intellectual capital and strategic orientation in respect to MSMEs' performance reveals that innovation capability serves as a partial mediator in the relationship between intellectual capital and technical orientation and organization performance. Conclusions: In general, intellectual capital and strategic orientation have a positive substantial influence on innovation, according to the findings. Furthermore, they have a considerable favorable influence on the performance of MSMEs. It's just that intellectual capital has no discernible impact on knowledge sharing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.353-354
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• 2021

In this study, in order to overcome the limitations of the particle size analysis method using a scanner, a microscope, or a laser, and to reduce the cost, a high-quality sampling of micro minerals is performed using an ultra-high-pixel DSLR camera and a MACRO lens. Using this, digital photos taken of standard mineral particles are analyzed to distinguish the size and shape of mineral particles at the level of grain of sand (a few mm ~ 0.063 mm). In addition, various photographing techniques for the production of three-dimensional images of mineral particles were sought, and an attempt was made to produce learning materials and images for mineral classification.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.4
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• pp.351-359
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• 2011

In a structure, damage can occur at several scales from micro-cracking to corrosion or loose bolts. This makes the identification of damage difficult with one mode of sensing. Hence, a multi-mode actuated sensing system is proposed based on a self-sensing circuit using a piezoelectric sensor. In the self sensing-based multi-mode actuated sensing, one mode provides a wide frequency-band structural response from the self-sensed impedance measurement and the other mode provides a specific frequency-induced structural wavelet response from the self-sensed guided wave measurement. In this study, an experimental study on the pipeline system is carried out to verify the effectiveness and the robustness of the proposed structural health monitoring approach. Different types of structural damage are artificially inflicted on the pipeline system. To classify the multiple types of structural damage, a supervised learning-based statistical pattern recognition is implemented by composing a two-dimensional space using the damage indices extracted from the impedance and guided wave features. For more systematic damage classification, several control parameters to determine an optimal decision boundary for the supervised learning-based pattern recognition are optimized. Finally, further research issues will be discussed for real-world implementation of the proposed approach.