• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1056-1060
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• 2012

Online monitoring system is becoming an essential element of railway traction system for utilized to condition based malignance management and various techniques currently employed in railway traction system. Among the various techniques, it is efficient to detect partial discharge signals by electromagnetic wave detection in order to detect insulation fault of rectifier. Although VHF (Very High Frequency), UHF (Ultra High Frequency) sensors were adopted to detect partial discharge of power facilities, due to characteristics of urban railway, excessive noise occurs from 500 MHz to 1.5 GHz on UHF bandwidth. In this paper a new measurement system able to monitoring the conditions of power facilities on DC substation in metro was studied and set up. The system uses UHF sensors to measure the partial discharge of the rectifier due to electric faulting and dielectric breakdown. Comparison and estimation for performance of SHF sensor which had devised to detect partial discharge signal of urban railway rectifier has conducted. In order to estimate performance of SHF sensor, we have compared the sensor with existing UHF sensor on sensitivity upon frequency bandwidth generated by pulse generator, and also we have verified performance of the SHF sensor by detection results of partial discharge signal from urban railway rectifier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.421-441
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• 2021

Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.442-460
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• 2021

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE⁺ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

• ETRI Journal
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• v.41 no.4
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.413-421
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• 2021

This research empirically analyzed whether the foods which came from a non-Muslim majority country, such as South Korea, could play an important role in affecting the consumer intention in a predominantly Muslim country. Online survey methods were used to investigate the proposed hypothesis. 318 responses were used for further analysis. Forty-six reflective constructs were adapted from literature and designed by using a five-point Likert scale to facilitate measurement. Estimation models and structural models were examined through SEM-PLS analysis techniques using SmartPLS 3.0 application as the data processing tool. The results showed that religiosity and halal awareness had a positive and significant effect on attitude toward halal labels, including the mediating effect from consumer attitudes towards halal labels which had a positive but insignificant effect on purchase intention. Halal awareness plays an important role for Muslims in the decision-making process for purchasing food. In contrast to the initial hypothesis, the country of origin actually did not have a positive effect on attitudes towards the halal label. In a Muslim-majority country like Indonesia, findings halal food is not difficult, so this research basically is a reminder to marketers to follow those halal principles in implementing their marketing strategies.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.341-348
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• 2021

The aim of this study is to fill gaps in emerging empirical evidence and negative electronic word of mouth (NeWOM) in repurchase intention (RI) moderated by the roles of social network sites (SNS) and company mitigation response (CMR). This type of research is descriptive. The sample used in this study is online consumers who buy energy drinks, based on the questionnaire obtained by 145 respondents. Based on the results of testing the estimation of the structural equation model, it was found that the negative variable brand experience sharing had no significant effect on NeWOM; the negative variable electronic reviews had a significant effect on the electronic word of mouth variable, the negative variable electronic reviews had a significant effect on the negative electronic variable word of mouth, the variable intensity of the use of social networking sites can strengthen the direction of the causal influence between the negative variables sharing brand experiences on negative electronic words of mouth. The variable social networking sites usage intensity can strengthen the direction of the effect of causality between negative electronic review variables on negative electronic word of mouth, the negative brand experience sharing variable does not have a significant effect on the repurchase intention variable.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.6
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• pp.69-79
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• 2022

The purpose of the study is to look into the likelihood of private sector enterprises going bankrupt due to COVID-19 pandemic-related issues. The data for this study was taken from the World Bank's Enterprise Survey, which was intended to assess the impact of the COVID-19 pandemic on the business sector. This study uses the Ordinal Logit Method to analyze the model with dependent variables having ordinal values. The determinants reflect business performance, innovation, business relationships, and government support. According to the estimation results, a lower probability of business closures, illiquidity, and payment delays are found in businesses that maintain sales growth, operating hours, temporary workers, product portfolio, consumer demand, and input supply. Meanwhile, the increase in online business activities and receiving support from financial institutions and the government do not help businesses reduce the risk. Moreover, higher survival is found in manufacturing and developing countries. This implies the fragility of businesses in the retail and service sectors, especially for mega-enterprises in developed countries. In addition, the negative impact of the COVID-19 pandemic on businesses in Europe and West Asia is less severe than in other regions. The results imply policies to support the private sector during the pandemic, such as increasing labor market flexibility or rapidly implementing supportive policies.

• Steel and Composite Structures
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• v.49 no.4
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• pp.407-417
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• 2023

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. Many domain schemes based on the measured vibration computations, including least squares estimation and neural fuzzy logic control, have been studied and found to be effective for online/offline monitoring of structural damage. Traditional strategies require all external stimulus data (input data) which have been measured available, but this may not be the generalized for all structures. In this article, a new method with unknown inputs (excitations) is provided to identify structural matrix such as stiffness, mass, damping and other nonlinear parts, unknown disturbances for example. An analytical solution is thus constructed and presented because the solution in the existing literature has not been available. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of Distribution Science
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• v.22 no.1
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• pp.37-46
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• 2024

Purpose: This study aims to address the overlooked micro-level aspects within Smart Manufacturing (SM) research, rectifying the misalignment in manufacturing firms' estimation of their technological adoption capabilities. Drawing upon the Social-Technical Systems (STS) theory, this paper utilises innovation capability as a mediating variable, constructing a human-centric organizational model to bridge this research gap. Research design, data and methodology: This study collected data from 233 Chinese manufacturing firms via online questionnaires. Introducing innovation capability as a mediating variable, it investigates the impact of social-technical system dimensions (work design, social subsystems, and technical subsystems) on SM adoption willingness. Smart PLS 4.0 was employed for data analysis, and Structural Equation Modelling (SEM) validated the theoretical model's assumptions. Results: In direct relationships, social subsystems, technical subsystems, and work design positively influence firms' innovation capabilities, which, in turn, positively impact SM adoption. However, innovation capability does not mediate the relationship between technical subsystems and SM adoption. Conclusions: This study focuses on the internal micro-level of organisational employees, constructing a human-centric framework that emphasises the interaction between organisations and technology. The study fills empirical gaps in Smart Manufacturing adoption, providing organisations with a means to examine the integration of employees and the organisational social-technical system.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.391-398
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• 2024

Accurate positioning is essential for unmanned underwater vehicle (UUV) operations, particularly for long-term survey missions. To reduce the inherent positioning errors from the inertial navigation systems of UUVs, or dead reckoning, underwater terrain observations from sonar sensors are typically exploited. Within the framework of pose-graph optimization, we can generate submaps of the seafloor and use them to add loop-closure constraints to the pose graph by determining the best match between the submaps. However, this approach results in error accumulation in long-term operations because the quality of local submaps depends on the dead reckoning. Hence, we can adopt external acoustic positioning systems, such as an ultrashort baseline (USBL), to add global constraints to the existing pose graph. We assume that the acoustic transponder is installed on a UUV and that the acoustic transceiver is equipped in an unmanned surface vehicle trailing the UUV to maintain an acoustic connection between the vehicles. We simulate the terrain and USBL measurements as well as evaluate the performance of the UUV's pose estimation via online pose-graph optimization.