• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.3
• /
• pp.877-893
• /
• 2022

With the development of deep learning, face inpainting has been significantly enhanced in the past few years. Although image inpainting framework integrated with generative adversarial network or attention mechanism enhanced the semantic understanding among facial components, the issues of reconstruction on corrupted regions are still worthy to explore, such as blurred edge structure, excessive smoothness, unreasonable semantic understanding and visual artifacts, etc. To address these issues, we propose a Learnable Structure Knowledge of Fusion Network (LSK-FNet), which learns a prior knowledge by edge generation network for image inpainting. The architecture involves two steps: Firstly, structure information obtained by edge generation network is used as the prior knowledge for face inpainting network. Secondly, both the generated prior knowledge and the incomplete image are fed into the face inpainting network together to get the fusion information. To improve the accuracy of inpainting, both of gated convolution and region normalization are applied in our proposed model. We evaluate our LSK-FNet qualitatively and quantitatively on the CelebA-HQ dataset. The experimental results demonstrate that the edge structure and details of facial images can be improved by using LSK-FNet. Our model surpasses the compared models on L1, PSNR and SSIM metrics. When the masked region is less than 20%, L1 loss reduce by more than 4.3%.

• Nuclear Engineering and Technology
• /
• v.54 no.8
• /
• pp.2828-2839
• /
• 2022

This study developed a safety assessment tool for geological disposal systems called APro, a systemically integrated modeling system based on modularizing and coupling the processes which need to be considered in a geological disposal system. Thermal, hydraulic, chemical, canister failure, radionuclide release and transport processes were considered in the current version of APro. Each of the unit processes in APro consists of a single Default Module, and several Alternative Modules which can increase the flexibility of the model. As an initial stage of developing the modularization concept and modeling interface, the Default Modules of each unit process were described, with one Alternative Module of chemical process. The computation part of APro is mainly a MATLAB workspace controlling COMSOL and PHREEQC, which are coupled by an operator splitting scheme. The APro model domain is a stylized geological disposal system employing the Swedish disposal concept (KBS-3 type), but the repository layout can be freely adjusted. In order to show the applicability of APro to the total system performance assessment of geological disposal system, some sample simulations were conducted. From the results, it was confirmed that coupling of the thermal and hydraulic processes and coupling of the canister failure and the radionuclide release processes were well reflected in APro. In addition, the technical connectivity between COMSOL and PHREEQC was also confirmed.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.141-151
• /
• 2022

In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

• Journal of Drive and Control
• /
• v.19 no.1
• /
• pp.51-61
• /
• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.

• Journal of East Asia Management
• /
• v.3 no.2
• /
• pp.1-19
• /
• 2022

This study suggests an integrated theoretical framework for the relationship between political risk and multinational corporation (MNC) subsidiary's performance in the emerging market. The political risk would have a negative impact on MNC subsidiary's performance in the emerging countries that are developing in Asia, the Commonwealth of Independent States, Africa, and South America. The major reason is that political risks could generate a loss of benefit or a loss of control for MNC's subsidiary. In this study, I suggest that corporate social responsibility (CSR) strategy would be a solution to overcome various political risks. Specifically, the affiliated firms with diversified industries or greater financial resources could mitigate the negative impact of political risk than unaffiliated firms. Because they can use their tangible or nontangible asset such as information, technology, and construction in order to gain legitimacy and trust from local government, local community, and local firms in the emerging market. Finally, I claimed the costs of the affiliated firms would exceed the benefits at the initial stages, while the benefits of affiliated firms would exceed the costs over time when political risks become higher. The reason is that the trust gained from local stakeholders accumulates over time and the impact of CSR strategy would become an important solution to overcome the risks in and unstable context.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.25-25
• /
• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• Journal of Distribution Science
• /
• v.20 no.3
• /
• pp.95-106
• /
• 2022

Purpose: This study aims to verify the effects of omnichannel characteristics on pleasure, resistance and repurchase intention in the omnichannel situation in order to provide the innovative commercial business. Research design, data and methodology: The study examined relations between research concepts centered on previous studies, set hypotheses, developed a research model, and verified the model through a questionnaire survey. A total of 297 questionnaires were used for the final analysis, excluding the questionnaires showing insincere or outliers. Results: First, Omnichannel showed multi-dimensional characteristics consisting of consistency, innovation, economy, and integration. Second, innovation and economic feasibility had a positive effect on pleasure. Third, only economic feasibility had a negative effect on user resistance. Fourth, consumers' shopping pleasure had a negative effect on user resistance. Fifth, repurchase intention of consumers was positively affected by innovation. Conclusions: This research contributed to extend academic framework of distribution research by examining causal relationship through adoption of economic and innovation factors as new characteristics from the integrated perspective beyond the research frame of the existing omnichannel distribution environment. Companies should provide meaningful experiences by resolving concerns about side effects caused by human-computer interaction and providing smart information that matches the products most suitable for consumer needs.

• Journal of Distribution Science
• /
• v.20 no.4
• /
• pp.33-50
• /
• 2022

Purpose: This pilot study aims to conceptualize a new green marketing mix for retail consumers based on Stimulus-Organism-Response (SOR) model. Moreover, it also aims to conceptualize a testable research model of new green marketing mix with consumers' green purchasing behavior, and to validate the measurement model with traditional as well as modern suggested validating techniques. Research design, data and methodology: A pilot test data from 75 respondents of retail buyers of energy-efficient electric appliances in Pakistan were tested for the confirmatory factor analysis (CFA) by examining a measurement model of the construct through different validation techniques (like Composite Reliability, McDonald's Omega (ω), rho (ρA), HTMT, etc.) as heretofore these scales were not validated through these modern methods. Results: The results revealed that the instrument has a certain degree of reliability and validity through different validating techniques. All the measurement items reach the suggested threshold values. Conclusions: Therefore, this study conceptualized an integrated framework of all the three stakeholders of the environment (government, companies, and public or consumers) to achieve environmental sustainability. Hence, future studies can extend these findings and conduct a full-scale study to establish an empirical relationship between the 5G's of green marketing for retailing businesses and consumers' green purchase behavior.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.736-743
• /
• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• Annual Conference of KIPS
• /
• 2023.11a
• /
• pp.264-267
• /
• 2023

The circular economy approach in the coffee supply chain promotes a more sustainable, environmentally friendly, and socially responsible coffee industry. It aims to reduce the environmental impact of coffee production and consumption while ensuring the long-term viability of coffee farming communities and ecosystems. However, there are many challenges in the traditional circular economy coffee supply chain. Hence, this paper undertakes a comparative analysis between the traditional circular economy coffee supply chain and its integration with blockchain. As a result, we display the benefits of incorporating blockchain technology into the conventional circular economy framework of the coffee supply chain. Additionally, this integration promises to overcome the challenges in the traditional circular economy coffee supply chain.