• Corrosion Science and Technology
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• v.19 no.4
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• pp.174-188
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• 2020

Atmospheric corrosion is a natural surface degradation process of metal due to changes in environmental parameters in the surrounding atmosphere. It is very sensitive to environmental parameters such as temperature, relative humidity, sulphur dioxide, and chloride, making it a major global economic challenge. Existing forecasting empirical corrosion models including the ISO standard are based on statistical analysis of experimental studies without considering the behavior of atmospheric parameters. The present study proposes a reliable global empirical model for estimating short and long-term atmospheric corrosion rates based on environmental parameters and corrosion mechanisms obtained from a parametric study. Repercussion of atmospheric corrosion rate due to individual and combined influences of environmental parameters specifies their importance in the estimation. New global empirical coefficients obtained for environmental parameters are statistically established (R² =0.998) with 95% confidence limit. They are validated using experimental datasets of existing studies observed at 88 different continental locations. The current proposed model can predict atmospheric corrosion by means of corrosion formation mechanisms influenced by combined effects of environmental parameters, further abating applicability limitations of location and time.

• Journal of Power Electronics
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• v.17 no.3
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• pp.756-765
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• 2017

The control of the power conversion system (PCS) in a battery energy storage system has a challenge due to the existence of grid impedance. This paper studies an impedance model of an LCL-based PCS in the d-q domain. The feature of a PCS connected to a weak grid is unveiled by use of an impedance model and a generalized Nyquist criterion. It is shown that the interaction between grid impedance and the PCS destabilizes the cascaded system in certain cases. Therefore, this paper proposes a novel control method that adopts virtual resistors to overcome this issue. The improvement in the control loop leads the PCS to a more stable condition than the conventional method. Impedance measurement is implemented to verify the correctness of the theoretical analysis. Experimental results obtained from a down-scaled prototype indicate that the proposed control method can improve the performance of the PCS under a weak grid.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.515-518
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• 2019

Semantic segmentation has remained as a challenging problem in the field of computer vision. Given the immense power of Convolution Neural Network (CNN) models, many complex problems have been solved in computer vision. Semantic segmentation is the challenge of classifying several pixels of an image into one category. With the help of convolution neural networks, we have witnessed prolific results over the time. We propose a convolutional neural network model which uses Fully CNN with deconvolutional pixel layers. The goal is to create a hierarchy of features while the fully convolutional model does the primary learning and later deconvolutional model visually segments the target image. The proposed approach creates a direct link among the several adjacent pixels in the resulting feature maps. It also preserves the spatial features such as corners and edges in images and hence adding more accuracy to the resulting outputs. We test our algorithm on Karlsruhe Institute of Technology and Toyota Technologies Institute (KITTI) street view data set. Our method achieves an mIoU accuracy of 92.04 %.

• Journal of Fashion Business
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• v.25 no.6
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• pp.25-45
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• 2021

Due to the fashion industry taking responsibility for their garment manufacturing, a significant number of UK universities are focusing on combining sustainability in their curriculum to support future employees' skills and knowledge in sustainable fashion. A proper understanding of educational and instructional theories is needed to develop effective teaching and learning materials and environments. Therefore, this study aimed to evaluate the Fashion Design module created with consideration of sustainability using ADDIE instructional model. For evaluation, the teaching materials, including the module brief and the PowerPoint slides for each session, were used. Ten students were interviewed and observed along with two tutors, also interviewed to analyze the strengths and weaknesses of the module from a variety of viewpoints. With sustainable fashion being embedded into specialized higher education courses, tutors decided to incorporate sustainability into the module as an introduction to this topical subject in order to build a stronger foundation of knowledge and challenge traditional ways of working. Results showed that combining sustainability into the design and technical sessions had a positive influence on students who built upon their existing knowledge. Tutors researched the need for change within the industry in line with the Sustainable Development Goals and aligned the content to inform the students of the current crisis. This study could provide a guideline to create instructional material for sustainable fashion design courses.

• Steel and Composite Structures
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• v.29 no.6
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• pp.689-701
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• 2018

Corrosion of steel reinforcement is a principal cause of deterioration of RC columns. Making these corrosion-damaged columns conform to new safety regulations and functions is a tremendous technological challenge. This study presented an experimental investigation on steel-concrete jacketed corrosion-damaged RC columns. The influences of steel jacket thickness and concrete strength on the enhancement performance of the strengthened specimens were investigated. The results showed that the use of steel-concrete jacketing is efficient since the stub strengthened columns behaved in a more ductile manner. Moreover, the ultimate strength of the corrosion-damaged RC columns is increased by an average of 5.3 times, and the ductility is also significantly improved by the strengthening method. The bearing capacity of the strengthening columns increases with the steel tube thickness increasing, and the strengthening concrete strength has a positive impact on both bearing capacity, whereas a negative influence on the ductility. Subsequently, a numerical model was developed to predict the behavior of the retrofitted columns. The model takes into account corrosion-damage of steel rebar and confining enhancement supplied by the steel tube. Comparative results with the experimental results indicated that the developed numerical model is an effective simulation. Based on extensive verified numerical studies, a design equation was proposed and found to predict well the ultimate eccentric strength of the strengthened columns.

• Fashion & Textile Research Journal
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• v.23 no.4
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• pp.480-490
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• 2021

Sales forecasting is crucial for many retail operations. For apparel retailers, accurate sales forecast for the next season is critical to properly manage inventory and plan their supply chains. The challenge in this increases because apparel products are always new for the next season, have numerous variations, short life cycles, long lead times, and seasonal trends. In this study, a sales forecasting model is proposed for apparel products using machine learning techniques. The sales data pertaining to outerwear items for four years were collected from a Korean sports brand and filtered with outliers. Subsequently, the data were standardized by removing the effects of exogenous variables. The sales patterns of outerwear items were clustered by applying K-means clustering, and outerwear attributes associated with the specific sales-pattern type were determined by using a decision tree classifier. Six types of sales pattern clusters were derived and classified using a hybrid model of clustering and decision tree algorithm, and finally, the relationship between outerwear attributes and sales patterns was revealed. Each sales pattern can be used to predict stock-keeping-unit-level sales based on item attributes.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.683-693
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• 2021

Shipping has become an important sector in supporting social, economic, government, defense, security, cultural and other sectors to unite separate islands and broad seas. Thus, ports automatically become an important facility in Indonesia. The purpose of this research is to test and explain the effect of Organizational readiness for change, Service Innovation, and Corporate Image on Perceived opportunity and challenge. The research model with inferential analysis uses Structural Equation Modeling (SEM) analysis with the WarpPLS approach, expected to answer the statements of problem and be able to test the desired hypothesis. The model development in this research was based on the background, statements of problem, conceptual framework and research hypotheses. The model referred to is "Complete and Comprehensive Port." Its development was carried out through studying and synthesizing various sources. The most important source is the results of literature review in the form of theoretical developments and research results, then continued with compilation. The use of Organizational Change Readiness, Service Innovation, and Corporate Image in improving Port Competitiveness is seen as one of the novelties of this research, specifically the use of the Organizational Change Readiness variable which is often used in high-flexibility companies but now used in port companies as well.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.205-211
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• 2021

Plants are important for humanity. They intervene in several areas of human life: medicine, nutrition, cosmetics, decoration, etc. The large number of varieties of these plants requires an efficient solution to identify them for proper use. The ease of recognition of these plants undoubtedly depends on the classification of these species into family; however, finding the relevant characteristics to achieve better automatic classification is still a huge challenge for researchers in the field. In this paper, we have developed a new automatic plant classification technique based on artificial neural networks. Our model uses leaf texture characteristics as parameters for plant family identification. The results of our model gave a perfect classification of three plant families of the Ivorian flora, with a determination coefficient (R2) of 0.99; an error rate (RMSE) of 1.348e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and an accuracy (Accuracy) of 100%. The same technique was applied on Flavia: the international basis of plants and showed a perfect identification regression (R2) of 0.98, an error rate (RMSE) of 1.136e-14, a sensitivity of 84.85%, a specificity of 100%, a precision of 100% and a trueness (Accuracy) of 100%. These results show that our technique is efficient and can guide the botanist to establish a model for many plants to avoid identification problems.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1101-1113
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• 2021

The rapid growth of sense-and-respond applications and the emerging cloud computing model present a new challenge: providing publish/subscribe middleware as a scalable and elastic cloud service. The publish/subscribe interaction model is a promising solution for scalable data dissemination over wide-area networks. In addition, there have been some work on the publish/subscribe messaging paradigm that guarantees reliability and availability in the face of node and link failures. These publish/subscribe systems are commonly used in information-centric networks and edge-fog-cloud infrastructures for IoT. The IoT has an edge-fog cloud infrastructure to efficiently process massive amounts of sensing data collected from the surrounding environment. In this paper. we propose a quorum-based hierarchical fault-tolerant publish/subscribe systems (QHFPS) to enable reliable delivery of messages in the presence of link and node failures. The QHFPS efficiently distributes IoT messages to the publish/subscribe brokers in fog overlay layers on the basis of proposing extended stepped grid (xS-grid) quorum for providing tolerance when faced with node failures and network partitions. We evaluate the performance of QHFPS in three aspects: number of transmitted Pub/Sub messages, average subscription delay, and subscritpion delivery rate with an analytical model.