• Journal of the Korea Institute of Building Construction
• /
• v.20 no.6
• /
• pp.481-488
• /
• 2020

The arc thermal metal spraying method (ATMSM) has been used in Korea to improve the electromagnetic pulse (EMP) shielding effect of building and infrastructure by three machines of MS, KMS and HMS (Metal Spray, Korea Metal Spray, High-frequency Meta Spray). The adhesion ability of sprayed metal was found to be 58.32, 64.66 and 87.62% for MS, KMS and HMS, respectively. The metal sprayed area per hour of MS, KMS and HMS was calculated and found to be 14.3, 19.25 and 22.16㎡/h, respectively. The HMS showed the highest metal spray area attributed to the diameter of metal wire i.e. 1.6mm which sprayed 80 mm with 18 m/min. There was minimum loss of metal and the highest efficiency by HMS compared to others. Therefore, it is suggested to use HMS for metal spraying by ATMSM to enhance the EMP shielding effect.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.5
• /
• pp.1610-1629
• /
• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.44 no.1
• /
• pp.45-50
• /
• 2021

Machines and facilities are physically or chemically degenerated by continuous usage. The representative type of the degeneration is the wearing of tools, which results in the process mean shift. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore, a preventive maintenance is necessary at some point. The problem of determining the maintenance period (or wear limit) which minimizes the total cost is called the 'process mean shift problem'. The total cost includes three items: maintenance cost (or adjustment cost), non-conforming cost due to the non-conforming products, and quality loss cost due to the difference between the process target value and the product characteristic value among the conforming products. In this study, we set the production volume as a decreasing function rather than a constant. Also we treat the process variance as a function to the increasing wear rather than a constant. To the quality loss function, we adopted the Cpm+, which is the left and right asymmetric process capability index based on the process target value. These can more reflect the production site. In this study, we presented a more extensive maintenance model compared to previous studies, by integrating the items mentioned above. The objective equation of this model is the total cost per unit wear. The determining variables are the wear limit and the initial process setting position that minimize the objective equation.

• Smart Structures and Systems
• /
• v.29 no.6
• /
• pp.757-766
• /
• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Journal of Software Assessment and Valuation
• /
• v.17 no.1
• /
• pp.75-82
• /
• 2021

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.17-28
• /
• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.24 no.4
• /
• pp.1-9
• /
• 2022

The purpose of this research is to improve sustainable clothes care by comparing household washer's standard course and quick course. Detergency at each course was classified by laundry weight, detergent concentration, and soils. Also, fabric damage from each course was compared. Washing experiments were carried out using two types of washing machines and three types of detergents. Using the standard soiled fabrics of EMPA 108 set, detergency was compared by laundry weight, soil, and detergent concentration. Additionally, fabric damage was evaluated using the mechanical action of MA-40. The results of the research were as follows. First, a standard course, having more working time exhibited better detergency than a quick course. However, the detergency deviation under 6kg laundry weight was as low as 9.0%. Second, detergency by the type of soil was more effective in standard course than in a quick course, but hydrophilic protein soils had a small detergency deviation at 7.6%. Moreover, hydrophobic oil, complex, and particulate soils had a higher deviation at 19.7% Third, fabric damage was in proportion to operating time. Fourth, a quick course showed approximately 80% detergency regardless of the type of detergent. in the case of using 50% of the recommended allowance by the detergent manufacturer. In conclusion, reducing the operating washing time and detergent concentration is in accordance with increasing sustainability, in the case of washing with lightly soiled fabrics under 6kg of laundry weight.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1377-1393
• /
• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.33 no.1
• /
• pp.87-97
• /
• 2023

Containers are an emerging virtualization technology that can build an isolated environment more lightweight and faster than existing virtual machines. For that reason, many organizations have recently adopted them for their services. Yet, the container architecture has also exposed many security problems since all containers share the same OS kernel. In this work, we focus on the fact that an attacker can abuse host resources to make them unavailable to benign containers-also known as host resource exhaustion attacks. Then, we analyze the impact of host resource exhaustion attacks through real attack scenarios exhausting critical host resources, such as CPU, memory, disk space, process ID, and sockets in Docker, the most popular container platform. We propose five attack scenarios performed in several different host environments and container images. The result shows that three of them put other containers in denial of service.

• Journal of the Ergonomics Society of Korea
• /
• v.36 no.5
• /
• pp.545-557
• /
• 2017

Objective: The aim of this study was to find risk factors in cultivating ginseng based on risk assessments and suggest safety measures for main risks. Background: Safety education and training is one of the practical and effective methods to prevent occupational accidents and injuries. In agricultural sector, there are few contents of safety education as compared to other industries. Especially, farm work has different cultivation characteristics according to the crops, so it needs special education materials for each crop. Among the various types of crops, ginseng contains various risk factors due to its long cultivating period and unique environment. Therefore, safety education material specified for ginseng is necessary to improve ginseng farmers' safety. Method: Risk assessment for cultivating tasks of ginseng was carried out through data obtained from various methods (site survey, interview, literature survey). To improve objectivity, the risk assessment was applied with 3-criteria (researcher estimate, interview, previous research results). Finally, the three high-risk tasks were selected and safety measures for those tasks were provided. Results: Three tasks, such as 'Mounting, maintenance and removing supports', 'Pest control' and 'Harvest', were selected as risky tasks among total tasks. (1) In 'Mounting' and maintenance and removing supports', the farmers found to be exposed to the risks of musculoskeletal disorders and accidents related to operating the tablet machine. (2) In 'Pest control', agrichemical poisoning, musculoskeletal disorders and hyperthermia were main risks. Finally, (3) In 'Harvest', the farmers are mainly exposed to the possibility of accidents of agricultural machines and risks of musculoskeletal disorders. Thus, it needs to apply appropriate safety measures to those risky tasks, such as safety guidelines, convenience equipment, protective kit, and so on. Conclusion: This study can be used as basic data for agricultural safety and expected that it would be useful for further study. In addition, the results of the research will be produced in the form of animation, which will enhance the safety consciousness for aged farmers. Application: The result of this study can be used in developing safety education materials for ginseng farmers which is essential to prevent occupational accidents and injuries among ginseng farmers.