• Advanced Industrial SCIence
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• v.1 no.2
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• pp.24-29
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• 2022

Recently, smart farm research that applies IoT technology to various farms is being actively conducted to improve agricultural cooling power and minimize cost reduction. In particular, methods for automatically and remotely controlling environmental information data around smart farms through IoT devices are being studied. This paper proposes a processing model that can maintain an optimal growth environment by monitoring environmental information data collected from smart farms in real time based on machine learning. Since the proposed model uses machine learning technology, environmental information is grouped into multiple blockchains to enable continuous data collection through rich big data securing measures. In addition, the proposed model selectively (or binding) the collected environmental information data according to priority using weights and correlation indices. Finally, the proposed model allows us to extend the cost of processing environmental information to n-layer to a minimum so that we can process environmental information in real time.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of dental hygiene science
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• v.22 no.3
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• pp.180-190
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• 2022

Background: Like direct infection from COVID-19, psychological concern about infection could affect health. Concern about COVID-19 infection was associated with individual habits to practice rules for preventing infection. Therefore, this study aimed to check occupational types and whether to practice tooth brushing after lunch depending on the occupation of economic workers and find correlations between concerns about infection due to COVID-19 pandemic and tooth brushing after lunch. Methods: The raw data was from the community health survey conducted in 2020. Among 229,269 adult participants aged 19 years and older, 138,970 economic workers were included in the final analysis. The chi-squared test was used to find differences in psychological concerns due to the COVID-19 pandemic. According to the participants, the rate of practicing tooth brushing after lunch was based on COVID-19-related psychological concerns. Multiple logistic regression analysis was conducted to check the influence of psychological concerns due to the COVID-19 pandemic on the rate of practicing tooth brushing after lunch. Results: According to occupational classifications, professionals and office workers and career soldiers had 1.551- and 1.581-times higher practicing rates than managers, respectively, whereas machine operators, agricultural and fishery sector workers, and daily laborers had lower practicing rates. Regarding COVID-19-related psychological concerns, the group with a lower concern about infection had a 1.076 times higher practicing rate than that with greater concern. The group with greater concern about blame from neighbors had 1.119 times higher practicing rate than that with lower concern. Conclusion: The correlations between higher economic workers' concerns about infection and blame from neighbors and higher recognition of the necessity to prevent COVID-19 and practice tooth brushing after lunch were confirmed. It is necessary to prepare measures for practicing tooth brushing after lunch suitable to the characteristics of occupational types and work environments of economic workers.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.175-181
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• 2022

COVID-19 is an acute respiratory syndrome that affects the host's breathing and respiratory system. The novel disease's first case was reported in 2019 and has created a state of emergency in the whole world and declared a global pandemic within months after the first case. The disease created elements of socioeconomic crisis globally. The emergency has made it imperative for professionals to take the necessary measures to make early diagnoses of the disease. The conventional diagnosis for COVID-19 is through Polymerase Chain Reaction (PCR) testing. However, in a lot of rural societies, these tests are not available or take a lot of time to provide results. Hence, we propose a COVID-19 classification system by means of machine learning and transfer learning models. The proposed approach identifies individuals with COVID-19 and distinguishes them from those who are healthy with the help of Deep Visual Embeddings (DVE). Five state-of-the-art models: VGG-19, ResNet50, Inceptionv3, MobileNetv3, and EfficientNetB7, were used in this study along with five different pooling schemes to perform deep feature extraction. In addition, the features are normalized using standard scaling, and 4-fold cross-validation is used to validate the performance over multiple versions of the validation data. The best results of 88.86% UAR, 88.27% Specificity, 89.44% Sensitivity, 88.62% Accuracy, 89.06% Precision, and 87.52% F1-score were obtained using ResNet-50 with Average Pooling and Logistic regression with class weight as the classifier.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.57-65
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• 2022

DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU's input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.58-75
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• 2009

As practicalization and commercialization of the technologies invented from the national R&D(Research & Development) project has been emerging as an important issue, the need for a tool for R&D project management has been increased. Technology Readiness Levels model(TRL) has currently been used for R&D project management because it provides distinctive definition of the nine levels in the progress of technology development starting from the basic research level to the utilization level. However, it is difficult to adopt the model for a large-scale national R&D project in which multiple research projects are involved simultaneously. In addition, TRL demands evaluation of research projects done by relevant experts and offers no specific measures determining the level of technology development. This study uses Delphi method to develop the measurement system helping to determine technology readiness levels for the technologies invented in a large scale national R&D project. The proposed model includes definition and measurement scles for each level in TRL.

• Clinics in Shoulder and Elbow
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• v.26 no.2
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• pp.169-174
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• 2023

Background: Sleep quality, quantity, and efficiency have all been demonstrated to be adversely affected by rotator cuff pathology. Previous measures of assessing the impact of rotator cuff pathology on sleep have been largely subjective in nature. This study was undertaken to objectively analyze this relationship through the use of activity monitors. Methods: Patients with full-thickness rotator cuff tears at a single institution were prospectively enrolled between 2018 and 2020. Waist-worn accelerometers were provided for the patients to use each night for 14 days. Sleep efficiency was calculated using the ratio of the time spent sleeping to the total amount of time that was spent in bed. Retraction of the rotator cuff tear was classified using the Patte staging system. Results: This study included 36 patients: 18 with Patte stage 1 disease, 14 with Patte stage 2 disease, and 4 patients with Patte stage 3 disease. During the study, 25 participants wore the monitor on multiple nights, and ultimately their data was used for the analysis. No difference in the median sleep efficiency was appreciated amongst these groups (P>0.1), with each cohort of patients demonstrating a generally high sleep efficiency. Conclusions: The severity of retraction of the rotator cuff tear did not appear to correlate with changes in sleep efficiency for patients (P>0.1). These findings can better inform providers on how to counsel their patients who present with complaints of poor sleep in the setting of full-thickness rotator cuff tears.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Journal of The Korean Society of Integrative Medicine
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• v.11 no.3
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• pp.35-47
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• 2023

Purpose : As the population aging deepens, the number of elderly people with arthritis is also continuously increasing. Accordingly, this study intended to identify the factors influencing the health-related quality of life (HINT-8) of the elderly with arthritis according to household type and provide baseline data for developing a measure to enhance the life quality of the elderly with arthritis. Methods : The factors influencing the health-related quality of life (HINT-8) of the elderly with arthritis were identified based on the raw data from the Korea national health and nutrition examination survey conducted in 2021. Data were analyzed with SPSS Statistics ver 25.0 for windows (IBM Corp), and the significance level (α) was set to .05. Statistical analysis was performed with t-test, ANOVA, multiple regression analysis, and post-hoc analysis with Duncan test. Results : The factors that influenced the health-related quality of life (HINT-8) of single-households were medical aid (β=-.17, p=.045), restriction of activity (β=-.17, p=.023), self-rated health status (β=.29, p<.001), and anxiety scale (β=-.36, p<.001). The factors that influenced the health-related quality of life (HINT-8) of multi-households were an age of 75 or over (β=-.14, p=.011), living in rural (β=-.14, p=.003), the outpatient department treatment experience (β=-.09, p=.047), self-rated health status (β=.26, p<.001), anxiety scale (β=-.29, p<.001), and stress (β=-.22, p<.001). Conclusion : Factors affecting the health-related quality of life (HINT-8) of the elderly with arthritis were found to be different between single-person households and multi-person household. Therefore, it is necessary to prepare measures to improve the quality of life of the elderly with arthritis by considering the factors influencing the health-related quality of life (HINT-8) of the elderly with arthritis according to the household type identified in this study.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1242-1242
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• 2022

Annually, several construction workers fall ill, are injured, or die due to heat-related exposure. The prevalence of work-related heat illness may rise and become an issue for workers operating in temperate climates, given the increase in frequency and intensity of heatwaves in the US. An increase in temperature negatively impacts physical exertion levels and mental state, thereby increasing the potential of accidents on the job site. To reduce the impact of heat stress on workers, it is critical to develop and implement measures for monitoring physical exertion levels and mental state in hot conditions. For this, limited studies have evaluated the utility of wearable biosensors in measuring physical exertion and mental workload in hot conditions. In addition, most studies focus solely on male participants, with little to no reference to female workers who may be exposed to greater heat stress risk. Therefore, this study aims to develop a process for objective and continuous assessment of worker physical exertion and mental workload using wearable biosensors. Physiological data were collected from eight (four male and four female) participants performing a simulated drilling task at 92oF and about 50% humidity level. After removing signal artifacts from the data using multiple filtering processes, the data was compared to a perceived muscle exertion scale and mental workload scale. Results indicate that biosensors' features can effectively detect the change in worker physical and mental state in hot conditions. Therefore, wearable biosensors provide a feasible and effective opportunity to continuously assess worker physical exertion and mental workload.