• Journal of Navigation and Port Research
• /
• v.45 no.6
• /
• pp.298-305
• /
• 2021

Identifying the effect of turning characteristics on collision avoidance for Maritime Autonomous Surface Ships (MASS) can provide a key to avoid the collision of MASS. The purpose of this study was to derive a method to identify the effect of turning characteristics, which can be changed by various rudder angles and the ship's speed, on collision avoidance. The turning circle was observed using a mathematical model of a 161-meter-long ship, and it was analyzed that the turning circle had an effect on collision avoidance through numerical simulations of collision avoidance for four collision situations of two ships. The evaluation results using the two variables, the minimum relative distance between two ships and the minimum time at the minimum relative distance, demonstrated that the rudder angle has a major influence on the change of the minimum relative distance, and the ship's speed has a major influence on the change of the minimum time. The evaluation method proposed in this study was expected to be applicable to collision avoidance as a measures in remote control of MASS.

• Smart Structures and Systems
• /
• v.30 no.6
• /
• pp.613-626
• /
• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of Korean Society for Quality Management
• /
• v.50 no.4
• /
• pp.777-792
• /
• 2022

Purpose: This paper investigates the effects of leader feedback on employee creative problem-solving behavior. It also explores the relevant conditions that maximize the above relationship from the psychological trait and task nature perspectives. Specifically we examine how employee feedback acceptance and task complexity moderate the relationship between leader's feedback behavior on follower creative problem-solving behavior. Finally the three-way interaction among leader's feedback behavior, employee feedback acceptance and task complexity is analyzed for the best conditions to maximize the positive effect of leader's feedback on creative problem solving behavior. Methods: This paper used a cross-sectional design with questionnaires administered to 411 employees working in Korean manufacturing and service firms. It applied a hierarchical regression analysis to test the hypothesized relationships including three-way interaction effect among leader's feedback behavior, follower feedback acceptance and task complexity on follower creative problem-solving behavior. Results: The empirical results of the paper indicated that the leader feedback behavior had enhanced employee creative problem-solving behavior. It was also found that follower feedback acceptance and task complexity positively moderated the relationship between leader's feedback and follower problem solving behavior. In addition, the test of three-way interaction effects also revealed that the higher the levels of both employee feedback acceptance and task complexity, the greater the positive effect of leader feedback behavior on employee creative problem solving behavior. Conclusion: This paper contributes to the leadership and creativity literatures by identifying the role of leader's behavior enhancing employee creative problem-solving behavior and the specific conditions strengthening the positive effect of leader feedback behavior on employee creative problem-solving behavior.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.6
• /
• pp.887-892
• /
• 2022

Due to various reasons (normally financial constraints in developing countries), it becomes common to change of the business model from state-run projects to Private Investment Projects (Public Private Partnership) in the global railway businesses. However, due to the nature of railroads compared with other types of infrastructure such as roads and others, railway business require considerable construction cost and O&M cost through the business development, construction, and operation and management stages. Therefore, private investment railway projects, especially in developing countries, can be problematic in terms of the potential for uncertainty when return on investment cannot be guaranteed. In order to strengthen the competitiveness of domestic companies when entering overseas railroad PPP projects, this study proposes PPP-related risks and their countermeasures by reviewing global railroad trends and identifying Korea's weakness in managing international railroad projects.

• Korean Journal of Organic Agriculture
• /
• v.30 no.2
• /
• pp.241-253
• /
• 2022

The negative impact of climate change on the agricultural sector is rapidly increasing, and it is urgent to prepare policies at the government level to mitigate it. In the case of Rwanda's agricultural sector, which lacks the government's budget and farmers' capital, efficient and effective policy implementation is of paramount importance. To this end, rather than establishing related policies in the public sector from the top down, it is necessary to establish a bottom-up customized policy that is reflected in policy establishment by identifying the characteristics and behaviors of farmers who actually participate in adaptation activities. In this study, the effects of farmers' characteristics and farmers' perception status/adaptation status to climate change on the selection of adaptation methods for climate change were analyzed. 357 rice farmers randomly selected from Eastern Rwanda were surveyed to explore the information related to farmers' perception to climate change and adaptation methods as well as basic information of the farm. Research shows that the probability of selecting a variety of adaptation methods rather than not responding to climate change increases the younger the age, the higher the education level, and the easier access to climate information and credit. As a policy proposals, it is judged that public support such as strengthening agricultural technology support services, including more detailed guidance for elderly and low-educated farmers, and improving access to farm loan services by agricultural financial institutions is needed. In addition, it is necessary to adjust the planting time and cultivation method, provide timely information related to climate change, and provide crop variety improvement services to farmers.

• Advances in nano research
• /
• v.12 no.2
• /
• pp.185-195
• /
• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3738-3760
• /
• 2022

Due to increasing spectrum demand for new wireless devices applications, cooperative spectrum sensing (CSS) paradigm is the most promising solution to alleviate the spectrum shortage problem. However, in the interweave cognitive radio (CR) system, the inherent nature of CSS opens a hole to Byzantine attack, thereby resulting in a significant drop of the CSS security and efficiency. In view of this, a weighted differential sequential single symbol (WD3S) algorithm based on MATLAB platform is developed to accurately identify malicious users (MUs) and benefit useful sensing information from their malicious reports in this paper. In order to achieve this, a dynamic Byzantine attack model is proposed to describe malicious behaviors for MUs in an interweave CR system. On the basis of this, a method of data transmission consistency verification is formulated to evaluate the global decision's correctness and update the trust value (TrV) of secondary users (SUs), thereby accurately identifying MUs. Then, we innovatively reuse malicious sensing information from MUs by the weight allocation scheme. In addition, considering a high spectrum usage of primary network, a sequential and differential reporting way based on a single symbol is also proposed in the process of the sensing information submission. Finally, under various Byzantine attack types, we provide in-depth simulations to demonstrate the efficiency and security of the proposed WD3S.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.4
• /
• pp.351-361
• /
• 2021

The purpose of this study is to provide basic data for resolving individual and regional health inequalities by identifying factors that affect healthy living practices, and to protect the access to health equity and the access to health equity and the people's right to health. Raw data from the 2019 Community Health Survey were used, and descriptive statistical analysis and multivariate logistic regression analysis were performed using SAS 9.4 and IBM SPSS ver. 21. The healthy living practice rate was 33.8% overall, and there was a difference of 11~20% by region. In terms of individual factors, healthy living practices were significantly different in gender, age, occupation, sleep time, subjective health status, and subjective stress level. In the interpersonal factor, there was a difference in social activity for healthy living practice, and in the community factor, positive attitude toward the local physical environment, annual unsatisfied medical care, and use of health institutions were significant. In order to increase the practice of healthy living by region based on the research results, comprehensive policies and cooperative measures that can be approached at the individual, social and national level should be implemented along with specific strategies.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.5
• /
• pp.431-435
• /
• 2022

The fields of STEM (Science, Technology, Engineering and Medicine) are changing rapidly. Recently, with the remarkable development of Internet and Web technologies, an environment that can be accessed worldwide has been created, thereby lowering the barriers to share STEM knowledge and information. The purpose of this study is to derive improvements by evaluating users' satisfaction with the information system developed by applying the open access model in the STEM field. Through an online survey using a structured questionnaire, a total of 204 users participated from January to February. The collected data were analyzed using quantitative statistical techniques. IPA (Importance Performance Analysis) technique was used. By identifying the importance and satisfaction (performance) between variables, areas with relatively low satisfaction compared to importance were derived. Users' overall satisfaction with the open access information system was 81.2 points and social reliability was 85.9 points, which were relatively high, respectively. What should be paid attention to in this study is the satisfaction with the system use environment, which is the most vulnerable area.

• The Korean Journal of Physiology and Pharmacology
• /
• v.26 no.5
• /
• pp.347-355
• /
• 2022

Abdominal aortic aneurysm (AAA) is a life-threatening disorder worldwide. Fibroblast growth factor 21 (FGF21) was shown to display a high level in the plasma of patients with AAA; however, its detailed functions underlying AAA pathogenesis are unclear. An in vitro AAA model was established in human aortic vascular smooth muscle cells (HASMCs) by angiotensin II (Ang-II) stimulation. Cell counting kit-8, wound healing, and Transwell assays were utilized for measuring cell proliferation and migration. RT-qPCR was used for detecting mRNA expression of FGF21 and activating transcription factor 4 (ATF4). Western blotting was utilized for assessing protein levels of FGF21, ATF4, and markers for the contractile phenotype of HASMCs. ChIP and luciferase reporter assays were implemented for identifying the binding relation between AFT4 and FGF21 promoters. FGF21 and ATF4 were both upregulated in Ang-II-treated HASMCs. Knocking down FGF21 attenuated Ang-II-induced proliferation, migration, and phenotype switch of HASMCs. ATF4 activated FGF21 transcription by binding to its promoter. FGF21 overexpression reversed AFT4 silencing-mediated inhibition of cell proliferation, migration, and phenotype switch. ATF4 transcriptionally upregulates FGF21 to promote the proliferation, migration, and phenotype switch of Ang-II-treated HASMCs.