• Journal of Veterinary Clinics
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• v.39 no.6
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• pp.411-416
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• 2022

A 2.2 kg adult female Amur leopard cat (Prionailurus bengalensis) injured in a road accident was rescued. Severe abrasions on the left chin were washed daily with an antiseptic and silver sulfadiazine ointment was applied. Corneal edema of the oculus sinister (OS) was treated with ofloxacin and 5% sodium chloride eye drops. The skin wounds gradually improved, but the eye condition did not improve and the lens was also found to be detached. In addition, on the 6th day of hospitalization, melena was observed. On radiographic examination, foreign bodies in the stomach and a fracture of the sternum were confirmed. Subsequently, endoscopic removal of foreign bodies and enucleation of the OS using an electrothermal vessel sealer were performed sequentially over several days. However, corrective surgery of the fractured sternum was not carried out because a natural union of the fracture had occurred, which was found to be fairly stable. The patient completely recovered on the 83rd day and was successfully released into the wild on the 97th day, after two weeks of adaptation training in a natural environment. This report describes the rehabilitation of a wild Amur leopard cat injured in a road accident through a series of diagnostic and treatment steps until its eventual return to the wild and highlights some improvements needed in the process.

• Physical Therapy Korea
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• v.30 no.1
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• pp.8-14
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• 2023

Background: Excessive hamstring (HS) activation due to the weakness of the gluteus maximus (GM) causes pain in the hip joint. A single-leg deadlift is a hip extensor exercise, especially GM, that includes functional movements in daily life and complex multi-joint training. In single-leg deadlift, the muscle activity depends on the forward trunk lean angle, and it's necessary to study which muscles are used dominantly depending on the angle. Objects: The purpose of this study was to compare the effect on the muscle activity of the GM and HS during single-leg deadlift according to different forward trunk lean angles and the ratio of the GM vs HS (GM/HS). Methods: Twenty-one healthy female participants were recruited. The muscles activities of the GM, HS and the GM/HS ratio were measured through electromyography during single-leg deadlift according to three condition of forward trunk lean angles (30°, 60°, and 90°). Results: The GM and HS activities significantly differed among three conditions (p < 0.05). GM/HS ratio was significantly higher at 30° and 60° of forward trunk lean compared to 90°. Moreover, the GM activity was significantly higher at 60° of forward trunk lean than at 30° (p < 0.05). Conclusion: The single-leg deadlift at 60° of forward trunk lean is a proper GM muscle strengthening exercise.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.170-170
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• 2020

Due to the development of technologies, complex computation of huge data set is possible with a prevalent personal computer. Therefore, machine learning methods have been widely applied in the hydrologic field such as regression-based regional frequency analysis (RFA). The main purpose of this study is to compare two frameworks of RFA based on the artificial neural network (ANN) models: quantile regression technique (QRT-ANN) and parameter regression technique (PRT-ANN). As an output layer of the ANN model, the QRT-ANN predicts quantiles for various return periods whereas the PRT-ANN provides prediction of three parameters for the generalized extreme value distribution. Rainfall gauging sites where record length is more than 20 years were selected and their annual maximum rainfalls and various hydro-meteorological variables were used as an input layer of the ANN model. While employing the ANN model, 70% and 30% of gauging sites were used as training set and testing set, respectively. For each technique, ANN model structure such as number of hidden layers and nodes was determined by a leave-one-out validation with calculating root mean square error (RMSE). To assess the performances of two frameworks, RMSEs of quantile predicted by the QRT-ANN are compared to those of the PRT-ANN.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.737-745
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• 2023

This paper discusses a study on developing an artificial intelligence model to detect incidents of fires in distribution switchboard using thermal images. The objective of the research is to preprocess collected thermal images into suitable data for object detection models and design a model capable of determining the occurrence of fires within distribution panels. The study utilizes thermal image data from AI-HUB's industrial complex for training. Two CNN-based deep learning object detection algorithms, namely Faster R-CNN and RetinaNet, are employed to construct models. The paper compares and analyzes these two models, ultimately proposing the optimal model for the task.

• Journal of Radiation Protection and Research
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• v.48 no.3
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• pp.107-116
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• 2023

This article reviews the experience of applying the Identification, Diagnosis, Prospective, and Action (IDPA) facilitating method as a means of promoting practices of dialogue between stakeholders in the radiological protection field. After presenting the characteristics of the IDPA method and its ability to promote active listening, participation, and dialogue among stakeholders facing complex situations, as well as the procedural aspects associated with its practical implementation, the article describes three examples of the application of the method in the field of radiological protection. The first one presents how the IDPA method supported a debate among decision-makers, authorities, experts, professionals, and representatives of non-governmental organizations about how to engage stakeholders in radiological protection. The second example presents how the IDPA method was used in a series of dialogue meetings to explore the challenges of the post-nuclear accident situation resulting from the Fukushima Daiichi Nuclear Power Plant accident. The third one presents the application of the method in the context of a training course organized by Nagasaki University in the affected area close to the damaged plant. Experience has shown that the IDPA method makes it possible to develop responses to problems posed in very different contexts and, in many cases, to find compromises regarding their solutions. The IDPA method has the merit of allowing each of the participants to better understand the situation they are faced with, even if such a positive result is not always achieved.

• Journal of Wellbeing Management and Applied Psychology
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• v.7 no.1
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• pp.27-33
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• 2024

Purpose: This study investigates human error accidents in the Korean railway sector, emphasizing the need for systematic management to prevent such incidents, which can have fatal consequences, especially in driving-related jobs. Research design, data and methodology: This paper analyzed data from the Aviation and Railway Accident Investigation Board and the Korea Transportation Safety Authority, examining 240 human error accidents that occurred over the last five years (2018-2022). The analysis focused on accidents in the driving, facility, electric, and control fields. Results: The findings indicate that the majority of human error accidents stem from negligence in confirmation checks, issues with work methods, and oversight in facility maintenance. In the driving field, errors such as signal check neglect and braking failures are prevalent, while in the facility and electric fields, the main issues are maintenance delays and neglect of safety measures. Conclusions: The paper concludes that human error accidents are complex and multifaceted, often resulting from a high workload on engineers and systemic issues within the railway system. Future research should delve into the causal relationships of these accidents and develop targeted prevention strategies through improved work processes, education, and training.

• Journal of Ocean Engineering and Technology
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• v.38 no.1
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• pp.18-29
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• 2024

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.803-829
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• 2022

With the advances in technology and the trend towards increased computer-based experiential learning in education settings, business games are being increasingly used by business educators. This article utilizes Toulmin's Sensemaking Framework to investigate the sensemaking process of business professionals to reveal how they consciously reason about the value of business games for learning complex business concepts and principles. Using the analysis of responses from 43 business professionals, our study identifies key areas where business professionals find value in business games and the limitations of using business games. First, business games are found to be an effective tool when teaching practical business skill sets to business professionals. Second, business games enhance the overall learning process in professional business training. Third, despite the advantages, some pitfalls in applying business games to practice are found. We also found sub-themes, claims, and argument patterns of how business professionals evaluate the value of business games through a grounded theory qualitative analysis method. Analysis results show several ground-warrant patterns exist in the arguments on values of business games including general principle - causal reasoning, personal experience - generalization, and personal projection - generalization. With these findings, we believe this paper contributes to the theory and practice of business game design, development, and the game playing and learning process.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.253-262
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• 2024

Dynamic properties are pivotal in soil analysis, yet their experimental determination is hampered by complex methodologies and the need for costly equipment. This study aims to predict dynamic soil properties using static properties that are relatively easier to obtain, employing machine learning techniques. The static properties considered include soil cohesion, friction angle, water content, specific gravity, and compressional strength. In contrast, the dynamic properties of interest are the velocities of compressional and shear waves. Data for this study are sourced from 26 boreholes, as detailed in a geotechnical investigation report database, comprising a total of 130 data points. An importance analysis, grounded in the random forest algorithm, is conducted to evaluate the significance of each dynamic property. This analysis informs the prediction of dynamic properties, prioritizing those static properties identified as most influential. The efficacy of these predictions is quantified using the coefficient of determination, which indicated exceptionally high reliability, with values reaching 0.99 in both training and testing phases when all input properties are considered. The conventional method is used for predicting dynamic properties through Standard Penetration Test (SPT) and compared the outcomes with this technique. The error ratio has decreased by approximately 0.95, thereby validating its reliability. This research marks a significant advancement in the indirect estimation of the relationship between static and dynamic soil properties through the application of machine learning techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.