• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.35-42
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• 2021

In the article, the authors investigate the economic security system in the conditions of the powers transformation. It is substantiated that economic security acts as a certain system that includes components and at the same time acts as a subsystem of the highest order. It is determined that the economic security system of regions acting as a system has its subsystems, which include: production, financial, environmental, innovation, investment and social subsystems. The parameters of the economic security system include relative economic independence, economic stability and self-development of economic systems, and it is proved that an important feature of economic security in addition to its systemic nature is multi-vector. It is substantiated that the monitoring of ensuring the economic security system of the development of economic systems of different levels in the conditions of the powers transformation should contain the analysis of social, economic and ecological development of regions; spheres of possible dangers of the development of regional economic systems; the nature of the threats; the degree of the possibility of threats; time perspective of economic development threats; possible consequences of losses for economic entities; the impact of threats to the object of the economic entities' activity; possible asymmetry of economic development of regional economic entities. Possible threats as a consequence of the powers transformation have been identified. A PEST analysis of the impact of factors of different nature on economic security and the development of regional economic systems in the powers transformation is carried out. A recurrent ratio is proposed for the economic security system in the conditions of the powers transformation.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.28 no.3
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• pp.69-77
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• 2022

Background: Ankle sprains, and the resulting ankle instability worsen to chronic due to recurrent ankle injuries or sprains, 78% of which are accompanied by posture instability and damage due to changes in the position of the talus of the ankle. The purpose of this study is to investigate the immediate effect of applying MWM taping on the patient's muscle strength and balance ability in patients with chronic ankle instability. Methods: 15 people with MWM taping and 15 people with Kinesio taping were applied, and after applying the taping of the ankle, 10 minutes of walking treadmill and 10 times of forward lunge operation, the change in ankle muscle strength and balance ability was confirmed. The strength test of the ankle was performed using a test device called Biodex system 4 (USA) for the movement of the dorsi-flexion and plantar flexion of the foot, and the balance of the two groups was measured using Biodex balance system (USA) to test balance ability. Results: The comparison of muscle strength changes in the ankle does not show a significant increase in the group applying MWM compared to the group applying kinesio taping (p<.05). In the comparison of equilibrium capabilities, the MWM taping group also showed a significant increase in the MWM taping group compared to the kinesio taping group (p<.05). Conclusion: When applying MWM taping and kinesio taping to patients with chronic ankle instability, there was no significant difference in comparison of muscle strength changes, but there was a significant difference in comparison of balance ability.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.355-357
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• 2022

In this paper, as a positioning technology for predicting the movement path of a moving object using a recurrent neural network (RNN) model, which is a deep learning network, in an indoor environment, continuous location information is used to predict the path of a moving vehicle within a local path. We propose a movement path generation technique that can reduce decision errors. In the case of an indoor environment where GPS information is not available, the data set must be continuous and sequential in order to apply the RNN model. However, Wi-Fi radio fingerprint data cannot be used as RNN data because continuity is not guaranteed as characteristic information about a specific location at the time of collection. Therefore, we propose a movement path generation technique for a vehicle moving a local path in an indoor environment by giving the necessary sequential location continuity to the RNN model.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.19-33
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• 2022

With the explosive growth of social media, its abundant text-based data generated by web users has become an important source for data analysis. For example, we often witness online movie reviews from the 'Naver Movie' affecting the general public to decide whether they should watch the movie or not. This study has conducted analysis on the Naver Movie's text-based review data to predict the actual ratings. After examining the distribution of movie ratings, we performed semantics analysis using Korean Natural Language Processing. This research sought to find the best review rating prediction model by comparing machine learning and deep learning models. We also compared various regression and classification models in 2-class and multi-class cases. Lastly we explained the causes of review misclassification related to movie review data characteristics.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3620-3630
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• 2022

The diagnosis of abnormalities in a nuclear power plant is essential to maintain power plant safety. When an abnormal event occurs, the operator diagnoses the event and selects the appropriate abnormal operating procedures and sub-procedures to implement the necessary measures. To support this, abnormality diagnosis systems using data-driven methods such as artificial neural networks and convolutional neural networks have been developed. However, data-driven models cannot always guarantee an accurate diagnosis because they cannot simulate all possible abnormal events. Therefore, abnormality diagnosis systems should be able to detect their own potential misdiagnosis. This paper proposes a rulebased diagnostic validation algorithm using a previously developed two-stage diagnosis model in abnormal situations. We analyzed the diagnostic results of the sub-procedure stage when the first diagnostic results were inaccurate and derived a rule to filter the inconsistent sub-procedure diagnostic results, which may be inaccurate diagnoses. In a case study, two abnormality diagnosis models were built using gated recurrent units and long short-term memory cells, and consistency checks on the diagnostic results from both models were performed to detect any inconsistencies. Based on this, a re-diagnosis was performed to select the label of the second-best value in the first diagnosis, after which the diagnosis accuracy increased. That is, the model proposed in this study made it possible to detect diagnostic failures by the developed consistency check of the sub-procedure diagnostic results. The consistency check process has the advantage that the operator can review the results and increase the diagnosis success rate by performing additional re-diagnoses. The developed model is expected to have increased applicability as an operator support system in terms of selecting the appropriate AOPs and sub-procedures with re-diagnosis, thereby further increasing abnormal event diagnostic accuracy.

• Journal of Oral Medicine and Pain
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• v.47 no.3
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• pp.148-151
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• 2022

Obstructive sialadenitis, one of the diseases that most frequently causes swelling and pain in the salivary glands, is mainly caused by structural obstructions. Sialolithiasis is the most frequent cause of the disease, and other causes include calculus formation, duct strictures, foreign bodies, and anatomical variations. Although there is a possibility that facial fillers directly block the salivary ducts, no cases of obstructive sialadenitis associated with them have been reported yet. We report the case of a 34-year-old female patient who complained of recurrent swelling and pain in the left buccal mucosa. She had undergone facial filler injection procedures on her facial area for cosmetic purposes several years before. Based on the findings of magnetic resonance imaging (MRI) and MR sialography, she was diagnosed with obstructive sialadenitis due to facial fillers. Through this case, we should remember to obtain a thorough history including filler treatments in the case of parotid gland swelling. We also suggest proper utilization of advanced imaging such as MRI in evaluating the location of facial fillers.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.25 no.3
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• pp.251-262
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• 2022

Purpose: Endoscopic drainage is an established treatment modality for adult patients with pancreatic fluid collections (PFCs). Available data regarding the efficacy and safety of endoscopic drainage in pediatric patients are limited. In this systematic review and meta-analysis, we aimed to analyze the outcomes of endoscopic drainage in children with PFCs. Methods: A literature search was performed in Embase, PubMed, and Google Scholar for studies on the outcomes of endoscopic drainage with or without endoscopic ultrasonography (EUS) guidance in pediatric patients with PFCs from inception to May 2021. The study's primary objective was clinical success, defined as resolution of PFCs. The secondary outcomes included technical success, adverse events, and recurrence rates. Results: Fourteen studies (187 children, 70.3% male) were included in this review. The subtypes of fluid collection included pseudocysts (60.3%) and walled-off necrosis (39.7%). The pooled technical success rates in studies where drainage of PFCs were performed with and without EUS guidance were 95.3% (95% confidence interval [CI], 89.6-98%; I²=0) and 93.9% (95% CI, 82.6-98%; I²=0), respectively. The pooled clinical success after one and two endoscopic interventions were 88.7% (95% CI, 82.7-92.9%; I²=0) and 92.3% (95% CI, 87.4-95.4%; I²=0), respectively. The pooled rate of major adverse events was 6.3% (95% CI, 3.3-11.4%; I²=0). The pooled rate of recurrent PFCs after endoscopic drainage was 10.4% (95% CI, 6.1-17.1%; I²=0). Conclusion: Endoscopic drainage is safe and effective in children with PFCs. However, future studies are required to compare endoscopic and EUS-guided drainage of PFCs in children.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.456-462
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• 2022

In this paper, we introduce a method of predicting time offset by applying LSTM, a deep learning model, to a precision time comparison technique based on measurement data extracted from code signals transmitted from GPS satellites to determine Universal Coordinated Time (UTC). First, we introduce a process of extracting time information from code signals received from a GPS satellite on a daily basis and constructing a daily time offset into one time series data. To apply the deep learning model to the constructed time offset time series data, LSTM, one of the recurrent neural networks, was applied to predict the time offset of a GPS satellite. Through this study, the possibility of time offset prediction by applying deep learning in the field of GNSS precise time transfer was confirmed.