• Journal of Korea Port Economic Association
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• v.38 no.3
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• pp.87-104
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• 2022

Considering the complex structure and high volatility in the shipping market, it is important to investigate the connectedness amongst influencing factors. This study explores the dynamic relationship between supply-demand factors and shipping freight indices. We choose Capesize and Panamax in the bulk carrier market and use quarterly data of GDP, world fleet, BCI, and BPI from 1999 to 2021. Applying the wavelet analysis and wavelet Granger causality test, the simultaneous examination of co-movement and causality between two factors and the shipping market in both the time and frequency domains is achieved. We find that co-movement and causality vary across time and frequencies, thereby existing dynamic relationships between variables. Second, compared to multiple coherencies using demand and supply factors together, partial coherencies indicate noticeable causalities. It implies that analyzing demand and supply factors separately is essential. Finally, shipping freight indices show a high correlation with the demand factor in a good market and with the supply factor in a bad market. Generally, GDP positively leads shipping freights in the recovery phase while the world fleet negatively leads shipping freights in the downturn. The research is meaningful in that the rarely-applied wavelet analysis is adopted in the shipping market and that it gives a reasonable ground to explain the role of supply and/or demand factors in different phases of the market cycle.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.

• Journal of Naturopathy
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• v.9 no.2
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• pp.37-45
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• 2020

Purpose: Symptoms of idiopathic Parkinson's disease (PD) include tremors, bradykinesia, and rigidity. The purpose was to explore the effects of breathing, meditation and qigong on the improving of insight, behavior, mood discomfort, depression, anxiety, and olfactory dysfunction, which are PD non-motor symptoms. Methods: Three stages of An's-4444 healing breathing, An's Gwanjeong healing meditation, and healing qigong performed 12 times for 80 minutes at a time in subjects with PD (11 patients), and pre- and post-measurements compared and evaluated. Results: The Integrated Parkinson's Rating Scale (UPDRSI) for mood discomfort after 12 healings was 69%. The Depression Scale (61%) for HAMD, and 64% for Anxiety (HAMA)), and the smell identification test (TSI) for a trial for olfactory dysfunction, improved to 82%, respectively. However, the numerical values after one month after 12 healing were almost same in all four scales. This means that the healing effect maintained until after one month. Conclusions: An's healing breathing, meditation and qigong therapy significantly improved insight, behavior, and mood discomfort, and non-motor symptoms such as depression, anxiety, and olfactory dysfunction. These results suggest that An's breathing, meditation and qigong therapy are valuable as a primary therapy to improve and heal non-motor symptoms in Parkinson's disease patients. Further research in biomedical science is needed.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.119-125
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• 2022

With the recent increase in dementia patients due to aging, measures to prevent their wandering behavior and disappearance are urgently needed. To solve this problem, various authentication methods and location detection techniques have been introduced, but the security problem of personal authentication and a system that can check indoor and outdoor overall was lacking. In order to solve this problem, various authentication methods and location detection techniques have been introduced, but it was difficult to find a system that can check the security problem of personal authentication and indoor/outdoor overall. In this study, we intend to propose a system that can identify personal authentication, basic health status, and overall location indoors and outdoors by using wristband-type wearable devices in a private blockchain environment. In this system, personal authentication uses ECG, which is difficult to forge and highly personally identifiable, Bluetooth beacon that is easy to use with low power, non-contact and automatic transmission and reception indoors, and DGPS that corrects the pseudorange error of GPS satellites outdoors. It is intended to detect wandering behavior and abnormal signs by locating the patient. Through this, it is intended to contribute to the prompt response and prevention of disappearance in case of wandering behavior and abnormal symptoms of dementia patients living at home or in nursing homes.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.538-544
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• 2021

Technology readiness assessment is a procedure for managing defense project risk factors based on the preemptive identification of technical risks. Under current regulations, technology readiness is determined based on considerations of the ratings of factors itemized on a checklist, whether unsatisfied factors have a fatal impact on the project, and whether countermeasures for unsatisfied factors have been established. However, objective criteria for assessing the impact of unsatisfied factors have not been presented, and thus, at present, the results of technology readiness level determinations are largely subjective. In addition, the importance of questions on the checklist is dependent on individual project characteristics and this is not considered during the assessment process. In this paper, we propose an improved technology readiness assessment procedure that considers the characteristics of each project. Using the proposed procedure, we quantitatively determined the importance of each checklist item using a weighting method. We found the devised procedure improved the reliability and objectivity of technology readiness assessment results. A case analysis of a complex weapons system is presented to demonstrate these improvements.

• Therapeutic Science for Rehabilitation
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• v.11 no.3
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• pp.65-80
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• 2022

Objective : This study examined the psychometric properties of the 12-item Coping Orientation to Problems Experienced Scale (COPE) using Rasch analysis. COPE is one of the instruments used to measure stress-coping skills. Methods : The study participants were 480 community-dwelling older adults. We tested the instrument's unidimensionality assumption using principal component analysis (PCA). Item fit was examined using infit-and-outfit mean-square (MnSq) and standardized fit statistics (ZSTD). The precision and item difficulty hierarchies of the instrument were examined. The item-difficulty hierarchy was investigated to identify the easy and difficult items. We tested differential item functioning (DIF) for sex and age groups. Results : PCA revealed that the instrument met the unidimensionality assumption (eigenvalue = 1.78). Among the 12 items, item 2 was removed because of misfit (Infit MnSq = 1.33, Infit ZSTD = 5.05, Outfit MnSq = 1.56, Outfit ZSTD = 7.15). The remaining 11 items demonstrated a conceptual item-difficulty hierarchy. The person strata value was 3.10, which is equivalent to a reliability index value of 0.81. There was no DIF for the sex and age groups (DIF contrast <0.27). Conclusion : The findings indicated that the revised COPE-11 has adequate item-level psychometric properties and can accurately measure stress coping skills.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.921-926
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• 2022

Sugar solutions (5%, 10%, 15% and 20%) were tested by seven sensors of Astree E-Tongue for selecting a sensor for sweetness. NMS sensor was chosen as a sensor for sweetness among two sensors (PKS and NMS sensors selected in first stage) by considering precision, linearity and accuracy. Sugar, fructose, glucose and xylitol (5%, 10% and 15%) were tested by E-tongue. The principal component analysis (PCA) result by E-Tongue with seven sensors at 5% concentration level of four sweetners was not satisfactory (Discrimination index was -0.1). On the other hand, the relative NMS sensor response values were derived as 1.08 (fructose), 0.99 (glucose) and 1.00 (xylitol) comparing to sugar. Only the E-Tongue relative glucose response 0.99 was different from 0.5~0.75 of the relative sweetness range reported as the human sensory test results. Considering the excellent precision (%RSD, 1.53~3.64%) of E-Tongue using NMS single sensor for three types of sweeteners compared to sugar in the concentration range of 5% to 15%, replacing sensory test of sweetened beverages by E-Tongue might be possible for new product development and quality control.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.887-901
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• 2022

Estimation of the parameters for individual rainfall-rainfall events can lead to a different set of parameters for each event which result in lack of parameter identifiability. Moreover, it becomes even more ambiguous to determine a representative set of parameters for the watershed due to enhanced variability exceeding the range of model parameters. To reduce the variability of estimated parameters, this study proposed a parameter optimization framework with the simultaneous use of multiple rainfall-runoff events based on NSE as an objective function. It was found that the proposed optimization framework could effectively estimate the representative set of parameters pertained to their physical range over the entire watershed. It is found that the difference in NSE value of optimization when it performed individual and multiple rainfall events, is 0.08. Furthermore, In terms of estimating the observed floods, the representative parameters showed a more improved (or similar) performance compared to the results obtained from the single-event optimization process on an NSE basis.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.