• Journal of Korea Port Economic Association
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• v.39 no.3
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• pp.191-210
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• 2023

The Covid-19 pandemic has had a significant impact on global logistics and supply chains, leading to major discrepancies in logistics performance across countries worldwide. Through an examination of logistics performance index and supply chain tracking data, this study aimed to identify the changes in global supply chains and logistics environments during the pandemic. The analysis of the logistics performance index showed that overall, countries around the world, especially developed nations, showed improvements in metrics such as customs and border management efficiency, the quality of trade and transport infrastructure, capability and quality of logistics services, and cargo tracking abilities. However, the competitive pricing feasibility of international transportation and the on-time delivery frequency of goods saw a decline due to the pandemic's effects. The supply chain tracking data revealed that ports in Asian countries demonstrated high processing efficiency. In contrast, the U.S. and European countries took comparatively more time. Particularly for air cargo, parcels, and express shipments, the U.S. showed relatively longer processing times, leading to logistical delays. In conclusion, during the Covid-19 pandemic, Asian countries maintained relatively high efficiency in their logistics and trade environments. Conversely, the U.S. and some European countries showed delays and decreased efficiency in various metrics. In the future, efforts should be made to address delays and congestion, namely, the deceleration of logistics processes.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1085-1095
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• 2023

Time-series data generated from satellite data are crucial resources for change detection and monitoring across various fields. Existing research in time-series data generation primarily relies on single-image analysis to maintain data uniformity, with ongoing efforts to enhance spatial and temporal resolutions by utilizing diverse image sources. Despite the emphasized significance of time-series data, there is a notable absence of automated data collection and preprocessing for research purposes. In this paper, to address this limitation, we propose a system that automates the collection of satellite information in user-specified areas to generate time-series data. This research aims to collect data from various satellite sources in a specific region and convert them into time-series data, developing an automatic satellite image collection system for this purpose. By utilizing this system, users can collect and extract data for their specific regions of interest, making the data immediately usable. Experimental results have shown the feasibility of automatically acquiring freely available Landsat and Sentinel images from the web and incorporating manually inputted high-resolution satellite images. Comparisons between automatically collected and edited images based on high-resolution satellite data demonstrated minimal discrepancies, with no significant errors in the generated output.

• Composites Research
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• v.36 no.5
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• pp.297-302
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• 2023

There are several methods for shaping foams, but the most commonly used methods involve the use of resin mixed with a foaming agent, which is then foamed under high temperature and pressure in the case of compression foaming, or foamed under high temperature without applying pressure in the case of atmospheric foaming. The polymers used for foaming require design and analysis of optimal foaming conditions in order to achieve foaming under ambient pressure. Environmentally friendly bio-based polymers face challenges when it comes to foaming on their own, which has led to ongoing research in blending them with resins capable of traditional foam production. This study investigates changes in the characteristics of bio-based polymer-EVA blend foams based on variations in the content of bio-based polymers and explores the optimal foaming conditions according to crosslinking. The correlation between foaming characteristics and mechanical properties of the foams was examined. Through this research, we gained insights into how the content of bio-based polymers affects the properties of foams containing bio-based polymers and identified differences between ambient pressure and high-pressure foaming processes. Additionally, the feasibility of commercializing bio-based polymer-EVA composite foams was confirmed.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.55-62
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• 2023

Recent ransomware attacks employ various techniques and pathways, posing significant challenges in early detection and defense. Consequently, the scale of damage is continually growing. This paper introduces a machine learning-based approach for effective ransomware detection by focusing on file encryption and encryption patterns, which are pivotal functionalities utilized by ransomware. Ransomware is identified by analyzing password behavior and encryption patterns, making it possible to detect specific ransomware variants and new types of ransomware, thereby mitigating ransomware attacks effectively. The proposed machine learning-based encryption behavior detection technique extracts encryption and encryption pattern characteristics and trains them using a machine learning classifier. The final outcome is an ensemble of results from two classifiers. The classifier plays a key role in determining the presence or absence of ransomware, leading to enhanced accuracy. The proposed technique is implemented using the numpy, pandas, and Python's Scikit-Learn library. Evaluation indicators reveal an average accuracy of 94%, precision of 95%, recall rate of 93%, and an F1 score of 95%. These performance results validate the feasibility of ransomware detection through encryption behavior analysis, and further research is encouraged to enhance the technique for proactive ransomware detection.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.478-488
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• 2023

This paper investigates the naval application of laser communication as a potential replacement for traditional acoustic wave communication in underwater environments. We developed a laser transceiver using Arduino and MATLAB, conducting a water tank experiment to validate communication feasibility across diverse underwater conditions. In the first experiment, when transmitting data through a laser, the desired message was converted into data and transmitted, received, and confirmed to be converted into the correct message. In the second experiment, the operation of communication in underwater situations was confirmed, and in the third experiment, the intensity of light was measured using the CDS illuminance sensor module and the limits of laser communication were measured and confirmed in various underwater situations. Additionally, MATLAB code was employed to gather data on salinity, water temperature, and water depth for calculating turbidity. Optimal wavelength values (532nm, 633nm, 785nm, 1064nm) corresponding to calculated turbidity levels (5, 20, 55, 180) were determined and presented. The study then focuses on analyzing potential applications in naval tactical communication, remote sensing, and underwater drone control. Finally, we propose measures for overcoming current technological limitations and enhancing performance.

• Analyses & Alternatives
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• v.8 no.1
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• pp.87-109
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• 2024

At the 8th Party Congress in 2021, North Korea announced a plan to build 50,000 housing units in Pyongyang, and this paper analyzes the economic effects and related informal costs of of the project. Currently, Pyongyang is experiencing a significant housing shortage. It is estimated that the number of households in Pyongyang increased by 184,000 between 1994 and 2020, while the estimated new housing supply during the period was only 30,000. Pyongyang's 50,000 housing units construction project is characterized by the goal of improving the living conditions of workers, the application of the new city construction method, and the largest state-led housing construction since the Arduous March. The project is expected to generate economic effects such as increasing workers' motivation to work, increasing tourism resources, and generating income from related industries. On the one hand, a significant portion of the construction cost of the 50,000-unit housing project in Pyongyang is passed on to companies and households in the form of informal cost such as quasi-taxes and manpower mobilization. In addition, there may be congestion in the power supply and sewerage facilities that occur when moving in. If these costs are not taken into account, the feasibility of a housing construction project may not be properly assessed, making it difficult to sustain it in the long term.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.901-910
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• 2022

Objective: This study aimed to assess the technical feasibility, efficacy, and safety of the safe triangular working zone (STWZ) approach applied in percutaneous vertebroplasty (PV) for spinal metastases involving the posterior part of the vertebral body. Materials and Methods: We prospectively enrolled 87 patients who underwent PV for spinal metastasis involving the posterior part of the vertebral body, with or without the STWZ approach, from January 2019 to April 2022. Forty-nine patients (27 females and 22 males; mean age ± standard deviation [SD], 57.2 ± 11.6 years; age range, 31-76 years) were included in group A (with STWZ approach), accounting for 54 vertebrae. Thirty-eight patients (18 females and 20 males; 59.1 ± 10.9 years; 29-81 years) were included in group B (without STWZ approach), accounting for 57 vertebrae. Patient demographics, procedure-related variables, and pain relief as assessed using the visual analog scale (VAS) were collected at different time points. Tumor recurrence in the vertebrae after PV was analyzed using Kaplan-Meier curves. Results: The STWZ approach was successful from T1 to L5 without severe complications. Cement filling was satisfactory in 47/54 (87.0%) and 25/57 (43.9%) vertebrae in groups A and B, respectively (p < 0.001). Cement leakage was not significantly different between groups A and B (p = 1.000). Mean VAS score ± SD before and 1 week and 1, 3, 6, 9, and 12 months after PV were 7.6 ± 1.8, 4.2 ± 2.0, 2.7 ± 1.9, 1.9 ± 1.5, 1.7 ± 1.4, 1.7 ± 1.1, and 1.6 ± 1.3, respectively, in group A and 7.2 ± 1.7, 4.0 ± 1.3, 3.4 ± 1.6, 2.4 ± 1.2, 1.8 ± 1.0, 1.4 ± 0.5, and 1.7 ± 0.9, respectively, in group B. Kaplan-Meier analysis showed a lower tumor recurrence rate in group A than in group B (p = 0.001). Conclusion: The STWZ approach may represent a new, safe, alternative/auxiliary approach to target the posterior part of the vertebral body in the PV for spinal metastases.

• Korean Journal of Radiology
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• v.24 no.12
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• pp.1190-1199
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• 2023

Objective: This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy. Materials and Methods: Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation. Results: Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade (P < 0.001), larger mean lesion size (P = 0.038), larger mean MS (P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732). Conclusion: High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1281-1289
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• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.