• Journal of Navigation and Port Research
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• v.47 no.3
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• pp.120-127
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• 2023

Maritime pilots are always exposed to unpredictable risks present in the marine environment because they are boarding ships through pilot ladders or accommodation ladders at sea. Since the pilot plays an important role in securing the safety of a ship entering or departing from a port, an injury to the pilot substantially affects the overall safety of the ship. The purpose of this study is to analyze pilot injuries and predict accidents. For this purpose, pilot injury cases are analyzed and potential situations are identified through a survey. Pilot injuries are also predicted. The survey was analyzed using the IPA (Importance-Performance Analysis) methodology, and the binomial distribution and Poisson distribution were used to predict injury trends. As a result of the study, 316.8 nearmiss occurred per pilot injury, and if the current accident management system is maintained, the probability of pilot injury occurring within 3 months is 64.4%. Based on the research results, the need for a management system to prevent pilot injuries and reinforcement of maintenance and installation for pilot ladders was suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.79-88
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• 2006

In this study, a methodology is developed for flood runoff analysis considering the interaction between interior floodplain and channel. Riparian lowland is modeled as storage areas by HEC-RAS and is connected with main channel through gravity drainage structure and pumping stations. As a result, we were able to compute the difference between runoff into the interior floodplain and delayed runoff to main channel from interior floodplain. This allowed us to compute the storage change in the interior floodplain and corresponding inundation areas. Furthermore, the levee is modeled as a lateral structure and the flood from the main channel to interior floodplain is modeled by installing a weir on top of it. In addition, levee breach is also modeled so that flooding from main channel to interior floodplain can be considered. Computed flooding depth in the storage areas are compared with elevation to identify the inundated areas and flood maps can then be produced for a desired time or for the extent of flooding given a flooding depth. Output from this modeling effort can provide many useful information for flood planning such as flow depth in main channel, flooding depth and area in interior floodplain. The method was applied to Sapgyo river basin and the comparison with observed flood events showed that it can reproduce the observation fairly well, hence proving the utility of the method.

• Korean Journal of Heritage: History & Science
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• v.56 no.4
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• pp.216-228
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• 2023

The purpose of this study is to apply the handheld Raman spectrometer to identify the coloring materials used in a large Buddhist painting (of Maitreya Buddha) at Janggoksa Temple through cross-validation with HH-XRF. An in situ investigation was performed together with use of a digital microscope and HH-XRF analysis to verify the properties of pigments used in the gwaebul ("large Buddhist painting") via a non-destructive method. However, the identification of coloring materials composed of light elements and mixed or overlaid pigments is difficult using only non-destructive analysis data. Unlike in situ investigation, laboratory analysis often required samples yet the sampling is restricted to a small quantity due to the cultural heritage characteristic. Thus, it is necessary to develop a non-destructive in situ method to supplement the HH-XRF data. The large Buddhist painting at Janggoksa Temple was painted mainly using white, red, yellow, green, and blue colors. The Raman spectroscopy provides molecular information, while XRF spectroscopy provides information about elemental composition of the pigments. Analysis results identified various coloring materials: inorganic pigment, such as lead white, minium, cinnabar, and orpiment, as well as organic pigment such as gamboge and indigo. Therefore, it is possible to obtain more information for the identification of pigments; organic pigment and mixed or overlaid pigments, while at the same time minimizing the collection sample and simplifying the analysis procedure compared to previously used methods. The results of this study will be used as basic data for the analysis of painting cultural heritage through a non-destructive in situ method in the future.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.127-135
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• 2023

With the development of information and communication technology, the amount of information produced and shared in the battlefield and stored and managed in the system dramatically increased. This means that the amount of information which cansupport situational awareness and decision making of the commanders has increased, but on the other hand, it is also a factor that hinders rapid decision making by increasing the information overload on the commanders. To overcome this limitation, this study proposes a method to automatically search, select, and summarize documents that can help the commanders to understand the battlefield situation reports that he or she received. First, named entities are discovered from the battlefield situation report using a named entity recognition method. Second, the documents related to each named entity are discovered. Third, a language model and collaborative filtering are used to select the documents. At this time, the language model is used to calculate the similarity between the received report and the discovered documents, and collaborative filtering is used to reflect the commander's document reading history. Finally, sentences containing each named entity are selected from the documents and sorted. The experiment was carried out using academic papers since their characteristics are similar to military documents, and the validity of the proposed method was verified.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.42-49
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• 2024

In recent years, the construction industry has diligently focused on improving the quality and safety of buildings through smart technologies. However, there is a growing trend of leakage defects, especially in educational facilities, due to aging. The objective of this study is to analyze the causes of these defects in educational environments using the Fault Tree Analysis (FTA) technique and propose preventive measures based on the findings. The FTA technique is explained through a review of domestic literature, and data from the Educational Support Center from 2019 to 2021 are examined to identify major defects. The construction of the Fault Tree (FT) for leakage defects resulted in the identification of 12 basic events. Subsequently, a comprehensive understanding of the causes of leakage is achieved through FTA analysis, leading to the identification of the primary causes of defects. Leakage defects accounted for 46.8% of all reported issues in educational facilities, with roof (ceiling) leaks being the most common problem. FTA analysis revealed that poor substrate treatment was the main cause of roof (ceiling) leaks, which could be attributed to cracks in the waterproof layer, joint cracks, and microvoids in the waterproof layer. The primary achievement of this research is to provide essential data for preventing leakage defects in educational facilities and developing preventive measures through the FTA technique. These results are expected to significantly enhance the management of educational facilities and the prevention of leakage issues.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.137-145
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• 2024

Recently, there has been an increase in the use of Automatic Identification Systems. Class A AIS is used for ships engaged in international voyages, while Class B AIS is utilized for smaller vessels navigating domestic coastlines. AtoN AIS is used for aids to navigation, AIS is employed for search and rescue aircraft, and AIS-SART is widely used worldwide. Accordingly, in 2022, the Maritime Safety Committee(MSC) of the International Maritime Organization(IMO) revised the performance standards for the satellite emergency positioning radio beacon(EP IRB) to include AIS signals along with 121.5 MHz for aircraft, which has been used as a homing signal. It was recommended to use together as a homing signal, and from July 1, 2022, it was decided that AIS-EP IRB that satisfies the revised performance standards will replace the existing EP IRB. Consequently, starting from July 1, 2022, it was decided that AIS-EPIRB, which meets the revised performance standards, will replace the existing EP IRB. This paper aims to verify the feasibility of implementing AIS-EPIRB, which has not yet been developed domestically. To achieve this, a dedicated chipset for AIS was used to additionally implement frequency generation of 161.975 MHz and 162.025 MHz and GMSK modulation to satisfy the requirements.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.91-101
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• 2024

The utilization of container virtualization technology ensures the consistency and portability of data-intensive and memory volatile workflows. Kubernetes serves as the de facto standard for orchestrating these container applications. Cloud users often overprovision container applications to avoid container restarts caused by resource shortages. However, overprovisioning results in decreased CPU and memory resource utilization. To address this issue, oversubscription of container resources is commonly employed, although excessive oversubscription of memory resources can lead to a cascade of container restarts due to node memory scarcity. Container restarts can reset operations and impose substantial overhead on containers with high memory volatility that include numerous stateful applications. This paper proposes a technique to mitigate container restarts in a memory oversubscription environment based on Kubernetes. The proposed technique involves identifying containers that are likely to request memory allocation on nodes experiencing high memory usage and temporarily pausing these containers. By significantly reducing the CPU usage of containers, an effect similar to a paused state is achieved. The suspension of the identified containers is released once it is determined that the corresponding node's memory usage has been reduced. The average number of container restarts was reduced by an average of 40% and a maximum of 58% when executing a high memory volatile workflow in a Kubernetes environment with the proposed method compared to its absence. Furthermore, the total execution time of a container workflow is decreased by an average of 7% and a maximum of 13% due to the reduced frequency of container restarts.

• Journal of Aerospace System Engineering
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• v.18 no.1
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• pp.1-10
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• 2024

In a previous study, a structure of a superplastic yoke consisting of a thin FR4 layer laminated with viscoelastic tape on both sides of a shape memory alloy (SMA) was proposed to reduce residual vibration generated by a deployable solar panel during high motion of a satellite. Damping properties of viscoelastic tapes will change with temperature, which can directly affect vibration reduction performance of the yoke. To check damping performance of the yoke at different temperatures, free damping tests were performed under various temperature conditions to identify the temperature range where the damping performance was maximized. Based on above temperature test results, this paper predicts temperature of the yoke through orbital thermal analysis so that the yoke can have effective damping performance even if it is exposed to an orbital thermal environment. In addition, the thermal design method was described so that the yoke could have optimal vibration reduction performance.