• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.1-11
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• 2022

In this paper, we propose an NFT(Non-Fungible Token)-based access control method for safely sharing data between users in blockchain environment. Since all data stored in the blockchain can be accessed by anyone due to the nature of the technology, it is necessary to control access except for authorized users when sharing sensitive data. For that, we generate each data as NFT and controls access to the data through the smart contract. In addition, in order to overcome the limitations of single ownership of the existing NFT, we separated the NFT into ownership and use rights, so that data can be safely shared between users. Ownership is represented as an original NFT, use rights is represented as a copied NFT, and all data generated as NFT is encrypted and uploaded, so data can be shared only through the smart contract with access control. To verify this approach, we set up a hypothetical scenario called Building Information Modeling (BIM) data trade, and deployed a smart contract that satisfies 32 function call scenarios that require access control. Also, we evaluated the stability in consideration of the possibility of decryption through brute-force attack. Through our approach, we confirmed that the data can be safely shared between users in blockchain environment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1267-1273
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• 2022

Although the Korea shipbuilding industry has recently been receiving most of the orders for ships in the world, production processes are being disrupted due to a shortage of manpower at the production site. This is because the workers quit the shipyard as both work and wages were reduced due to the long slump in the shipbuilding industry. The main reason for the increase in orders was the large-scale orders for Qatar LNG carriers, and the situation in which the technical specifications required for ships are becoming more complex is also working to an advantage. Because the contract delivery time is of utmost importance for ships, the dock launch plan is the most important management item among the shipyard's major processes. The structure to be built in the dock may be a hull that has left the design work or a finished vessel, and in some cases, it is often at the level of some blocks of the hull. When launching, the hull is affected by the hogging or sagging moment due to the fluid force, and securing the safety of the structural strength of the block connection is of utmost importance. In a normal process, the connecting member launches after welding has been completed, but in actual shipbuilders, quick decision-making is needed on the conditions for securing structural safety to comply with the docking schedule. In this study, a detailed analysis method and applicability using a bending stress evaluation method and finite element analysis modelling were analyzed to rationally judge the above-mentioned problems from an engineering point of view. The main contents mentioned in the thesis can be used as good examples when conducting similar structural strength evaluations in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.601-610
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• 2023

The comprehension of water-level behavior in rivers is essential for effective flood and river environmental management. The objective of this study is to propose a methodology that can be used by field engineers engaged in actual practice, to readily identify the characteristics of water-level behavior during flood events. To this end, a total of 45 historical water-level records from 2010 to 2022 year, which provide flood information for the flood vulnerable districts of the Han River, were obtained. A Water-level Rise Behavior Curve (WRBC) was developed and suggested to quantify the amount of water-level rise per unit time during flood. As a result, the water-level rises by more than 80% of the total rise within the first 6.2 hours, followed by a gradual rise. The time required to achieve a particular equilibrium varied depending on the area and runoff characteristics of the upstream. Furthermore, the study revealed that the WRBC provides a statistical representation of the water-level rise trend during floods, and can be effectively utilized for flood mitigation measures in waterfront spaces and irrigation facilities.

• Journal of the Korean Geotechnical Society
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• v.24 no.6
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• pp.57-67
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• 2008

Two test methods, nonrepetitive plate loading test (NPLT) and repetitive plate loading test (RPLT) are being used to control the quality of compaction through the evaluation of the stiffness of subgrade soils in the Korea railway industry. Subgrade reaction modulus ($k_{30}$) from the NPLT and strain modulus ($E_v$) from the RPLT are the index values to check them. The methods have similar aspects, but they differ in the modulus evaluation method, the numbers of loading stage, termination procedures, etc. This paper analyses the differences of the two test methods and evaluates the relationship between subgrade reaction modulus and strain modulus. In order to develop the relationship, total 22 tests were performed including the NPLT and the RPLT at the 6 original grounds, and 5 upper or lower subgrades in Kyungbu High Speed Railway II stage construction sites. According to the soil conditions, the relationship between subgrade reaction modulus and strain modulus was proposed with corrections by considering strain states, mean confining pressures, and Poisson's ratio.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.325-336
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• 2023

In August 2022, even though flood damage occurred in the metropolitan area due to heavy rain, drought warnings were issued in Jeolla province, which indicates that the regional drought is intensified recent years. To cope with regarding intensified regional droughts, many studies have been conducted to identify spatial patterns of the occurrence of meteorological drought, however, case studies of spatial clustering for water shortage are not sufficient. In this study, using the estimations of water shortage in the Han River Basin in 2030 of the Master Plans for National Water Management, the spatial characteristics of water shortage were analyzed to identify the hotspot areas based on the Local Moran's I and Getis-Ord Gi^*, which are representative indicators of spatial clustering analysis. The spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The overall results of for three anayisis periods (S0(1967-1983), S1(1984-2000), S2(2001-2018)) indicated that the lower Imjin River (#1023) was the hotspot for water shortage, and there are moving patterns of water shortage from the east of lower Imjin River (#1023) to the west during S2 compared to S0 and S1. In addition, the Yangyang-namdaecheon (#1301) was the HL area that is adjacent to a high water shortage area and a low water shortage area, and had water shortage pattern in S2 compared to S0 and S1.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.469-476
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• 2008

Recently, TOD gains popularity as a traffic solution measure of high density urban regeneration projects. The purpose of this study is to investigate traffic impacts of high density TOD projects, and to identify the issues to be resolved. For a case study, it chooses Gangnamgucheong station in Gangnam area served by two subway lines, and designates 400m radius from the station as a site for high-density development. The MOEs chosen for this study is traffic volume, time, distance, speed, and mode share. The SECOM model is adopted for traffic simulation. The analysis results show that high-density TOD is an effective tool for traffic improvement even with only one station area being implemented. It is found that the traffic volume increases near the station in nature where high-density development occurs, but it declines overall in the rest of Gangam area. The total travel time and distance of passenger vehicles decline, meaning that the traffic condition becomes better than before. With regulation on parking supply, the improvement becomes more vivid. In terms of the changes of traffic speed, both alternatives show 4.1% increase in speed, but the difference between alternatives is not quite noticeable because of the induced vehicle demand driven to the streets with improved traffic condition. The mode share changes occur for the benefit of subway ridership, because the study station is equipped with two subway line services. When mixed with parking supply restriction, the impact becomes clearer.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.1
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• pp.1-9
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• 2023

Patients with neurocognitive disorder show behavioral psychological symptoms such as agitation, aggression, depression, and wandering, as well as cognitive decline, which puts a considerable burden on patients and their families. For the treatment of behavioral psychological symptoms, patient-centered, non-pharmacological treatment should be used as a first line approach. This paper describes non-pharmacological interventions to manage and treat behavioral psychological symptoms in patients with neurocognitive disorder. In order to control behavioral psychological symptoms such as agitation, depression, apathy, insomnia, and wandering, it is important to identify and evaluate factors such as environmental changes and drugs, and then solve such problems. Non-pharmacological interventions include reassurance, encourage, distraction, and environmental change. It is necessary to understand behavior from a patient's point of view and to approach the patient's needs and abilities appropriately. Reminiscence therapy, music therapy, aroma therapy, multisensory stimulation therapy, exercise therapy, light therapy, massage therapy, cognitive intervention therapy, and pet therapy are used as non-pharmacological interventions, and these approaches are known to improve symptoms such as depression, apathy, agitation, aggression, anxiety, wandering, and insomnia. However, the quality of the evidence base for non-pharmacological approaches is generally lower than for pharmacological treatments. Therefore, more extensive and accurate effectiveness verification studies are needed in the future.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.543-551
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• 2023

As climate change continues to prompt an increasing demand for advancements in disaster and safety management technologies to address abnormal high water temperatures, typhoons, floods, and droughts, sea surface temperature has emerged as a pivotal factor for swiftly assessing the impacts of summer harmful algal blooms in the seas surrounding Korean Peninsula and the formation and dissipation of cold water along the East Coast of Korea. Therefore, this study sought to gauge predictive performance by leveraging statistical methods and deep learning algorithms to harness sea surface temperature data effectively for marine anomaly research. The sea surface temperature data employed in the predictions spans from 2018 to 2022 and originates from the Heuksando Tidal Observatory. Both traditional statistical ARIMA methods and advanced deep learning models, including long short-term memory (LSTM) and gated recurrent unit (GRU), were employed. Furthermore, prediction performance was evaluated using the attention LSTM technique. The technique integrated an attention mechanism into the sequence-to-sequence (s2s), further augmenting the performance of LSTM. The results showed that the attention LSTM model outperformed the other models, signifying its superior predictive performance. Additionally, fine-tuning hyperparameters can improve sea surface temperature performance.