• Journal of Satellite, Information and Communications
• /
• v.12 no.2
• /
• pp.21-27
• /
• 2017

The combat systems in Korea navy have been operating some kinds of tactical data link systems such as Link-11, ISDL and JTDLS. Each tactical data link system has the ability to transmit and receive tactical information like track, engagement, weapon information by using unique message of theirs. And each unique message has their own format. But a number of tactical data link system make combat effectiveness worse because their major functions are duplicated unnecessarily. So, many advanced countries are trying to make united data link system. Similarly, the combat systems in Korea navy will operate C4I data link system, and it is combined version current ISDL, KNCCS and JTDLS data link system. In this paper, we consider the development current tactical data link systems in Korea navy. Also, compare the characteristics between I-message used in ISDL and Host-Interface message used in C4I. From these results, we analyze advanced points about C4I data link system.

• Proceedings of the Membrane Society of Korea Conference
• /
• 1998.10a
• /
• pp.15-18
• /
• 1998

Membrane reactors are systems which combine a chemical reactor with a membrane separation process allowing to carry out simultaneously conversion and product separation. The catalyst can be immobilized on the membrane or simply compartmentalized in a reaction space by the membrane. Membrane reactors are today investigated to produce optically pure isomers and/or resolve racemic mixture of enantiomers. The interest towards these systems is due to the increasing demand of enantiomerically pure compounds to be used in the pharmaceutical, food, and agrochemical industries. In fact, enantiomers can have different biological activities, which often influence the efficacy or toxicity of the compound. On the basis of current literature there are basically two schemes on the use of membrane technology to produce enantiomers. In one case, the membrane itseft is intrinsically enantioselective: the membrane is the chiral system which selectively separates the wanted isomer on the basis of its conformation. In the other, a kinetic resolution using an enantiospecific biocatalyst is combined with a membrane separation process; the membrane separates the product from the substrate on the basis of their relative chemical properties (i.e. solubility). This kind of configuration is widely used to carry out kinetic resolutions of low water soluble substrams in biphasic membrane reactors [Giomo, 1995, 1997; Lopez, 1997]. These are systems where enzyme-loaded membranes promote reactions between two separate phases thanks to the properties of enzymes, such as lipases, to catalyse reactions at the org ic/aqueous interface; the two phases are maintained in contact and separated at the membrane level by operating at appropriate transmembrane pressure. A schematic representation of biphasic membrane reactor is shown in figure 1, while an example of enantiospecific reaction and product separation carried out with these systems is reported in figure 2.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.2
• /
• pp.352-359
• /
• 2008

RTLS(Real Time Location Systems) is a technology that identifies a location of a target object and provides peat visibility at a work place. Unlike those of the overseas, domestic ports and shipyards have narrow work places and thus, the efficient utilization of these spaces is one of the most important considerations for improving productivity. Companies considering implementation of RTLS should understand its limitations or applicability. In this paper, problems of RTLS such as fading factors which were caused from the features of RF, and limitations caused from the preconditions of RTLS were explained. To overcome those problems, three types of solutions such as movable RTLS, semi-movable RTLS and combined RTLS with other technologies were suggested.

• Journal of the Korea Safety Management & Science
• /
• v.19 no.4
• /
• pp.273-282
• /
• 2017

This paper is aimed at proposing a new approach to connecting the measurements of customer satisfaction on healthcare services with the prioritized identification of healthcare service processes to be improved. As customers' requirements for healthcare services have become too diverse and healthcare service systems have been increasingly complex, there has been growing interest in the customer-oriented evaluation of healthcare service quality and the systematic improvement of healthcare service processes. Most of the previous studies on service quality evaluation are based on SERVQUAL model. However, because of the unique characteristics and constraints inherent in healthcare service systems, it has been reported that SERVQUAL would be inadequate to be applied to healthcare service systems. As an alternative, SERVPERF has recently been widely used in the evaluation of healthcare service quality. However, there is a lack of studies on how to use the measurements of healthcare service quality systematically to improve service functions and processes. With this issue in mind, we firstly measured the customer-perceived satisfaction on the healthcare service quality from the six dimensions based on SERVPERF. Then we identified the relationships between the subjective measurements and healthcare service processes through brainstorming and expert interview. By using the relationships, we developed a customer journey map in healthcare services that visually describe the interaction between customers and healthcare service systems. The developed customer journey map would help service designers easily identify a healthcare service process that needs to be improved with priority. It is expected that the design improvement process proposed in this study would be a useful method for enhancing the quality of healthcare services.

• Archives of Plastic Surgery
• /
• v.49 no.5
• /
• pp.614-616
• /
• 2022

As of November 2021, TikTok has one billion monthly active users and is recognized as the most engaging social media platform. TikTok has seen a surge in users and content creators, ranging from athletes to medical professionals. In the past year, content creators have utilized the app to advocate for social reforms, education, and other uses that were not previously considered. Breast cancer is the most commonly diagnosed cancer in women, with an expected 281,550 new cases of invasive breast cancer in 2021. As more individuals with breast cancer choose to undergo resection, the demand for autologous fat grafting in breast reconstruction has increased due to the natural look and feel of breast tissue. The purpose of this article is to analyze content related to breast reconstruction with fat grafting found on TikTok and recommend methods to improve patient education, care, and outcomes. We searched TikTok on November 1, 2021, for videos using the phrase "breast reconstruction with fat grafting." The top 200 videos retrieved from the TikTok search algorithm were analyzed, and all commentaries, duplicates, and nonrelevant videos were removed. Video characteristics were collected, and two independent reviewers generated a DISCERN score A total of 131 videos were included in the study. They were found to have a combined 1,871,980 likes, 41,113 comments, and 58,662 shares. The videos had an average DISCERN score of 2.16. Content creators had an overall low DISCERN score in items involving the use of references, disclosure of risks for not obtaining treatment, and support for shared decision-making. When stratified, the DISCERN score was higher for videos created by physicians (DISCERN average 2.48) than for videos created by nonphysicians (DISCERN average 1.99; p < 0.001).

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1310-1338
• /
• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.1
• /
• pp.13-25
• /
• 2024

In traffic management, gaps in understanding traffic conditions continue to exist. While the self-belonging problem indicator develops relative to speed, belonging, and self-based relative inclination, it does not apply elimination criteria that may indicate situations that contrast with attribute-specific problems. In this study, we develop integrated indicators that specify communication situations and safety levels for modeling. We review indicators of changes in traffic conditions and raise safety issues, reviewing the indicators so that ITS data can be applied, analyzing the relationships between indicators through factor analysis. We develop combined, integrated indicators that can show changes and stability in traffic situations and that can be applied in traffic information centers to contribute to the development of a traffic environment that can monitor related traffic conditions.

• Ocean Systems Engineering
• /
• v.14 no.2
• /
• pp.171-210
• /
• 2024

This paper focuses on the rigorous and holistic fatigue analysis of mooring chains for a deep draft semi-submersible platform in the challenging environment of the central Gulf of Mexico (GoM). Known for severe hurricanes and strong loop/eddy currents, this region significantly impacts offshore structures and their mooring systems, necessitating robust designs capable of withstanding extreme wind, wave and current conditions. Wave scatter and current bin diagrams are utilized to assess the probabilistic distribution of waves and currents, crucial for calculating mooring chain fatigue. The study evaluates the effects of Vortex Induced Motion (VIM), Out-of-Plane-Bending (OPB), and In-Plane-Bending (IPB) on mooring fatigue, alongside extreme single events such as 100-year hurricanes and loop/eddy currents including ramp-up and ramp-down phases, to ensure resilient mooring design. A detailed case study of a deep draft semi-submersible platform with 16 semi-taut moorings in 2,500 meters of water depth in the central GoM provides insights into the relative contributions of wave scatter diagram, VIMs from current bin diagram, the combined stresses of OPB/IPB/TT and extreme single events. By comparing these factors, the study aims to enhance understanding and optimize mooring system design for safety, reliability, and cost-effectiveness in offshore operations within the central GoM. The paper addresses a research gap by proposing a holistic approach that integrates findings from various contributions to advance current practices in mooring design. It presents a comprehensive framework for fatigue analysis and design optimization of mooring systems in the central GoM, emphasizing the critical importance of considering environmental conditions, OPB/IPB moments, and extreme single events to ensure the safety and reliability of mooring systems for offshore platforms.

• International Journal of Computer Science & Network Security
• /
• v.24 no.9
• /
• pp.169-185
• /
• 2024

Due to levitate and rapid growth of E-Commerce, most of the organizations are moving towards cashless transaction Unfortunately, the cashless transactions are not only used by legitimate users but also it is used by illegitimate users and which results in trouncing of billions of dollars each year worldwide. Fraud prevention and Fraud Detection are two methods used by the financial institutions to protect against these frauds. Fraud prevention systems (FPSs) are not sufficient enough to provide fully security to the E-Commerce systems. However, with the combined effect of Fraud Detection Systems (FDS) and FPS might protect the frauds. However, there still exist so many issues and challenges that degrade the performances of FDSs, such as overlapping of data, noisy data, misclassification of data, etc. This paper presents a comprehensive survey on financial fraud detection system using such data mining techniques. Over seventy research papers have been reviewed, mainly within the period 2002-2015, were analyzed in this study. The data mining approaches employed in this research includes Neural Network, Logistic Regression, Bayesian Belief Network, Support Vector Machine (SVM), Self Organizing Map(SOM), K-Nearest Neighbor(K-NN), Random Forest and Genetic Algorithm. The algorithms that have achieved high success rate in detecting credit card fraud are Logistic Regression (99.2%), SVM (99.6%) and Random Forests (99.6%). But, the most suitable approach is SOM because it has achieved perfect accuracy of 100%. But the algorithms implemented for financial statement fraud have shown a large difference in accuracy from CDA at 71.4% to a probabilistic neural network with 98.1%. In this paper, we have identified the research gap and specified the performance achieved by different algorithms based on parameters like, accuracy, sensitivity and specificity. Some of the key issues and challenges associated with the FDS have also been identified.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2009.10a
• /
• pp.258-263
• /
• 2009

This paper describes an analysis of the stability and performance of a large-capacity flywheel energy storage system (FESS) supported by active magnetic bearings. We designed and manufactured the system that can store up to 5kWh of usable energy at the maximum speed of 18,000 rpm. In order to analyze the stability of the systems accurately, we derived a rigid body rotor model, flexible rotor model using finite-element method, and a reduced-order model using modal truncation. The rotor model is combined with those of active magnetic bearings, amplifiers, and position sensors, resulting in a system simulation model. This simulation model is validated against experimental measurements. The stability of the system is checked from the pole locations of the closed-loop transfer functions. We also investigated the sensitivity function to quantify the robustness of the systems to the disturbances such as mass imbalance and sensor noises.