• Journal of the Society of Naval Architects of Korea
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• v.61 no.4
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• pp.267-277
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• 2024

The Sortie Generation Rate (SGR) is a critical performance indicator for carrier-based aircraft and is a key factor for the carrier design process. This study aims to analyze the factors that affect SGR and establish a representative Sortie Generation Process (SGP) along with simulation results to calculate SGR for a naval ship equipped to carry aircraft. Detailed SGR factors are identified from the perspectives of the aircraft, aviation personnel, and aircraft carrier during the flight preparation stage, and the SGP is established accordingly. As a representative, Korean Navy's CVX basic design is chosen for detailed analysis. The physical dimension and spots for the deck design with time and probabilistic data of SGP are considered to develop a queueing network model for SGR calculation. To consider the specific probabilistic features, the model was solved with discrete event simulation tools(SimPy and AnyLogic) where the results show great agreement. Such findings on SGR factors and calculation are expected to be incorporated in the future development of SGR calculation algorithms and also present guidelines for proper design of aircraft carrier based on concrete operation concept.

• The Journal of the Acoustical Society of Korea
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• v.43 no.5
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• pp.484-493
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• 2024

This paper discusses the process and results of experimental research aimed at reducing Underwater Radiated Noise (URN) using air injection technology. Air Lubrication System (ALS) is an air injection technology mainly installed and operated to improve the propulsion efficiency of large commercial ship, such as LNGC. Recently, research institutes have been studying the potential of reducing URN using ALS. This paper performs an experiment as part of such research. The experiment was conducted in the Large Cavitation Tunnel (LCT), and the major devices applied in the experiment fall into two categories: ALS, which is directly applied to the model in use for LNGC and a modified air injection belt developed from the Masker-Air System (MAS), which is being developed to reduce URN of naval ships. The environmental conditions for the experiment mainly include the air injection flow rate and flow speed in the LCT. The flow rate was set to the actual air injection conditions of ALS, and the flow speed was adjusted to two different levels, considering the actual speeds of LNGC. The noise reduction performance was confirmed by calculating insertion loss with and without air injection.

• Asia-Pacific Journal of Business
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• v.15 no.3
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• pp.205-231
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• 2024

Purpose - The purpose of this study is to investigate the relationship between workplace bullying and defensive silence/acquiescent silence, and to examine a mediation model of job burnout. Design/methodolgy/approach - The survey questionnaires were distributed to 974 employees of a public R&D institute, financial industry, and public officials in Daejeon and Chungnam, and a total of 322 surveys were collected. 288 valid responses were used for the final data analysis using SPSS 21.0 and Amos 22.0. Descriptive statistics were used to identify demographic characteristics of the sample. Reliability analysis of the measurement was conducted using Cronbach's alpha coefficient, and confirmatory factor analysis (CFA) was performed to check the validity of the measurement. Hierarchical regressions were used to examine the relationship between the variables including the moderating effect of job calling. The mediating effect of job burnout and the moderated mediation effect of job calling was analyzed using bootstrapping with PROCESS Macro installed on SPSS 21.0. Findings - The findings of the study are as follows: First, workplace bullying had a significant positive effect on both defensive silence and acquiescent silence. Second, workplace bullying had a significant positive effect on job burnout. Third, job burnout had a significant positive effect on both defensive silence and acquiescent silence. Fourth, job burnout significantly mediated the relationship between workplace bullying and defensive silence, and between workplace bullying and acquiescent silence. Research implications or Originality - The results of this study show that workplace bullying is an important variable that must be managed, because workplace bullying leads to job burnout that can hinder the performance improvement and innovation activities of the organization, which in turn leads to organizational silence. On the other hand, in the research model, the job calling was used as a moderating variable to alleviate the positive effect of workplace bullying and job burnout on the defensive and acquiescent silence, but there was no significant moderating effect. In addition to the job calling, which is the moderating variable used in this study, it is judged that it is necessary to consider specific measures to prevent members from reaching job burnout and reduce silent behavior by investing appropriate job resources such as supporting colleagues, supervisors and leadership in the workplace.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.1-12
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• 2005

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. This paper presents a new algorithm for robust registration of the images acquired by multiple sensors having different modalities; the EO (electro-optic) and IR(infrared) ones in the paper. The two feature-based and intensity-based approaches are usually possible for image registration. In the former selection of accurate common features is crucial for high performance, but features in the EO image are often not the same as those in the R image. Hence, this approach is inadequate to register the E0/IR images. In the latter normalized mutual Information (nHr) has been widely used as a similarity measure due to its high accuracy and robustness, and NMI-based image registration methods assume that statistical correlation between two images should be global. Unfortunately, since we find out that EO and IR images don't often satisfy this assumption, registration accuracy is not high enough to apply to some applications. In this paper, we propose a two-stage NMI-based registration method based on the analysis of statistical correlation between E0/1R images. In the first stage, for robust registration, we propose two preprocessing schemes: extraction of statistically correlated regions (ESCR) and enhancement of statistical correlation by filtering (ESCF). For each image, ESCR automatically extracts the regions that are highly correlated to the corresponding regions in the other image. And ESCF adaptively filters out each image to enhance statistical correlation between them. In the second stage, two output images are registered by using NMI-based algorithm. The proposed method provides prospective results for various E0/1R sensor image pairs in terms of accuracy, robustness, and speed.