• Convergence Security Journal
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• v.24 no.2
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• pp.201-207
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• 2024

Voice is a critical element of human communication, and the development of speech recognition models is one of the significant achievements in artificial intelligence, which has recently been applied in various aspects of human life. The application of speech recognition models in the military field is also inevitable. However, before artificial intelligence models can be applied in the military, it is necessary to research their vulnerabilities. In this study, we evaluates the military applicability of the multilingual speech recognition model "Whisper" by examining its vulnerabilities to battlefield noise, white noise, and adversarial attacks. In experiments involving battlefield noise, Whisper showed significant performance degradation with an average Character Error Rate (CER) of 72.4%, indicating difficulties in military applications. In experiments with white noise, Whisper was robust to low-intensity noise but showed performance degradation under high-intensity noise. Adversarial attack experiments revealed vulnerabilities at specific epsilon values. Therefore, the Whisper model requires improvements through fine-tuning, adversarial training, and other methods.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.170-179
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• 2014

Recently the U-Healthcare system has been rapidly advanced to manage emergence rescue for seniors. We can access emergency rescue systems with high quality services anytime, anywhere under ubiquitous healthcare systems. The more the various systems develop, the more software security systems become important. Therefore, the safety-critical system has been widely spread to the world by advancement of the information and communication technologies. There are a lot kind of fault analysis methods to evaluate software security systems. However due to characteristics of software that is not applied by human error, it can be prevented the enormous damages and losses from improving the safety of safety-critical system. So this paper proposes an integration method of FTA and Forward and Backward FMECA. This method has each strength of FTA and FMECA which is visual and numeric in normalization. First, by use of FTA, we can redraw FTA with Forward FMECA and Backward FMECA in consideration of occurrence, severity, detection, correctness, robustness, and security. Also according to value of NRVP at each event, we can modify FTA diagrams as shown critical paths given by severity and occurrence. Also, we propose the improved emergency rescue service platform of ubiquitous healthcare systems through identifying priorities of the criticality according to normalized risk priority values (NRPV).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4036-4044
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• 2012

This study is aimed to investigate the association between web-based visual stress and MBTI personality types. The stressor deriving visual stress is built by 14 vowels out of Korean alphabet as a content and parallel striples as a background on the screen, which is given to each subject during 5min. The dependent variable indicating how much human takes visual stress is the reduction rate of flicker fusion frequency, which is evaluated with visual flicker fusion frequency tester. The independent variables are gender and 8 MBTI personality types(E-I, S-N, T-F, and J-P), and hypotheses are based on human information processing model and previous studies. The results address that the reduction rate is not significantly affected by gender, S-N, and J-P, but E-I and T-F have significant influences on it. The reduction rate in I-type is almost 2 times as much as that in E-type and T-type has the rate 2.2 times more than F-type. This study can be applicable to determine the adequate personnel for jobs requiring less sensibility to visual stressors in areas that human error may lead to critical damages to an overall system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4762-4767
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• 2012

The AF track circuit that detecting train position and transmitting various train control data for DTG to the train on-board is composed of single operation system. If a failure occurs on this system, the driver should be operate the train by manually until the system is restored, because the system cannot control switch machines and signals by automatically. In this process the human error affects to the train delay, collision, derailment and critical safety accident. Therefore, this document has analyzed the effects that each failure mode influences on system and train, and quantified the failure valuation point and class. Basis on this quantified analysis result, MTBF increased and MTTR decreased and failure number also decreased by adopting the independent installation of power supply, the replacement of defected capacitors, the installation of resister cooling system and the improvement of maintenance methods. And the failure factors of AF track circuits were decreased by conducting the preventive maintenance which is a quantitative way of maintenance system by experience.

• Genomics & Informatics
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• v.10 no.2
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• pp.117-122
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• 2012

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.104-115
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• 1999

This paper proposes an efficient method for 3D modeling of a human face from trinocular images by reconstructing face surface using range data. By using a trinocular camera system, we mitigated the tradeoff between the occlusion problem and the range resolution limitation which is the critical limitation in binocular camera system. We also propose an MPC_MBS (Matching Pixel Count Multiple Baseline Stereo) area-based matching method to reduce boundary overreach phenomenon and to improve both of accuracy and precision in matching. In this method, the computing time can be reduced significantly by removing the redundancies. In the model generation sub-pixel accurate surface data are achieved by 2D interpolation of disparity values, and are sampled to make regular triangular meshes. The data size of the triangular mesh model can be controlled by merging the vertices that lie on the same plane within user defined error threshold.

• Journal of Korean Society of Steel Construction
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• v.20 no.1
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• pp.93-104
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• 2008

study deals with the reliability assesment for the 5-year phases of a suspension bridge construction in Korea. The main objectives of this study are; (1) the evaluation of the reliability of a suspension bridge by considering an ultimate limit state for the fracture of main cable wires, (2) the determination of the critical phases among 28 construction stages for the deck erection, and (3) the evaluation of the reliability of the limit state for the erection control during construction stages. The research and the design of the suspension bridge have been focused on the state of construction mainly based on empirical data. Based on the recent survey of the distribution of accidents in Korean railways, over 80% of the accidents related to the uncertainties in human error, planning, design, materials and loads during construction have ben reported before the completion of construction. While many researches have evaluated the safety of bridges, the uncertainties in the construction phases have not been well treated in a guidelines or a specifications. An improved adaptive response surface method is used for the risk assessment in the construction phases of the target suspension bridge.

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.11-20
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• 2022

There is a critical need for AI assistance in guard operations of Army base perimeters, which is exacerbated by changes in the national defense and security environment such as force reduction. In addition, the possibility for human error inherent to perimeter guard operations attests to the need for an innovative revamp of current systems. The purpose of this study is to propose a real-time object detection AI tailored to military CCTV surveillance with three unique characteristics. First, training data suitable for situations in which relatively small objects must be recognized is used due to the characteristics of military CCTV. Second, we utilize a data augmentation algorithm suited for military context applied in the data preparation step. Third, a noise reduction algorithm is applied to account for military-specific situations, such as camouflaged targets and unfavorable weather conditions. The proposed system has been field-tested in a real-world setting, and its performance has been verified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.53-60
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• 2020

The bogies of railway vehicles are one of the most critical components for service. Fatigue defects in the bogie can be initiated for various reasons, such as material imperfection, welding defects, and unpredictable and excessive overloads during operation. To prevent the derailment of a railway vehicle, it is necessary to evaluate and detect the defect of a connection weldment between the car body and bogie accurately. The safety of the bogie weldment was checked using an ultrasonic test, and it is necessary to determine the occurrence of defects using a learning method. Recently, studies on deep learning have been performed to identify defects with a high recognition rate with respect to a fine and similar defect. In this paper, the databases of weldment specimens with artificial defects were constructed to detect the defect of a bogie weldment. The ultrasonic inspection using the wedge angle was performed to understand the detection ability of fatigue cracks. In addition, the convolutional neural network was applied to minimize human error during the inspection. The results showed that the defects of connection weldment between the car body and bogie could be classified with more than 99.98% accuracy using CNN, and the effectiveness can be verified in the case of an inspection.

• Journal of the Korea Society for Simulation
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• v.25 no.2
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• pp.31-40
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• 2016

Wireless sensor networks have been used in many applications such as marine environment, army installation, etc. The sensor data is very important, because all these applications depend on sensor data. The possibility of communication failures becomes high since the surrounding environment of a wireless sense network has an sensitive effect on its communications. In particular, communication failures in underwater communications occur more frequently because of a narrow bandwidth, slow transmission speed, noises from the surrounding environments and so on. In cases of communication failures, the sensor data can be lost in the sensor data delivery process and these kinds of sensor data losses can make critical huge physical damages on human or environments in applications such as fire surveillance systems. For this reason, although a few of studies for storing and compressing sensor data have been proposed, there are lots of difficulties in actual realization of the studies due to none-existence of the framework using network communications. In this paper, we propose a framework for reducing loss of the sensor data and analyze its performance. The our analyzed results in non-framework application show a decreasing data recovery rate, T/t, as t time passes after a network failure, where T is a time period to fill the storage with sensor data after the network failure. Moreover, all the sensor data generated after a network failure are the errors impossible to recover. But, on the other hand, the analyzed results in framework application show 100% data recovery rate with 2~6% data error rate after data recovery.