• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.1
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• pp.38-60
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• 1999

The main objective of this study is systemization of the technique of ROC quantification and optimization of baseline design by applying CE principle to the acquisition process of a weapon system. QFD and TOA techniques can be employed to a good working example of the conceptual design of a future main battle tank. In this paper, Product Planning Phase, the first phase of four QFD phases, is deployed in terms of eight steps including customer requirements and final product control characteristics. TOA is carried out considering only combat weight. In order to perform combat weight analysis and performance TOA, Preliminary Configuration Synthesis Methodology is used. Preliminary Configuration Synthesis Methodology employs the method of least squares and described linear equations of weight interrelation equation for each component of tank. As a result of QFD based upon the ROC, it was cleared that armor piercing power, main armament, type of ammunition, cruising range, combat weight, armor protection, power loading, threat detection and cost are primary factors influencing design and that combat weight is the most dominant one. The results of TOA based on the combat weight constraint show that 5100 lb reduction was required to satisfy the ROC. The baseline design of a future main battle tank is illustrated with assumption that all phases of QFD are employed to development and production process of subsystems, components, and parts of main battle tank. TOA is applied in iterative process between initial baseline design and ROC. The detailed design of each component is illustrated for a future main battle tank.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.797-802
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• 2015

Background: The purpose of this study was to prospectively evaluate the predictive value of perfusion computed tomography (CT) for response of local advanced esophageal carcinoma to radiotherapy and chemotherapy. Materials and Methods: Before any treatment, forty-three local advanced esophageal squamous cell carcinomas were prospectively evaluated by perfusion scan with 16-row CT from June 2009 to January 2012. Perfusion parameters, including perfusion (BF), peak enhanced density (PED), blood volume (BV), and time to peak (TTP) were measured using Philips perfusion software. Seventeen cases received definitive radiotherapy and 26 received concurrent chemo-radiotherapy. The response was evaluated by CT scan and esophagography. Differences in perfusion parameters between responders and non-responders were analyzed, and ROCs were used to assess predictive value of the baseline parameters for treatment response. Results: There were 25 responders (R) and 18 non-responders (NR). Responders showed significantly higher BF (R:34.1 ml/100g/min vs NR: 25.0 ml/100g/min, p=0.001), BV (23.2 ml/100g vs 18.3 ml/100g, p=0.009) and PED (32.5 HU vs 28.32HU, P=0.003) than non-responders. But the baseline TTP (R: 38.2s vs NR: 44.10s, p=0.172) had no difference in the two groups. For baseline BF, a threshold of 36.1 ml/100g/min achieved a sensitivity of 56%, and a specificity of 94.4% for detection of clinical responders from non-responders. Conclusions: The results suggest that the perfusion CT can provide some helpful information for identifying tumors that may respond to radio-chemotherapy.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.303-304
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• 2007

In this paper, we implement an automatic distance speech recognition system for voiced-enabled services. We first construct a baseline automatic speech recognition (ASR) system, where acoustic models are trained from speech utterances spoken by using a cross-talking microphone. In order to improve the performance of the baseline ASR using distance speech, the acoustic models are adapted to adjust the spectral characteristics of speech according to different microphones and the environmental mismatches between cross-talking and distance speech. Next we develop a voice activity detection algorithm for distance speech. We compare the performance of the base-line system and the developed ASR system on a task of PBW (Phonetically Balanced Word) 452. As a result it is shown that the developed ASR system provides the average word error rate (WER) reduction of 30.6 % compared to the baseline ASR system.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.361-368
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• 2018

As autonomous driving research is rapidly developing, pedestrian detection study is also successfully investigated. However, most of the study utilizes color image datasets and those are relatively easy to detect the pedestrian. In case of color images, the scene should be exposed by enough light in order to capture the pedestrian and it is not easy for the conventional methods to detect the pedestrian if it is the other case. Therefore, in this paper, we propose deep neural network (DNN)-based multi-spectrum pedestrian detection method using color and thermal images. Based on single-shot multibox detector (SSD), we propose fusion network structures which simultaneously employ color and thermal images. In the experiment, we used KAIST dataset. We showed that proposed SSD-H (SSD-Halfway fusion) technique shows 18.18% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 2.1% lower miss rate compared to the conventional halfway fusion method.

• Korean Journal of Health Education and Promotion
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• v.26 no.1
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• pp.15-26
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• 2009

Objectives: The current study evaluated the effects of an integrated breast health program according to levels of breast cancer risk appraisal on knowledge on breast cancer, early detection behaviors, and diet patterns and attitudes in Korean healthy women. Method: A nonequivalent control group pre-posttest design was used. A total of 413 women aged 40-59, registering at the Life Long Health Center in two cities, were classified into intervention groups of 179 women and control groups of 234 women. The integrated breast health program included education, counseling on breast cancer, early detection behaviors, and appropriate diet with multimedia and individual practice session using breast models, reflecting characteristics of each level according to levels of risk appraisal. The knowledge on breast cancer, early detection behaviors, and diet were investigated using questionnaires at baseline and three months after intervention. Results: In both normal and borderline-risk group, intervention groups reported significantly higher scores of knowledge on breast cancer and higher stages of BSE behaviors than control groups. Conclusion: The results showed positive effects on knowledge and early detection behaviors of breast cancer in normal and borderline-risk groups. Further studies should investigate longitudinal effects of the intervention program on dietary change.

• Computers and Concrete
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• v.20 no.5
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• pp.563-572
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• 2017

Ordinary reinforced concrete (RC) and prestressed concrete bridges are two popular and typical types of short- and medium-span bridges that accounts for the vast majority of all existing bridges. The cost of maintaining, repairing or replacing degraded existing RC bridges is immense. Detecting the abnormality of RC bridges at an early stage and taking the protective measures in advance are effective ways to improve maintenance practices and reduce the maintenance cost. This study proposes a systematic method from influence line (IL) identification to damage detection with applications to RC bridges. An IL identification method which integrates the cubic B-spline function with Tikhonov regularization is first proposed based on the vehicle information and the corresponding moving vehicle induced bridge response time history. Subsequently, IL change is defined as a damage index for bridge damage detection, and information fusion technique that synthesizes ILs of multiple locations/sensors is used to improve the efficiency and accuracy of damage localization. Finally, the feasibility of the proposed systematic method is verified through experimental tests on a three-span continuous RC beam. The comparison suggests that the identified ILs can well match with the baseline ILs, and it demonstrates that the proposed IL identification method has a high accuracy and a great potential in engineering applications. Results in this case indicate that deflection ILs are superior than strain ILs for damage detection of RC beams, and the performance of damage localization can be significantly improved with the information fusion of multiple ILs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.962-969
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• 2006

To address the importance of the process fault detection for productivity, support vector machines (SVMs) is employed to assist the decision to determine process faults in real-time. The reactive ion etching (RIE) tool data acquired from a production line consist of 59 variables, and each of them consists of 10 data points per second. Principal component analysis (PCA) is first performed to accommodate for real-time data processing by reducing the dimensionality or the data. SVMs for eleven steps or etching m are established with data acquired from baseline runs, and they are further verified with the data from controlled (acceptable) and perturbed (unacceptable) runs. Then, each SVM is further utilized for the fault detection purpose utilizing control limits which is well understood in statistical process control chart. Utilizing SVMs, fault detection of reactive ion etching process is demonstrated with zero false alarm rate of the controlled runs on a run to run basis.

• The KIPS Transactions:PartA
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• v.18A no.2
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• pp.61-68
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• 2011

Memory error debugging is one of the most critical processes in improving software quality. However, due to the extensive time consumed to debug, the enhancement often leads to a huge bottle neck in the development process of mobile devices. Most of the existing memory error detection tools are based on static error detection; however, the tools cannot be used in mobile devices due to their use of large working memory. Therefore, it is challenging for mobile device vendors to deliver high quality mobile devices to the market in time. In this paper, we introduce "PinMemcheck", a pin-based memory error detection tool, which detects all potential memory errors within $1.5{\times}$ execution time overhead compared with that of a baseline configuration by applying the Pin's binary instrumentation process and a simple data structure.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.229-238
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• 2015

Web shells are programs that are written for a specific purpose in Web scripting languages, such as PHP, ASP, ASP.NET, JSP, PERL-CGI, etc. Web shells provide a means to communicate with the server's operating system via the interpreter of the web scripting languages. Hence, web shells can execute OS specific commands over HTTP. Usually, web attacks by malicious users are made by uploading one of these web shells to compromise the target web servers. Though there have been several approaches to detect such malicious web shells, no standard dataset has been built to compare various web shell detection techniques. In this paper, we present a collection of web shell files, WebSHArk 1.0, as a standard dataset for current and future studies in malicious web shell detection. To provide baseline results for future studies and for the improvement of current tools, we also present some benchmark results by scanning the WebSHArk dataset directory with three web shell scanning tools that are publicly available on the Internet. The WebSHArk 1.0 dataset is only available upon request via email to one of the authors, due to security and legal issues.