• Journal of the Korean Society of Safety
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• v.34 no.1
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• pp.82-89
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• 2019

The importance of aviation safety has been emphasized recently due to the development of aviation industry. Despite the efforts of each country and the improvement of screening equipment, screening tasks are still difficult and detection failures are frequent. The purpose of this study was to examine the effect of feedback on improving signal detection performance applying a Simulated Baggage Screening Program(SBSP) for improving aviation safety. SBSP consists of three parts: image combination, option setting and experiment. The experimental images were color-coded to reflect the items' transmittance of the x-rays and could be combined as researchers' need. In the option, the researcher could set up the information, incentive, and comments needed for training to be delivered on a number of tasks and times. Experiment was conducted using SBSP and participant's performance information (hit, missed, false alarms, correct rejection, reaction time, etc.) was automatically calculated and stored. A total of 50 participants participated and each participant was randomly assigned to feedback and non-feedback group. Participants performed a total of 200 tasks and 20(10%) contained target object(gun and knife). The results showed that when the feedback was provided, the hit, correct rejection ratio and d′ were increased, however, the false alarms and miss decreased. However, there was no significant difference in response criteria(${\beta}$). In addition, implications, limitations of this study and future research were discussed.

• The Journal of Korean Philosophical History
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• no.38
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• pp.7-38
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• 2013

From the philosophy of Laozi, we can infer the two types of the individuals, such as Youwei individual and Wuwei individual. The Youwei individual characterizes its expandibility, which appears as an aggressive character, and the society where this has set in is a false society. The Wuwei individual discards a false power and authority, concentrates on its realities and life, and further restricts its rights in a voluntary way. Their behavioral pattern like this allows the other party to secure an autonomous space and ensures that he or she can live a full life in person. The society these Wuwei individuals have formed through their own relationships is Xuantong Society. The Xuantong Society proposed by Laozi restricts individual rights, but it rather guarantees individual's autonomy, life and happiness, and suggests an individual-community model in which common good is created endlessly even though it does not establish the common good. This is very different from the points of view which guarantees individual rights and at the same time attempt to realize the common good together.

• Journal of IKEEE
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• v.26 no.4
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• pp.553-559
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• 2022

In this paper, RC snubber circuit design technology for oscillation suppression in half-bridge configuration of cascode gallium nitride (GaN) field effect transistors (FETs) is analyzed. A typical wide band-gap (WBG) device, cascode GaN FET, has excellent high-speed switching characteristics. However, due to such high-speed switching characteristics, a false turn-off problem is caused, and an RC snubber circuit is essential to suppress this. In this paper, the commonly used experimental-based RC snubber design technique and the RC snubber design technique using the root locus method are compared and analyzed. In the general method, continuous circuit changes are required until the oscillation suppression performance requirement is met based on experimental experience . However, in root locus method, the initial value can be set based on the non-oscillation R-C map. To compare the performance of the two aforementioned design methods, a simulation experiment and a switching experiment using an actual double pulse circuit are performed.

• Structural Engineering and Mechanics
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• v.91 no.5
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.269-274
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• 2015

As smart device companies such as Google or Apple develop competitive mobile-based automotive operating systems and navigation systems, the range of choice for users in such markets is expanding. Navigation systems equipped with mobile operating systems have increased convenience for users. However, since an API for the POI databases used in navigation systems doesn't exist, the number of applications using POI data is insufficient. In this paper, we designed and implemented system calls for navigation operating systems with a focus on POI search, in order to resolve such limitations. The system calls support set-based POI search functions, and therefore provides solutions to search performance degradation problems caused by false inputs. As a result of performance evaluation, not only did the search performance improve, but there was also no problem in applying APIs in applications.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.981-999
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• 2017

In the standard control chart assuming a simple random model, we estimate the process variance without considering the between-sample variance. If the between-sample exists in the process, the process variance is under-estimated. When the process variance is under-estimated, the narrower control limits result in the excessive false alarm rate although the sensitivity of the control chart is improved. In this paper, using the variance component model to incorporate the between-sample variance, we set the control limits using both the within- and between-sample variances, and evaluate the efficiency of the control chart in terms of the average run length (ARL). Considering the most widely used control chart types such as ${\bar{X}}$, EWMA and CUSUM control charts, we compared the differences between two cases, Case I and Case II, where the between-sample variance is ignored and considered, respectively. We also considered the two cases when the process parameters are given and estimated. The results showed that the false alarm rate of Case I increased sharply as the between-sample variance increases, while that of Case II remains the same regardless of the size of the between-sample variance, as expected.

• Journal of Radiation Protection and Research
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• v.45 no.3
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• pp.117-129
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• 2020

Background: Radiation portal monitors (RPMs) involving plastic scintillators installed at the border inspection sites can detect illicit trafficking of radioactive sources in cargo containers within seconds. However, RPMs may generate false alarms because of the naturally occurring radioactive materials. To manage these false alarms, we previously suggested an energy-weighted algorithm that emphasizes the Compton-edge area as an outstanding peak. This study intends to evaluate the identification of radioactive sources using an improved energy-weighted algorithm. Materials and Methods: The algorithm was modified by increasing the energy weighting factor, and different peak combinations of the energy-weighted spectra were tested for source identification. A commercialized RPM system was used to measure the energy-weighted spectra. The RPM comprised two large plastic scintillators with dimensions of 174 × 29 × 7 ㎤ facing each other at a distance of 4.6 m. In addition, the in-house-fabricated signal processing boards were connected to collect the signal converted into a spectrum. Further, the spectra from eight radioactive sources, including special nuclear materials (SNMs), which were set in motion using a linear motion system (LMS) and a cargo truck, were estimated to identify the source identification rate. Results and Discussion: Each energy-weighted spectrum exhibited a specific peak location, although high statistical fluctuation errors could be observed in the spectrum with the increasing source speed. In particular, ¹³⁷Cs and ⁶⁰Co in motion were identified completely (100%) at speeds of 5 and 10 km/hr. Further, SNMs, which trigger the RPM alarm, were identified approximately 80% of the time at both the aforementioned speeds. Conclusion: Using the modified energy-weighted algorithm, several characteristics of the energy weighted spectra could be observed when the used sources were in motion and when the geometric efficiency was low. In particular, the discrimination between ⁶⁰Co and ⁴⁰K, which triggers false alarms at the primary inspection sites, can be improved using the proposed algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.2
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• pp.703-711
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• 2016

IDS (Intrusion Detection System) is used to detect network attacks through network data analysis. The system requires a high accuracy and detection rate, and low false alarm rate. In addition, the system uses a range of techniques, such as expert system, data mining, and state transition analysis to analyze the network data. The purpose of this study was to compare the performance of two data mining methods for detecting network attacks. They are Support Vector Machine (SVM) and a neural network called Forward Additive Neural Network (FANN). The well-known KDD Cup 99 training and test data set were used to compare the performance of the two algorithms. The accuracy, detection rate, and false alarm rate were calculated. The FANN showed a slightly higher false alarm rate than the SVM, but showed a much higher accuracy and detection rate than the SVM. Considering that treating a real attack as a normal message is much riskier than treating a normal message as an attack, it is concluded that the FANN is more effective in intrusion detection than the SVM.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.79-85
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• 2022

Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.

• Journal of Korean Society of Forest Science
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• v.105 no.4
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• pp.422-428
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• 2016

Ginkgo (Ginkgo biloba L.) is one of the most appropriate roadside trees because of a good transplantation nature and ability to grow well in urban environment. Ginkgo is a dioecious species. Sex discrimination of ginkgo is possible through comparing morphological characters of reproductive organs. However, it needs more than about twenty years for reproductive organs to appear after sexual maturity. Until now, ginkgo trees for roadside plantation have been planted without discriminating the sex because ginkgo trees have been usually planted before sexual maturity. Ginkgo nuts from the female ginkgo trees planted along the roadside emit a foul odor, and make much pollution on the streets. Thus in this study a novel SCAR marker (SCAR-GBM) for the early sex discrimination was developed. Primers were developed on the basis of the sequence of male-specific RAPD variants reported previously. False-negative problem of SCAR marker, probably caused by dominant nature, was resolved by using multiplex PCR using primers of both the SCAR-GBM and a universal primer set of atp1 region in mitochondria DNA, which resulted in improved discrimination efficiency. The results showed that DNA bands of 1,039 bp were commonly amplified by the atp1 primer set in male and female trees, and SCAR-GBM markers of 675 bp were specifically amplified only in male trees. Reproducible and specific discrimination of the multiplex PCR was finally confirmed by applying multiple male and female individuals.