• Journal of Advanced Marine Engineering and Technology
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• v.37 no.7
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• pp.760-770
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• 2013

Detecting grammatical errors from a text is a long-history application. In this paper, we compare the performance of two grammatical error detection techniques, which are implemented as a sub-module of an automated English scoring system. One is to use a full syntactic parser, which has not only grammatical rules but also extra-grammatical rules in order to detect syntactic errors while paring. The other one is to use a finite state machine which can identify an error covering a small range of an input. In order to compare the two approaches, grammatical errors are divided into three parts; the first one is grammatical error that can be handled by both approaches, and the second one is errors that can be handled by only a full parser, and the last one is errors that can be done only in a finite state machine. By doing this, we can figure out the strength and the weakness of each approach. The evaluation results show that a full parsing approach can detect more errors than a finite state machine can, while the accuracy of the former is lower than that of the latter. We can conclude that a full parser is suitable for detecting grammatical errors with a long distance dependency, whereas a finite state machine works well on sentences with multiple grammatical errors.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.2
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• pp.94-99
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• 2016

Application layer attacks have for years posed an ever-serious threat to network security, since they always come after a technically legitimate connection has been established. In recent years, cyber criminals have turned to fully exploiting the web as a medium of communication to launch a variety of forbidden or illicit activities by spreading malicious automated software (auto-ware) such as adware, spyware, or bots. When this malicious auto-ware infects a network, it will act like a robot, mimic normal behavior of web access, and bypass the network firewall or intrusion detection system. Besides that, in a private and large network, with huge Hypertext Transfer Protocol (HTTP) traffic generated each day, communication behavior identification and classification of auto-ware is a challenge. In this paper, based on a previous study, analysis of auto-ware communication behavior, and with the addition of new features, a method for classification of HTTP auto-ware communication is proposed. For that, a Not Only Structured Query Language (NoSQL) database is applied to handle large volumes of unstructured HTTP requests captured every day. The method is tested with real HTTP traffic data collected through a proxy server of a private network, providing good results in the classification and detection of suspicious auto-ware web access.

• Journal of IKEEE
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• v.23 no.2
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• pp.448-453
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• 2019

Under the conflicting objectives of maximum user satisfaction and fast launching, there exist great needs for automated mobile app testing. In automated app testing, detection of usage-state changes is one of the most important issues for minimizing human intervention and testing of various usage scenarios. Because conventional approaches utilizing pre-collected training examples can not handle the rapid evolution of apps, we propose a novel method detecting changes in usage-state through graph-entropy. In the proposed method, widgets in a screen shot are recognized through DNNs and 'onverted graphs. We compared the performance of the proposed method with a SIFT (Scale-Invariant Feature Transform) based method on 20 real-world apps. In most cases, our method achieved superior results, but we found some situations where further improvements are required.

• Smart Structures and Systems
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• v.33 no.6
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• pp.449-463
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• 2024

Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.100-107
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• 2007

AABR(automated auditory brainstem response) test is used for the screening purpose of hearing ability of neonates. In this paper, algorithm using Rolle's theorem is suggested for automatic detection of the ensemble averaged ABR waveform. The ABR waveforms were recorded from 55 normal-hearing ears of neonates at screening levels varying from 30 to 60 dBnHL. Recorded signals were analyzed by expert audiologist and by the proposed algorithm. The results showed that the proposed algorithm correctly identified latencies of the major ABR waves (III, V) with latent difference below 0.2 ms. No significant differences were found between the two methods. We also analyzed the ABR signals using derivative algorithm and compared the results with proposed algorithm. The number of detected candidate waves using the proposed algorithm was 47 % less than that of the existing one. The proposed method had lower relative errors (0.01 % error at 60dBnHL) compared to the existing one. By using proposed algorithm, clinicians can detect and label waves III and V more objectively and quantitatively than the manual detection method.

• Korean Journal of Microbiology
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• v.44 no.2
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• pp.130-134
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• 2008

Modern automated culture systems have increased the isolation rate of microorganisms and shortened the time to detection, reducing experimental errors in diagnosis of infecting agents. BacT/ALERT 3D system is based on the colorimetric detection of $CO_2$ produced by the growing microorganisms. In order to evaluate the efficiency of the detection system, sterility test were performed using 6 bacteria. With standard aerobic and anaerobic bottles containing the liquid media, both three aerobic bacteria (P. aeruginosa, M. luteus, B. subtilis) and a facultative bacterium S. aureus were detected up to 1 CFU in 31.44 hr. In addition, growth of anaerobic C. sporogenes was recognized up to 1 CFU in 15.96 hr. The slowly growing bacteria P. acnes was detected up to 10,000 CFU in 129.36 hr. In comparison with conventional culture method, BacT/ALERT 3D automated culture system was more sensitive and saved detection time up to$2\sim10$ hr. Therefore, this automated culture system enables to efficiently detect bacteria in clinical samples and biological medicines.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.201-204
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• 1995

In this study, we proposed an algorithm for P-wave detection. It is essential to decide the existence of P-wave and to extract some parameter about P-wave in automated ECG interpreter. Especially this paper describes the detection and estimation of three case, artrial flutter, coupled and non-coupled, which are very crucial things in ECG diagnosis. The performance of algorithm is showed by applying it to CSE(Common Standards for Quantative Electrocardiography) database.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.653-657
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• 2005

AGV is very useful equipment to transfer containers in automated container terminal. AGV must have Obstacle Detection System (ODS) for port automation. ODS needs the function to classify some specified object from background in acquired data. And it must be able to track classified moving objects. Finally, ODS could determine its next action for safe driving whether it should do emergency stop or speed down, or it should change its deriving lane. For these functions, ODS can have many different kinds of algorithm. In this paper, we present one of AGV to be used in automated container terminal.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.314-321
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• 2020

Logic automated stimulus and response (LASAR) software is an automatic test program development tool for logic function test and fault detection of avionics components digital circuit cards. LASAR software needs to the information for the logic circuit function and input and output of the device. If there is no component information, normal component modeling is impossible. In this paper, component modeling is carried out through reverse design of programmable logic device (PLD) device without element information. The developed LASAR program identified failure detection rates through fault simulation results and single-seated fault insertion methods. Fault detection rates have risen by 3% to 91% for existing limited modeling and 94% for modeling through the reverse design. Also, the 22 case of stuck fault with the I/O pin of EP310 PLD were detected 100% to confirm the good performance.

• Food Science and Biotechnology
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• v.16 no.3
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• pp.337-342
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• 2007

Conventional methods for pathogen detection and identification are labor-intensive and take days to complete. Biosensors have shown great potential for the rapid detection of foodborne pathogens. Fiber-optic biosensors have been used to rapidly detect pathogens because they can be very sensitive and are simple to operate. However, many fiber-optic biosensors rely on manual sensor handling and the sandwich assay, which require more effort and are less sensitive. To increase the simplicity of operation and detection sensitivity, a binding inhibition assay method for detecting Listeria monocytogenes in food samples was developed using an automated, fiber-optic-based immunosensor: RAPTOR (Research International, Monroe, WA, USA). For the assay, fiber-optic biosensors were developed by the immobilization of Listeria antibodies on polystyrene fiber waveguides through a biotin-avidin reaction. Developed fiber-optic biosensors were incorporated into the RAPTOR to evaluate the detection of L. monocytogenes in frankfurter samples. The binding inhibition method combined with RAPTOR was sensitive enough to detect L. monocytogenes ($5.4{\times}10^7\;CFU/mL$) in a frankfurter sample.