• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.715-721
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• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.

• Journal of Communications and Networks
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• v.15 no.6
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• pp.606-613
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• 2013

This paper proposes a complete solution of a contention-based medium access control in wireless local networks to provide station level quality of service guarantees in both downstream and upstream directions. The solution, based on the mature distributed coordination function protocol, includes a new fixed contention window backoff scheme, a tuning procedure to derive the optimal parameters, a super mode to mitigate the downstream bottleneck at the access point, and a simple admission control algorithm. The proposed system guarantees that the probability of the delay bound violation is below a predefined threshold. In addition, high channel utilization can be achieved at the same time. The numerical results show that the system has advantages over the traditional binary exponential backoff scheme, including efficiency and easy configuration.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.111-119
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• 2013

Face recognition has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous applications, such as access control, surveillance, security, credit-card verification, and criminal identification. In this paper, we propose a simple descriptor called an ECSP(Extended Center-Symmetric Pattern) for illumination-robust face recognition. The ECSP operator encodes the texture information of a local face region by emphasizing diagonal components of a previous CS-LBP(Center-Symmetric Local Binary Pattern). Here, the diagonal components are emphasized because facial textures along the diagonal direction contain much more information than those of other directions. The facial texture information of the ECSP operator is then used as the input image of an image covariance-based feature extraction algorithm such as 2D-PCA(Two-Dimensional Principal Component Analysis). Performance evaluation of the proposed approach was carried out using various binary pattern operators and recognition algorithms on the Yale B database. The experimental results demonstrated that the proposed approach achieved better recognition accuracy than other approaches, and we confirmed that the proposed approach is effective against illumination variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.576-599
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• 2013

Protocol reverse engineering is useful for many security applications, including intelligent fuzzing, intrusion detection and fingerprint generation. Since manual reverse engineering is a time-consuming and tedious process, a number of automatic techniques have been proposed. However, the accuracy of these techniques is limited due to the complexity of binary instructions, and the derived formats have missed constraints that are critical for security applications. In this paper, we propose a new approach for protocol format extraction. Our approach reasons about only the evaluation behavior of a program on the input message from concolic execution, and enables field identification and constraint inference with high accuracy. Moreover, it performs binary analysis with low complexity by reducing modern instruction sets to BIL, a small, well-specified and architecture-independent language. We have implemented our approach into a system called Icefex and evaluated it over real-world implementations of DNS, eDonkey, FTP, HTTP and McAfee ePO protocols. Experimental results show that our approach is more accurate and effective at extracting protocol formats than other approaches.

• Imaging Science in Dentistry
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• v.48 no.4
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• pp.277-281
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• 2018

Purpose: This study was performed to investigate the relationship between sleep bruxism(SB) and pulpal calcifications in young women. Materials and Methods: A total of 100 female participants between 20 and 31 years of age who were referred to our radiology clinic for a dental check-up, including 59 SB and 41 non-SB patients, were sampled for the analysis. SB was diagnosed based on the American Academy of Sleep Medicine criteria. All teeth were evaluated on digital panoramic radiographs to detect pulpal calcifications, except third molars, teeth with root canal treatment, and teeth with root resorption. Binary logistic regression analysis was used to determine the risk factors for pulpal calcifications. The Spearman correlation coefficient was applied and the Pearson chi-square test was used for categorical variables. To test intra-examiner reproducibility, Cohen kappa analysis was applied. P values <.05 were considered to indicate statistical significance. Results: A total of 2800 teeth were evaluated (1652 teeth from SB patients and 1148 from non-SB patients), and 61% of patients had at least 1 dental pulpal calcification. No statistically significant relationship was found between SB and pulpal calcifications (P>0.05). In SB patients, the total number of pulpal calcifications was 129, while in non-SB patients, it was 84. Binary logistic analysis showed that SB was not a risk factor for the presence of pulpal calcifications(odds ratio, 1.19; 95% CI, 0.52-2.69, P>.05). Conclusion: No relationship was found between SB and pulpal calcifications.

• Journal of The Korean Association For Science Education
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• v.14 no.2
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• pp.123-132
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• 1994

The experimental evidence evaluation of the 11th grade students(N:91) was investigated. Specially, the influence of students' ideas about optical phenomena and presented evidence types on their evidence evaluation, and the influence of students' ideas on their causal inference modes were investigated. After eliciting the students' ideas about shadow phenomena and conformity of their idea, the experimental results with a binary outcome were presented as the evidence. Then the students were asked to evaluate the evidence. Again students' ideas were elicited. Most of students had causal ideas such that the shape of object(96%) and the inclination of screen(75%) were causes of shadow shape, not the shape(70%) and color(92%) of light source. In the case of the shape of object and the color of light source, most students(70%) believed strongly their ideas. Most responses(80%) in the evidence were evidence-based, and 12% of them were theory-based. There was no significant difference of reponses types between students with causal ideas(81%) and students with non-causal ideas(78%), between covariable and non-covariable evidence. But in the case of non-causal ideas, covariable evidence was more likely to yield evidence-based reponses than non-covariable evidence. If students had preconcepts inconsistent(84%) with the evidence, they were more likely to make evidence-based responses than the students with consistent ideas (75%) with the evidence. Especially in the case perceptually biased evidence, this tendency was marked. In the case of covariable evidence, many students made inclusion inferences(40%) rather than uncertainty inferences(32%). In the case of uncertainty inferences(94%), students more likely to make evidence-based reponses than inclusion inferences(83%) and exclusion infernces(88%). In the case of inclusion inferences and exclusion infernces, students tended to make idea-based responses and distort the evidences. In conclusion, when the students evaluate the experimental evidences, their ideas influence the causal inference modes. Especially, according to the conformity of the preconcepts and logical relation of evidences, the inference modes are more strongly depended upon the preconcepts rather than evidences.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.171-179
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• 2022

Self-Modifying-Code is a code that changes the code by itself during execution time. This technique is particularly abused by malicious code to bypass static analysis. Therefor, in order to effectively detect such malicious codes, it is important to identify self-modifying-codes. In the meantime, Self-modify-codes have been analyzed using dynamic analysis methods, but this is time-consuming and costly. If static analysis can detect self-modifying-code it will be of great help to malicious code analysis. In this paper, we propose a static analysis method to detect self-modified code for binary executable programs converted to LLVM IR and apply this method by making a self-modifying-code benchmark. As a result of the experiment in this paper, the designed static analysis method was effective for the standardized LLVM IR program that was compiled and converted to the benchmark program. However, there was a limitation in that it was difficult to detect the self-modifying-code for the unstructured LLVM IR program in which the binary was lifted and transformed. To overcome this, we need an effective way to lift the binary code.

• Journal of Information Technology and Architecture
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• v.10 no.2
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• pp.159-167
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• 2013

Web Services offer many benefits over other types of middleware in distributed computing. However, usage of Web Services results in large network bandwidth since Web Services use XML-based protocol which is heavier than binary protocols. Even though there have been many researches to minimize the network traffic and bandwidth usages of Web Services messages, none of them are solving problem clearly yet. In this paper, we propose a transparent proxy with cache to avoid transfer of repeated SOAP data, sent by Web Service to an application. To maintain the cache consistency, we introduce publish/subscribe paradigm using WS-Eventing between the proxy and Web Service. The implemented system based on our proposed architecture will not compromise the standards of Web Service. The evaluation of our system shows that caching SOAP messages not only reduces the network traffic but also decreases the request delays.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.250-255
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• 2007

Steam generator tubes play an important role in safety because they constitute one of the primary barriers between the radioactive and non-radioactive sides of the nuclear power plant. For this reason, the integrity of the tubes is essential in minimizing the leakage possibility of radioactive water. The integrity of the tubes is evaluated based on NDE (non-destructive evaluation) inspection results. Especially ECT (eddy current test) method is usually used for detecting the flaws in steam generator tubes. However, detection capacity of the NDE is not perfect and all of the "real flaws" which actually existing in steam generator tunes is not known by NDE results. Therefore reliability of NDE system is one of the essential parts in assessing the integrity of steam generators. In this study POD (probability of detection) of ECT system for ODSCC in steam generator tubes is evaluated using multivariate logistic regression. The cracked tube specimens are made using the withdrawn steam generator tubes. Therefore the cracks are not artificial but real. Using the multivariate logistic regression method, continuous POD surfaces are evaluated from hit (detection) and miss (no detection) binary data obtained from destructive and non-destructive evaluation of the cracked tubes. Length and depth of cracks are considered in multivariate logistic regression and their effects on detection capacity are evaluated.