• Journal of Yeungnam Medical Science
• /
• v.3 no.1
• /
• pp.179-183
• /
• 1986

The frozen section technique is a means of intraoperative pathological diagnosis, and a procedure of great value to the surgeon. This method should be accurate, rapid and reliable. This method serves useful purposes, such as determining the presence of tumor, its type(especially whether it is benign or malignant), the adequacy of a biopsy of a suspected lesion, and the conditions of the surgical margins. But, it bears many disadvantages, the most of which is the danger of incorrect diagnosis. We studied the indications, the limitations, and the accuracy of the frozen section method and the materials studies was total of frozen section during recent 3 years. The overall accuracy of the frozen section diagnosis of 809 cases was 98.1% with 0.5% of false negative, 0% of false positive, 0.5% of incorrect histological diagnosis or grading errors, and 0.9% of deferred cases. The tissues submitted were lymph node, gastrointestinal tract, skin subcutaneous tissues in decreasing oder of frequency. The false positive case is not present, while the false negative cases were 4.

• The KIPS Transactions:PartC
• /
• v.12C no.1 s.97
• /
• pp.45-52
• /
• 2005

A damage scale of information property has been increasing rapidly by various illegal actions of information systems, which result from dysfunction of a knowledge society. Reinforcement in criminal investigation requests of network security has accelerated research and development of Intrusion Detection Systems(IDSs), which report intrusion-detection about these illegal actions. Due to limited designs of early IDSs, it is hard for the IDSs to cope with tricks to go around IDS as well as false-positive and false-negative trials in various network environments. In this paper, we showed that this kind of problems can be solved by using a Virtual Protocol Stack(VPS) that possesses automatic learning ability through an optimum-adaptive mobile code. Therefore, the enhanced IDS adapts dynamically to various network environments in consideration of monitored and self-learned network status. Moreover, it is shown that Insertion/Evasion attacks can be actively detected. Finally, we discussed that this method can be expanded to an intrusion detection technique that possesses adaptability in the various mixed network environments.

• Journal of Internet Computing and Services
• /
• v.19 no.3
• /
• pp.15-23
• /
• 2018

A code reuse attack is an attack technique that can execute arbitrary code without injecting code directly into the stack by combining executable code fragments existing in program memory and executing them continuously. ROP(Return-Oriented Programming) attack is typical type of code reuse attack and serveral defense techniques have been proposed to deal with this. However, since existing methods use Rule-based method to detect attacks based on specific rules, there is a limitation that ROP attacks that do not correspond to previously defined rules can not be detected. In this paper, we introduce a method to detect ROP attack by learning command pattern used in ROP attack code using RNN(Recurrent Neural Network). We also show that the proposed method effectively detects ROP attacks by measuring False Positive Ratio, False Negative Ratio, and Accuracy for normal code and ROP attack code discrimination.

• Journal of Biomedical Engineering Research
• /
• v.18 no.4
• /
• pp.485-491
• /
• 1997

This paper describes the relationship between skin impedance signal, behavioral signal, and subjective evaluation depending on arousal level. Nz and reaction time had similar trend with mKSS level, but eyeblink rate was different from these two parameters. eye-blink rate increased slowly from mKSS level 1 to 5, and had high increasing rate at mKSS 7. But it showed steep descent at mKSS level 9. Each subject showed different eye-blink rates, but changing rates of EBR was similar at eachm KSS level. Therefore it suggests that rising rate of EBR can be used arousal level criterion. From the result of reaction time test. human performance was decreased rapidly above the mKSS level 5, and false positive and false negative data was observed above the mKSS level 3. It is desirable to give a subject some stimuli such as sound or aroma to rise arousal level between mKSS level 3 and mKSS level 5.

• Imaging Science in Dentistry
• /
• v.48 no.2
• /
• pp.121-125
• /
• 2018

Purpose: Detecting laryngeal cartilages (triticeous and thyroid cartilages) on panoramic radiographs is important because they may be confused with carotid artery calcifications in the bifurcation region, which are a risk factor for stroke. This study assessed the efficiency of panoramic radiography in the diagnosis of calcified laryngeal cartilages using cone-beam computed tomography (CBCT) as the reference standard. Materials and Methods: A total of 312 regions(142 bilateral, 10 left, 18 right) in 170 patients(140 males, 30 females) were examined. Panoramic radiographs were examined by an oral and maxillofacial radiologist with 11 years of experience. CBCT scans were reviewed by 2 other oral and maxillofacial radiologists. The kappa coefficient(${\kappa}$) was calculated to determine the level of intra-observer agreement and to determine the level of agreement between the 2 methods. Diagnostic indicators(sensitivity, specificity, accuracy, and false positive and false negative rates) were also calculated. P values <.05 were considered to indicate statistical significance. Results: Eighty-two images were re-examined to determine the intra-observer agreement level, and the kappa coefficient was calculated as 0.709 (P<.05). Statistically significant and acceptable agreement was found between the panoramic and CBCT images (${\kappa}=0.684$ and P<.05). The sensitivity, specificity, diagnostic accuracy rate, the false positive rate, and the false negative rate of the panoramic radiographs were 85.4%, 83.5%, 84.6%, 16.5%, and 14.6%, respectively. Conclusion: In most cases, calcified laryngeal cartilages could be diagnosed on panoramic radiographs. However, due to variation in the calcifications, diagnosis may be difficult.

• The KIPS Transactions:PartC
• /
• v.13C no.1 s.104
• /
• pp.45-54
• /
• 2006

Nowadays, a lot of techniques have been applied for the detection of malicious behavior. However, the current techniques taken into practice are facing with the challenge of much variations of the original malicious behavior, and it is impossible to respond the new forms of behavior appropriately and timely. There are also some limitations can not be solved, such as the error affirmation (positive false) and mistaken obliquity (negative false). With the questions above, we suggest a new method here to improve the current situation. To detect the malicious code, we put forward dealing with the basic source code units through the conceptual graph. Basically, we use conceptual graph to define malicious behavior, and then we are able to compare the similarity relations of the malicious behavior by testing the formalized values which generated by the predefined graphs in the code. In this paper, we show how to make a conceptual graph and propose an efficient method for similarity measure to discern the malicious behavior. As a result of our experiment, we can get more efficient detection rate.

• Journal of Digital Contents Society
• /
• v.18 no.2
• /
• pp.373-382
• /
• 2017

As the number of scientific and technical contents increases, services that support efficient search of scientific and technical contents are required. When an author's affiliation is used as a keyword, not only the contents produced by the affiliation can be searched, but also the identification rate of the search result using the author and the term as keyword can be improved. Because of the ambiguity and vagueness of the data used as a search keyword, the search result may include false negative or false positive. However, the previous research on the control through identification of the search keyword is mainly focused on the author data and terminology data. In this paper, we propose the algorithm to identify affiliations and experiment with show the experiment with scientific and technological contents held by the Korea Institute of Science and Technology Information.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.3
• /
• pp.55-65
• /
• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.15 no.2
• /
• pp.117-123
• /
• 2022

Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.

• Korean Journal of Radiology
• /
• v.21 no.7
• /
• pp.891-899
• /
• 2020

Objective: To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. Materials and Methods: We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). Results: All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.