• Pediatric Gastroenterology, Hepatology & Nutrition
• /
• v.7 no.2
• /
• pp.137-142
• /
• 2004

Purpose: The resistance of H. pylori to clarithromycin is one of the major causes of eradication failure. In H. pylori, clarithromycin resistance is due to point mutation in 23S rRNA. The aims of this study were to investigate the mutation of 23S rRNA and to examine the association of cagA, vacA genotype and clarithromycin resistant genes. Methods: H. pylori DNA was extracted from antral biopsy specimens from 27 children with H. pylori infection. Specific polymerase chain reaction (PCR) assays were used for cagA and vacA. Mutations associated with clarithromycin resistance were detected by using PCR restriction fragment length polymorphism (RFLP) analysis of 23S rRNA gene. Results: A2143G mutation was detected in one case and A2144G in 4, indicating 18.5% were clarithromycin resistant. Among the total of 27, cagA was present in 25 (93%), vacA s1a/m1 in 6 (22%), s1a/m2 in 3 (11%), s1c/m1 in 16 (59%), and s1c/m2 in 1 (4%). All of the 5 clarithromycin resistant strains were cagA (+), among which 2 were s1a/m1 and 2 were s1c/m1. There was no relation between genotypes and clarithromycin resistant genes. Conclusion: Detection of H. pylori resistance to clarithromycin using PCR RFLP from biopsy specimens might be useful for the selection of antibiotics. Clarithromycin resistant genes are not associated with genotypes of cagA and vacA.

• Journal of the Korean Magnetics Society
• /
• v.23 no.6
• /
• pp.200-204
• /
• 2013

In this study, we have fabricated magnetoresistive sensors of $50{\mu}m{\times}50{\mu}m$ cross type by trilayer structure of antiferromagnetic/nonmagnetic/ferromagnetic. The magnetic signal and magnetic domain of this sensor is measured. The sensor hysteresis loop is not in symmetrical at 0 Oe. This is may be due to the exchange coupling between ferromagnetic layer and anti ferromagnetic layer. This exchange bias value is 20 Oe. The sensor signal is measured at between the applied magnetic field and current. The sensor signal is measured between the applied magnetic field and current at $20^{\circ}$ and $90^{\circ}$ angles. The sensitivity of sensor signals is $20{\mu}V/Oe$ and $7{\mu}V/Oe$ at $20^{\circ}$ and $90^{\circ}$ angles, respectively. In addition, this sensor is also applied for the detection of magnetic bacteria at $20^{\circ}$ angle. From these results, we calculate the stray field of single bacteria is to be $5{\times}10^{-5}$Oe.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.2
• /
• pp.185-195
• /
• 2018

As machines have been automated in the field of industries in recent years, it is a paramount importance to manage and maintain the automation machines. When a fault occurs in sensors attached to the machine, the machine may malfunction and further, a huge damage will be caused in the process line. To prevent the situation, the fault of sensors should be monitored, diagnosed and classified in a proper way. In the paper, we propose a sensor fault detection scheme based on SVM and CNN to detect and classify typical sensor errors such as erratic, drift, hard-over, spike, and stuck faults. Time-domain statistical features are utilized for the learning and testing in the proposed scheme, and the genetic algorithm is utilized to select the subset of optimal features. To classify multiple sensor faults, a multi-layer SVM is utilized, and ensemble technique is used for CNN. As a result, the SVM that utilizes a subset of features selected by the genetic algorithm provides better performance than the SVM that utilizes all the features. However, the performance of CNN is superior to that of the SVM.

• Journal of the Korean earth science society
• /
• v.30 no.2
• /
• pp.210-222
• /
• 2009

Lightning data, observed from total lightning detection system (TLDS) of KMA, for the recent five years (2002-2006) have been analyzed for temporal and spatial characteristics of frequency, intensity, duration, and flash rate. Lightning frequency varies largely with years (most frequent in 2006) and the lightning during the summer accounts for 75% of total flashes and only 0.6% of lightnings strike in cold season. In rainy season (JJAS), the ratio of positive flashes to negative ones is as low as 0.15, but it increases up to 0.98 in February. The seasonal variation of lightning duration is strongly linked with lightning occurrences, whereas flashes rates show weak seasonal variability. In a daily scale, lightning, on average, occurs more often at dawn (2 am, 5-7 am) and in the mid-afternoon (15 pm), and the lightning at dawn (around 5 am) is most intense during the day. The western inland areas md the West/South Sea show high lightning density during JJAS, whereas eastern part and the East Sea exhibit a low density of lightning. Considering the low ratio of positive flashes (0.15) for the whole analysis domain during summer period, Chungnam and Jeonbuk areas have a high ratio of flashes over 0.4. However, these should be analyzed with much caution because weak positive cloud-to-cloud discharges can be regarded as cloud-to-ground flashes. The western inland also exhibits long annual flash hours (15-24). And the W3st Sea has high flash rates as a result of large density and low flash hours. The most frequent time of lightning occurrence over most inland areas lies between mid-afternoon and early-evening, whereas mountainous and coastal areas, and the northern Kyoungki and Hwanghae provinces show the maximum lightning strikes in the morning and at dawn, respectively.

• Journal of Intelligence and Information Systems
• /
• v.18 no.1
• /
• pp.91-107
• /
• 2012

As the Internet becomes more popular, many people use it to communicate. With the increasing number of personal homepages, blogs, and social network services, people often expose their personal information online. Although the necessity of those services cannot be denied, we should be concerned about the negative aspects such as personal information leakage. Because it is impossible to review all of the past records posted by all of the people, an automatic personal information detection method is strongly required. This study proposes a method to detect or classify online documents that contain personal information by analyzing features that are common to personal information related documents and learning that information based on the Na$\ddot{i}$ve Bayes algorithm. To select the document classification algorithm, the Na$\ddot{i}$ve Bayes classification algorithm was compared with the Vector Space classification algorithm. The result showed that Na$\ddot{i}$ve Bayes reveals more excellent precision, recall, F-measure, and accuracy than Vector Space does. However, the measurement level of the Na$\ddot{i}$ve Bayes classification algorithm is still insufficient to apply to the real world. Lewis, a learning algorithm researcher, states that it is important to improve the quality of category features while applying learning algorithms to some specific domain. He proposes a way to incrementally add features that are dependent on related documents and in a step-wise manner. In another experiment, the algorithm learns the additional dependent features thereby reducing the noise of the features. As a result, the latter experiment shows better performance in terms of measurement than the former experiment does.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.10
• /
• pp.2551-2561
• /
• 2014

Several algorithms have been developed to detect AFIB(Atrial Fibrillation) which either rely on the linear and frequency analysis. But they are more complex than time time domain algorithm and difficult to get the consistent rule of irregular RR interval rhythm. In this study, we propose algorithm for optimal value detection of irregular RR interval for AFIB classification based on linear analysis. For this purpose, we detected R wave, RR interval, from noise-free ECG signal through the preprocessing process and subtractive operation method. Also, we set scope for segment length and detected optimal value and then classified AFIB in realtime through liniar analysis such as absolute deviation and absolute difference. The performance of proposed algorithm for AFIB classification is evaluated by using MIT-BIH arrhythmia and AFIB database. The optimal value indicate ${\alpha}=0.75$, ${\beta}=1.4$, ${\gamma}=300ms$ in AFIB classification.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.25 no.2
• /
• pp.178-188
• /
• 2019

When using a distributed system, it is very important to know the intention of a target ship in order to prevent collisions. The action taken by a certain ship for collision avoidance and the action of the target ship it intends to avoid influence each other. However, it is difficult to establish a collision avoidance plan in consideration of multiple-ship situations for this reason. To solve this problem, a Distributed Stochastic Search Algorithm (DSSA) has been proposed. A DSSA searches for a course that can most reduce cost through repeated information exchange with target ships, and then indicates whether the current course should be maintained or a new course should be chosen according to probability and constraints. However, it has not been proven how the parameters used in DSSA affect collision avoidance actions. Therefore, in this paper, I have investigated the effect of the parameters and weight factors of DSSA. Experiments were conducted by combining parameters (time window, safe domain, detection range) and weight factors for encounters of two ships in head-on, crossing, and overtaking situations. A total of 24,000 experiments were conducted: 8,000 iterations for each situation. As a result, no collision occurred in any experiment conducted using DSSA. Costs have been shown to increase if a ship gives a large weight to its destination, i.e., takes selfish behavior. The more lasting the expected position of the target ship, the smaller the sailing distance and the number of message exchanges. The larger the detection range, the safer the interaction.

• Smart Media Journal
• /
• v.9 no.4
• /
• pp.97-101
• /
• 2020

Using passive electric field sensor which operates in non-contact mode, we can measure the electric potential induced from the change of electric charges on a sensor caused by the movement of human body or hands. In this study, we propose a new method, which utilizes PLN induced to the sensor around the moving object, to detect one's hand movement and extract gesture frames from the detected signals. Signals from the EPS sensors include a large amount of power line noise usually existing in the places such as rooms or buildings. Using the fact that the PLN is shielded in part by human access to the sensor, signals caused by motion or hand movement are detected. PLN consists mainly of signals with frequency of 60 Hz and its harmonics. In our proposed method, signals only 120 Hz component in frequency domain are chosen selectively and exclusively utilized for detection of hand movement. We use FFT to measure a spectral-separated frequency signal. The signals obtained from sensors in this way are continued to be compared with the threshold preset in advance. Once motion signals are detected passing throng the threshold, we determine the motion frame based on period between the first threshold passing time and the last one. The motion detection rate of our proposed method was about 90% while the correct frame extraction rate was about 85%. The method like our method, which use PLN signal in order to extract useful data about motion movement from non-contact mode EPS sensors, has been rarely reported or published in recent. This research results can be expected to be useful especially in circumstance of having surrounding PLN.

• Journal of the Korean Institute of Gas
• /
• v.26 no.4
• /
• pp.41-57
• /
• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.785-796
• /
• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.