• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2080-2085
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• 2018

There have been numerous studies that extract the R-peak from electrocardiogram (ECG) signals. All of these studies can extract R-peak from ECG. However, these methods are complicated and difficult to implement in a real-time portable ECG device. After filtration choosing a threshold value for R-peak detection is a big challenge. Fixed threshold scheme is sometimes unable to detect low R-peak value and adaptive threshold sometime detect wrong R-peak for more adaptation. In this paper, a simple and robustness algorithm is proposed to detect R-peak with less complexity. This method also solves the problem of threshold value selection. Using the adaptive filter, the baseline drift can be removed from ECG signal. After filtration, an appropriate threshold value is automatically chosen by using the minimum and maximum value of an ECG signals. Then the neighborhood searching scheme is applied under threshold value to detect R-peak from ECG signals. Proposed method improves the detection and accuracy rate of R-peak detection. After R-peak detection, we calculate heart rate to know the heart condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1056-1060
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• 2012

Online monitoring system is becoming an essential element of railway traction system for utilized to condition based malignance management and various techniques currently employed in railway traction system. Among the various techniques, it is efficient to detect partial discharge signals by electromagnetic wave detection in order to detect insulation fault of rectifier. Although VHF (Very High Frequency), UHF (Ultra High Frequency) sensors were adopted to detect partial discharge of power facilities, due to characteristics of urban railway, excessive noise occurs from 500 MHz to 1.5 GHz on UHF bandwidth. In this paper a new measurement system able to monitoring the conditions of power facilities on DC substation in metro was studied and set up. The system uses UHF sensors to measure the partial discharge of the rectifier due to electric faulting and dielectric breakdown. Comparison and estimation for performance of SHF sensor which had devised to detect partial discharge signal of urban railway rectifier has conducted. In order to estimate performance of SHF sensor, we have compared the sensor with existing UHF sensor on sensitivity upon frequency bandwidth generated by pulse generator, and also we have verified performance of the SHF sensor by detection results of partial discharge signal from urban railway rectifier.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.851-865
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• 2021

Enterprise networks in the PyeongChang Winter Olympics were hacked in February 2018. According to a domestic security company's analysis report, attackers destroyed approximately 300 hosts with the aim of interfering with the Olympics. Enterprise have no choice but to rely on digital vaccines since it is overwhelming to analyze all programs executed in the host used by ordinary users. However, traditional vaccines cannot protect the host against variant or new malware because they cannot detect intrusions without signatures for malwares. To overcome this limitation of signature-based detection, there has been much research conducted on the behavior analysis of malwares. However, since most of them rely on a sandbox where only analysis target program is running, we cannot detect malwares intruding the host where many normal programs are running. Therefore, this study proposes a method to detect malware variants in the host through logs rather than the sandbox. The proposed method extracts common behaviors from variants group and finds characteristic behaviors optimized for querying. Through experimentation on 1,584,363 logs, generated by executing 6,430 malware samples, we prove that there exist the common behaviors that variants share and we demonstrate that these behaviors can be used to detect variants.

• Hip & pelvis
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• v.30 no.4
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• pp.226-232
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• 2018

Purpose: Pain caused by osteoarthritis is primarily nociceptive pain; however, it is considered that a component of this pain is due to neuropathic pain (NP). We investigated the effects of total hip arthroplasty (THA) in patients with NP diagnosed by the PainDETECT questionnaire. Materials and Methods: One hundred sixty-three hips (161 patients) were evaluated. All patients were asked to complete the PainDETECT questionnaire based on their experience with NP, and clinical scores were evaluated using the Japanese Orthopaedic Association (JOA) Hip Score before and after THA. Results: The patients of 24.5% reported NP before THA; 5.5% reported NP 2 months after THA. Prior to THA, there was no significant correlation between the PainDETECT score and the radiographic severity; however, there was a significant correlation between the PainDETECT score and JOA score. NP at 2 months after THA was not significantly correlated with pain scores at 1 week after THA; however, a significant correlation was observed between the preoperative pain score and NP at 2 months after THA. Conclusion: THA was useful for relieving nociceptive pain and for relieving NP in patients with hip osteoarthritis. Preoperative pain was a risk factor for NP after THA. Controlling preoperative pain may be effective for reducing postoperative NP.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• Annals of Clinical Neurophysiology
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• v.23 no.1
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• pp.46-52
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• 2021

Background: We aimed to investigate candidates for serological biomarkers of neuropathic pain in individuals with neuromyelitis optica spectrum disorder (NMOSD). Methods: We analyzed 38 sera samples from 38 participants with NMOSD in National Cancer Center. Neuropathic pain was evaluated using the painDETECT questionnaire. Pain with neuropathic components (painDETECT score ≥ 13) was observed in 22 participants, among whom 17 had definite neuropathic pain (painDETECT score ≥ 19). The remaining 16 participants had non-neuropathic pain (painDETECT score < 13). Serum glial fibrillary acidic protein (GFAP) levels were assessed using a single-molecule array assay. Several cytokines, including tumor necrosis factor-alpha (TNF-α), interleukin (IL)-6, IL-10, and IL-17A, were measured by a multiplex bead-based immunoassay. Results: In comparison of NMOSD participants with neuropathic pain components (or definite neuropathic pain) and those with non-neuropathic pain, the absolute values of serum GFAP, TNF-α, IL-6, and IL-10 levels were higher in participants with neuropathic pain components (or definite neuropathic pain), but these findings did not reach statistical significance. Conclusions: Further larger-scale investigations to find reliable serological biomarkers for neuropathic pain in NMOSD are warranted.

• Smart Structures and Systems
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• v.29 no.6
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• pp.785-790
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• 2022

Condition monitoring of permanent magnet synchronous motors (PMSMs) and detecting faults such as eccentricity and demagnetization are essential for ensuring system reliability. Motor current signal analysis is the most commonly used precursor for detecting faults in the PMSM drive system. However, the current signature responds sensitively to the load and temperature of the motor, thereby making it difficult to monitor faults in real- applications. Therefore, in this study, a condition monitoring methodology that detects motor faults, including their classification with standstill conditions, is proposed. The objective is to detect and classify faults of PMSMs by using programmable inverter without additional sensors and systems for detection. Both DC and AC were applied through the d-axis of a three-phase motor, and the change in incremental inductance was investigated to detect and classify faults. Simulation with finite element analysis and experiments were performed on PMSMs in healthy conditions as well as with eccentricity and demagnetization faults. Based on the results obtained from experiments, the proposed method was confirmed to detect and classify types of faults, including their severity.

• Journal of the Korean Society of Safety
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• v.37 no.6
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• pp.25-31
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• 2022

In this study, the spill and leak system is developed to provide real-time remote monitoring of industrial complexes where chemical accidents have been occurring every year. The spill and leak monitoring system uses IR-RCD equipment mounted on a 70m-high steel tower to detect chemical substances, thereby detecting chemical accidents such as leaks, fires, and explosions in real time. If IR-RCD equipment can actually detect chemical substances at a long distance, accurate and rapid initial response can be expected. Therefore, in order to confirm that IR-RCD equipment can detect chemical leakage accidents occurring at a long distance, acetone was selected as the experimental substance and a detection experiment was designed. The experiment was conducted using the acetone pool evaporation method at the wharf which was located 1.5 km away from IR-RCD equipment, and it was confirmed whether IR-RCD equipment could detect acetone in real time through the control monitor.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.603-622
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• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• The Plant Pathology Journal
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• v.40 no.4
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• pp.337-345
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• 2024

Soybean (Glycine max L.) is one of the most widely planted and used legumes in the world, being used for food, animal feed products, and industrial production. The soybean mosaic virus (SMV) is the most prevalent virus infecting soybean plants. This study developed a diagnostic method for the rapid and sensitive detection of SMV using a reverse transcription-recombinase polymerase amplification (RT-RPA) technique combined with a lateral flow strip (LFS). The RT-RPA and RT-RPA-LFS conditions to detect the SMV were optimized using the selected primer set that amplified part of the VPg protein gene. The optimized reaction temperature for the RT-RPA primer and RT-RPA-LFS primer used in this study was 38℃ for both, and the minimum reaction time was 10 min and 5 min, respectively. The RT-RPA-LFS was as sensitive as RT-PCR to detect SMV with 10 pg/µl of total RNA. The reliability of the developed RT-RPA-LFS assay was evaluated using leaves collected from soybean fields. The RT-RPA-LFS diagnostic method developed in this study will be useful as a diagnostic method that can quickly and precisely detect SMV in the epidemiological investigation of SMV, in the selection process of SMV-resistant varieties, on local farms with limited resources.