• Journal of Power Electronics
• /
• v.17 no.1
• /
• pp.262-271
• /
• 2017

This paper studies the performances of five typical Phase-locked Loops (PLLs) for distorted and unbalanced grid, which are the Decoupled Double Synchronous Reference Frame PLL (DDSRF-PLL), Double Second-Order Generalized Integrator PLL (DSOGI-PLL), Double Second-Order Generalized Integrator Frequency-Lock Loop (DSOGI-FLL), Double Inverse Park Transformation PLL (DIPT-PLL) and Complex Coefficient Filter based PLL (CCF-PLL). Firstly, the principles of each method are meticulously analyzed and their unified small-signal models are proposed to reveal their interior relations and design control parameters. Then the performances are compared by simulations and experiments to investigate their dynamic and steady-state performances under the conditions of a grid voltage with a negative sequence component, a voltage drop and a frequency step. Finally, the merits and drawbacks of each PLL are given. The compared results provide a guide for the application of current control, low voltage ride through (LVRT), and unintentional islanding detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.175-179
• /
• 2007

To existing method which uses energy variation and spectrum deviation to detect the underwater transient signal is useful to detect white noise environment, but it is not useful to do colored noise environment. To improve capacity of detecting the underwater transient signal both in white noise environment and colored noise environment, this study takes advantage of Double Density Discrete Wavelet Transform and CFAR Power-Law.

• Proceedings of the Optical Society of Korea Conference
• /
• 2009.10a
• /
• pp.347-348
• /
• 2009

• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 2004.11a
• /
• pp.125-125
• /
• 2004

• ETRI Journal
• /
• v.42 no.1
• /
• pp.46-53
• /
• 2020

Gait analysis is an effective clinical tool across a wide range of applications. Recently, inertial measurement units have been extensively utilized for gait analysis. Effective gait analyses require good estimates of heel-strike and toe-off events. Previous studies have focused on the effective device position and type of triaxis direction to detect gait events. This study proposes an effective heel-strike and toe-off detection algorithm using a smart insole with inertial measurement units. This method detects heel-strike and toe-off events through a time-frequency analysis by limiting the range. To assess its performance, gait data for seven healthy male subjects during walking and running were acquired. The proposed heel-strike and toe-off detection algorithm yielded the largest error of 0.03 seconds for running toe-off events, and an average of 0-0.01 seconds for other gait tests. Novel gait analyses could be conducted without suffering from space limitations because gait parameters such as the cadence, stance phase time, swing phase time, single-support time, and double-support time can all be estimated using the proposed heel-strike and toe-off detection algorithm.

• International Journal of Computer Science & Network Security
• /
• v.21 no.9
• /
• pp.125-131
• /
• 2021

Detecting fraudulent insurance claims is difficult due to small and unbalanced data. Some research has been carried out to better cope with various types of fraudulent claims. Nowadays, technology for detecting fraudulent insurance claims has been increasingly utilized in insurance and technology fields, thanks to the use of artificial intelligence (AI) methods in addition to traditional statistical detection and rule-based methods. This study obtained meaningful results for a fraudulent insurance claim detection model based on machine learning (ML) and deep learning (DL) technologies, using fraudulent insurance claim data from previous research. In our search for a method to enhance the detection of fraudulent insurance claims, we investigated the reinforcement learning (RL) method. We examined how we could apply the RL method to the detection of fraudulent insurance claims. There are limited previous cases of applying the RL method. Thus, we first had to define the RL essential elements based on previous research on detecting anomalies. We applied the deep Q-network (DQN) and double deep Q-network (DDQN) in the learning fraudulent insurance claim detection model. By doing so, we confirmed that our model demonstrated better performance than previous machine learning models.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.2
• /
• pp.771-789
• /
• 2019

Text detection has been a popular research topic in the field of computer vision. It is difficult for prevalent text detection algorithms to avoid the dependence on datasets. To overcome this problem, we proposed a novel unsupervised text detection algorithm inspired by bootstrap learning. Firstly, the text candidate in a novel form of superpixel is proposed to improve the text recall rate by image segmentation. Secondly, we propose a unique text sample selection model (TSSM) to extract text samples from the current image and eliminate database dependency. Specifically, to improve the precision of samples, we combine maximally stable extremal regions (MSERs) and the saliency map to generate sample reference maps with a double threshold scheme. Finally, a multiple kernel boosting method is developed to generate a strong text classifier by combining multiple single kernel SVMs based on the samples selected from TSSM. Experimental results on standard datasets demonstrate that our text detection method is robust to complex backgrounds and multilingual text and shows stable performance on different standard datasets.

• Korean Journal of Clinical Oncology
• /
• v.14 no.2
• /
• pp.83-88
• /
• 2018

Purpose: Double primary colorectal cancer (CRC) and gastric cancer (GC) represent the most common multiple primary malignant tumors (MPMT) in Korea. The recognition and screening of hidden malignancies other than the primary cancer are critical. This study aimed to investigate the clinicopathologic characteristics and survival rates in patients with synchronous or metachronous double primary CRC and GC. Methods: Between January 1994 and May 2018, 11,050 patients were diagnosed with CRC (n=5,454) or GC (n=5,596) at Gil Medical Center. MPMT and metastatic malignant tumors were excluded from this study. A total of 103 patients with double primary CRC and GC were divided into two groups: the synchronous group (n=40) and the metachronous group (n=63). The incidence, clinicopathologic characteristics, and survival rate of the two groups were analyzed. Results: The incidence of synchronous and metachronous double primary CRC and GC was 0.93%. Double primary CRC and GC commonly occurred in male patients aged over 60 years with low comorbidities and minimal previous cancer history. There were significant differences between the synchronous and metachronous groups in terms of age, morbidity, and overall survival. Metachronous group patients were 6 years younger on average (P=0.009), had low comorbidities (P=0.008), and showed a higher 5-year overall survival rate (94.8% and 61.3%, P<0.001) in contrast to synchronous group. Conclusion: When primary cancer (CRC or GC) is detected, it is important to be aware of the possibility of the second primary cancer (GC or CRC) development at that time or during follow-up to achieve early detection and better prognosis.

• Membrane Journal
• /
• v.12 no.4
• /
• pp.240-246
• /
• 2002

New polysulfone scintillation proximity membranes were prepared by impregnating Cerium Activated Yttrium Silicate (CAYS), an inorganic fluor, in a membrane structure. The membranes were applied to detect the radionuclide contamination directly without the aid of a scintillation cocktail. The preparation of membranes was divided into two processes. A supporting polymer film was made of casting solutions consisting of polysulfone and solvent, their cast film being solidified by vacuum evaporation. CAYS-dispersed polymer solutions were cast over the first, solidified polymer films and coagulated either by evaporating solvent or by exchanging solvent in the solution with nonsolvent in a coagulation bath. The prepared membranes had two distinguished, but tight1y attached, double layers: one is the supporting layer of dense polymer film and the other the detecting layer consisting of CAYS and polymer. The radionuclide counting results revealed that the prepared membranes were efficient to monitor radioactivity contamination with reliable counting ability.

• Journal of Microbiology and Biotechnology
• /
• v.31 no.12
• /
• pp.1709-1715
• /
• 2021

Outbreaks of food poisoning due to the consumption of norovirus-contaminated shellfish continue to occur. Male-specific (F+) coliphage has been suggested as an indicator of viral species due to the association with animal and human wastes. Here, we compared two methods, the double agar overlay and the quantitative real-time PCR (RT-PCR)-based method, for evaluating the applicability of F+ coliphage-based detection technique in microbial contamination tracking of shellfish samples. The RT-PCR-based method showed 1.6-39 times higher coliphage PFU values from spiked shellfish samples, in relation to the double agar overlay method. These differences indicated that the RT-PCR-based technique can detect both intact viruses and non-particle-protected viral DNA/RNA, suggesting that the RT-PCR based method could be a more efficient tool for tracking microbial contamination in shellfish. However, the virome information on F+ coliphage-contaminated oyster samples revealed that the high specificity of the RT-PCR- based method has a limitation in microbial contamination tracking due to the genomic diversity of F+ coliphages. Further research on the development of appropriate primer sets for microbial contamination tracking is therefore necessary. This study provides preliminary insight that should be examined in the search for suitable microbial contamination tracking methods to control the sanitation of shellfish and related seawater.