• Asian Pacific Journal of Cancer Prevention
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• v.15 no.11
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• pp.4545-4548
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• 2014

Objective: To investigate the changes of serum vascular endothelial growth factor (VEGF), soluble interleukin-2 receptor (SIL-2R) and hepatocyte growth factor (HGF) contents in patients with primary hepatocellular carcinoma (HCC) before and after percutaneous microwave coagulation therapy (PMCT) and determine their clinical significance. Materials and Methods: Fasting venous blood (3 mL) from 81 patients with primary HCC diagnosed by pathology was collected in the mornings 1 day before PMCT, and 1 day, 7 days and 1 month after PMCT, and then the serum was separated and stored in $-70^{\circ}C$. The contents of VEGF, SIL-2R and HGF were detected by enzyme linked immunosorbent assay (ELISA). Results: The serum VEGF, SIL-2R and HGF contents in 81 patients with primary HCC had obviously dynamic changes before and after PMCT. By comparison to 1 day after PMCT with pre-operation, there was no statistical significance regarding VEGF and SIL-2R contents (P>0.05), but HGF content showed significant difference (P<0.01). Compared with pre-operation, VEGF, SIL-2R and HGF contents 7 days and 1 month after PMCT all manifested significant differences (P<0.01). By comparison to 7 days with 1 month after PMCT, there was no statistical significance regarding the VEGF content (P>0.05), whereas SIL-2R and HGF contents showed significant change (P<0.01). Conclusions: The contents of serum VEGF, SIL-2R and HGF have obviously dynamic changes in primary HCC before and after PMCT, and their joint detection is expected to be an effective hematologic evaluation index of PMCT for primary HCC.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.719-722
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• 2015

Objective: To evaluate the values of 4 tumor markers in serum and ascites and their ascites/serum ratios in the identification and diagnosis of benign and malignant ascites. Materials and Methods: A total of 76 patients were selected as subjects and divided into malignant ascites group (45 cases) and benign ascites group (31 cases). Samples of ascites and serum of all hospitalized patients were collected before treatment. The levels of carcinoembryonic antigen (CEA), alpha fetoprotein (AFP), cancer antigen 125 (CA125) and carbohydrate antigen 19-9 (CA19-9) were detected by chemiluminescence (CLIA). Results: CEA, AFP and CA19-9 in both serum and ascites as well as CA125 in ascites were evidently higher in the malignant ascites group than in the benign ascites group (P<0.01). Malignant ascites was associated with elevated ascites/serum ratios for AFP and CA125 (P<0.01). The areas under receiver operating characteristic (AUROCs) of CEA and CA125 in ascites and the ratios of ascites/serum of AFP, CEA, CA125 and CA19-9 were all >0.7, suggesting certain values, while those of ascites CA19-9 and serum CEA were 0.697 and 0.629 respectively, indicating low accuracy in the identification and diagnosis of benign and malignant ascites. However, the AUROCs of the remaining indexes were <0.5, with no value for identification and diagnosis. Compared with single index, the sensitivity of combined detection increased significantly (P<0.05), in which the combined detection of CEA, CA19-9 and CA125 in ascites as well as the ratio of ascites/serum of CEA, CA19-9, CA125 and AFP had the highest sensitivity (98.4%) but with relevantly low specificity. Both sensitivity and specificity of combined detection should be comprehensively considered so as to choose the most appropriate index. Conclusions: Compared with single index, combined detection of tumor markers in serum and ascites can significantly improve the diagnostic sensitivity and specificity.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.569-572
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• 2008

The possibility to extent the solution in human detection problem for plug-in on vision-based Human Computer Interaction domain is very attractive, since the successful of the machine leaning theory and computer vision marriage. Bayesian logistic regression is a powerful classifier performing sparseness and high accuracy. The difficulties of finding people in an image will be conquered by implementing this Bavesian model as classifier. The comparison with other massive classifier e.g. SVM and RVM will introduce acceptance of this method for human detection problem. Our experimental results show the good performance of Bavesian logistic regression in human detection problem, both in trade-off curves (ROC, DET) and real-implementation compare to SVM and RVM.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.75-81
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• 2020

Approximately 1.3 million people die from traffic accidents each year, and smartphone usage while driving is one of the main causes of such accidents. Therefore, detection of smartphone usage by drivers has become an important part of distracted driving detection. Previous studies have used single camera-based methods to collect the driver images. However, smartphone usage detection by employing a single camera can be unsuccessful if the driver occludes the phone. In this paper, we present a driver smartphone usage detection system that uses multiple cameras to collect driver images from different perspectives, and then processes these images with ensemble convolutional neural networks. The ensemble method comprises three individual convolutional neural networks with a simple voting system. Each network provides a distinct image perspective and the voting mechanism selects the final classification. Experimental results verified that the proposed method avoided the limitations observed in single camera-based methods, and achieved 98.96% accuracy on our dataset.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.1055-1061
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• 2011

In this study, main parameters: aperture, first bend and second bend which express a structure of ear canal are extracted in order to modeling and manufacture the ready-made ear shells of hearing aids. The proposed parameter extraction method consists of 2 important algorithms, aperture detection and feature detection. In the aperture detection algorithm, aperture of 3-D scanned virtual ear impression and parameters relating to ear shell of hearing aid are determined. The feature detection algorithm detects first bend, second bend, and related parameters. Through these two algorithms, parameters for aperture, first bend, and second bend are extracted to model the ready-made ear shell of hearing aid. The values of these extracted parameters from 36 people's right ear impression are analyzed and measured statistically. As a result of the analysis, it has been found that it is possible to classify ready-made ear shell parameters by age and size. The ready-made ear shell parameters are classified 3-size for 20 years old and 2-size for 60 years olde. Using 3D rhino program, virtual ready-made ear shell is reconstructed by parameters of every type, and simulated to model it. A final product was produced by transferring simulation result with rapid prototyping system. The modeled ready-made ear shell is evaluated with the objective and subjective method. Objective method is the comparison volume ratio and overlapped volume ratio of ear impression from randomly chosen 18 people and ready-made ear shell. And subjective method is that the final product of ready-made ear shell is used by users and the satisfaction number drawn from well fitting and comfortable testing was evaluated. In the result of the evaluation, it has been found that volume ration is 70%, big and middle size ready-made ear shell products are possible, and the satisfaction number is high.

• Smart Media Journal
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• v.12 no.3
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• pp.9-18
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• 2023

Due to the deepening of COVID-19, high-intensity social distancing has been prolonged and many social problems have been cured. In particular, physical and psychological isolation occurred due to the non-face-to-face system and a lot of damage occurred. The various social problems caused by Corona acted as severe stress for all those affected by Corona 19, and eventually acted as a factor threatening mental health such as depression. While the number of people suffering from mental illness is increasing, the actual use of mental health services is low. Therefore, it is necessary to establish a system for people suffering from mental health problems. Therefore, in this study, depression detection and emergency detection models were constructed based on sensor information using smartphones from depressed subjects and general subjects. For the detection of depression and emergencies, VAE, DAGMM, ECOD, COPOD, and LGBM algorithms were used. As a result of the study, the depression detection model had an F1 score of 0.93 and the emergency situation detection model had an F1 score of 0.99. direction.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.65-72
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• 2011

In this paper, we consist of three dimensional acceleration sensor as a small-sized sensor module to acquire base technologies that need to estimate exhibition audience' moving distance. and that we developed algorism and device that can calculate acceleration in gravity direction with attaching it to people's body part without regard to three dimensional direction. By making use of the sensor module, we have to process the data that let it quantitatively process possible to measure people's walk and movement by computer system. We normalized sensor output data in the process of change from sensor module to acquisition of data, rectangular coordinates and single scalar acceleration value in gravity direction. Printed out sensor data attaching sensor module to people's body part is used for motion pattern detection after normalization, Motion sensor devised mode change algorism because it print data of other pattern according to attached position of body. For algorism design, we collected data occurring during walking about subject and we also defined occurring problem domain after analyzing the data. We settle defined problem domain and that we simulated the walking number measuring instrument with highly efficient in restricted environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2414-2433
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• 2021

This article focuses on providing remedial solutions for COVID disease through the data collection process. Recently, In India, sudden human losses are happening due to the spread of infectious viruses. All people are not able to differentiate the number of affected people and their locations. Therefore, the proposed method integrates robotic technology for monitoring the health condition of different people. If any individual is affected by infectious disease, then data will be collected and within a short span of time, it will be reported to the control center. Once, the information is collected, then all individuals can access the same using an application platform. The application platform will be developed based on certain parametric values, where the location of each individual will be retained. For precise application development, the parametric values related to the identification process such as sub-interval points and intensity of detection should be established. Therefore, to check the effectiveness of the proposed robotic technology, an online monitoring system is employed where the output is realized using MATLAB. From simulated values, it is observed that the proposed method outperforms the existing method in terms of data quality with an observed percentage of 82.

• Journal of Microbiology and Biotechnology
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• v.34 no.6
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• pp.1322-1327
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• 2024

The accurate and rapid detection of methicillin-resistant Staphylococcus aureus (MRSA) holds significant clinical importance. This work presents a new method for detecting methicillin-resistant Staphylococcus aureus (S. aureus) in clinical samples. The method uses an aptamer-based colorimetric assay that combines a recognizing probe to identify the target and split DNAzyme to amplify the signal, resulting in a highly sensitive and direct analysis of methicillin-resistance. The identification of the PBP2a protein on the membrane of S. aureus in clinical samples leads to the allosterism of the recognizing probe, and thus provides a template for the proximity ligation of split DNAzyme. The proximity ligation of split DNAzyme forms an intact DNAzyme to identify the loop section in the L probe and generates a nicking site to release the loop sequence ("3" and "4" fragments). The "3" and "4" fragments forms an intact sequence to induce the catalytic hairpin assembly, exposing the G-rich section. The released the G-rich sequence of LR probe induces the formation of G-quadruplex-hemin DNAzyme as a colorimetric signal readout. The absorption intensity demonstrated a strong linear association with the logarithm of the S. aureus concentration across a wide range of 5 orders of magnitude dynamic range under the optimized experimental parameters. The limit of detection was calculated to be 23 CFU/ml and the method showed high selectivity for MRSA.