• Smart Media Journal
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• v.13 no.2
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• pp.85-94
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• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

• Journal of Preventive Medicine and Public Health
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• v.33 no.2
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• pp.226-230
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• 2000

Objectives : The purpose of this study was to suggest a proper method for the detection of heaptitis B surface antibody(anti-HBs) in a screening program for hepatitis B vaccination. Methods : Sensivitity, specificity and predictive values were compared between Immunochromatographic assay (ICA) and passive hemagglutination(PHA) in 978 subjects(565 males, 413 females, 19-78 years ranging in age, mean 46.5 years old). EIA was used as a standard method for the detection of HBsAb. Results : Sensitivity in the detection of anti-HBs of PHA and ICA was 88.7%, and 94.9%, specificity was 94.3% and 96.6%, negative predictive value was 96.5%, and 98.0%, and positive predictive value was 82.3%, and 91.3%,, respectively. False negative rate(11.3%) of PHA was higher than that(5.1%) of ICA. The higher the titer of anti-HBs in EIA was, the lower the false negative rate was. There was no false negative result in the cases with $101mIU/{\beta}c$ or more in EIA Conclusion : We suggest that ICA should be the choice of screening method in the detection of anti-HBs in Hepatitis B vaccination program.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.958-966
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• 2003

Change detection algorithms based on frame difference are frequently used for finding moving objects in image sequences. These algorithms detect the change of frames using estimated statistical background model. But, if this estimated background model is different from the actual statistical distribution, false detections are generated. In this paper, we propose an improved change detection algorithm using euler number and structure tensor. The proposed mapping method which is based on the euler number can be used for reducing the false detections that generated by nonparametric change detection algorithm. In this paper, the change in the region of moving object also can be detected by the proposed method using structure tensor. Experimental result shows that the proposed method reduces the false detections effectively by 90% on "Weather", by 34% on "Mother & daughter" and by 43% on "Aisle" than an existing method does.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.192-198
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• 2018

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7381-7384
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• 2015

This study was conducted to 1206 women who had cervical cancer screening at Chonburi Cancer Hospital. The spilt-sample study aimed to compare the efficacy of abnormal cervical cells detection between liquid-based cytology (LBC) and conventional cytology (CC). The collection of cervical cells was performed by broom and directly smeared on a glass slide for CC then the rest of specimen was prepared for LBC. All slides were evaluated and classified by The Bethesda System. The results of the two cytological tests were compared to the gold standard. The LBC smear significantly decreased inflammatory cell and thick smear on slides. These two techniques were not difference in detection rate of abnormal cytology and had high cytological diagnostic agreement of 95.7%. The histologic diagnosis of cervical tissue was used as the gold standard in 103 cases. Sensitivity, specificity, positive predictive value, negative predictive value, false positive, false negative and accuracy of LBC at ASC-US cut off were 81.4, 75.0, 70.0, 84.9, 25.0, 18.6 and 77.7%, respectively. CC had higher false positive and false negative than LBC. LBC had shown higher sensitivity, specificity, PPV, NPV and accuracy than CC but no statistical significance. In conclusion, LBC method can improve specimen quality, more sensitive, specific and accurate at ASC-US cut off and as effective as CC in detecting cervical epithelial cell abnormalities.

• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.823-834
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• 2013

In this paper, we propose a detection method for Digital Radio Mondiale (DRM) Plus suitable for hybrid mode broadcasting which services both DRM Plus and analog FM within the same frequency band. The guard-interval correlation method of Orthogonal Frequency Division Multiplexing (OFDM) is good for DRM Plus signal detection, but the possibility for false alarm increases when FM signal is received. The proposed method includes a reference block in the guard-interval correlation which increases the identification rate of weak DRM Plus signals and decreases the possibility of false alarm when analog FM is received. The performance of the proposed method is verified through simulations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.2
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• pp.83-92
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• 2018

Using ground moving target indicators equipped with synthetic aperture radars for locating moving targets within a wide background clutter in a short time is an excellent method for monitoring traffic. Although the displaced phase center antenna (DPCA) technique and along track interferometry (ATI) are real time methods with low computational complexity, they are essential for reducing cases of false alarm that can result in poor performance. In this paper, we propose two detection methods using DPCA and ATI-the parallel fusion method and serial fusion method. Simulation results demonstrate that the proposed detection methods are characterized by low probability of false alarm along with good performance. In particular, the serial fusion method possesses high detection probability along with low probability of false alarm (1/5th of the false alarm probability of the DPCA technique).

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.