• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• ETRI Journal
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• v.46 no.2
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• pp.205-217
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• 2024

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short-term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine-tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root-mean-square error of only 2.12%.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.139-144
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• 2024

In this study, four types of fall detection systems - designed with YOLOPose, principal component analysis (PCA), convolutional neural network (CNN), and long short-term memory (LSTM) architectures - were developed and compared in the detection of everyday falls. The experimental dataset encompassed seven types of activities: walking, lying, jumping, jumping in activities of daily living, falling backward, falling forward, and falling sideways. Keypoints extracted from YOLOPose were entered into the following architectures: RAW-LSTM, PCA-LSTM, RAW-PCA-LSTM, and PCA-CNN-LSTM. For the PCA architectures, the reduced input size stemming from a dimensionality reduction enhanced the operational efficiency in terms of computational time and memory at the cost of decreased accuracy. In contrast, the addition of a CNN resulted in higher complexity and lower accuracy. The RAW-LSTM architecture, which did not include either PCA or CNN, had the least number of parameters, which resulted in the best computational time and memory while also achieving the highest accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.863-871
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• 2018

Existing vulnerability analysis tools are prone to missed detections, incorrect detections, and over-detection, which reduces accuracy. In this paper, cross-checking based on a vulnerability detection method using static and dynamic analysis is proposed, which develops and manages safe applications and can resolve and analyze these problems. Risks due to vulnerabilities are computed, and an intelligent vulnerability detection technique is used to improve accuracy and evaluate risks under the final version of the application. This helps the development and execution of safe applications. Through incorporation of tools that use static analysis and dynamic analysis techniques, our proposed technique overcomes weak points at each stage, and improves the accuracy of vulnerability detection. Existing vulnerability risk-evaluation systems only evaluate self-risks, whereas our proposed vulnerability risk-evaluation system reflects the vulnerability of self-risk and the detection accuracy in a complex fashion to evaluate relative. Our proposed technique compares and analyzes existing analysis tools, such as lists for detections and detection accuracy based on the top 10 items of SANS at CWE. Quantitative evaluation systems for existing vulnerability risks and the proposed application's vulnerability risks are compared and analyzed. We developed a prototype analysis tool using our technique to test the application's vulnerability detection ability, and to show that our proposed technique is superior to existing ones.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.1
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• pp.123-129
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• 2014

This paper presents an altitude detection accuracy for long range and multifunction radar. The accuracy is difficult to estimate because it is affected by an time varying atmosphere refractivity. We analyze altitude accuracy with a raytracing simulator with atmosphere refractivity. An altitude error is simulated with measured and modeled refractivity, and the modeled refractivity is used for compensate an altitude accuracy. As a result, the error is modeled with normal distribution function, and analyzed.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.28-33
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• 2012

The visual sensing of human hands plays an important part in many man-machine interaction/interface systems. Most existing visionbased hand detection techniques depend on the color cues of human skin. The RGB color image from a vision sensor is often transformed to another color space as a preprocessing of hand detection because the color space transformation is assumed to increase the detection accuracy. However, the actual effect of color space transformation has not been well investigated in literature. This paper discusses a comparative evaluation of the pixel classification performance of hand skin detection in four widely used color spaces; RGB, YIQ, HSV, and normalized rgb. The experimental results indicate that using the normalized red-green color values is the most reliable under different backgrounds, lighting conditions, individuals, and hand postures. The nonlinear classification of pixel colors by the use of a multilayer neural network is also proposed to improve the detection accuracy.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.251-252
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• 2023

As technology improves with the 4th industrial revolution, smart construction is becoming a key part of safety management in the architecture and civil engineering. By using object detection technology with CCTV data, construction sites can be managed efficiently. In this study, super resolution technology based on deep learning is proposed to improve the accuracy of object detection in construction sites. As the resolution of a train set data and test set data get higher, the accuracy of object detection model gets better. Therefore, according to the scale of construction sites, different object detection models can be considered.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2752-2768
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• 2020

Phishing websites can have devastating effects on governmental, financial, and social services, as well as on individual privacy. Currently, many phishing detection solutions are evaluated using small datasets and, thus, are prone to sampling issues, such as representing legitimate websites by only high-ranking websites, which could make their evaluation less relevant in practice. Phishing detection solutions which depend only on the URL are attractive, as they can be used in limited systems, such as with firewalls. In this paper, we present a URL-only phishing detection solution based on a convolutional neural network (CNN) model. The proposed CNN takes the URL as the input, rather than using predetermined features such as URL length. For training and evaluation, we have collected over two million URLs in a massive URL phishing detection (MUPD) dataset. We split MUPD into training, validation and testing datasets. The proposed CNN achieves approximately 96% accuracy on the testing dataset; this accuracy is achieved with URL schemes (such as HTTP and HTTPS) removed from the URL. Our proposed solution achieved better accuracy compared to an existing state-of-the-art URL-only model on a published dataset. Finally, the results of our experiment suggest keeping the CNN up-to-date for better results in practice.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.399-408
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• 2012

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.