• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.97-103
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• 2012

In this paper, we propose a novel voice activity detection (VAD) algorithm to effectively distinguish speech from nonspeech in various noisy environments. Global speech absence probability (GSAP) derived from likelihood ratio (LR) based on the statistical model is widely used as the feature parameter for VAD. However, the feature parameter based on conventional GSAP is not sufficient to distinguish speech from noise at low SNRs (signal-to-noise ratios). The presented VAD algorithm utilizes GSAP based on Teager energy (TE) as the feature parameter to provide the improved performance of decision for speech segments in noisy environment. Performances of the proposed VAD algorithm are evaluated by objective test under various environments and better results compared with the conventional methods are obtained.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.566-574
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• 2021

The arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. On the contray, Deep neural network (DNN) direcly utilizes raw data without feature extraction, based on end-to-end learning. However, a disadvantage of the DNN is processing complexity, posing the difficulty of being migrated into a termnial device. To solve this, this paper proposes an arc detection method using a logistic regression that is one of simple machine learning methods.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.83-89
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• 2021

In this paper, we propose an algorithm that provide services to consumers who want to conduct business by scientifically and systematically analyzing the number of pedestrians in a specific area over a specific period of time. In this paper, we proposed the algorithm to analyze the commercial area using the pedestrian-detect algorithm in the particular region using YOLO, one of the deep learning techniques. And with one image per minute in the images, the number of pedestrians is identified and this information is used for the analysis of business district on interesting area and time, systematically and objectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.97-99
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• 2022

Recently, the amount of data is increasing with the development of industries and technologies. Research on the processing and transmission of large amounts of data is attracting attention. Therefore, in this paper, compressed sensing was used to reduce the amount of data and its effect on Mask R-CNN algorithm was analyzed. We confirmed that as the compressed sensing rate increases, the amount of data in the image and the resolution decreases. However, it was confirmed that there was no significant degradation in the performance of object detection.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.277-278
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• 2022

최근 전동킥보드 보급이 이루어지면서 이와 관련된 교통사고가 증가하고 있다. 이에 따라 전동킥보드 주행 시 헬멧 착용을 의무화하는 도로교통법 개정안이 시행되고 있지만, 물리적으로 대부분 현장에서 단속이 어렵다. 본 논문에서는 딥러닝 영상인식 기술을 활용한 객체검출(object detection) 모델인 YOLOv4를 기반으로 전동킥보드 사용자의 헬멧 미착용 검출시스템을 제안하였다. 이를 통해 전동킥보드 주행 시 헬멧 착용 여부를 효율적으로 단속하는데 활용 할 수 있을 것으로 기대한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.95-105
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• 2003

A novel reference point detection method is proposed by exploiting tile gradient probabilistic model that captures the curvature information of fingerprint. The detection of reference point is accomplished through searching and locating the points of occurrence of the most evenly distributed gradient in a probabilistic sense. The uniformly distributed gradient texture represents either the core point itself or those of similar points that can be used to establish the rigid reference from which to map the features for recognition. Key benefits are reductions in preprocessing and consistency of locating the same points as the reference points even when processing arch type fingerprints. Moreover, the new feature extraction method is proposed by improving the existing feature extraction using filterbank method. Experimental results indicate the superiority of tile proposed scheme in terms of computational time in feature extraction and verification rate in various noisy environments. In particular, the proposed gradient probabilistic model achieved 49% improvement under ambient noise, 39.2% under brightness noise and 15.7% under a salt and pepper noise environment, respectively, in FAR for the arch type fingerprints. Moreover, a reduction of 0.07sec in reference point detection time of the GPM is shown possible compared to using the leading the poincare index method and a reduction of 0.06sec in code extraction time of the new filterbank mettled is shown possible compared to using the leading the existing filterbank method.

• The Journal of Korean Society of Virology
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• v.27 no.1
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• pp.19-27
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• 1997

To develop the polymerase chain reaction (PCR) for the detection of type D simian retrovirus (SRV) infection, an oligonucleotide primer pair was designed to hybridize to the sequences within env gene of SRV subtype 1 (SRV-1). The 3' proximal env sequences annealing to the primers had been rather conserved among three different subtypes of SRV, SRV-1, SRV-2, and SRV-3 (Mason-Pfizer Monkey Virus: MPMV). The PCR using the primer pair targeting an env region successfully detected and amplified all three subtypes of SRV with excellent specificity after single round of reaction. The tests with peripheral blood mononuclear cells infected either with simian immunodeficiency virus or simian T-Iymphotropic virus type 1, major immunosuppressive viral agents together with SRV in simian, verified the specificity of the PCR by excluding any cross reactivity. Semiquantitative titration PCR, amplifying serially diluted plasmid DNA of each subtype, was performed to evaluate sensitivity limits of the reaction. Based on molecular weight of each cloned SRV genome, the PCR should be able to detect one SRV-infected cell per more than $5-7{\times}10^4$ uninfected cells after simple ethidium bromide staining of resulting products. The PCR must be very efficient screening system with its quickness, certainty, and sensitivity for SRV-infected animals used in human AIDS research model. Second round amplification of the reaction products from the first PCR, or Southern hybridization by radiolabeled probes shall render to compete its efficacy to ELISA which has been the most sensitive technique to screen SRV infection but with frequent ambiguity problem.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.944-953
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• 2020

Object detection plays a crucial role in a self-driving system. With the advances of image recognition based on deep convolutional neural networks, researches on object detection have been actively explored. In this paper, we proposed a lightweight model of the mask R-CNN, which has been most widely used for object detection, to efficiently predict location and shape of various objects on the road environment. Furthermore, feature maps are adaptively re-calibrated to improve the detection performance by applying an attention module to the neural network layer that plays different roles within the mask R-CNN. Various experimental results for real driving scenes demonstrate that the proposed method is able to maintain the high detection performance with significantly reduced network parameters.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.737-745
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• 2023

This paper discusses a study on developing an artificial intelligence model to detect incidents of fires in distribution switchboard using thermal images. The objective of the research is to preprocess collected thermal images into suitable data for object detection models and design a model capable of determining the occurrence of fires within distribution panels. The study utilizes thermal image data from AI-HUB's industrial complex for training. Two CNN-based deep learning object detection algorithms, namely Faster R-CNN and RetinaNet, are employed to construct models. The paper compares and analyzes these two models, ultimately proposing the optimal model for the task.