• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.291-292
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• 2024

국내의 산재 사고 사망 비율 중 대부분은 건설업이 차지하고 있으며 사망 원인 중 42.9%는 추락사가 차지하고 있다. 따라서 국내 사고 사망을 예방하기 위해서는 노동자의 생명을 지켜주는 안전 장비의 착용 여부가 중요하다. 본 논문에서는 객체 탐지에 사용되는 YOLO v4와 YOLO v4-TINY 알고리즘과 영상 처리에 사용되는 OpenCV를 이용하여 실시간 영상에서 안전모 미착용 인원을 감지하고 관리자에게 알려주는 시스템을 개발하였다. 이 시스템을 활용하여 건설 현장에서 현장 카메라로 안전모 미착용 인원을 실시간으로 검출하여 경고하므로써 작업자의 안전에 기여할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.257-258
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• 2024

현재 시각장애인들이 현금을 사용하게 될 시 지폐가 얼마인지 확인할 방법이 없어 불편을 겪거나 금전적 사기를 당할 위험이 잦다. 한국은행에서는 이러한 사고를 막기 위해 점자 지폐를 만들어 발부하고 있지만 시각장애인 91%가 식별하지 못해 많은 불편을 겪고 있다. 본 논문에서는 딥러닝을 활용하여 화폐를 인식하고 TTS 기술을 사용하여 지폐의 값이 얼마인지 소리로 알려주는 시스템을 개발하였다. 지폐 인식을 위해 데이터를 직접 수집하여 YOLOv5 알고리즘을 활용하여 학습시킨 Weights 파일을 사용하였다. 이를 활용하여 시각장애인들은 더 안전하게 현금을 사용하고, 금전적인 문제를 예방할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.297-298
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• 2024

펜데믹, 전쟁 등을 포함한 국제 정세 변화에 따른 물류대란, 원자재가격 상승 및 환율 급등으로 인해 2023년 기준 대한민국의 물가는 크게 오르고 있는 추세이다. 물가 상승은 사업장의 인건비 부담 증가로 이어지고 있고 특히 노동 집약 산업인 농업 분야에서의 인건비 부담 문제는 더욱 심각한 실정이다. 외국인 근로자 고용이 대안이 될 수 있지만 인건비 절감 효과는 미미하기에 농업계 관계자들은 자동화 시스템 도입에 관심이 집중되고 있다. 따라서, 본 논문에서는 사과 분류 작업 자동화 체계의 핵심 요소에 해당하는 사과 품질 실시간 모니터링 시스템을 제안한다. 제안한 방식에서는 딥러닝 기반의 영상 분석 기법 및 무게 센서 데이터 분석을 통해 사과의 품질에 따른 등급 책정을 자동화 한다.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.280-286
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• 2024

In this paper, layered UNet with warmup and dropout tricks was used to segment teeth instantly by using data labeled for each individual tooth and increase performance of the result. The layered UNet proposed before showed very good performance in tooth segmentation without distinguishing tooth number. To do instance segmentation of teeth, we labeled teeth CBCT data according to tooth numbering system which is devised by FDI World Dental Federation notation. Colors for labeled teeth are like AI-Hub teeth dataset. Simulation results show that layered UNet does also segment very well for each tooth distinguishing tooth number by color. Layered UNet model using warmup trick was the best with IoU values of 0.80 and 0.77 for training, validation data. To increase the performance of instance segmentation of teeth, we need more labeled data later. The results of this paper can be used to develop medical software that requires tooth recognition, such as orthodontic treatment, wisdom tooth extraction, and implant surgery.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.358-366
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• 2023

This study aimed to provide a solution for improving ship collision alert of the 'accident vulnerable ship monitoring service' among the 'intelligent marine traffic information system' services of the Ministry of Oceans and Fisheries. The current ship collision alert uses a supervised learning (SL) model with survey labels based on large ship-oriented data and its operators. Consequently, the small ship data and the operator's opinion are not reflected in the current collision-supervised learning model, and the effect is insufficient because the alarm is provided from a longer distance than the small ship operator feels. In addition, the supervised learning (SL) method requires a large number of labeled data, and the labeling process requires a lot of resources and time. To overcome these limitations, in this paper, the classification model of collision alerts for small ships using unlabeled data with the semi-supervised learning (SSL) algorithms (Label Propagation and TabNet) was studied. Results of real-time experiments on small ship operators using the classification model of collision alerts showed that the satisfaction of operators increased.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.91-96
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• 2016

Network security is an interesting area in Information Technology. It has an important role for the manager monitor and control operating of the network. There are many techniques to help us prevent anomaly or malicious activities such as firewall configuration etc. Intrusion Detection System (IDS) is one of effective method help us reduce the cost to build. The more attacks occur, the more necessary intrusion detection needs. IDS is a software or hardware systems, even though is a combination of them. Its major role is detecting malicious activity. In recently, there are many researchers proposed techniques or algorithms to build a tool in this field. In this paper, we improve the performance of IDS. We explore and analyze the impact of activation functions applying to recurrent neural network model. We use to KDD cup dataset for our experiment. By our experimental results, we verify that our new tool of IDS is really significant in this field.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.101-108
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• 2017

The study proposed a system that filters the data that is entered when analyzing big data such as SNS and BLOG. Personal information includes impersonal personal information, but there is also personal information that distinguishes it from personal information, such as religious institution, personal feelings, thoughts, or beliefs. Define these personally identifiable information as sensitive information. In order to prevent this, Article 23 of the Privacy Act has clauses on the collection and utilization of the information. The proposed system structure is divided into two stages, including Big Data Processing Processes and Sensitive Information Filtering Processes, and Big Data processing is analyzed and applied in Big Data collection in four stages. Big Data Processing Processes include data collection and storage, vocabulary analysis and parsing and semantics. Sensitive Information Filtering Processes includes sensitive information questionnaires, establishing sensitive information DB, qualifying information, filtering sensitive information, and reliability analysis. As a result, the number of Big Data performed in the experiment was carried out at 84.13%, until 7553 of 8978 was produced to create the Ontology Generation. There is considerable significan ce to the point that Performing a sensitive information cut phase was carried out by 98%.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4300-4314
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• 2019

With the development of video-related applications, media content has increased dramatically through applications. There is a substantial amount of near-duplicate videos (NDVs) among Internet videos, thus NDVR is important for eliminating near-duplicates from web video searches. This paper proposes a novel NDVR system that supports large-scale retrieval and contributes to the efficient and accurate retrieval performance. For this, we extracted keyframes from each video at regular intervals and then extracted both commonly used features (LBP and HSV) and new image features from each keyframe. A recent study introduced a new image feature that can provide more robust information than existing features even if there are geometric changes to and complex editing of images. We convert a vector set that consists of the extracted features to binary code through a set of hash functions so that the similarity comparison can be more efficient as similar videos are more likely to map into the same buckets. Lastly, we calculate similarity to search for NDVs; we examine the effectiveness of the NDVR system and compare this against previous NDVR systems using the public video collections CC_WEB_VIDEO. The proposed NDVR system's performance is very promising compared to previous NDVR systems.