• Title/Summary/Keyword: 안전한 기계 학습

Search Result 139, Processing Time 0.025 seconds

A Study on Smoke Detection using LBP and GLCM in Engine Room (선박의 기관실에서의 연기 검출을 위한 LBP-GLCM 알고리즘에 관한 연구)

  • Park, Kyung-Min
    • Journal of the Korean Society of Marine Environment & Safety
    • /
    • v.25 no.1
    • /
    • pp.111-116
    • /
    • 2019
  • The fire detectors used in the engine rooms of ships offer only a slow response to emergencies because smoke or heat must reach detectors installed on ceilings, but the air flow in engine rooms can be very fluid depending on the use of equipment. In order to overcome these disadvantages, much research on video-based fire detection has been conducted in recent years. Video-based fire detection is effective for initial detection of fire because it is not affected by air flow and transmission speed is fast. In this paper, experiments were performed using images of smoke from a smoke generator in an engine room. Data generated using LBP and GLCM operators that extract the textural features of smoke was classified using SVM, which is a machine learning classifier. Even if smoke did not rise to the ceiling, where detectors were installed, smoke detection was confirmed using the image-based technique.

A Study on Auto-Classification of Aviation Safety Data using NLP Algorithm (자연어처리 알고리즘을 이용한 위험기반 항공안전데이터 자동분류 방안 연구)

  • Sung-Hoon Yang;Young Choi;So-young Jung;Joo-hyun Ahn
    • Journal of Advanced Navigation Technology
    • /
    • v.26 no.6
    • /
    • pp.528-535
    • /
    • 2022
  • Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

Design and Implementation of an OpenCV-based Digital Doorlock (OpenCV기반 디지털 도어락 시스템의 설계 및 구현)

  • Park, Sang-Young;Kang, Hwa-Young;Lee, Kang-Hee
    • Proceedings of the Korean Society of Computer Information Conference
    • /
    • 2019.07a
    • /
    • pp.321-324
    • /
    • 2019
  • 최근 국내에는 실업률 상승, 혼인률 하락 등 청년층 생애주기 변화, 단독거주, 고령층의 증가에 따라 1인 가구가 빠른 속도로 증가하고 있다. 이러한 추세는 지속될 것으로 예상되어 1인 가구를 겨냥한 맞춤형 보안솔루션에 대한 관심이 고조되고 있다. 본 논문에서는 사물 인터넷 기술을 적극적으로 접목할 수 있을 것으로 기대되는 디지털 도어락의 구현에 관한 연구를 수행하였다. 사물 인터넷 기술은 5G 시대의 도래에 따라 다시금 주목받고 있다. 이는 4차 산업혁명 시대의 핵심 기반 기술로 주요 IT 기업들이 상용화 기술 확보를 추진하고 있는 상황이다. 한편 디지털 도어락은 열쇠가 필요하지 않으며 위급상황이나 안전상황에 클릭 한번으로 출동 요원의 출동을 곧바로 요청할 수 있어 고객에게 편의성과 보안성을 제공한다. 하지만 비밀번호 방식의 디지털 도어락은 주기적으로 비밀번호를 교체해주지 않는 이상 지속적으로 같은 자리의 버튼만을 누르게 된다. 이렇게 되면 해당 위치에 지문이 남아서 비밀번호가 노출될 위험이 있다. 그러나 사물 인터넷 기술을 이용한 디지털 도어락을 사용하게 된다면 안전한 도어락 사용으로 주거 보안을 실현할 수 있다. 따라서 1인 가구를 노리는 범죄를 예방하기 위해 라즈베리 파이와 아두이노의 UART 통신, 머신러닝 CV를 이용하여 얼굴 인식으로 동일인임을 판단하는 디지털 도어락을 구현했다.

  • PDF

Application Study of Vessel Traffic Service: Dynamic Analysis of AIS for Shocheongcho Ocean Research Station (해상교통관제정보 활용 연구: 빅데이터 기반 해양 공간 선박 활동 특성 해석)

  • Park, Ju-Han;Kim, Seung-Ryong;Yang, Chan-Su
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
    • /
    • 2019.05a
    • /
    • pp.206-207
    • /
    • 2019
  • 우리나라에서 해상교통관제시스템(Vessel Traffic Service, VTS) 구역을 설정하여, 관제사를 중심으로 한 VTS와 선박사이의 해상교통상황 등의 교환을 통해 항만의 안전과 항만운영의 효율을 높이고 있다. 향후, 연안으로 확대될 예정이다. 더 넓은 해역에 대해서는 해양안전종합정보시스템(GICOMS)이 있으며, 선박자동식별장치 (AIS), 장거리위치추적시스템 (LRIT) 등에서 송신하는 선박의 운항정보를 수신하여 전자해도에 표시하고 있다. 이와 같은 선박관제정보는 빅데이터로 향후 자동화된 분석과 제원체계가 요구된다. 여기서는 해상교통관제정보 기초 활용 연구로, 소청초 종합해양과학기지주변의 AIS (Automatic Identification System)정보를 사용하여 선박 활동 특성 해석을 진행하였다.

  • PDF

Prediction of Ship Roll Motion using Machine Learning-based Surrogate Model (기계학습기반의 근사모델을 이용한 선박 횡동요 운동 예측)

  • Kim, Young-Rong;Park, Jun-Bum;Moon, Serng-Bae
    • Journal of Navigation and Port Research
    • /
    • v.42 no.6
    • /
    • pp.395-405
    • /
    • 2018
  • Seakeeping safety module in Korean e-Navigation system is one of the ship remote monitoring services that is employed to ensure the safety of ships by monitoring the ship's real time performance and providing a warning in advance when the abnormal conditions are encountered in seakeeping performance. In general, seakeeping performance has been evaluated by simulating ship motion analysis under specific conditions for its design. However, due to restriction of computation time, it is not realistic to perform simulations to evaluate seakeeping performance under real-time operation conditions. This study aims to introduce a reasonable and faster method to predict a ship's roll motion which is one of the factors used to evaluate a ship's seakeeping performance by using a machine learning-based surrogate model. Through the application of various learning techniques and sampling conditions on training data, it was observed that the difference of roll motion between a given surrogate model and motion analysis was within 1%. Therefore, it can be concluded that this method can be useful to evaluate the seakeeping performance of a ship in real-time operation.

Vibration Data Denoising and Performance Comparison Using Denoising Auto Encoder Method (Denoising Auto Encoder 기법을 활용한 진동 데이터 전처리 및 성능비교)

  • Jang, Jun-gyo;Noh, Chun-myoung;Kim, Sung-soo;Lee, Soon-sup;Lee, Jae-chul
    • Journal of the Korean Society of Marine Environment & Safety
    • /
    • v.27 no.7
    • /
    • pp.1088-1097
    • /
    • 2021
  • Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

Transfer Learning Backbone Network Model Analysis for Human Activity Classification Using Imagery (영상기반 인체행위분류를 위한 전이학습 중추네트워크모델 분석)

  • Kim, Jong-Hwan;Ryu, Junyeul
    • Journal of the Korea Society for Simulation
    • /
    • v.31 no.1
    • /
    • pp.11-18
    • /
    • 2022
  • Recently, research to classify human activity using imagery has been actively conducted for the purpose of crime prevention and facility safety in public places and facilities. In order to improve the performance of human activity classification, most studies have applied deep learning based-transfer learning. However, despite the increase in the number of backbone network models that are the basis of deep learning as well as the diversification of architectures, research on finding a backbone network model suitable for the purpose of operation is insufficient due to the atmosphere of using a certain model. Thus, this study applies the transfer learning into recently developed deep learning backborn network models to build an intelligent system that classifies human activity using imagery. For this, 12 types of active and high-contact human activities based on sports, not basic human behaviors, were determined and 7,200 images were collected. After 20 epochs of transfer learning were equally applied to five backbone network models, we quantitatively analyzed them to find the best backbone network model for human activity classification in terms of learning process and resultant performance. As a result, XceptionNet model demonstrated 0.99 and 0.91 in training and validation accuracy, 0.96 and 0.91 in Top 2 accuracy and average precision, 1,566 sec in train process time and 260.4MB in model memory size. It was confirmed that the performance of XceptionNet was higher than that of other models.

Application and evaluation of design projects: A case study in a mechanics of materials course (디자인 프로젝트의 적용과 평가: 재료역학 수업의 사례연구)

  • Kim Ju-Hu
    • Journal of Engineering Education Research
    • /
    • v.6 no.1
    • /
    • pp.15-21
    • /
    • 2003
  • This paper reports the results of course restructuring employing design projects in an introductory mechanics of materials course at Pennsylvania State University. Unlike traditional lecture courses, students were encouraged to learn the rudiments of mechanical design and how materials standards, economics, manufacturing, environmental, legal (liability) and societal (safety) concerns relate to design. Through conducting collaborative design projects, the instructors helped students to acquire more advanced skills such as team-based decision making, integration and establishment of criteria, use of modern design theory, consideration of alternative solutions, and application of realistic constraints. In order to examine the impact of new course changes on students' learning, a survey was conducted in 1998 Fall semester. According to the results of survey analyses, students reported high values on this introductory mechanics of materials course. However, they did not give high values on the design projects. Rather, they preferred lecture sessions. Additionally, it was also found that students who earned higher grades from a prerequisite course(statics) showed lower values on the design projects. Implications for engineering educators and suggestions for future research studies were discussed.

Design of umbrella arch method based on adaptive SVM and reliability concept (Adaptive SVM 기법 및 신뢰성 개념을 적용한 강관다단공법의 설계기법 연구)

  • Lee, Jun S.;Sagong, Myung;Park, Jeongjun;Choi, Il Yoon
    • Journal of Korean Tunnelling and Underground Space Association
    • /
    • v.20 no.4
    • /
    • pp.701-715
    • /
    • 2018
  • A reliability based design approach of the tunnel reinforcement with umbrella arch method was considered to better represent the uncertainties of the weak rock properties around the tunnel. For this, a machine learning approach called an Adaptive Support Vector Machine (ASVM) together with the limit equilibrium method were introduced to minimize the iteration numbers during the classification training of the tunnel stability. The proposed method was compared with the results of typical Monte Carlo simulations. It was concluded that the ASVM was very efficient and accurate to calculate the probability of failure having auxiliary umbrella arches and uncertain material properties of the tunnel. Future work will be concentrated on the refinement of the fast adaptation of the SVM classification so that the minimum number of numerical analyses can be used where the limit solution is not available.

Machine learning based radar imaging algorithm for drone detection and classification (드론 탐지 및 분류를 위한 레이다 영상 기계학습 활용)

  • Moon, Min-Jung;Lee, Woo-Kyung
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.25 no.5
    • /
    • pp.619-627
    • /
    • 2021
  • Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.