• 로봇학회논문지
• /
• 제19권1호
• /
• pp.79-91
• /
• 2024

Large warehouses are building automation systems to increase efficiency. However, small warehouses, military bases, and local stores are unable to introduce automated logistics systems due to lack of space and budget, and are handling tasks manually, failing to improve efficiency. To solve this problem, this study designed small loading and unloading equipment that can be mounted on transportation vehicles. The equipment can be controlled remotely and is automatically controlled from the point where pallets loaded with cargo are visible using real-time video from an attached camera. Cargo recognition and control command generation for automatic control are achieved through a newly designed deep learning model. This model is designed to be optimized for loading and unloading equipment and mission environments based on the YOLOv3 structure. The trained model recognized 10 types of palettes with different shapes and colors with an average accuracy of 100% and estimated the state with an accuracy of 99.47%. In addition, control commands were created to insert forks into pallets without failure in 14 scenarios assuming actual loading and unloading situations.

• 한국산업정보학회논문지
• /
• 제18권5호
• /
• pp.45-50
• /
• 2013

최근 시장이 확대되고 있는 SMART TV에는 다양한 기능을 위하여 내장형 카메라가 들어가게 된다. 하지만, 이로 인한 불량 또한 발생하게 되며, 특히 TV power up sequence 문제로 인한 내장형 카메라의 화면 무감 불량현상은 기존 검사장비에서 검출되기 힘든 특징을 가지고 있다. 이를 위해 오디오 쪽 컨트롤 신호를 재현할 수 있는 새로운 검사장비가 필요하지만, 시간과 많은 비용이 소요되며, 생산에 큰 영향을 준다. 본 논문에서는 이와 같은 문제점을 해결하고자 FPGA (Field Programmable Gate Array)를 활용한 불량 검출 장비를 개발하여 문제점을 빠르고 정확하게 검출하는 방법을 제시한다. 이를 통해 새로운 장비를 대체하는 비용 절감 효과와 기존 검출 테스트 시간을 약 20여초에서 10초미만으로 크게 단축시킴으로써 개발기간의 최소화 및 공정에 적용을 통한 불량률 감소를 이룰 수 있다.

• KIEE International Transactions on Power Engineering
• /
• 제3A권4호
• /
• pp.214-221
• /
• 2003

In view of the fact that gas insulated switchgear (GIS) is an important piece of equipment in a substation, it is highly desirable to continuously monitor the state of equipment by measuring the partial discharge (PD) activity in a GIS, as PD is a symptom of an insulation weakness/breakdown. However, since the PD signal is relatively weak and the external noise makes detection of the PD signal difficult, it therefore requires careful attention in its detection. In this paper, the algorithm for detection of PD in the GIS using the wavelet transform (WT) is proposed. The WT provides a direct quantitative measure of the spectral content and dynamic spectrum in the time-frequency domain. The most appropriate mother wavelet for this application is the Daubechies 4 (db4) wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, is very well suited to detecting high frequency signals of very short duration, such as those associated with the PD phenomenon. The proposed algorithm is based on utilizing the absolute sum value of coefficients, which are a combination of D1 (Detail 1) and D2 (Detail 2) in multiresolution signal decomposition (MSD) based on WT after noise elimination and normalization.

• 한국통신학회논문지
• /
• 제38C권5호
• /
• pp.471-478
• /
• 2013

본 논문은 국내 철도 건널목의 현황 조사와 현재 운영되고 있는 안전설비의 문제점 분석을 통해 이를 해결할 수 있는 대안으로 레이저레이더기반의 지장물 검지장치를 설계 및 구현하였다. 철도 안전사고의 큰 비중을 차지하는 철도 건널목에서의 효과적인 지장물 검지를 통해 철도교통의 안전성 및 신뢰성 향상을 목표로 하며, 기존 안전 설비의 한계와 노후화로 인한 문제점을 극복하는 시스템을 제안한다. 레이저레이더 스캐너를 활용한 철도 건널목 지장물 검지장치를 설계하였고 시제품 시험 및 성능평가를 통해 이를 입증하였다.

• 전기저널
• /
• 2호통권146호
• /
• pp.11-18
• /
• 1989

• 디지털산업정보학회논문지
• /
• 제16권2호
• /
• pp.27-33
• /
• 2020

Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
• /
• pp.238-240
• /
• 2006

For the method of train position detection, ground-based train position estimation mainly has been applied so far. Ground-based position detection is the way to detect train current positions by installing train position equipments on railroad lines. However, the ground-based methods should install detection equipments on each section, and can only be able to detect train positions from main command center. So this method has several disadvantages such as an discontinuous position detection, an increment in cost of installation and maintenance. To make possible continuous train position detection, and to minimize amount of the cost, the vehicle-based position detection method should be chosen to determine train positions by loading position equipments on vehicles. In this paper, to realize the vehicle-based train position detection method, configuration scheme of train position detection equipment is suggested by using GPS, inertial sensor, speed sensor and its performance is verified by simulations.

• 융합보안논문지
• /
• 제21권2호
• /
• pp.11-18
• /
• 2021

대다수의 기업은 유무형의 자산을 보호하기 위한 방안으로, IT서비스망에 다양한 보안 장비를 구축하여 정보보호 모니터링을 수행하고 있다. 그러나 서비스 망 고도화 및 확장 과정에서 보안 장비 투자와 보호해야 할 자산이 증가하면서 전체 서비스망에 대한 공격 노출 모니터링이 어려워지는 한계가 발생하고 있다. 이에 대응하기 위한 방안으로 외부자의 공격과 장비 불법통신을 탐지할 수 있는 다양한 연구가 진행되었으나, 대규모 서비스망에 대한 효과적인 서비스 포트 오픈 감시 및 불법 통신 모니터링 체계 구축에 대한 연구는 미진한 편이다. 본 연구에서는 IT서비스망 전체 데이터 흐름의 관문이 되는 네트워크 백본장비의 'Netflow 통계 정보'를 분석하여, 대규모 투자 없이 광범위한 서비스망의 정보 유출 및 불법 통신 시도를 감시할 수 있는 프레임워크를 제안한다. 주요 연구 성과로는 Netflow 데이터에서 운영 장비의 텔넷 서비스 오픈 여부를 6개의 ML 머신러닝 알고리즘으로 판별하여 분류 정확도 F1-Score 94%의 높은 성능을 검증하였으며, 피해 장비의 불법 통신 이력을 연관하여 추적할 수 있는 모형을 제안하였다.

• 대한기계학회논문집B
• /
• 제22권11호
• /
• pp.1564-1570
• /
• 1998

In-situ particle monitors(ISPMs) are widely used for monitoring contaminant particles in vacuum-based semiconductor manufacturing equipment. In the present research, the performance of a Particle Measuring Systems(PMS) Vaculaz-2 ISPM at subatmospheric pressures has been studied. We created uniform upstream conditions of particle concentration and measured the detection efficiency, the lower detection limit, and the size response of the ISPM using uniform sized methylene blue aerosol particles. The effect of particle size, particle velocity, particle concentration, and system pressure on the detection efficiency was examined. Results show that the detection efficiency of the ISPM decreases with decreasing chamber pressure, and with increasing mass flow rate. The lower detection limit of the ISPM, determined at 50 % of the measured maximum detection efficiency, was found to be about $0.15{\sim}0.2{\mu}m$, which is similar to the minimum detectable size of $0.17{\mu}$ given by the manufacturer.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2001년도 하계종합학술대회 논문집(1)
• /
• pp.425-428
• /
• 2001

In this paper, FMCW radar signal processing technique for the vehicle detection system are studied. And FMCW radar sensor is used as a equipment for vehicle detection. To test the performance of developed algorithm, the evaluation of the algorithm is done by simulation for signal processing technique of vehicle detection system. RADAR signal of a driving vehicle is generated by using the Matlab. Distance and velocity of vehicles are calculated with developed a1gorithm. Also the signal processing procedure is done for the virtual data with FM-AM converted noise.