• 한국지리정보학회지
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• 제23권4호
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• pp.1-15
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• 2020

도시가 확장되고 지하시설물 관련 공사가 늘어남에 따라 지하시설물 관로 네트워크를 3차원으로 분석할 수 있는 기술이 절실히 요구되고 있다. 국내에서는 2015년부터 15종의 지하정보(지하시설물, 지하구조물, 지반)를 3차원 기반 지하공간통합지도로 구축하는 사업을 진행하고 있다. 하지만 현재 3차원 지하시설물 구축은 수작업 기반으로서 구축 방법이 매우 복잡하고 지자체별 수백만 건이 넘는 대용량 데이터를 처리하기에는 시간과 비용이 많이 발생한다. 본 논문은 3차원 지하시설물 모델의 가공 및 갱신 자동화 구축 프레임워크를 제시함으로써 3차원 지하시설물 모델을 최소비용으로 신속하게 구축하는 방안을 수립하였다. 본 연구에서 개발된 지하시설물 가공 및 부분갱신 자동화 기술들은 지하공간통합지도 구축사업에 즉시 현장적용이 가능할 것으로 기대한다.

• 대한전자공학회논문지
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• 제27권6호
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• pp.944-953
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• 1990

This paper describes design and implementation of a real-time high-precision vision system for an automation of SMT(surface mounting technology ). Also, a part inspection algorithm which calculates the position and direction of SMD(surface mounted device) accurately and performs the ruling using those information are presented, and a parallel processing technique for implementing those algorithms is also described. For a real-time implementation of iage acquisition and processing, several hardware modules, namely, multi-functional A/D-D/A board, frame memory board are developed. Particularly, a PE (processing element) board which employs the DSP56001 DSP (digital signal processor) is developed for the purpose of concurrent processing of part inspection algorithms. A stand-alone vision system is built by integration of the developed hardware modules and related softwares.

• 연구논문집
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• 통권25호
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• pp.121-128
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• 1995

The early day of manufacturing automation systems are NC machine tool Based automation, and now it become FA, DNC, FMC, FMS, CIM and IMS. Manufacturing Automation Technology is applied the increase of all industrial manufacturing competitve power. so the demand of this skill is repidly increase. This Technology can solve lack of manpower, decrease of productivity, and become weakened international competitive. But, automation rate of our country is 30-40%, because the level of domestic technology is low, so we need systemic research of manufacturing automation. The targets of this study are increase the data processing ability of CNC controller, and development of autonomous manufacturing system that can decision making between production module such as setup, manufacturing, inspection and transportation and part that object of manufacturing.

• Journal of Information Processing Systems
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• 제18권2호
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• pp.258-267
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• 2022

Weak over-the-horizon perception and blind spot are the main problems in intelligent connected vehicles (ICVs). In this paper, a V2V image transmission-based road condition warning method is proposed to solve them. The encoded road emergency images which are collected by the ICV are transmitted to the on-board unit (OBU) through Ethernet. The OBU broadcasts the fragmented image information including location and clock of the vehicle to other OBUs. To satisfy the channel quality of the V2X communication in different times, the optimal fragment length is selected by the OBU to process the image information. Then, according to the position and clock information of the remote vehicles, OBU of the receiver selects valid messages to decode the image information which will help the receiver to extend the perceptual field. The experimental results show that our method has an average packet loss rate of 0.5%. The transmission delay is about 51.59 ms in low-speed driving scenarios, which can provide drivers with timely and reliable warnings of the road conditions.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.575-586
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• 2022

Uneven light in real-world causes visual degradation for underexposed regions. For these regions, insufficient consideration during enhancement procedure will result in over-/under-exposure, loss of details and color distortion. Confronting such challenges, an unsupervised low-light image enhancement network is proposed in this paper based on the guidance of the unpaired low-/normal-light images. The key components in our network include super-resolution module (SRM), a GAN-based low-light image enhancement network (LLIEN), and denoising-scaling module (DSM). The SRM improves the resolution of the low-light input images before illumination enhancement. Such design philosophy improves the effectiveness of texture details preservation by operating in high-resolution space. Subsequently, local lightness attention module in LLIEN effectively distinguishes unevenly illuminated areas and puts emphasis on low-light areas, ensuring the spatial consistency of illumination for locally underexposed images. Then, multiple discriminators, i.e., global discriminator, local region discriminator, and color discriminator performs assessment from different perspectives to avoid over-/under-exposure and color distortion, which guides the network to generate images that in line with human aesthetic perception. Finally, the DSM performs noise removal and obtains high-quality enhanced images. Both qualitative and quantitative experiments demonstrate that our approach achieves favorable results, which indicates its superior capacity on illumination and texture details restoration.

• Journal of Information Processing Systems
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• 제19권1호
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• Journal of Information Processing Systems
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• 제19권6호
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• pp.791-802
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• 2023

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3791-3805
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• 2016

The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.

• Journal of Power Electronics
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• 제17권1호
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• pp.200-211
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• 2017

Conventional boost-full-bridge and boost-hybrid-bridge two-stage inverters are widely applied in order to adapt to the wide dc input voltage range of photovoltaic arrays. However, the efficiency of the conventional topology is not fully optimized because additional switching losses are generated in the voltage conversion so that the input voltage rises and then falls. Moreover, the electrolytic capacitors in a dc-link lead to a larger volume combined with increases in both weight and cost. This paper proposes a higher efficiency inverter with time-sharing synchronous modulation. The energy transmission paths, wheeling branches and switching losses for the high-frequency switches are optimized so that the overall efficiency is greatly improved. In this paper, a contrastive analysis of the component losses for the conventional and proposed inverter topologies is carried out in MATLAB. Finally, the high-efficiency under different switching frequencies and different input voltages is verified by a 3 kW prototype.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.300-306
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• 2000

This paper introduces a new concept of automation for bio-production with tele-operative system. The proposed system showed practical and feasible way of automation for the volatile bio-production process. Based on the proposition, recognition of the job environment with object identification was performed using computer vision system. A man-machine interactive hybrid decision-making, which utilized a concept of tele-operation was proposed to overcome limitations of the capability of computer in image processing and feature extraction from the complex environment image. Identifying watermelons from the outdoor scene of the cultivation field was selected to realize the proposed concept. Identifying watermelon from the camera image of the outdoor cultivation field is very difficult because of the ambiguity among stems, leaves, shades, and especially fruits covered partly by leaves or stems. The analog signal of the outdoor image was captured and transmitted wireless to the host computer by R.F module. The localized window was formed from the outdoor image by pointing to the touch screen. And then a sequence of algorithms to identify the location and size of the watermelon was performed with the local window image. The effect of the light reflectance of fruits, stems, ground, and leaves were also investigated.