• Journal of the Korean Society of Safety
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• v.11 no.1
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• pp.133-141
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• 1996

In spite of a tendency automatizing manaufacturing processes, since power presses are highly repetitive at high speeds, they have still been using to a large extent in many industries. More often than not, press workers have to make decisions whether work materials are located well or not, they should rearrange them or not, and their bodies would be safe or not. If the decision would be wrong, of course, they cause severe damages to human workers so that many workers haven't been willing to work with them. However, with the help of computer technologies, it would be possible to aid the press workers' decisions, and to allow or prohibit them from inserting their hands between slide rams and dies. Thus, this research was aimed to evaluate and analyze possibilities of applying Image Processing Techniques for prevention of press accidents. Through a series of procedures including Capturing work sites and material, Image Enhancement, Contouring, and Edge Finding, work characteristics were obtained and analyzed. The results showed that there were somewhat differences in image characteristics between accident-induced work scenes and accident-free ones. Consequently, if the image analyses are well carried out in real time, they would give a successful help to human press workers.

• Journal of the Korean Physical Society
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• v.73 no.12
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• pp.1821-1826
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• 2018

Uniformity is a key feature of state-of-the-art infrared focal planed array (IRFPA) and infrared imaging system. Unlike traditional infrared telescope facility, a ground-based infrared radiant characteristics measurement system with an IRFPA not only provides a series of high signal-to-noise ratio (SNR) infrared image but also ensures the validity of radiant measurement data. Normally, a long integration time tends to produce a high SNR infrared image for infrared radiant characteristics radiometry system. In view of the variability of and uncertainty in the measured target's energy, the operation of switching the integration time and attenuators usually guarantees the guality of the infrared radiation measurement data obtainted during the infrared radiant characteristics radiometry process. Non-uniformity correction (NUC) coefficients in a given integration time are often applied to a specified integration time. If the integration time is switched, the SNR for the infrared imaging will degenerate rapidly. Considering the effect of the SNR for the infrared image and the infrared radiant characteristics radiometry above, we propose a-wide-dynamic-range NUC algorithm. In addition, this essasy derives and establishes the mathematical modal of the algorithm in detail. Then, we conduct verification experiments by using a ground-based MWIR(Mid-wave Infared) radiant characteristics radiometry system with an Ø400 mm aperture. The experimental results obtained using the proposed algorithm and the traditional algorithm for different integration time are compared. The statistical data shows that the average non-uniformity for the proposed algorithm decreased from 0.77% to 0.21% at 2.5 ms and from 1.33% to 0.26% at 5.5 ms. The testing results demonstrate that the usage of suggested algorithm can improve infrared imaging quality and radiation measurement accuracy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.251-254
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• 2001

본 논문에서는 Self Organizing Map을 이용하여 fMRI data를 분석해 보았다. fMRl (functional Magnetic Resonance Imaging)는 인간의 뇌에 대한 비 침투적 연구 방법 중 최근에 각광받고 있는 것이다. Motor task를 수행하고 있는 피험자로부터 image data를 얻어내어 SOM을 적용하여 clustering한 결과 motor cortex 영역이 뚜렷하게 clustering 되었음을 알 수 있었다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.861-864
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• 1993

This research provides the results of a trial to generate the chaos by using nonlinear function constructed by fuzzy inference rules. The chaos generation function or chaotic behavior can be obtained by using Takagi-Sugeno fuzzy model with some constraint of the relationship of its parameters. Two examples are shown in this research. The first is simple example that construct of logistic image by fuzzy model. The second is more complicated one that provide the chaotic time series by non-linear autoregression based on fuzzy model. Simulated results are shown in these examples.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.807-818
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• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.171-179
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• 2021

We outline a process for estimating Cole-Cole parameters from time-domain induced polarization (IP) data. The IP transients are all inverted to 2D Cole-Cole earth models that include resistivity, chargeability, relaxation time, and the frequency exponent. Our inversion algorithm consists of two stages. We first convert the measured voltage decay curves into time series of current-on time apparent resistivity to circumvent the negative chargeability problem. As a first step, a 4D inversion recovers the resistivity model at each time channel that increases monotonically with time. The desired intrinsic Cole-Cole parameters are then recovered by inverting the resistivity time series of each inversion block. In the second step, the Cole-Cole parameters can be estimated readily by setting the initial model close to the true value through a grid search method. Finally, through inversion procedures applied to synthetic data sets, we demonstrate that our algorithm can image the Cole-Cole earth models effectively.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1931-1942
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• 2021

As the utility of an optical satellite image with a high spatial resolution (i.e., fine-scale) has been emphasized, recently, various studies of the land surface monitoring using those have been widely carried out. However, the usefulness of fine-scale satellite images is limited because those are acquired at a low temporal resolution. To compensate for this limitation, the spatiotemporal data fusion can be applied to generate a synthetic image with a high spatio-temporal resolution by fusing multiple satellite images with different spatial and temporal resolutions. Since the spatio-temporal data fusion models have been developed for mid or low spatial resolution satellite images in the previous studies, it is necessary to evaluate the applicability of the developed models to the satellite images with a high spatial resolution. For this, this study evaluated the applicability of the developed spatio-temporal fusion models for KOMPSAT-3A and Sentinel-2 images. Here, an Enhanced Spatial and Temporal Adaptive Fusion Model (ESTARFM) and Spatial Time-series Geostatistical Deconvolution/Fusion Model (STGDFM), which use the different information for prediction, were applied. As a result of this study, it was found that the prediction performance of STGDFM, which combines temporally continuous reflectance values, was better than that of ESTARFM. Particularly, the prediction performance of STGDFM was significantly improved when it is difficult to simultaneously acquire KOMPSAT and Sentinel-2 images at a same date due to the low temporal resolution of KOMPSAT images. From the results of this study, it was confirmed that STGDFM, which has relatively better prediction performance by combining continuous temporal information, can compensate for the limitation to the low revisit time of fine-scale satellite images.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.690-706
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• 2021

Classification is a substantial operation in data mining, and each element is distributed taking into account its feature values in the corresponding class. Metaheuristics have been widely used in attempts to solve satellite image classification problems. This article proposes a hybrid approach, the flower pigeons-inspired optimization algorithm (FPIO), and the local search method of the flower pollination algorithm is integrated into the pigeon-inspired algorithm. The efficiency and power of the proposed FPIO approach are displayed with a series of images, supported by computational results that demonstrate the cogency of the proposed classification method on satellite imagery. For this work, the Davies-Bouldin Index is used as an objective function. FPIO is applied to different types of images (synthetic, Alsat-2B, and Sentinel-2). Moreover, a comparative experiment between FPIO and the genetic algorithm genetic algorithm is conducted. Experimental results showed that GA outperformed FPIO in matters of time computing. However, FPIO provided better quality results with less confusion. The overall experimental results demonstrate that the proposed approach is an efficient method for satellite imagery classification.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• Current Optics and Photonics
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• v.8 no.5
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• pp.463-471
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• 2024

Multimode fibers (MMFs) possess high information throughput and small core diameter, making them highly promising for applications such as endoscopy and communication. However, modal dispersion hinders the direct use of MMFs for image transmission. By training neural networks on time-series waveforms collected from MMFs it is possible to reconstruct images, transforming blurred speckle patterns into recognizable images. This paper proposes a fully convolutional neural-network model, MSMDFNet, for image restoration in MMFs. The network employs an encoder-decoder architecture, integrating multiscale convolutional modules in the decoding layers to enhance the receptive field for feature extraction. Additionally, attention mechanisms are incorporated from both spatial and channel dimensions, to improve the network's feature-perception capabilities. The algorithm demonstrates excellent performance on MNIST and Fashion-MNIST datasets collected through MMFs, showing significant improvements in various metrics such as SSIM.