• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.751-756
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• 2018

This research extracted traffic light from input video, recognized colors of traffic light, and suggested traffic light color recognizing algorithm applicable to manless autonomous vehicle or ITS by distinguishing signs. To extract traffic light, suggested algorithm extracted the outline with CEA(Canny Edge Algorithm), and applied HCT(Hough Circle Transform) to recognize colors of traffic light and improve the accuracy. The suggested method was applied to the video of stream acquired on the road. As a result, excellent rate of traffic light recognition was confirmed. Especially, ROI including traffic light in input video was distinguished and computing time could be reduced. In even area similar to traffic light, circle was not extracted or V value is low in HSV space, so it's failed in candidate area. So, accuracy of recognition rate could be improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4395-4412
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• 2020

The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.171-176
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• 2022

With the recent development of deep learning technology, the areas of recommendation systems have also diversified. This paper studied algorithms to improve the learning rate and studied the significance results according to words through comparison with the performance characteristics of the Word2Vec model. The problem recommendation algorithm was implemented with the values expressed through the reflection of meaning and similarity test between texts, which are characteristics of the Word2Vec model. Through Word2Vec's learning results, problem recommendations were conducted using text similarity values, and problems with high similarity can be recommended. In the experimental process, it was seen that the accuracy decreased with the quantitative amount of data, and it was confirmed that the larger the amount of data in the data set, the higher the accuracy.

• Computers and Concrete
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• v.29 no.6
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• pp.361-374
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• 2022

On the premise of ensuring that the automatic and quantitative discharging function of concrete conveyors is met, the accuracy of the weight forecast by the mathematical model of the screw conveying volume is improved, and the error of the weight of the concrete parts and the accumulation thickness is reduced. In this paper, the discrete element method (DEM) is used to simulate the macroscopic flow of concrete. Using the concrete discrete element model, the size of the screw conveyor is set, and establish the response model between the influencing factors (process and structure) and the concrete mass flow rate according to the design points of the screw discharging experiment. The nonlinear data fitting method is used to obtain the volumetric efficiency function under the influence of process and structural factors, and the traditional screw conveying volume model is improved. The mass flow rate of concrete predicted by the improved mathematical model of screw conveying volume is consistent with the test results. The model can accurately describe the conveying process of concrete and achieve the purpose of improving the accuracy of forecasting the weight of discharged concrete.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.201-209
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• 2023

Time comparison techniques are required for generating and keeping Coordinated Universal Time (UTC) and to distribute standard clocks. These techniques play an important role in various fields, including science, finance, military, and communication. Among these techniques, Two-Way Satellite Time and Frequency Transfer (TWSTFT) ensures a relatively high accuracy, with a time comparison accuracy at a nanosecond level. However, TWSTFT systems have some limitations, such as the dependency on extra network links. In this paper, we propose an improved method for TWSTFT system operation and design a message structure for the suggestion. Additionally, we estimate the data rate and redundancy for the new TWSTFT signal with the designed message structure.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.345-346
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• 2023

As the shape and size of detectable objects diversifying recognition and segmentation algorithms have been developed to acquire accurate shape information. Although a high density of data captured by the repetition of scanning improves the accuracy of algorithms the high dense data decreases the efficiency due to its large size. This paper proposes standardization algorithms using the feature of structural members on indoor point cloud data to improve the process. First of all we determine the reduction rate of the density based on the features of the target objects then the data reduction algorithm compresses the data based on the reduction rate. Second the data arrangement algorithm rotates the data until the normal vector of data is aligned along the coordinate axis to allow the following algorithms to operate properly. Final the data arrangement algorithm separates the rotated data into their leaning axis. This allows reverse engineering of indoor point clouds to obtain the efficiency and accuracy of refinement processes.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• ETRI Journal
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• v.45 no.6
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• pp.1022-1034
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• 2023

In this study, the impact of varying lighting conditions on recognition and decision-making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor-based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL-based images, significant feature points were selected using the scale-invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.69-80
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• 2024

Currently rebar spacing inspection is carried out by human inspectors who heavily rely on their individual experience, lacking a guarantee of objectivity and accuracy in the inspection process. In addition, if incorrectly placed rebars are identified, the inspector need to correct them. Recently, laser scanning and AR technologies have been widely used because of their merits of measurement accuracy and visualization. This study proposes a technology for rebar spacing inspection and fixing by combining laser scanning and AR technology. First, scan data acquisition of rebar layers is performed and the raw scan data is processed. Second, AR-based visualization and fixing are performed by comparing the design model with the model generated from the scan data. To verify the developed technique, performance comparison test is conducted by comparing with existing drawing-based method in terms of inspection time, error detection rate, cognitive load, and situational awareness ability. It is found from the result of the experiment that the AR-based rebar inspection and fixing technology is faster than the drawing-based method, but there was no significant difference between the two groups in error identification rate, cognitive load, and situational awareness ability. Based on the experimental results, the proposed AR-based rebar spacing inspection and fixing technology is expected to be highly useful throughout the construction industry.