• Journal of Information Processing Systems
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• v.20 no.2
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• pp.215-225
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• 2024

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional Random Forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P-I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.

• 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).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.371-376
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• 1995

Computer vision is applicated to measure the position and orientation of the SMD on 8mm Camcoder PCB and advanced image processing algorithms for high accuracy and real time processing are proposed. Illumination conditions are optimized for the best image formation and a set of LEDs is used as economic illuminator, which is regarded as a summation of many point sources. Conctete optical system is constructed and the performance of the proposed algorithm is verified by several experiments.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.97-105
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• 1998

The fiber oriented condition inside fiber reinforced composite material is a basic factor of mechanical properties of composite materials. It is very important to meausure the fiber orientation angle for the determination of molding conditions, mechanical characteristics, and the design of composite materials. In the work, the fiber orientation distribution of simulation figure plotted by PC is measured using image processing in order to examine the accuracy of intersection counting method. The fiber orientation function measured by intersection counting method using image processing is compared with the calculated fiber orientation function. The results show that the measured value of fiber orientation function using intersection counting method is lower than the calculated value, because the number of intersection between the scanning line and the fiber with smaller fiber aspect ratio is counted less than with larger fiber aspect ratio.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.51 no.2
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• pp.55-59
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• 2002

Parallel processing is able to be used effectively on computationally intense power system problems. But this technology is not still available is not only parallel computer but also parallel processing scheme. Testing these algorithms to ensure accuracy, and evaluation of their performance is also an issue. Although a significant amount of parallel algorithms of power system problem have been developed in last decade, actual testing on parallel computer architectures lies in the beginning stages because no clear cut paths. This paper presents Jacobian modeling method to supply the base being able to treat power flow by newton's method by the computer. This method is to assign and to compute teared blocks of sparse matrix at each parallel processors. The testing to insure accuracy of developed method have been done on serial computer by trying to simulate a parallel environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.556-560
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• 1996

In order to examine thd accuracy of intersection counting method, the fiber orientation distribution of simulation figure platted by PC is measured using image processing. The fiber orientation distribution obtained by an image processing method is compared with those by the intersection counting method. The result shows that the errors of the intersection counting method are large because its measurement is made by the cross point of the scanning line and the fiber.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.97-97
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• 2000

In this paper, a signal processing algorithm for software GPS receiver is proposed. The signal processor takes snapshot of the sampled If signal from the RF section of the GPS receiver. All the processing for code and carrier tracking and correlation are implemented using the digital signal processing techniques. In order to achieve fast code acquisition, correlation of the incoming GPS signal is performed using the FFT method, After code acquisition, to reduce the Doppler shift effect and increase the accuracy, the interpolation or the tracking are performed. The performance of the proposed processing algorithm is first evaluated using matlab/simulink. A signal acquisition board for sampling and logging GPS IF signal form the Mitel GPS RF chip set is constructed. In order to analyze the performance of the designed algorithm the experiments are performed and the results are analyzed.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Proceedings of the Korean Society of Laser Processing Conference
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• 2003.05a
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• pp.111-115
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• 2003

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.245-254
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• 2018

Impact of tropospheric correction techniques on accuracy of the GPS (Global Positioning System) derived ellipsoidal heights has been experimentally assessed in this paper. To this end, 247 baselines were constructed from a total of 88 CORS (Continuously Operating Reference Stations) in Korea. The GPS measurements for seven days, acquired from the so-called integrated GNSS (Global Navigation Satellite Systems) data center via internet connection, have been processed by two baseline processing software packages with an application of the empirical models, such as Hopfield, modified Hopfield and Saastamoinen, and the estimation techniques based on the DD (Double-Differenced) measurements and the PPP (Precise Point Positioning) technique; hence a total number of the baseline processed and tested was 8,645. Accuracy and precision of the estimated heights from the various correction schemes were analyzed about baseline lengths and height differences of the testing baselines. Details of these results are summarized with a view to hopefully providing an overall guideline of a suitable selection of the modeling scheme with respect to processing conditions, such as the baseline length and the height differences.