• Smart Media Journal
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• v.12 no.1
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• pp.47-53
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• 2023

Recently, research on a smart farm that creates new values by combining information and communication technology(ICT) with agriculture has been actively done. In order for domestic smart farm technology to have productivity at the same level of advanced agricultural countries, automated decision-making using machine learning is necessary. However, current smart greenhouse data collection technologies in our country are not enough to perform big data analysis or machine learning. In this paper, we design and implement a smart greenhouse data preprocessing system for autonomous machine learning. The proposed system applies target data to various preprocessing techniques. And the proposed system evaluate the performance of each preprocessing technique and store optimal preprocessing technique for each data. Stored optimal preprocessing techniques are used to perform preprocessing on newly collected data

• Journal of Astronomy and Space Sciences
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• v.19 no.2
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• pp.163-172
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• 2002

This article investigates the problem of data preprocessing for the precision orbit determination (POD) of low earth orbit satellite using GPS .aw data. Several data preprocessing algorithms have been developed to edit the GPS data automatically such that outlier deletion, cycle slip identification and correction, and time tag error correction. The GPS data are precisely edited for the accuracy of POD. Some methods of data preprocessing are restricted to the rate of the collections of the pseudorange and carrier phase measurements. This study considers the preprocessing efficiency varied with the rate, the quality of receiver and the altitude of the satellite's orbit. We also propose the proper methods in accordance with the rate for single frequency and dual frequency receivers.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.785-792
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• 2023

Because hyperspectral imaging provides detailed spectral information across a broad range of wavelengths, it can be utilized in numerous applications, including environmental monitoring, food quality inspection, medical diagnosis, material identification, art authentication, and crime scene analysis. However, hyperspectral images often contain various types of distortions due to the environmental conditions during image acquisition, which necessitates the proper removal of these distortions through a data preprocessing process. In this study, a preprocessing method was investigated to effectively correct the distortion caused by artificial light sources used in indoor hyperspectral imaging. For this purpose, a halogen-tungsten artificial light source was installed indoors, and hyperspectral images were acquired. The acquired images were then corrected for distortion using a preprocessing that does not require complex auxiliary equipment. After the corrections were made, the results were analyzed. According to the analysis, a statistical transformation technique using mean and standard deviation with reference to a reference signal was found to be the most effective in correcting distortions caused by artificial light sources.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.789-793
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• 1990

Preprocessing technique of the measurement bearing data is presented to improve the tar-get estimation accuracy for the bearing-only target notion analysis (TMA). Computer simulation is performed to compare with respect to the extended Kalman filter. By computer simulation, the target filter estimator with preprocessing Is both stable and robust to the measurement bearing noise.

• The Journal of Bigdata
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• v.8 no.1
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• pp.61-71
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• 2023

With the development of data-driven decision-making and sophisticated big data processing technique, there is a growing demand for information on how to process data. However, recent studies with data preprocessing mentioned only as a means to achieve a result. Therefore, in this study, we aimed to write in detail about the data processing pipeline, include preprocessing data. In particular, we shares the context and domain knowledge to aid fluent understand of the research.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.144-152
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• 2017

Among the technologies for implementing autonomous vehicles, advanced driver assistance system is a key technology to support driver's safe driving. In the technology using the vision sensor having a high utility, various preprocessing methods are used prior to feature extraction for lane detection. However, in the existing methods, the unnecessary lane candidates such as cars, lawns, and road separator in the road area are false positive. In addition, there are cases where the lane candidate itself can not be extracted in the area under the overpass, the lane within the dark shadow, the center lane of yellow, and weak lane. In this paper, we propose an efficient preprocessing method using k-means clustering for image division and the HSV color model. When the proposed preprocessing method is applied, the true positive region is maximally maintained during the lane detection and many false positive regions are removed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.69-74
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• 2022

SNN (Spiking Neural Network) operating in neuromorphic architecture was created by mimicking human neural networks. Neuromorphic computing based on neuromorphic architecture requires relatively lower power than typical deep learning techniques based on GPUs. For this reason, research to support various artificial intelligence models using neuromorphic architecture is actively taking place. This paper conducted a performance analysis of the speech recognition model based on neuromorphic architecture according to the speech data preprocessing technique. As a result of the experiment, it showed up to 84% of speech recognition accuracy performance when preprocessing speech data using the Fourier transform. Therefore, it was confirmed that the speech recognition service based on the neuromorphic architecture can be effectively utilized.

• Journal of Digital Convergence
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• v.10 no.2
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• pp.287-292
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• 2012

In this paper, user personalized services through the emotion perception required to support location-based sensing data preprocessing techniques and emotion data preprocessing techniques is studied for user's emotion data building and preprocessing in V-A emotion model. For this purpose the granular context tree and string matching based emotion pattern matching techniques are used. In addition, context-aware and personalized recommendation services technique using probabilistic reasoning is studied for personalized services based on context awareness.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.69-76
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• 1998

Due to the drastic improvement of computer hardware and operating system, it is easy to break through the main defects of limited computer memory and processing time, etc. To keep up with this situation, this paper is focused on developing the preprocessor program with the input method based on vector graphic editor and the preprocessing technique including automatic node generation algorithm for the $\rho$-version finite element program. To develop this preprocessor program, the special data structure and the OOP(Object Oriented Programming) have been used by the Visual Basic 4.0. The Special data structure is proposed to describe the geometric data of node numberings and coordinates suitable for the $\rho$-version finite element program, which are quite different from the comvential h-version finite element program.