• Advanced Industrial SCIence
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• v.3 no.2
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• pp.17-22
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• 2024

Recently, as the ICT field has been used in various environments, it has become possible to analyze pests by crops, use robots when harvesting crops, and predict by big data by utilizing ICT technologies in a sustainable agricultural environment. However, in a sustainable agricultural environment, efforts to solve resource depletion, agricultural population decline, poverty increase, and environmental destruction are constantly being demanded. This paper proposes an artificial intelligence-based big data processing analysis method to reduce the production cost and increase the efficiency of crops based on a sustainable agricultural environment. The proposed technique strengthens the security and reliability of data by processing big data of crops combined with AI, and enables better decision-making and business value extraction. It can lead to innovative changes in various industries and fields and promote the development of data-oriented business models. During the experiment, the proposed technique gave an accurate answer to only a small amount of data, and at a farm site where it is difficult to tag the correct answer one by one, the performance similar to that of learning with a large amount of correct answer data (with an error rate within 0.05) was found.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.489-498
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• 2022

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.6
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• pp.1529-1538
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• 1995

In this study, a liquid sheet type co-axial nozzle has been used to investigate the turbulent atomization characteristics which could result in the experimental data to be used in designing a jet nozzle with high performance. Image processing technique and immersion sampling method were employed to measure droplet size. In atomizing characteristics, droplet size distributions and absolute droplet sizes, SMD(Sauter Mean Diameter) have been investigated in the wide ranges of flow field depending upon the air-water mass ratios. And the comparisons between the present data and the semi-empirical curves have been conducted semi-empirical correlation for SMD has been derived in the present analysis.

• The Journal of the Acoustical Society of Korea
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• v.19 no.2
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• pp.68-72
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• 2000

Matched field processing technique is performed by considering complex underwater environments. Specially, the performance of minimum variance processor is greatly degraded by eigenvalue problem. In this paper, we propose the minimum variance matched field processor using shaping matrix. This shaping matrix makes that the input covariance matrix is invertible and enhances the desired acoustic source component. It was proved effectively range/depth localization of the proposed method with simulated data and vertical array data collected by NATO SACLANT Center north of the island of Elba off the Italian west coast.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.210-215
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• 2003

A fine hot-wire is used both as a heating element and a temperature sensor in transient hot-wire method. The traditional sensor system is unnecessarily big so that it takes large fluid volume to measure the thermal conductivity. To dramatically reduce this fluid volume, a new sensor fabrication and a data processing method are proposed in this article. Contrast to the conventional and most popular two wire sensor, the new sensor system is made up of divided multiple long and short wires. Through validation experiments, it is found that the measured thermal conductivities of the glycerin are exactly same each other between the conventional and proposed new method. Also some technical considerations in arranging the multiple wires are briefly discussed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.84-89
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• 2014

Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.1-11
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• 2006

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• Convergence Security Journal
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• v.21 no.3
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• pp.57-66
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• 2021

As the detection performance using deep learning and machine learning of the intrusion detection field has been verified, the cases of using it are increasing day by day. However, it is difficult to collect the data required for learning, and it is difficult to apply the machine learning performance to reality due to the imbalance of the collected data. Therefore, in this paper, A mixed sampling technique using t-SNE visualization for imbalanced data processing is proposed as a solution to this problem. To do this, separate fields according to characteristics for intrusion detection events, including payload. Extracts TF-IDF-based features for separated fields. After applying the mixed sampling technique based on the extracted features, a data set optimized for intrusion detection with imbalanced data is obtained through data visualization using t-SNE. Nine sampling techniques were applied through the open intrusion detection dataset CSIC2012, and it was verified that the proposed sampling technique improves detection performance through F-score and G-mean evaluation indicators.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.4
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• pp.1-8
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• 2008

The USN is important in technique, unmanned observation using wireless network camera, detection technique that use intrusion detection sensor. But these encrypted data transmission and processing technique through sensor network, method of the staff's location recognition and arrangement aren't serviced still as a integrated system in facility security industry. This paper proposed that improve facility management, the staff present recognition and system efficiency using RFID, USN and wireless camera.

• Korean Journal of Remote Sensing
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• v.2 no.2
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• pp.69-77
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• 1986

This study was emphasized on the land use classification by Remote Sensing Technique. Land cover maps about the major urbans, Seoul, Tajeon regions, its of each classified classes were extracted by use of Landsat MSS Data and Digital Image Processing System. From the results of this study, it was proved that land use classification by Remote Sensing technique could be used to obtain fully fruitful Results.