• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1572-1581
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• 2022

Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.14-19
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• 2019

In this paper, we propose a system for controlling the brightness of street lights by predicting pedestrian paths, identifying the position of pedestrians with motion sensing sensors and obtaining motion vectors based on past walking directions, then predicting pedestrian paths through the route prediction smart street lighting system. In addition, by using motion vector data, the pre-treatment process using linear interpolation method and the fuzzy system and neural network system were designed in parallel structure to increase efficiency and the rough set was used to correct errors. It is expected that the system proposed in this paper will be effective in securing the safety of pedestrians and reducing light pollution and energy by predicting the path of pedestrians in the detection of movement of pedestrians and in conjunction with smart street lightings.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.206.1-206.1
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• 2005

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.291-292
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• 2011

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.1445-1446
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• 2011

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1196-1200
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• 2003

The degree of supersaturation is an important measure for the operation of crystallization processes, because it is directly related to the control of crystal size distribution and shape. A conventional technique utilizing solution composition and temperature has a variety of problems caused from the measurement error and the handling of analyzing samples. A monitoring system of the supersaturation using a quartz crystal sensor is proposed here, and its performance is examined applying different manipulations of coolant temperature. The experimental outcome and photographic examination indicate that the measurements of resonant frequency and resistance of the sensor can be used for the prediction of the formation and growth of solid crystal from the crystallization process. The monitoring system eliminates the intrinsic error source of the conventional system to give the improved measurement and on-line application availability.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.588-590
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• 2006

Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto-associative kernel regression by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.05a
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• pp.67-68
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• 2019

The mechanical and durability characteristics of concrete structures depend on the construction environment, material conditions, design conditions, and temperature and humidity environment after casting. However, wired communicati-on sensors which are mainly used in the field have many limitations in their usability and monitoring. In this study, all temperature and humidity data measured from embedded sensors are monitored via a wireless sensor network. Based on the measured temperature data, the predicted compressive strength of the concrete was compared with the actual compressive strength. As a result, The error between predicted strength and experimental strength has decreased over time.