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Analysis of Odor Data Based on Mixed Neural Network of CNNs and LSTM Hybrid Model

  • Sang-Bum Kim;Sang-Hyun Lee
    • 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%.

Temperature Prediction of Underground Working Place Using Artificial Neural Networks (인공신경망을 이용한 심부 갱내온도 예측)

  • Kim, Yun-Kwang;Kim, Jin
    • Tunnel and Underground Space
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    • v.17 no.4
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    • pp.301-310
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    • 2007
  • The prediction of temperature in the workings for the propriety examination for the development of a deep coal bed and the ventilation design is fairly important. It is quite demanding to obtain precise thermal conductivity of rock due to the variety and the complexity of the rock types contiguous to the coal bed. Therefore, to estimate the thermal conductivity corresponding to this geological situation and complex gallery conditions, a computing program which is TemPredict, is developed in this study. It employs Artificial Neural Network and calculates the climatic conditions in galleries. This advanced neural network is based upon the Back-Propagation Algorithm and composed of the input layers that are acceptant of the physical and geological factors of the coal bed and the hidden layers each of which has the 5 and 3 neurons. To verify TemPredict, the calculated result is compared with the measured one at the entrance of -300 ML 9X of Jang-sung production department, Jang-sung Coal Mine. The difference between the results calculated by TemPredict ($25.65^{\circ}C$) and measured ($25.7^{\circ}C$) is only $0.05^{\circ}C$, which is less than the allowable error 5%. The result has more than 95% of very high reliability. The temperature prediction for the main carriage gallery 9X in -425 ML under construction when it is completed is made. Its result is $28.2^{\circ}C$. In the future, it would contribute to the ventilation design for the mine and the underground structures.

Predict DGPS Algorithm using Machine Learning (기계학습을 통한 예측 DGPS 항법 알고리즘)

  • Kim, HongPyo;Jang, JinHyeok;Koo, SangHoon;Ahn, Jongsun;Heo, Moon-Beom;Sung, Sangkyung;Lee, Young Jae
    • Journal of Advanced Navigation Technology
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    • v.22 no.6
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    • pp.602-609
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    • 2018
  • Differential GPS (DGPS) is known as a positioning method using pseudo range correction (PRC) which is communicating between a refence receiver and moving receivers. In real world, a moving receiver loses communication with the reference receiver, resulting in loss of PRC real-time communication. In this paper, we assume that the transmission of the pseudo range correction isinterrupted in the middle of real-time positioning situations, in which calibration information is received in the DGPS method. Under the disconnected communication, we propose 'predict DGPS' that real-time virtual PRC model which is modeled by a machine learning algorithm with previously acquired PRC data from a reference receiver. To verify predict DGPS method, we compared and analyzed positioning solutions acquired from real PRC and the virtual PRC. In addition, we show that positioning using the DGPS prediction method on a real road can provide an improved positioning solution assuming a scenario in which PRC communication was cut off.

Evaluation of Prediction Methods for Containment Integrated Leakage Rate (격납건물 종합누설률 예측방법 평가)

  • Yang, Seung-Ok;Lee, Kwang-Dae;Oh, Eung-Se
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.562-564
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    • 2004
  • The containment leakage rate test performed on the nuclear power plants consists of following phases : pressurizing the containment, stabilizing the atmosphere, conducting a Type A test, conducting a verification test, depressurizing the containment. It takes more than 48 hours from the pressurization to the depressurization and the prediction of the results will help to prepare the next test phase. In this paper, to predict the leakage rate, the prediction methods based on the least square method are evaluated according to the input variables and the measurement period.

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A Method to Predict Road Traffic Noise Using the Weibull Distribution (Weibull분포를 이용한 도로교통소음의 예측에 관한 연구)

  • 김갑수
    • Journal of Korean Society of Transportation
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    • v.5 no.2
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    • pp.73-80
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    • 1987
  • Various procedures for evaluation of traffic noise annoyance have been proposed. However, most of the studies of this type are restricted for improving traffic flow. In this paper, a method to predict the road traffic noise is proposed in terms of equivalent continuous A-Weighted sound pressure level (Leq), based on a probability model. First, distribution of the road traffic noise level are investigated. second, the weibull distribution parameters are estimated by using the quantification theory. Finally, a prediction model of the road traffic noise is proposed based on the weibull distribution model The predicted values of the Leq are closely matched the measured data.

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Prediction Models for Corrosion of Reinforcing Bars (철근의 부식 예측 모델에 관한 연구)

  • 김도겸;이종석;고경택;이장화;송영철;조명석
    • Proceedings of the Korea Concrete Institute Conference
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    • 1999.10a
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    • pp.739-742
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    • 1999
  • A reinforcement corrosion prediction model was proposed using the results from accelerated testing and mathematical equation from the Fick's 2nd law for chloride-induced corrosion of reinforcement in concrete. The input data included the chloride concentration, mix characteristics of concrete, and environmental conditions. This model can be used to predict the chloride concentration pertaining to corrosion time and loading age for marine concrete structures. This model can also be used to predict the service life.

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Waterborne Noise Prediction of the Reinforced Cylindrical Shell Using the SEA Technique (SEA 기법을 이용한 보강 원통형 셸의 수중방사소음 해석)

  • 배수룡;전재진;이헌곤
    • Journal of KSNVE
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    • v.3 no.2
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    • pp.155-161
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    • 1993
  • The vibration generated by the machinery on board is transmitted to the hull and into the water. At the early design stage, the prediction of the hull vibration and the radiated noise level is very important to reduce their levels. In this study, SAE(Statistical Energy Analysis) technique is applied to predict structureborne noise level of the hull considering fluid loading. Rayleigh integral is applied to predict the radiated noise level. The results of comparision between the predictions and measurements for the reinforced cylindrical shell have shown good agreements.

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Evaluation of GLEAMS nutrient submodel to predict nutrient losses from land application of poultry litter (계분살포시 수질자료를 이용한 GLEAMS 영양물질 부모형 평가)

  • 윤광식
    • Proceedings of the Korean Society of Agricultural Engineers Conference
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    • 1998.10a
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    • pp.484-489
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    • 1998
  • The GLEAMS nutrient submodel was evaluated to predict nutrient losses in surface runoff following application of two rates (9 and 18 t/ha) of poultry litter and a recommended rate of commercial fertilizer on corn plots. Nutrient submodel was evaluated with calibrated runoff and sediment losses to the observed field data. Simulation of nitrogen transformation effects on nitrogen losses in surface runoff did not agree with field data. The model simulated higher NH$_4$-N than NO$_3$-N losses in surface runoff, while field data showed the opposite.

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Predicting typhoons in Korea (국내 태풍 예측)

  • Yang, Heejoong
    • Journal of the Korea Safety Management & Science
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    • v.17 no.1
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    • pp.169-177
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    • 2015
  • We develop a model to predict typhoons in Korea. We collect data for typhoons and classify those depending on the severity level. Following a Bayesian approach, we develop a model that explains the relationship between different levels of typhoons. Through the analysis of the model, we can predict the rate of typhoons, the probability of approaching Korean peninsular, and the probability of striking Korean peninsular. We show that the uncertainty for the occurrence of various types of typhoons reduces dramatically by adaptively updating model parameters as we acquire data.

A Theoretical Study on the Boiling Heat Transfer Performance of Tubes with Extended Surfaces (확대 전열관의 비등열전달에 관한 이론적 연구)

  • Jho, S.G.
    • Solar Energy
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    • v.19 no.2
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    • pp.45-56
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    • 1999
  • The performance of vertical and horizontal tubes with extended surface of rectangular and triangular cross section was investigated theoretically for boiling heat transfer. A simple method for numerical program assuming one-dimensional heat flow was used to predict the performance of these extended surface tubes. The object of this study was to predict the effects of the height, thickness, numbers and, clearance of the extended surface on boiling heat transfer. The results showed that extended surfaces are quite effective as compared to plane surfaces especially near the bum-out point and to promote heat flux in boiling heat transfer.

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