Development Of Sensor Models Research Articles

The Effect of Rainfall and Illumination on Automotive Sensors Detection Performance

Vehicle safety promises to be one of the Advanced Driver Assistance System’s (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, LiDAR and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. Most of the current studies are based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development

Industrial process data are naturally complex time series with high nonlinearities and dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) network is very suitable for soft sensor model development. However, the original LSTM does not consider variable and sample relevance for quality prediction. In order to overcome this problem, a spatiotemporal attention-based LSTM network is proposed for soft sensor modeling, which can, not only identify important input variables that are related to the quality variable at each time step, but also adaptively discover quality-related hidden states across all time steps. By taking the spatiotemporal quality-relevant interactions into consideration, the prediction performance can be improved for the soft sensor model. The effectiveness and flexibility of the proposed model is demonstrated on an industrial hydrocracking process to predict the initial boiling points of heavy naphtha and aviation kerosene.

Generic Sensor Model Development and Simulation of Degradation Effects

Generic Sensor Model Development and Simulation of Degradation Effects

Soft Sensor Model Development for Cobalt Oxalate Synthesis Process Based on Adaptive Gaussian Mixture Regression

Average particle size is a critical quality variable for optimal operation of the cobalt oxalate synthesis process. However, the measurement for this variable is often achieved from offline laboratory assaying procedure with low sampling rate and low reliability. Therefore, a data-driven soft sensor model based on adaptive Gaussian mixture regression (AGMR) is presented in this paper. Firstly, a GMR based soft sensor model is developed for predicting the average particle size. Secondly, the prediction uncertainty obtained from the GMR model is used to assess the performance of the current soft sensor model. Thirdly, a dual updating algorithm based on the model performance assessment is constructed to track the time-varying behavior of the synthesis process. In the updating method, bias updating and moving window model updating methods are performed in turns based on the results of model performance assessment. The dual updating mechanism can avoid blind updating. Finally, a numerical example and a real industrial cobalt oxalate synthesis process application are used to demonstrate the effectiveness of the proposed method.

Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression

For complex industrial plants with multiphase/multimode data characteristic, Gaussian mixture model (GMM) has been used for soft sensor modeling. However, almost all GMM-based soft sensor modeling methods only employ GMM for identification of different operating modes, which means additional regression algorithms like PLS should be incorporated for quality prediction in different localized modes. In this paper, the Gaussian mixture regression (GMR) model is introduced for multiphase/multimode soft sensor modeling. In GMR, operating mode identification and variable regression are integrated into one model; thus, there is no need to switch prediction models when the operating mode changes from one to another. To improve the GMR model fitting performance, a heuristic algorithm is adopted for parameter initialization and component number optimization. Feasibility and efficiency of GMR based soft sensor are validated through a numerical example and two benchmark processes.