In-Cylinder Oxygen Concentration Estimation Based on Virtual Measurement and Data Fusion Algorithm for Turbocharged Diesel Engines

In-cylinder oxygen concentration (ICOC) is critical for advanced combustion control of internal combustion engines, and is hard to be accessed in commercial measurements. In existing research, ICOC is predicted by conventional dynamical model based on mass/energy conservation, which suffers from uncertainties such as inaccuracy of volumetric efficiency or the error of orifice geometry. In this paper, we enhance the ICOC estimation by implementing two vital strategies. Firstly, we introduce a method called virtual measurement to resist the conventional model uncertainties, in this method we modeling the ICOC as a function of ignition delay which can be obtained by measuring the in-cylinder pressure. Secondly, we apply Kalman filter to fuse the ICOC results from the conventional dynamical model and the virtual measurement. The data fusion algorithm turns the estimation to a predictor-corrector fashion, which further improves the overall accuracy and robustness. The proposed approach is validated through a calibrated GT-Power engine model. The results show that the estimation error can be achieved form at worst 0.03 to at best 0.01 on steady state.

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Application of multi-resolution data fusion algorithm

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.02556 ◽

2010 ◽

Vol 30 (9) ◽

pp. 2556-2558 ◽

Cited By ~ 1

Author(s):

Ming-bo SHI ◽

Ji-hong CHEN ◽

Zheng-zheng JIANG

Keyword(s):

Data Fusion ◽

Fusion Algorithm ◽

Resolution Data

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Comparison of Diesel Engine Vibroacoustic Properties Powered by Bio and Standard Fuel

Energies ◽

10.3390/en14051478 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1478

Author(s):

Radoslaw Wrobel ◽

Gustaw Sierzputowski ◽

Zbigniew Sroka ◽

Radostin Dimitrov

Keyword(s):

Alternative Fuels ◽

Internal Combustion Engines ◽

Combustion Process ◽

Significant Other ◽

Engine Torque ◽

Engine Model ◽

Vehicle Operation ◽

History Of ◽

Profile Changes ◽

The Time Domain

Alternative fuels appeared soon after the first internal combustion engines were designed. The history of alternative fuels is basically as long as the history of the automotive industry. Initially, fuels whose physicochemical properties allowed for a change in parameters of the combustion process in order to achieve greater efficiency and reliability were searched for. Nowadays, there are significantly more variables; in addition to the above mentioned parameters, alternative fuels are being sought that will ensure environmental protection during vehicle operation and improve the ergonomics of use. This article outlines the results of the authors’ own comparative tests of vibrations of a vibroacoustic character. Based on a popular engine model, the vibration–acoustic responses of a system powered by two types of fuel, namely, diesel and biodiesel (B10), are compared. The research consists of comparing vibrations in both time and frequency domains. In the case of the time domain, the evaluation was performed with vibrations as a function of engine torque and speed. In the case of frequency analysis, the focus was on changes in the frequency response for the tested fuels. The research shows that the profile of vibroacoustic vibrations changes in the case of biodiesel power supply in relation to standard fuel. The vibration profile changes significantly as a function of speed and only slightly in relation to the engine load. The results presented in this article show different vibroacoustic responses of an engine powered by diesel and biodiesel; the change is minor for lower speeds but significant (other harmonics are dominant) for higher speeds (changes in the dominant harmonic magnitude of up to 10% at a crankshaft speed of 3000 rpm).

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A novel optimal data fusion algorithm and its application for the integrated navigation system of missile

Chinese Journal of Aeronautics ◽

10.1016/j.cja.2021.01.027 ◽

2021 ◽

Author(s):

Di Liu ◽

Xiyuan Chen ◽

Xiao Liu

Keyword(s):

Data Fusion ◽

Navigation System ◽

Integrated Navigation ◽

Fusion Algorithm ◽

Integrated Navigation System

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Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽

10.3390/f12030286 ◽

2021 ◽

Vol 12 (3) ◽

pp. 286

Author(s):

Sang-Jin Park ◽

Seung-Gyu Jeong ◽

Yong Park ◽

Sang-hyuk Kim ◽

Dong-kun Lee ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Data Fusion ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fusion Model ◽

Fusion Algorithm ◽

Study Region ◽

Modis Imagery ◽

Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

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An efficient intelligent data fusion algorithm for wireless sensor network

Procedia Computer Science ◽

10.1016/j.procs.2021.02.079 ◽

2021 ◽

Vol 183 ◽

pp. 418-424

Author(s):

Haitao Wang ◽

Lihua Song ◽

Jue Liu ◽

Tingting Xiang

Keyword(s):

Wireless Sensor Network ◽

Data Fusion ◽

Sensor Network ◽

Wireless Sensor ◽

Fusion Algorithm

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Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network

2020 IEEE/CIC International Conference on Communications in China (ICCC) ◽

10.1109/iccc49849.2020.9238952 ◽

2020 ◽

Author(s):

Xuexin Zhao ◽

Junhua Wu ◽

Maoli Wang ◽

Guangshun Li ◽

Haili Yu ◽

...

Keyword(s):

Neural Network ◽

Data Fusion ◽

Sensor Data ◽

Fusion Algorithm ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion

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Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽

10.3390/s21072480 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2480

Author(s):

Isidoro Ruiz-García ◽

Ismael Navarro-Marchal ◽

Javier Ocaña-Wilhelmi ◽

Alberto J. Palma ◽

Pablo J. Gómez-López ◽

...

Keyword(s):

Data Fusion ◽

Reference System ◽

Inertial Sensor ◽

Low Cost ◽

Sampling Rate ◽

Sensor Data ◽

Fusion Algorithm ◽

Improve Technique ◽

Worst Case ◽

Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

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