Prognostics of the remaining useful life (RUL) of lithium-ion batteries is a crucial role in the battery management systems (BMS). An artificial neural network (ANN) does not require much knowledge from the lithium-ion battery systems, thus it is a prospective data-driven prognostic method of lithium-ion batteries. Though the ANN has been applied in prognostics of lithium-ion batteries in some references, no one has compared the prognostics of the lithium-ion batteries based on different ANN. The ANN generally can be classified to two categories: the shallow ANN, such as the back propagation (BP) ANN and the nonlinear autoregressive (NAR) ANN, and the deep ANN, such as the long short-term memory (LSTM) NN. An improved LSTM NN is proposed in order to achieve higher prediction accuracy and make the construction of the model simpler. According to the lithium-ion data from the NASA Ames, the prognostics comparison of lithium-ion battery based on the BP ANN, the NAR ANN, and the LSTM ANN was studied in detail. The experimental results show: (1) The improved LSTM ANN has the best prognostic accuracy and is more suitable for the prediction of the RUL of lithium-ion batteries compared to the BP ANN and the NAR ANN; (2) the NAR ANN has better prognostic accuracy compared to the BP ANN.
The grading evaluation of metacarpophalangeal rheumatoid arthritis (RA) ultrasonic images is a diagnostic challenge that heavily relies on the expertise of trained sonographers. This study presents a grading method for detecting and estimating the geometric and texture features of synovium thickening and bone erosion. Unlike previous studies in this area, this work uses the metrics and texture features of region of interest (ROI). The highlighted feature of metacarpophalangeal bone and the dark feature of the synovial thickening are extracted simultaneously by the segmented method based on the Gaussian scale space. The segmented results are analyzed to extract three quantitative geometric parameters, which are combined with gray-level co-occurrence matrix (GLCM) statistic texture features to describe the ultrasonic image of metacarpophalangeal RA. To obtain the preferable ability of classification, we applied a support vector machine (SVM) and various feature descriptors, including GLCM, local binary patterns (LBP), and GLCM + LBP, to grade the ultrasonic image of metacarpophalangeal RA. Results show that the SVM, based on our feature descriptor, provides the highest accuracy of up to 92.50%, of the four descriptors. The SVM based on GLCM+LBP descriptor shows better accuracy (86.55%) than either SVM + LBP (85.43%) or SVM+GLCM (82.51%) for discriminating among four grade RA ultrasonic images. Overall, this methodology points to a significant grading of metacarpophalangeal RA ultrasound images without medical expert analysis or blood sample analysis, such as detecting C-reactive protein, measuring erythrocyte sedimentation rate, and testing rheumatoid factor. INDEX TERMS Computer aided diagnosis, metacarpophalangeal rheumatoid arthritis, machine learning, Ultrasonic imaging.
Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries can ensure the normal and effective operation of power systems using lithium-ion batteries. However, how to select battery prediction parameters through scientific methods and how to accurately predict battery RUL values under high and low temperature conditions are still a huge challenge. Thus according to the technique for order preference by similarity to ideal solution (TOPSIS) based on information entropy, improved particle swarm optimization (PSO) and moving average filter(MAF), a novel data-driven method for predict lithium-ion batteries' RUL is proposed. The TOPSIS method based on information entropy is proposed to select the best degradation parameters; a sliding average low-pass filter is used to solve the capacity regeneration and noise problem of the battery experimental data; the improved PSO algorithm is presented to predict the battery RUL accurately. Based on the batteries experimental data from NASA and University of Maryland, we have done many simulation experiments on parameters selection and RUL accuracy comparisons among several data-driven methods. The experimental results shows:(1) compared with the other prediction methods without degradation parameters selection, the proposed method with TOPSIS and MAF filtering is more accurate;(2) our proposed algorithm has higher prediction accuracy and use less training data than other data-driven algorithms;(3) this method has high prediction accuracy under both the high and low temperature conditions.
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