Least-Squares Based and Gradient Based Iterative Parameter Estimation Algorithms for a Class of Linear-in-Parameters Multiple-Input Single-Output Output Error Systems

The identification of a class of linear-in-parameters multiple-input single-output systems is considered. By using the iterative search, a least-squares based iterative algorithm and a gradient based iterative algorithm are proposed. A nonlinear example is used to verify the effectiveness of the algorithms, and the simulation results show that the least-squares based iterative algorithm can produce more accurate parameter estimates than the gradient based iterative algorithm

Wave interference network with a wave function for traffic sign recognition

In this paper, we successfully combine convolution with a wave function to build an effective and efficient classifier for traffic signs, named the wave interference network (WiNet). In the WiNet, the feature map extracted by the convolutional filters is refined into many entities from an input image. Each entity is represented as a wave. We utilize Euler's formula to unfold the wave function. Based on the wave-like information representation, the model modulates the relationship between the entities and the fixed weights of convolution adaptively. Experiment results on the Chinese Traffic Sign Recognition Database (CTSRD) and the German Traffic Sign Recognition Benchmark (GTSRB) demonstrate that the performance of the presented model is better than some other models, such as ResMLP, ResNet50, PVT and ViT in the following aspects: 1) WiNet obtains the best accuracy rate with 99.80% on the CTSRD and recognizes all images exactly on the GTSRB; 2) WiNet gains better robustness on the dataset with different noises compared with other models; 3) WiNet has a good generalization on different datasets

Remaining useful life indirect prediction of lithium-ion batteries using CNN-BiGRU fusion model and TPE optimization

The performance of lithium-ion batteries declines rapidly over time, inducing anxiety in their usage. Ascertaining the capacity of these batteries is difficult to measure directly during online remaining useful life (RUL) prediction, and a single deep learning model falls short of accuracy and applicability in RUL predictive analysis. Hence, this study proposes a lithium-ion battery RUL indirect prediction model, fusing convolutional neural networks and bidirectional gated recurrent units (CNN-BiGRU). The analysis of characteristic parameters of battery life status reveals the selection of pressure discharge time, average discharge voltage and average temperature as health factors of lithium-ion batteries. Following this, a CNN-BiGRU model for lithium-ion battery RUL indirect prediction is established, and the Tree-structured Parzen Estimator (TPE) adaptive hyperparameter optimization method is used for CNN-BiGRU model hyperparameter optimization. Overall, comparison experiments on single-model and other fusion models demonstrate our proposed model's superiority in the prediction of RUL in terms of stability and accuracy

Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mrow""mml:mo"±"/mml:mo""/mml:mrow""/mml:math"30cm, which meet the requirement of urban rail transit. Document type: Articl

Rapid Algorithm for Generating and Selecting Optimal Metro Train Speed Curves Based on Alpha Zero and Expert Experience

According to the current research status of urban rail transit’s fully automatic operation (FAO), the train driving speed curves are usually obtained through simulation and calculation. The train driving speed curves obtained by this method not only have low efficiency but also are not suitable for complex road conditions. Inspired by AlphaZero, a reinforcement learning algorithm that utilised vast amounts of artificial data to defeat AlphaGo, an AI Go program, this paper investigates and analyses methods for rapidly generating a large number of speed curves and selecting those with superior performance for train operation. Firstly, we use the powerful third-party library in Python as the basis, combined with the idea of AlphaZero, to produce artificial speed curves for metro train driving. Secondly, we set relevant parameters with reference to expert experience to quickly produce massive reasonable artificial speed curves. Thirdly, we analysed relevant indicators such as energy consumption, running time error and passenger comfort to select some speed curves with better comprehensive performance. Finally, through the many observations with different running distances and different speed limits, we found that the speed curves produced and selected by our algorithm are more productive, diverse and conducive to the research of train driving operation than the actual data from traditional manual driving and ATO (automatic train operation) system

Observational studies of the effects of wind mixing and biological process on the vertical distribution of dissolved oxygen off the Changjiang Estuary

Wind mixing is important in regulating dissolved oxygen (DO) variability; however, the transect response of DO dynamics to wind disturbance has seldom been documented with field data. In the summer of 2017, repeat transect observations off the Changjiang Estuary were conducted throughout a fresh wind (the maximum wind speed was 9.8 m s–1) event to reveal the role of physical mixing and biological activity in DO variations. After the wind event, hypoxia was alleviated presenting as the hypoxia thickness decreased from 30 m to 20 m. However, poorly ventilated near-bottom hypoxia was aggravated with a further decrease in DO. Generally, the saturation of dissolved oxygen (DOs) in depth-integrated water column increased by 9%–49% through physical diffusion with a weakened stratification and enhanced phytoplankton bloom. However, in this case, the wind-induced physical water mass mixing by transporting DO downward had a limited contribution to the water-column DO budget, while upwards nutrients induced by mixing fueled the larger vertical area of algae bloom and subsequent substantial oxygen consumption. As the wind speed increased, the air-sea exchange would be important in supplying DO, especially in nearshore areas, which could effectively offset the DO deficiency. In summary, frequently occurring fresh wind-mixing events off the Changjiang Estuary would alleviate hypoxia in the water column but probably exacerbate hypoxia at the bottom, as determined by competing ventilation and respiration roles. Such complex interactions likely occur and perform differently as wind stress varies. Thus, high-spatial and long-term process observations are required to better understand the net effects of bottom hypoxia evolution

Massive nutrients offshore transport off the Changjiang Estuary in flooding summer of 2020

Flood events significantly increase water discharges and terrigenous material inputs to coastal waters. Riverine nutrients in the Changjiang Estuary are transported by the dispersion of Changjiang Diluted Water (CDW) plumes and detached low-salinity water patches. However, the effects of flooding on nutrient offshore transports have not been well explored. Here, we present the nutrient conditions in the Changjiang Estuary and adjacent East China Sea in the historical flooding year 2020. Comparisons of nutrient distributions between flooding years, drought year and non-flooding years were also made. Our results showed that nitrate flux from the Changjiang River in August 2020 was 1.5 times that of the multi-year averaged flux in non-flooding years. Enormous riverine nutrient input resulted in much higher nutrient concentrations in the outer estuary than those in non-flooding years. In addition, a detached low-salinity water patch was observed, which made the salinity of the northern estuary even lower than that in the historical flooding year 1998. Surface dissolved inorganic nitrate (DIN) level in the low-salinity water patch was even ~16 times of that at nearby station in the drought year 2006. While phosphate (PO43−) concentrations were less than 0.1 μmol L−1 east of 123°E, which was probably caused by intensive biological uptake, as indicated by a high Chlorophyll a (Chl a) concentration (29.08 μg L−1). The depleted PO43− and high N/P of the low-salinity water patch suggested PO43− limitation even under flood conditions. A three end-member mixing model was adopted to identify the contributions of the CDW end-member (CDWend-member) and biological process to nutrient distributions. Our model results showed that the nutrient contribution of the CDWend-member to the estuary (122–124°E, 31–32.5°N) in flooding year 2020 was over double that in drought year 2006. Model-derived biological DIN uptake was as high as 24.65 μmol L−1 at the low-salinity water patch. Accordingly, the estimated net community production was 566–1131 mg C m−2 d−1 within the euphotic zone. The offshore transport of a low-salinity, high-DIN water patch during flooding could probably have a significant influence on biogeochemical cycles in the broad shelf, and even the adjacent Japan Sea

Cross-talk between PRMT1-mediated methylation and ubiquitylation on RBM15 controls RNA splicing

RBM15, an RNA binding protein, determines cell-fate specification of many tissues including blood. We demonstrate that RBM15 is methylated by protein arginine methyltransferase 1 (PRMT1) at residue R578 leading to its degradation via ubiquitylation by an E3 ligase (CNOT4). Overexpression of PRMT1 in acute megakaryocytic leukemia cell lines blocks megakaryocyte terminal differentiation by downregulation of RBM15 protein level. Restoring RBM15 protein level rescues megakaryocyte terminal differentiation blocked by PRMT1 overexpression. At the molecular level, RBM15 binds to pre-mRNA intronic regions of genes important for megakaryopoiesis such as GATA1, RUNX1, TAL1 and c-MPL. Furthermore, preferential binding of RBM15 to specific intronic regions recruits the splicing factor SF3B1 to the same sites for alternative splicing. Therefore, PRMT1 regulates alternative RNA splicing via reducing RBM15 protein concentration. Targeting PRMT1 may be a curative therapy to restore megakaryocyte differentiation for acute megakaryocytic leukemia

Deep Neural Fuzzy System Oriented toward High-Dimensional Data and Interpretable Artificial Intelligence

Fuzzy systems (FSs) are popular and interpretable machine learning methods, represented by the adaptive neuro-fuzzy inference system (ANFIS). However, they have difficulty dealing with high-dimensional data due to the curse of dimensionality. To effectively handle high-dimensional data and ensure optimal performance, this paper presents a deep neural fuzzy system (DNFS) based on the subtractive clustering-based ANFIS (SC-ANFIS). Inspired by deep learning, the SC-ANFIS is proposed and adopted as a submodule to construct the DNFS in a bottom-up way. Through the ensemble learning and hierarchical learning of submodules, DNFS can not only achieve faster convergence, but also complete the computation in a reasonable time with high accuracy and interpretability. By adjusting the deep structure and the parameters of the DNFS, the performance can be improved further. This paper also performed a profound study of the structure and the combination of the submodule inputs for the DNFS. Experimental results on five regression datasets with various dimensionality demonstrated that the proposed DNFS can not only solve the curse of dimensionality, but also achieve higher accuracy, less complexity, and better interpretability than previous FSs. The superiority of the DNFS is also validated over other recent algorithms especially when the dimensionality of the data is higher. Furthermore, the DNFS built with five inputs for each submodule and two inputs shared between adjacent submodules had the best performance. The performance of the DNFS can be improved by distributing the features with high correlation with the output to each submodule. Given the results of the current study, it is expected that the DNFS will be used to solve general high-dimensional regression problems efficiently with high accuracy and better interpretability