CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels

With the development of end-to-end control based on deep learning, it is important to study new system modeling techniques to realize dynamics modeling with high-dimensional inputs. In this paper, a novel Koopman-based deep convolutional network, called CKNet, is proposed to identify latent dynamics from raw pixels. CKNet learns an encoder and decoder to play the role of the Koopman eigenfunctions and modes, respectively. The Koopman eigenvalues can be approximated by eigenvalues of the learned state transition matrix. The deterministic convolutional Koopman network (DCKNet) and the variational convolutional Koopman network (VCKNet) are proposed to span some subspace for approximating the Koopman operator respectively. Because CKNet is trained under the constraints of the Koopman theory, the identified latent dynamics is in a linear form and has good interpretability. Besides, the state transition and control matrices are trained as trainable tensors so that the identified dynamics is also time-invariant. We also design an auxiliary weight term for reducing multi-step linearity and prediction losses. Experiments were conducted on two offline trained and four online trained nonlinear forced dynamical systems with continuous action spaces in Gym and Mujoco environment respectively, and the results show that identified dynamics are adequate for approximating the latent dynamics and generating clear images. Especially for offline trained cases, this work confirms CKNet from a novel perspective that we visualize the evolutionary processes of the latent states and the Koopman eigenfunctions with DCKNet and VCKNet separately to each task based on the same episode and results demonstrate that different approaches learn similar features in shapes.Comment: 8 pages, 7 figure

A deep learning framework based on Koopman operator for data-driven modeling of vehicle dynamics

Autonomous vehicles and driving technologies have received notable attention in the past decades. In autonomous driving systems, \textcolor{black}{the} information of vehicle dynamics is required in most cases for designing of motion planning and control algorithms. However, it is nontrivial for identifying a global model of vehicle dynamics due to the existence of strong non-linearity and uncertainty. Many efforts have resorted to machine learning techniques for building data-driven models, but it may suffer from interpretability and result in a complex nonlinear representation. In this paper, we propose a deep learning framework relying on an interpretable Koopman operator to build a data-driven predictor of the vehicle dynamics. The main idea is to use the Koopman operator for representing the nonlinear dynamics in a linear lifted feature space. The approach results in a global model that integrates the dynamics in both longitudinal and lateral directions. As the core contribution, we propose a deep learning-based extended dynamic mode decomposition (Deep EDMD) algorithm to learn a finite approximation of the Koopman operator. Different from other machine learning-based approaches, deep neural networks play the role of learning feature representations for EDMD in the framework of the Koopman operator. Simulation results in a high-fidelity CarSim environment are reported, which show the capability of the Deep EDMD approach in multi-step prediction of vehicle dynamics at a wide operating range. Also, the proposed approach outperforms the EDMD method, the multi-layer perception (MLP) method, and the Extreme Learning Machines-based EDMD (ELM-EDMD) method in terms of modeling performance. Finally, we design a linear MPC with Deep EDMD (DE-MPC) for realizing reference tracking and test the controller in the CarSim environment.Comment: 12 pages, 10 figures, 1 table, and 2 algorithm

Influence and Optimization of Packet Loss on the Internet-Based Geographically Distributed Test Platform for Fuel Cell Electric Vehicle Powertrain Systems

In view of recent developments in fuel cell electric vehicle powertrain systems, Internet-based geographically distributed test platforms for fuel cell electric vehicle powertrain systems become a development and validation trend. Due to the involvement of remote connection and the Internet, simulation with connected models can suffer great uncertainty because of packet loss. Such a test platform, including packet loss characteristics, was built using MATLAB/Simulink for use in this paper. The simulation analysis results show that packet loss affects the stability of the whole test system. The impact on vehicle speed is mainly concentrated in the later stage of simulation. Aiming at reducing the effect of packet loss caused by Internet, a robust model predictive compensator was designed. Under this compensator, the stability of the system is greatly improved compared to the system without a compensator

Robust Multimodal Failure Detection for Microservice Systems

Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches

Expert Consensus on Microtransplant for Acute Myeloid Leukemia in Elderly Patients -Report From the International Microtransplant Interest Group

Recent studies have shown that microtransplant (MST) could improve outcome of patients with elderly acute myeloid leukemia (EAML). To further standardize the MST therapy and improve outcomes in EAML patients, based on analysis of the literature on MST, especially MST with EAML from January 1st, 2011 to November 30th, 2022, the International Microtransplant Interest Group provides recommendations and considerations for MST in the treatment of EAML. Four major issues related to MST for treating EAML were addressed: therapeutic principle of MST (1), candidates for MST (2), induction chemotherapy regimens (3), and post-remission therapy based on MST (4). Others included donor screening, infusion of donor cells, laboratory examinations, and complications of treatment

microRNA Expression Profiling of Side Population Cells in Human Lung Cancer and Preliminary Analysis

Background and objective Recent studies indicate that the side population (SP) which is an enriched source of cancer stem cells (CSCs) is the root cause of tumor growth and development. SP appears to be highly resistant to chemo- and radio-therapy which becomes an important factor in tumor recurrence and metastasis. The aim of this study is to determine the difference of microRNA expression profiles between SP cells and non-SP cells so as to lay necessary basis for research on the function of miRNA in lung cancer stem cells. Methods SP and non-SP cells were isolated using flow cytometry and Hoechst 33342 dye efflux assay from human lung adenocarcinoma A549 cell. The total RNA was extracted. The microarray detection system was employed to analyze whether there was difference in miRNA expression profile between SP and non-SP cells. Results A total of 85 differentially expressed miRNA were found, including 32 over-expression and 53 low-expression miRNA in SP. Conclusion miRNA may play important roles in tumorigenesis of lung cancer stem cell. The study of miRNA contributes to elucidate the molecular mechanism of lung cancer stem cell

Analysis of Bacterial Community Composition of Corroded Steel Immersed in Sanya and Xiamen Seawaters in China via Method of Illumina MiSeq Sequencing

Metal corrosion is of worldwide concern because it is the cause of major economic losses, and because it creates significant safety issues. The mechanism of the corrosion process, as influenced by bacteria, has been studied extensively. However, the bacterial communities that create the biofilms that form on metals are complicated, and have not been well studied. This is why we sought to analyze the composition of bacterial communities living on steel structures, together with the influence of ecological factors on these communities. The corrosion samples were collected from rust layers on steel plates that were immersed in seawater for two different periods at Sanya and Xiamen, China. We analyzed the bacterial communities on the samples by targeted 16S rRNA gene (V3–V4 region) sequencing using the Illumina MiSeq. Phylogenetic analysis revealed that the bacteria fell into 13 phylotypes (similarity level = 97%). Proteobacteria, Firmicutes and Bacteroidetes were the dominant phyla, accounting for 88.84% of the total. Deltaproteobacteria, Clostridia and Gammaproteobacteria were the dominant classes, and accounted for 70.90% of the total. Desulfovibrio spp., Desulfobacter spp. and Desulfotomaculum spp. were the dominant genera and accounted for 45.87% of the total. These genera are sulfate-reducing bacteria that are known to corrode steel. Bacterial diversity on the 6 months immersion samples was much higher than that of the samples that had been immersed for 8 years (P < 0.001, Student’s t-test). The average complexity of the biofilms from the 8-years immersion samples from Sanya was greater than those from Xiamen, but not significantly so (P > 0.05, Student’s t-test). Overall, the data showed that the rust layers on the steel plates carried many bacterial species. The bacterial community composition was influenced by the immersion time. The results of our study will be of benefit to the further studies of bacterial corrosion mechanisms and corrosion resistance

A deep Koopman operator‐based modelling approach for long‐term prediction of dynamics with pixel‐level measurements

Abstract Although previous studies have made some clear leap in learning latent dynamics from high‐dimensional representations, the performances in terms of accuracy and inference time of long‐term model prediction still need to be improved. In this study, a deep convolutional network based on the Koopman operator (CKNet) is proposed to model non‐linear systems with pixel‐level measurements for long‐term prediction. CKNet adopts an autoencoder network architecture, consisting of an encoder to generate latent states and a linear dynamical model (i.e., the Koopman operator) which evolves in the latent state space spanned by the encoder. The decoder is used to recover images from latent states. According to a multi‐step ahead prediction loss function, the system matrices for approximating the Koopman operator are trained synchronously with the autoencoder in a mini‐batch manner. In this manner, gradients can be synchronously transmitted to both the system matrices and the autoencoder to help the encoder self‐adaptively tune the latent state space in the training process, and the resulting model is time‐invariant in the latent space. Therefore, the proposed CKNet has the advantages of less inference time and high accuracy for long‐term prediction. Experiments are performed on OpenAI Gym and Mujoco environments, including two and four non‐linear forced dynamical systems with continuous action spaces. The experimental results show that CKNet has strong long‐term prediction capabilities with sufficient precision

A Ga3+Self-Assembled Fluorescent Probe for ATP Imaging in Vivo

Adenosine 5′-triphosphate (ATP) is a functional molecule associated with many important biological processes. Fluorescent detection methods for ATP with facile performance and high selectivity are in demand. One of the possible multi-membered arrays assembled between DHBO and Ga3+ ions was conducted in aqueous solution, which can selectively recognize ATP with fluorescence enhancement from ADP, AMP and other structurally similar nucleoside triphosphates in vitro and in vivo. ATP facilitates the interaction between DHBO and Ga3+ ions, resulting in the fluorescence increase. The detection limit for ATP was calculated to be 5.49×10−7 M, which is much lower than that of intracellular concentrations (1–10 mM). In addition, DHBO–Ga3+ can be applied to detect ATP-relevant enzyme activity

Strain Transfer Characteristics of Resistance Strain-Type Transducer Using Elastic-Mechanical Shear Lag Theory

The strain transfer characteristics of resistance strain gauge are theoretically investigated. A resistance strain-type transducer is modeled to be a four-layer and two-glue (FLTG) structure model, which comprises successively the surface of an elastomer sensitive element, a ground adhesive glue, a film substrate layer, an upper adhesive glue, a sensitive grids layer, and a polymer cover. The FLTG model is studied in elastic–mechanical shear lag theory, and the strain transfer progress in a resistance strain-type transducer is described. The strain transitional zone (STZ) is defined and the strain transfer ratio (STR) of the FLTG structure is formulated. The dependences of the STR and STZ on both the dimensional sizes of the adhesive glue and structural parameters are calculated. The results indicate that the width, thickness and shear modulus of the ground adhesive glue have a greater influence on the STZ ratio. To ensure that the resistance strain gauge has excellent strain transfer performance and low hysteresis, it is recommended that the paste thickness should be strictly controlled, and the STZ ratio should be less than 10%. Moreover, the STR strongly depends on the length and width of the sensitive grids