A hierarchical MPC scheme for interconnected systems

This paper describes a hierarchical control scheme for interconnected systems. The higher layer of the control structure is designed with robust Model Predictive Control (MPC) based on a reduced order dynamic model of the overall system and is aimed at optimizing long-term performance, while at the lower layer local regulators acting at a higher frequency are designed for the full order models of the subsystems to refine the control action. A simulation experiment concerning the control of the temperature inside a building is reported to witness the potentialities of the proposed approach

CDC20, TOP2A and NEK2 Expression in Esophageal Squamous Cell Carcinoma and Its Clinical Significance

Objective: to study the expression and clinical significance CDC20,TOP2A, NEK2 esophageal squamous cell carcinoma. Methods: Toselect 70 patients with esophageal squamous cell carcinoma, Between August 2018 - August 2020, All intraoperative pathological specimens,A group -35 cases), Cancer tissue, B group, adjacent tissues), two groups of CDC20, TOP2A, NEK 2 expression were detected and analyzed by immunohistochemistry and semi-quantitative reverse transcription polymerase chain reaction -RT-PcR) assay.Results: the values of CDC20,TOP2A, NEK2 expression level in A group were significantly higher thanthose in B group -P<0.05). The expression level CDC20, TOP2A, NEK2esophageal squamous cell carcinoma was positively correlated with TNMstage and lymphatic metastasis, and negatively correlated with tumordifferentiation. Conclusion: CDC20, TOP2A, NEK2 high expression leveldirectly affects the metastasis, recurrence and prognosis of esophagealsquamous cell carcinoma. The combination of three indexes can accuratelyevaluate the pathological status of patients with esophageal squamous cellcarcinoma and help to judge the prognosis of patients accuratel

A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples

Human papillomavirus (HPV) is a sexually transmitted virus. Cervical cancer is one of the highest incidences of cancer, almost all patients are accompanied by HPV infection. In addition, the occurrence of a variety of cancers is also associated with HPV infection. HPV vaccination has gained widespread popularity in recent years with the increase in public health awareness. In this context, HPV testing not only needs to be sensitive and specific but also needs to trace the source of HPV infection. Through machine learning and deep learning, information from medical examinations can be used more effectively. In this review, we discuss recent advances in HPV testing in combination with machine learning and deep learning

Status of the singlino-dominated dark matter in general Next-to-Minimal Supersymmetric Standard Model

With the rapid progress of dark matter direct detection experiments, the attractiveness of the popular bino-dominated dark matter in economical supersymmetric theories is fading. As an alternative, the singlino-dominated dark matter in general Next-to-Minimal Supersymmetric Standard Model (NMSSM) is paying due attention. This scenario has the following distinct characteristics: free from the tadpole problem and the domain-wall problem of the NMSSM with a $Z_3$-symmetry, predicting more stable vacuum states than the $Z_3$-NMSSM, capable of forming an economical secluded dark matter sector to yield the dark matter experimental results naturally, and readily weaken the restrictions from the LHC search for SUSY. Consequently, it can explain the muon g-2 anomaly in broad parameter space that agrees with various experimental results while simultaneously breaking the electroweak symmetry naturally. In this study, we show in detail how the scenario coincides with the experiments, such as the SUSY search at the LHC, the dark matter search by the LZ experiment, and the improved measurement of the muon g-2. We provide a simple and clear picture of the physics inherent in the general NMSSM

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