Shenyang Institute of Automation,Chinese Academy Of Sciences
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Concentration optimization of combinatorial drugs using Markov chain-based models
No full textAbstractBackgroundCombinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs.ResultsIn this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments.ConclusionThis article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy
Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing
No full textCompared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic nondestructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology
Path planning of mobile robot based on improved DDQN
No full textAiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm
Adaptive Control for Stochastic Nonlinear Systems with Dead Zone Output
No full textThe design of a tracking-constrained-based adaptive controller for a class of stochastic nonlinear systems with dead zone output is developed in this study. In order to deal with the tracking error constraint, a stochastic Barrier Lyapunov function (BLF) is utilized to the adaptive fuzzy backstepping control design. And the Nussbaum-type function is introduced to compensate for the influence of dead zone output. Finally, all the closed-loop signals are remain bounded with the convergence of the tracking error is ensured by the developed controller, and the constraint is satisfied. A simulation example verifies the effectiveness of the control strategy obtained
Overview of unmanned surface vessel coverage path planning techniques
No full textWith the rapid development of communication technology, sensors and artificial intelligence, unmanned surface vessel have made a breakthrough technology, which has attracted more and more attention at home and abroad in recent years. This paper introduces the development status of unmanned surface vessel and coverage path planning, summarizes the methods of environmental modeling, and focuses on the coverage path planning strategy. The development trend and application prospect of unmanned surface vessel are prospected according to the existing technology of cover path planning
3D Liver Tissue Model with Branched Vascular Networks by Multimaterial Bioprinting
No full textComplicated vessels pervade almost all body tissues and influence the pathophysiology of the human body significantly. However, current fabrication strategies have limited success at multiscale vascular biofabrication. This study reports a methodology to fabricate soft vascularized tissue at centimeter scale using multimaterial bioprinting by a customized multistage-temperature-control printer. The printed constructs can be perfused via the branched endothelialized vasculatures to support the well-formed 3D capillary networks, which ensure cellular activities with sufficient nutrient supply and then mimic a mature and functional liver tissue in terms of synthesis of liver-specific proteins. Moreover, an inner and external pressure-bearing layer is printed to support the direct surgical anastomosis of the carotid artery to the jugular vein. In summary, a versatile platform to recapitulate the vasculature network is presented, in which case sustaining the optimal cellularization in engineered tissues is achievable.</p
Microanalysis of a ductile iron by microchip laser-induced breakdown spectroscopy
No full textAbstract
Laser beams with ns pulse width are generally employed as an excitation source in the process of detecting inclusions and elemental segregation on a workpiece surface by microanalysis of the laser-induced breakdown spectroscopy. In addition, the ablation crater interval of laser sampling on the sample surface is generally 20 μm or more. It is difficult to detect the morphology of inclusions smaller than 50 μm in diameter and the micro-segregation of elements. However, in this work, when the laser ablation crater is 10 μm and the sampling resolution of the laser on the sample surface is 5 μm, the morphology and distribution of spherical inclusions (20–60 μm) in ductile iron can be detected according to the difference of the Fe spectrum on the Fe matrix and the spheroidal inclusions. Moreover, the distribution of micro-segregation of Mg and Ti elements in ductile iron was also studied
Comprehensive monitoring of industrial processes using multivariable characteristics evaluation and subspace decomposition
No full textGaussianity, non-Gaussianity, linearity, and nonlinearity generally coexist within industrial process variables, and should be taken into account simultaneously for process modelling with monitoring. This paper presents a comprehensive monitoring method of industrial processes using multivariable characteristics evaluation and subspace decomposition. First, a multivariable characteristics evaluation method is presented to divide the process variables into the Gaussian linear, Gaussian nonlinear, non-Gaussian linear, and non-Gaussian nonlinear subspaces. Second, the PCA-ICA-KPCA-KICA-based multivariable subspace decomposition is proposed for process modelling. Furthermore, comprehensive monitoring is developed and final results are combined using comprehensive statistics. By multivariable characteristics evaluation and subspace decomposition, the proposed method could evaluate and seek the multivariable characteristics and enhance the performance of process monitoring. The effectiveness and feasibility of the proposed comprehensive monitoring method are demonstrated by a numerical system and the benchmark Tennessee Eastman (TE) process
A key performance indicator-relevant approach based on kernel entropy component regression model for industrial system
No full textKey performance indicator (KPI)-relevant fault detection method has been raised for decades to hugely increase the economic interest of modern industries. However, the typical data-driven approaches like the kernel principal component analysis (KPCA) and the kernel entropy analysis (KECA) are inefficient to consider the influence taken by the fault factor on the KPI. Thus, in this work, an algorithm called the kernel entropy regression (KECR) is proposed to enhance the interpretability between the fault and the KPI. The proposed algorithm captures the information relevant to the KPI state in the subspace and rewords the decomposition of the KECA method. The angular structure of the KECR method achieves an accurate partition for process variables to hugely decrease false detection results. In the end, an industrial case is utilized to demonstrate the effectiveness of the KECR method
Intrusion detection based on hybrid classifiers for smart grid
No full textIn this paper, a novel intrusion detection method combining a deep learning-based method and a feature-based method is proposed for smart grid. Specifically, long short-term memory and extreme gradient boosting are adopted for intrusion detection, and the results are fused based on the accuracies of these two models. As the XGBoost method is sensitive to its parameters and unsuitable selections greatly degrade its performance, in this paper, a Bayesian method is proposed to optimize these parameters. Moreover, a crossover scheme in a genetic algorithm is introduced to reduce the impact of falling into a local optimum of Bayesian optimization. Extensive experimental results show the effectiveness of the proposed algorithm