In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.
To determine the effect of bone mesenchymal stem cells (BMSCs) in transplantation therapy for lipopolysaccharide (LPS)-induced coagulation disorder and the underlying mechanism of high mobility group protein B1-receptors for advanced glycation end products/Toll-like receptors-nuclear factor-κB (HMGB1-RAGE/TLRs-NF-κB) signaling pathway.BMSCs of female Sprague-Dawley (SD) rats ageing 4-5 weeks old were extracted and cultivated in vitro, and the fourth-passaged BMSCs phenotype was identified by flow cytometry for transplantation in the following experimental study. The rats were randomly divided into normal saline (NS) control group, LPS group, and BMSC group according to the random number table with 15 rats in each group. Coagulation disorders model was reproduced by injection of 1 mg/kg LPS via saphenous vein, and the rats in the NS control group was injected with equal volume NS. Those in the BMSC group were infused BMSC 0.5 mL containing 1×106 cells via tail vein at 2 hours after LPS injection, and the rats in other groups were injected with equal volume NS. Abdominal aorta blood was collected at 1, 3 and 7 days post operation. Coagulation indexes such as platelet count (PLT), platelet volume distribution width (PDW), mean platelet volume (MPV), plateletcrit (PCT), platelet large cell ratio (P-LCR), activated partial thromboplastin time (APTT), prothrombin time (PT), thrombin time (TT), international normalized ratio (INR), and fibrinogen (FIB) were determined. The mRNA levels and contents of HMGB1, RAGE, TLR2/4 and NF-κB were determined by real-time reverse transcription-polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively.(1) The cells cultured in vitro were spindle shaped or flat. The fourth-passaged BMSCs phenotype was successfully identified by flow cytometry technology. (2) Coagulation indexes: compared with NS control group, PLT, PCT and FIB in LPS group were significantly decreased, PDW, MPV, P-LCP, and INR were significantly increased, and APTT, PT, and TT were significantly prolonged from the first day. Furthermore, those in LPS group were gradually ameliorated with prolongation of LPS induction time. The coagulation function abnormality induced by LPS was reversed by BMSCs with significant difference at 1 day as compared with LPS group [PLT (×109/L): 398.8±17.9 vs. 239.1±15.8, PCT (%): 0.35±0.04 vs. 0.23±0.06, FIB (g/L): 1.7±0.6 vs. 0.8±0.1, PDW (%): 12.4±1.6 vs. 16.2±1.5, MPV (fl): 11.0±1.6 vs. 13.7±1.1, P-LCP (%): 13.0±2.1 vs. 15.3±2.7, INR: 1.52±0.17 vs. 1.82±0.19, APTT (s): 66.3±4.1 vs. 89.5±4.5, PT (s): 18.3±0.7 vs. 25.1±1.9, TT (s): 87.5±7.8 vs. 115.0±9.7, all P < 0.05], till 7 days. (3) HMGB1-RAGE/TLRs-NF-κB signaling pathway related molecules: compared with NS control group, the mRNA expressions and contents of HMGB1, RAGE, TLR2/4 and NF-κB were significantly increased in LPS group from the first day. However, the mRNA expressions and contents of the molecules in LPS group were gradually decreased with prolongation of LPS induction time. After BMSC intervention, the mRNA expressions and contents of molecules at 1 day were significantly lower than those of LPS group [HMGB1 mRNA (2-ΔΔCt): 10.77±0.04 vs. 24.51±3.69, HMGB1 content (μg/L): 0.48±0.01 vs. 0.95±0.06; RAGE mRNA (2-ΔΔCt): 11.57±1.11 vs. 18.08±0.29, RAGE content (μg/L): 0.73±0.04 vs. 1.37±0.06; TLR2 mRNA (2-ΔΔCt): 2.60±0.22 vs. 12.61±0.27, TLR2 content (μg/L): 0.81±0.03 vs. 1.59±0.09; TLR4 mRNA (2-ΔΔCt): 2.95±0.52 vs. 4.06±0.11, TLR4 content (μg/L): 0.80±0.09 vs. 1.18±0.11; NF-κB mRNA (2-ΔΔCt): 1.29±0.06 vs. 7.79±0.25, NF-κB content (μg/L): 1.22±0.24 vs. 2.42±0.26, all P < 0.05], till 7 days.BMSCs administration could ameliorate the coagulation function in LPS-induced coagulation disorder rats and these might be associated with HMGB1-RAGE/TLRs-NF-κB signaling pathway inhibition.
In this work, we focus on iterative distributed model predictive control (DMPC) of large-scale nonlinear systems subject to delayed state feedback. The motivation for studying this control problem is the presence of delayed measurements in feedback control of large-scale chemical processes and the potential use of sensors and actuators in industrial applications to improve closed-loop performance. Under the assumption that there exists an upper bound on the maximum measurement delay, we design an iterative DMPC system for nonlinear systems subject to delayed state feedback. The design takes advantage of bi-directional communication between the distributed controllers used in the iterative DMPC system. Sufficient conditions under which the proposed distributed control design guarantees that the states of the closed-loop system are ultimately bounded in a region that contains the origin are provided. The theoretical results are illustrated through a catalytic alkylation of benzene process.
A novel type of 3 radiation ionization gas flowmeter is introduced based on the molecule-marked method measuring the air stream velocity. The faint current from a ionization room is detected to a computer through an A/D converter after being magnified and filtered by an analogue-signal-processing circuit. Through a peak-seeking software, the flow rate can be achieved by the non-touching method even under bad conditions. The detailed design of hardware and software is given.
This paper presents a multi–source data fusion model method which could improve the blast furnace (BF) burden surface model accuracy. First, the three sections of straight line are used to describe the cross section of BF burden surface, and apply the motion law of the furnace burden to constrain the specific parameters of the three sections of straight line. Secondly, a multi–source data fusion method based on co–universal kriging estimation method is proposed. The temperature and height data are combined to build the unbiased estimation for the burden surface shape. Finally, an example of surface shape model using our proposed method in a 2500 m³ BF of a steel plant is discussed. The application shows that, contrasted with the traditional model, the model accuracy has arisen by 8%, and the resolution of surface shape has arisen by 0.32. The novel method can provide necessary guidance for energy saving and emission reduction in operation of the BF.
In the field of 5G mmWave massive MIMO (Multiple Input Multiple Output) communication systems, we propose a new method whose goal is to reduce the computational complexity of the antenna-side hybrid beamforming algorithm while meeting the requirements of communication spectrum efficiency and error rate. This research is based on a partially connected hybrid beamforming structure. According to the proposed new ECC (Equivalent Channel Capacity) method, the optimal phase information of the analog network can be obtained directly from the fully digital precoder. According to the hardware structure, a method of multiple sub-optimizations is adopted to obtain the analog precoding sub-modules. The simulation results show that the ECC-based hybrid beamforming algorithm can reduce the computational complexity by 75% while achieving the same performance effect as the iterative algorithm.
Large-scale process monitoring has become a challenging issue due to the integration of sub-systems or subprocesses, leading to numerous variables with complex relationship and potential missing information in modern industrial processes. To avoid this, a distributed expectation maximization-principal component analysis scheme is proposed in this paper, where the process variables are first divided into several sub-blocks using two-layer process decomposition method, based on knowledge and generalized Dice’s coefficient. Then, the missing information of variables is estimated by expectation maximization algorithm in the principal component analysis framework, then the expectation maximization-principal component analysis method is applied for fault detection to each sub-block. Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach.
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