全地形轮式移动机器人运动学建模与分析

提出全地形轮式移动机器人的正逆运动学问题。将机器人看成一个混合串-并联多刚体系统,从每个轮-地接触点到机器人车体分别构成一个串联子系统,抛弃车轮纯滚动假设,在轮-地接触点处建立瞬时坐标系,考虑车轮的平面滑移,从而对每个串联子系统形成一个封闭的速度链。对于每个速度闭链,可直接在驱动轮轮心处写出从机器人各驱动轮到机器人本体之间的运动方程,将每个速度闭链的运动方程合并即可得到机器人的整体运动学模型。以一个具有被动柔顺机构的六轮全地形移动机器人为对象进行推导,该方法既考虑了地形不平的影响,又考虑了车轮的前向、侧向及转向滑移,已知机构参数后就可以直接写出机器人的速度方程,且便于运动学求解。该方法对于轮式移动机器人的运动学建模具有一般性,且具有物理意义明确、推导过程简洁等特点。

全地形移动机器人轮-地几何接触角估计

研究全地形移动机器人在不平坦地形中轮-地几何接触角的实时估计问题.本文以带有被动柔顺机构的六轮全地形移动机器人为对象,抛弃轮-地接触点位于车轮支撑臂延长线上这一假设,通过定义轮-地几何接触角δ来反映轮-地接触点在轮缘上位置的变化和地形不平坦给机器人运动带来的影响,将机器人看成是一个串-并联多刚体系统,基于速度闭链理论建立考虑地形不平坦和车轮滑移的机器人运动学模型,并针对轮-地几何接触角δ难以直接测量的问题,提出一种基于模型的卡尔曼滤波实时估计方法.利用卡尔曼滤波对机器人内部传感器的测量值进行噪声处理,基于机器人整体运动学模型对各个轮-地几何接触角进行实时估计,物理实验数据的处理结果验证了本文方法的有效性,从而为机器人运动学的精确计算和高质量的导航控制奠定了基础.

基于量子蒙特卡罗的地震波反演方法

The primary approaches for people to understand the inner properties of the earth and the distribution of the mineral resources are mainly coming from surface geology survey and geophysical/geochemical data inversion and interpretation. The purpose of seismic inversion is to extract information of the subsurface stratum geometrical structures and the distribution of material properties from seismic wave which is used for resource prospecting, exploitation and the study for inner structure of the earth and its dynamic process. Although the study of seismic parameter inversion has achieved a lot since 1950s, some problems are still persisting when applying in real data due to their nonlinearity and ill-posedness. Most inversion methods we use to invert geophysical parameters are based on iterative inversion which depends largely on the initial model and constraint conditions. It would be difficult to obtain a believable result when taking into consideration different factors such as environmental and equipment noise that exist in seismic wave excitation, propagation and acquisition. The seismic inversion based on real data is a typical nonlinear problem, which means most of their objective functions are multi-minimum. It makes them formidable to be solved using commonly used methods such as general-linearization and quasi-linearization inversion because of local convergence. Global nonlinear search methods which do not rely heavily on the initial model seem more promising, but the amount of computation required for real data process is unacceptable. In order to solve those problems mentioned above, this paper addresses a kind of global nonlinear inversion method which brings Quantum Monte Carlo (QMC) method into geophysical inverse problems. QMC has been used as an effective numerical method to study quantum many-body system which is often governed by Schrödinger equation. This method can be categorized into zero temperature method and finite temperature method. This paper is subdivided into four parts. In the first one, we briefly review the theory of QMC method and find out the connections with geophysical nonlinear inversion, and then give the flow chart of the algorithm. In the second part, we apply four QMC inverse methods in 1D wave equation impedance inversion and generally compare their results with convergence rate and accuracy. The feasibility, stability, and anti-noise capacity of the algorithms are also discussed within this chapter. Numerical results demonstrate that it is possible to solve geophysical nonlinear inversion and other nonlinear optimization problems by means of QMC method. They are also showing that Green’s function Monte Carlo (GFMC) and diffusion Monte Carlo (DMC) are more applicable than Path Integral Monte Carlo (PIMC) and Variational Monte Carlo (VMC) in real data. The third part provides the parallel version of serial QMC algorithms which are applied in a 2D acoustic velocity inversion and real seismic data processing and further discusses these algorithms’ globality and anti-noise capacity. The inverted results show the robustness of these algorithms which make them feasible to be used in 2D inversion and real data processing. The parallel inversion algorithms in this chapter are also applicable in other optimization. Finally, some useful conclusions are obtained in the last section. The analysis and comparison of the results indicate that it is successful to bring QMC into geophysical inversion. QMC is a kind of nonlinear inversion method which guarantees stability, efficiency and anti-noise. The most appealing property is that it does not rely heavily on the initial model and can be suited to nonlinear and multi-minimum geophysical inverse problems. This method can also be used in other filed regarding nonlinear optimization.

Effect of high dietary starch levels on the growth performance, blood chemistry and body composition of gibel carp (Carassius auratus var. gibelio)

An 8-week growth trial was carried out in a semi-recirculation system to investigate the effect of high dietary starch levels on the growth performance, blood chemistry, starch utilization and body composition of gibel carp (Carassius auratus var. gibelio). Five isonitrogenous and isocarloric experimental diets were formulated to contain different starch levels (24%, 28%, 32%, 36% and 40% respectively). Triplicate groups of fish (24 fish per tank with an average body weight, of 8.5 g) were assigned to each diet. The results showed that dietary carbohydrate levels significantly affected the growth performance, hepatopancreatic lipid content, pyruvate kinase (PK) activity and whole-body lipid content. Growth performance, body crude lipid and plasma glucose concentrations showed a decreasing trend with an increase in dietary starch from 24% to 40%. Pyruvate kinase activities and hepatopancreatic lipid content showed an increasing trend with the dietary starch increasing from 24% to 32%, and then a decreasing trend with the dietary starch increasing from 32% to 40%. No significant difference in the hepatopancreatic hexokinase (HK) activity, plasma triglyceride contents, body crude protein, ash and calcium (Ca) and phosphorus (P) contents was observed between different treatments. In conclusion, higher dietary starch levels (32-40%) significantly (P < 0.05) decreased the growth of gibel carp in the present study.

A DSWN-based specific-purpose neural computing system

The Double Synapse Weighted Neuron (DSWN) is a kind of general-purpose neuron model, which with the ability of configuring Hyper-sausage neuron (HSN). After introducing the design method of hardware DSWN synapse, this paper proposed a DSWN-based specific purpose neural computing device-CASSANN-IIspr. As its application, a rigid body recognition system was developed on CASSANN-IIspr, which achieved better performance than RIBF-SVMs system.

Formation and evolution of the modern warm current system in the East China Sea and the Yellow Sea since the last deglaciation

To reconstruct the formation and evolution process of the warm current system within the East China Sea (ECS) and the Yellow Sea (YS) since the last deglaciation, the paleoceangraphic records in core DGKS9603, core CSH1 and core YSDP102, which were retrieved from the mainstream of the Kuroshio Current (KC), the edge of the modern Tsushima Warm Current (TWC) and muddy region under cold waters accreted with the Yellow Sea Warm Current (YSWC) respectively, were synthetically analyzed. The results indicate that the formation and evolution of the modern warm current system in the ECS and the YS has been accompanied by the development of the KC and impulse rising of the sea level since the last deglaciation. The influence of the KC on the Okinawa Trough had enhanced since 16 cal kyr BP, and synchronously the modern TWC began to develop with the rising of sea level and finally formed at about 8.5 cal kyr BP. The KC had experienced two weakening process during the Heinrich event 1 and the Younger Drays event from 16 to 8.5 cal kyr BP. The period of 7-6 cal kyr BP was the strongest stage of the KC and the TWC since the last deglaciation. The YSWC has appeared at about 6.4 cal kyr BP. Thus, the warm current system of the ECS and the YS has ultimately formed. The weakness of the KC, indicated by the occurrence of Pulleniatina minimum event (PME) during the period from 5.3 to 2.8 cal kyr BP, caused the main stream of the TWC to shift eastward to the Pacific Ocean around about 3 cal kyr BP. The process resulted in the intruding of continent shelf cold water mass with rich nutrients. Synchronously, the strength of the YSWC was relatively weak and the related cold water body was active at the early-mid stage of its appearance against the PME background, which resulted in the quick formation of muddy deposit system in the southeastern YS. The strength of the warm current system in the ECS and the YS has enhanced evidently, and approached to the modern condition gradually since 3 cal kyr BP.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.