AUV自主导航航位推算算法的分析研究

地球形状的不规则性,各种导航传感器本身的误差,以及仪器的安装偏差等,使得AUV(自治水下机器人)在进行远距离自主航行时,自主导航的精度大大下降。针对以上问题及实际工程需要,论文对AUV自主导航的航位推算算法做了进一步研究并加以改进,以提高其自主导航精度。最后,利用2004年中国科学院沈阳自动化所水下机器人研究中心进行AUV湖试所获得的数据,对文中提出的算法进行了验证。结果表明,AUV的自主导航精度得到大大提高,可以用于修正原来的自主导航算法。

动基座机器人的末端受限鲁棒跟踪控制

结合具有扰动基座末端受限机器人的动力学特性，提出了虚构线性不确定系统的匹配模型概念．通过引入线性不确定系统的鲁棒跟踪控制器设计方法，发展了一种新的受约束机器人的力鲁棒跟踪控制方法．文中给出了动基座ＰＵＭＡ５６２机器人的实验结果。

移动机器人环境建模的结构基自由网格法

本文给出的结构基自由网格法综合了正规栅格法和自由空间法的基本思想,依环境的结构信息确定地解决自由空间分割过程中构造想象边界的任意性问题,由此消除了路径的不确定性;此外,结构基自由网格模型可以在一定程度上消除规划路径“绕大弯”的现象。

小波域地震资料的处理方法（WSSP）研究

The modeling formula based on seismic wavelet can well simulate zero - phase wavelet and hybrid-phase wavelet, and approximate maximal - phase and minimal - phase wavelet in a certain sense. The modeling wavelet can be used as wavelet function after suitable modification item added to meet some conditions. On the basis of the modified Morlet wavelet, the derivative wavelet function has been derived. As a basic wavelet, it can be sued for high resolution frequency - division processing and instantaneous feature extraction, in acoordance with the signal expanding characters in time and scale domains by each wavelet structured. Finally, an application example proves the effectiveness and reasonability of the method. Based on the analysis of SVD (Singular Value Decomposition) filter, by taking wavelet as basic wavelet and combining SVD filter and wavelet transform, a new de - noising method, which is Based on multi - dimension and multi-space de - noising method, is proposed. The implementation of this method is discussed the detail. Theoretical analysis and modeling show that the method has strong capacity of de - noising and keeping attributes of effective wave. It is a good tool for de - noising when the S/N ratio is poor. To give prominence to high frequency information of reflection event of important layer and to take account of other frequency information under processing seismic data, it is difficult for deconvolution filter to realize this goal. A filter from Fourier Transform has some problems for realizing the goal. In this paper, a new method is put forward, that is a method of processing seismic data in frequency division from wavelet transform and reconstruction. In ordinary seismic processing methods for resolution improvement, deconvolution operator has poor part characteristics, thus influencing the operator frequency. In wavelet transform, wavelet function has very good part characteristics. Frequency - division data processing in wavelet transform also brings quite good high resolution data, but it needs more time than deconvolution method does. On the basis of frequency - division processing method in wavelet domain, a new technique is put forward, which involves 1) designing filter operators equivalent to deconvolution operator in time and frequency domains in wavelet transform, 2) obtaining derivative wavelet function that is suitable to high - resolution seismic data processing, and 3) processing high resolution seismic data by deconvolution method in time domain. In the method of producing some instantaneous characteristic signals by using Hilbert transform, Hilbert transform is very sensitive to high - frequency random noise. As a result, even though there exist weak high - frequency noises in seismic signals, the obtained instantaneous characteristics of seismic signals may be still submerged by the noises. One method for having instantaneous characteristics of seismic signals in wavelet domain is put forward, which obtains directly the instantaneous characteristics of seismic signals by taking the characteristics of both the real part (real signals, namely seismic signals) and the imaginary part (the Hilbert transfom of real signals) of wavelet transform. The method has the functions of frequency division and noise removal. What is more, the weak wave whose frequency is lower than that of high - frequency random noise is retained in the obtained instantaneous characteristics of seismic signals, and the weak wave may be seen in instantaneous characteristic sections (such as instantaneous frequency, instantaneous phase and instantaneous amplitude). Impedance inversion is one of tools in the description of oil reservoir. one of methods in impedance inversion is Generalized Linear Inversion. This method has higher precision of inversion. But, this method is sensitive to noise of seismic data, so that error results are got. The description of oil reservoir in researching important geological layer, in order to give prominence to geological characteristics of the important layer, not only high frequency impedance to research thin sand layer, but other frequency impedance are needed. It is difficult for some impedance inversion method to realize the goal. Wavelet transform is very good in denoising and processing in frequency division. Therefore, in the paper, a method of impedance inversion is put forward based on wavelet transform, that is impedance inversion in frequency division from wavelet transform and reconstruction. in this paper, based on wavelet transform, methods of time - frequency analysis is given. Fanally, methods above are in application on real oil field - Sansan oil field.

长时记忆中图形不同特征的加工与提取

Information can be represented both conceptually and imaginarily in long-term memory. However, it seems that only conceptual representation appears, neglecting imaginary information, in most of the long-term memory (LTM) models. In the matter of fact, picture can be stored in LTM directly and conceptually. There is no evidence for what specific type of information, conceptual or imaginary, for the color, shape, or texture to be represented. However, it is evident that the shape and color can be represented separately in LMT. Further research is needed on whether features are represented separately or not, such as color and texture, texture and shape etc. Rehearsal plays important role in picture memory besides the types of storage and representation. Memory of picture is indeed enhanced by rehearsal. There are two types of rehearsal. One is for creating image, another is articulatory loop. Which one will be taken during picture memory process depends on the characteristics of stimuli, subjects' encoding preferences and/or task requirements. Nevertheless, the relation between two types of rehearsal is not very clear yet up to now. Different features could be activated at different time course or possibilities since they can be represented separately. Six experiments were conducted dealing with the characteristics of representation, rehearsal and retrieval of picture in LTM. From these experiments, further understanding of picture information processing was expected. It would add more evidence to the LTM models, and make practical sense to the computer visual identification. The first two experiments were based on the paradigm from Hanna et al.(1996) to investigate separable representation of texture and shape, texture and color. The results indicated that texture could be represented separately with color and shape respectively. It suggested that different features might be processed in different way during remembering. Another interest finding is that recognition performance for shape, color and texture are quite different. What for shape is highest, for color is lowest, and for texture is between of them. Three features of picture can be represented separately. How about the roles of rehearsal when they enter the LTM from short-term memory(STM)? The second three experiments assigned three different types of rehearsal, i. e. visual, verbal, and subject-run(might be both of visual and verbal). The findings are that performances of picture memory were affected significantly by different types of rehearsal. Both visual and verbal rehearsal played important role during remembering process. It seems that verbal rehearsal, which might enhance the relative strength of memory trace, was much more effective than visual one. In addition, subjects tended to choose those difficult-to-name, features to rehearse, to improve the memory performance. Only two features were changed in each of the first two experiments. They might interact (facilitate or disturb) each other when they were retrieved. So it was difficult to identify the retrieval difference between them. In the last experiment, easy-to-name pictures were studied, and only one feature could be recognized. The results indicated that the retrieval performances of three features(shape, color, and texture) were quite different. They were different on the relative strength of memory trace, with the shape was strongest, color was lightest, and texture was in between. No difference was found on the absolute strength of them.