基于宏观经济结构合理化的宁夏水资源合理配置

本文分析了宁夏目前经济结构下水资源利用效率及产出效益，研究在一定经济发展水平下，当资源配置最优时，最优的经济均衡增长途径。区域最优经济结构是由国民经济各部门相互依存的关联关系技术矩阵所决定的，其均衡增长率和均衡增长产出结构分别等于非负矩阵的弗罗比尼斯特征根和相对应的弗罗比尼斯向量。本文根据联合国经济发展统计数据，分析出宁夏未来调整第一产业与第二产业结构的比例，认为应适当减少宁夏高耗水水稻种植面积，逐渐把农业用水量向工业用水量转移。转变经济增长方式，从水资源的高效利用及配置出发走内涵式、节水型的社会发展道路，以解决宁夏水资源严重不足的问题。     

Integrated Smart Sensor Calibration

In many applications electronic sensors are used toimprove performance and reliability of measurement systems. Suchsensors should provide a correct transfer from the physical signalto be measured to the electrical output signal. One importantstep to achieve this, is to calibrate each sensor by applyingdifferent reference input signals and adjusting the sensor transferaccordingly. Besides expensive reference equipment the calibrationprocess takes much time and attention per individual sensor,which means a considerable increase in sensor production costs.By including at the sensor or sensor interface chip a programmablecalibration facility the calibration of such smart sensors caneasily be automated and can be executed for a batch of sensorsat a time, thus minimizing the calibration time and costs. Thispaper presents a calibration method and options for integrationin the smart sensor concept, in hardware as well as in software.An advantage of the proposed method is that it does not needa large matrix of calibration data, which needs to be storedin a look-up table or converted into a correction formula, butinstead it uses a step-by-step approach to correct the sensortransfer at each calibration measurement until the error is sufficientlysmall.     

Safe and effective navigation of autonomous robots in hazardous environments

The development of autonomous mobile machines to perform useful tasks in real work environments is currently being impeded by concerns over effectiveness, commercial viability and, above all, safety. This paper introduces a case study of a robotic excavator to explore a series of issues around system development, navigation in unstructured environments, autonomous decision making and changing the behaviour of autonomous machines to suit the prevailing demands of users. The adoption of the Real-Time Control Systems (RCS) architecture (Albus, 1991) is proposed as a universal framework for the development of intelligent systems. In addition it is explained how the use of Partially Observable Markov Decision Processes (POMDP) (Kaelbling et al., 1998) can form the basis of decision making in the face of uncertainty and how the technique can be effectively incorporated into the RCS architecture. Particular emphasis is placed on ensuring that the resulting behaviour is both task effective and adequately safe, and it is recognised that these two objectives may be in opposition and that the desired relative balance between them may change. The concept of an autonomous system having “values” is introduced through the use of utility theory. Limited simulation results of experiments are reported which demonstrate that these techniques can create intelligent systems capable of modifying their behaviour to exhibit either ‘safety conscious’ or ‘task achieving’ personalities.     

自治Agents的面向服务的协商模型

提出了一个基于自治Agents的面向服务的协商模型。模型主要包括两大模块：首先定义了一系列的战略和战术过程，使得Agents可以产生初始建议，评价对方的建议，以及在此基础上做出反建议；其次，定义了两个层次的Agent通信机制。二者相辅相成，实现了Agents间合作的动态形成和随机协调，使得Agents可以承担因环境的不确定性而需动态协作的复杂任务。     

Self-calibration based on invariant view recognition: Dynamic approach to navigation

We extend a dynamic approach of behavior generation to the representation of spatial information. Two levels of dynamics integrate dead-reckoning, dominant far from home bases, and piloting, dominant near home bases. When the view-based piloting system recognizes a home base, visual place information recalibrates the dead-reckoning system, inverting the hierarchical ordering of the two dynamic levels by time scale inversion. Reference views taken at discrete home bases are recognized invariantly under rotation of views. This process yields compass information. Continuous translational information is obtained as a neural place representation built from view correlations with a scattered set of local views. This self-calibrating cognitive map couples into a dynamics of heading direction integrating the behaviors of obstacle avoidance and target acquisition. Targets can be designated in terms of the cognitive map. We demonstrate the dynamical model in simulation.     

Seasonal variation in light penetration and subsurface chlorophyll-α in southern Lake Michigan observed by a glider

The Cooperative Institute for Great Lakes Research (CIGLR) in collaboration with the Great Lakes Observing System and National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory (NOAA GLERL) deployed an autonomous underwater glider in southern Lake Michigan several times per year between 2012 and 2019 to collect offshore (>30 m depth) limnological measurements, including temperature, photosynthetically active radiation (beginning during 2015), and chlorophyll-α fluorescence. From these data, we calculated mixed layer depth, several measures of light penetration (diffuse attenuation coefficient, first optical depth, euphotic zone depth), and depth of the subsurface chlorophyll-α maxima. During summer, mean offshore mixed layer depth was typically 10–15 m, K_d for PAR was 0.1–0.17 m^?1, first optical depth was 6–9 m, euphotic zone depth was 35–40 m, and depth of subsurface chlorophyll-α maxima was 30–35 m. We also observed substantial spatial and temporal variation in these values across the basin and within and among seasons. Glider-based observations provide a wider horizontal and vertical perspective than other methods (e.g., ship- and satellite-based observations, buoys, and fixed moorings), and are therefore a valuable, complementary tool for Great Lakes limnology. The set of observations reported here provide seasonal and basin-scale information that may help to identify anomalies useful for future glider-assisted investigation into the role of biophysical processes in Great Lakes limnology and ecology.     

无人机空中加油控制设计与仿真

无人机空中加油控制精度问题,无人受油机模型的各种不确定性与来自外部的各种干扰归结为扰动,造成控制精度差。为解决上述问题,提出一种免疫粒子群优化算法的自抗扰无人机自主空中加油飞行控制律设计方法。利用自抗扰控制能够自动检测并补偿内部与外部干扰影响的特点,并利用扩张状态观测器进行估计与补偿,从而增强了所设计飞行控制律的鲁棒性,用免疫粒子群优化算法对自抗扰控制器参数进行了优化研究,以提高设计效率。仿真结果表明,所设计的自抗扰自主空中加油控制系统具有优良的控制性能与较高的控制精度,能够满足无人机自主空中加油的要求。     

任务与兴趣驱动教学法在《计算机网络实用技术》教学中的应用

针对现阶段职业教育中计算机网络专业普遍存在的问题,从兴趣任务驱动教学法入手,以《计算机网络实用技术》课程为例,对课程的教学理念、教学实施过程及实施效果进行了探索。     

Learning control in spatial coordinates for the path-following of autonomous vehicles

We prove the existence of a P-type (proportional-type) space-learning control, which, on the basis of a kinematic third order nonlinear model of an autonomous nonholonomic vehicle and by a proper choice of the proportional control gain, guarantees asymptotic tracking of planar curves whose uncertain curvature is L$L$-periodic in the curvilinear abscissa. The behavior of a human driver, who repetitively learns the correct action from the past experience in the space, is mathematically reproduced. A stability analysis is presented while simulation results demonstrate the effectiveness of the presented approach.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.