“道”“器”融合:教师减负的技术旨归与实现路径

智能技术是一把双刃剑,在教师减负中通过分担工作任务、辅助教学过程发挥着“器用”价值,但也以其“用”“术”和“效”规约着减负活动,出现技术异化现象。教师减负的逻辑起点、行动过程和终极目标指向教师生命属性的彰显,其生命属性的彰显也离不开智能技术的支持。基于此,教师减负既应以形而下的“器物之学”发挥“器用”价值,又应以形而上的“生命属性”坚守“合道”之则,立足“器”与“道”的共生共长实现“道器融合”。具体而言,在借助技术为教师减负的过程中,应确立技术边界掌握教师减负的“生命尺度”,促进人机协同实现教师减负的“生命职责”,彰显价值理性回应教师减负的“生命诉求”,从而消除“器用”价值下,因技术异化而引起的教师生命离场的现象,力求智能技术与教师生命在减负活动中实现双向耦合。

State estimation of connected vehicles using a nonlinear ensemble filter

The concept of connected vehicles is with great potentials for enhancing the road transportation systems in the future. To support the functions and applications under the connected vehicles frame, the estimation of dynamic states of the vehicles under the cooperative environments is a fundamental issue. By integrating multiple sensors, localization modules in OBUs(on-board units) require effective estimation solutions to cope with various operation conditions. Based on the filtering estimation framework for sensor fusion, an ensemble Kalman filter(En KF) is introduced to estimate the vehicle's state with observations from navigation satellites and neighborhood vehicles, and the original En KF solution is improved by using the cubature transformation to fulfill the requirements of the nonlinearity approximation capability, where the conventional ensemble analysis operation in En KF is modified to enhance the estimation performance without increasing the computational burden significantly. Simulation results from a nonlinear case and the cooperative vehicle localization scenario illustrate the capability of the proposed filter, which is crucial to realize the active safety of connected vehicles in future intelligent transportation.

Optimal Sensor Decision Based on Particle Filter

A novel infrared and radar synergistic tracking algorithm, which is based on the idea of closed loop control, and target’s motion model identification and particle filter approach, was put forward. In order to improve the observability and filtering divergence of infrared search and tracking, the unscented Kalman filter algorithm that has stronger ability of non-linear approximation was adopted. The polynomial and least square method based on radar and IRST measurements to identify the parameters of the model was proposed, and a “pseudo sensor” was suggested to estimate the target position according to the identified model even if the radar is turned off. At last, the average Kullback-Leibler discrimination distance based on particle filter was used to measure the tracking performance, based on tracking performance and fuzzy stochastic decision, the idea of closed loop was used to retrieve the module parameter of “pseudo sensor”. The experimental result indicates that the algorithm can not only limit the radar activity effectively but also keep the tracking accuracy of active/passive system well.

DECISION FUSION FOR WIRELESS SENSOR NETWORKS UNDER NAKAGAMI FADING CHANNELS

Decision fusion rules for Wireless Sensor Networks (WSNs) under Nakagami fading channels are investigated in this paper. Considering the application limitation of Likelihood Ratio Test fusion rule based on information of Channel Statistics using Series expansion (LRT-CSS),and the detection performance limitation of the Censoring based Mixed Fusion rule (CMF),a new LRT fusion rule based on information of channel statistics has been presented using Laplace approximation (LRT-CSL). Theoretical analysis and simulations show that the proposed fusion rule provides better detection performance than the Censoring based Mixed Fusion (CMF) and LRT-CSS fusion rules. Furthermore,compared with LRT-CSS fusion rule,the proposed fusion rule expands the application range of likelihood ratio test fusion rule.