There are more and more Sensory Integrative Dysfunction(SID) children.The author combs out behaviors and influence factors of SID,analyses the advantage and limitations of present intervention approaches.And the author proposes combining Sensory Integrative Training and psychological intervention with social work to help SID children comprehensively.
There are more and more Sensory Integrative Dysfunction(SID) children.This paper attempts to find out social work intervention strategies which can effectively help the SID children by applying participation observation and interview to a longitudinal case study based on the social worker's experience of serving SID children.Author finds that social workers should estimate the needs of Clients from the Clients themselves and their surroundings,and discover and use positive factors such as client's interest,capability and successful experience to promote benign interaction between the Clients and their family,teachers,companions and relatives.
Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.
We report diode pumped high power 2-µm Tm(3+) fiber lasers with an all-fiber configuration. The all-fiber configuration is completed by specially designed fiber Bragg gratings with similar structure parameters matched to the gain fiber. The maximum output power is 137 W with an optical-to-optical slope efficiency of 62% with respect to absorbed 793-nm pump power. The laser wavelength is stabilized at ~2019 nm with a spectral linewidth less than 3 nm across all output levels. To the best of our knowledge, this is the highest 2-µm laser output from a single narrow bandwidth all-fiber laser system.
At present,a great waste existed in magnesium-related filed due to the abandonment of tremendous finely gruond minerals during the exploitation and calcination of magnesite.This concept proposes to solve the problem by using small tunnel kiln which allows air and gas to come and go alternatively.It's proved that the design is feasible in practice.
The next generation wireless networks demand full coverage from the sky to the deep ocean, in which the water-air cross-boundary communication is a critical component in a coordinated uncrewed vehicular network that connects both uncrewed aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs). In such a network, optical wireless communication (OWC) is an emerging and promising technology to implement direct high-speed wireless communication through the water-to-air interface. However, due to the high directionality of optical beams and the dynamics of the harsh oceanic environments, it faces significant challenges to achieve reliable and robust seamless cross-boundary communication, as the waves cause beam deflection and the mobility of the transceivers makes the link worse or even results in link disconnections. To deal with these challenges, in this paper, we investigate both static and mobile application scenarios to enable reliable water-air direct optical communications via the deep reinforcement learning (DRL) approaches. In the static scenario, we propose an active alignment method and derive the optimal beamwidth to satisfy the requirement of bit error rate (BER) and improve the link availability. To cope with the oceanic environment-induced channel dynamics, in the mobile scenario, we design a DRL-based UAV control strategy by incorporating the extended Kalman filter (EKF) prediction technique to enhance reliable communication between the mobile transceivers. The numerical simulations demonstrate that the proposed schemes achieve impressive performance in terms of energy consumption and reliable communications under both the static and mobile scenarios.
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