Abstract: A panoramic monitoring and feedback control system and a panoramic monitoring and feedback control method, which are configured to assist monitors to obtain real-time information of a monitored field and eliminate an abnormal situation. The panoramic monitoring and feedback control system includes: a host-system, configured to display real-time information of the monitored field; at least one sub-system, connected to the host-system, including a plurality of wide-angle cameras, configured to receive sensing conditions, operate signals, and automatic operation signals; and at least one external system including sensor modules and equipment devices.

Abstract: An environment managing and monitoring system and a method using same are provided. The environment managing and monitoring system is configured to assist monitors to obtain real-time information of the monitoring field and control device in the monitoring field. The environmental managing and monitoring system includes at least one sub-system and a host system. The host system is configured to output a region of interest condition and a monitoring condition to the sub-system, wherein the sub-system is configured to generate monitoring results according to the monitoring conditions, and selects an image range from the captured wide-angle dynamic real-time images according to the region of interest condition.

Abstract: The disclosure is directed to an ensemble learning prediction apparatus. The apparatus includes a loss module, a diversity module, a sample weight module, and an integrating weight module. The loss module, the diversity module and the sample weight module calculate a loss, a diversity and a sample weight, respectively. An ensemble weight is learned by an object function built by the loss, diversity and the sample weight. The integrating weight module calculates an adaptive ensemble weight by integrating the ensemble weight and previous ensemble weights at a plurality of previous time points.

Abstract: The disclosure is directed to an ensemble learning prediction apparatus. The apparatus includes a loss module, a diversity module, a sample weight module, and an integrating weight module. The loss module, the diversity module and the sample weight module calculate a loss, a diversity and a sample weight, respectively. An ensemble weight is learned by an object function built by the loss, diversity and the sample weight. The integrating weight module calculates an adaptive ensemble weight by integrating the ensemble weight and previous ensemble weights at a plurality of previous time points.