A Ka-band Solid-state Transmitter Cloud Radar and Data Merging Algorithm for Its Measurements

This study concerns a Ka-band solid-state transmitter cloud radar, made in China, which can operate in three different work modes, with different pulse widths, and coherent and incoherent integration numbers, to meet the requirements for cloud remote sensing over the Tibetan Plateau. Specifically, the design of the three operational modes of the radar（i.e., boundary mode M1, cirrus mode M2, and precipitation mode M3） is introduced. Also, a cloud radar data merging algorithm for the three modes is proposed. Using one month＇s continuous measurements during summertime at Naqu on the Tibetan Plateau,we analyzed the consistency between the cloud radar measurements of the three modes. The number of occurrences of radar detections of hydrometeors and the percentage contributions of the different modes＇ data to the merged data were estimated.The performance of the merging algorithm was evaluated. The results indicated that the minimum detectable reflectivity for each mode was consistent with theoretical results. Merged data provided measurements with a minimum reflectivity of -35 dBZ at the height of 5 km, and obtained information above the height of 0.2 km. Measurements of radial velocity by the three operational modes agreed very well, and systematic errors in measurements of reflectivity were less than 2 dB. However,large discrepancies existed in the measurements of the linear depolarization ratio taken from the different operational modes.The percentage of radar detections of hydrometeors in mid- and high-level clouds increased by 60% through application of pulse compression techniques. In conclusion, the merged data are appropriate for cloud and precipitation studies over the Tibetan Plateau.

THE MACHINE LEARNING SYSTEM OF TELEPHONE NETWORKS MANAGEMENT

The problem of fault information process in telephone networks manage ment system in AT & T in the US has been solved with stepanwise learning approach.This method makes the information decrease step by step by means of merge and sort, classifies the information to several typical classes and establishes the knowledge base (KB) eventually. If new fault information is inputted, we will call the knowl edge in KB and predict the related faults which will happen.

Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

Purpose–This study aims to evaluate the influence of connected and autonomous vehicle(CAV)merging algorithms on the driver behavior of human-driven vehicles on the mainline.Design/methodology/approach–Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing,which lacks consideration of realistic driving behaviors in the merging scenario.This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms.Findings–Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon.The results revealed significant variation of the algorithm influences.Specifically,the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles.Originality/value–To the best of the authors’knowledge,this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation.To achieve the research purpose,a novel multi-driver driving simulator was developed,which enables multi-drivers to simultaneously interact with each other during a virtual driving test.The results are expected to have practical implications for further improvement of the CAV merging algorithm.