Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for inp...Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.展开更多
This study aims to analyze the quality of positioning orthophoto generated through the technique of direct georeferencing using metric digital camera system coupled to the laser system, and both systems were aided ine...This study aims to analyze the quality of positioning orthophoto generated through the technique of direct georeferencing using metric digital camera system coupled to the laser system, and both systems were aided inertial navigation platform. For this, we compared the coordinates obtained in 16 control points collected in the field with dual-frequency GNSS （global navigation satellite system） receiver with those obtained in the orthophoto, flight conducted in June 2009 on the campus of the State University of Campinas--UNICAMP, using a medium format digital camera and laser system, with which we obtained images with a spatial resolution of 0.15 m. Taking into account the pattern accuracy cartographic used in Brazil, it is concluded that the products produced have a standard accuracy ＂A＂ to 1：2,000 scale, which represents the best quality level, both for planimetric as for altimetry, and that the procedure reached results consistent with cartographic products in 1：2,000 scale, representing a reduction of steps in the mapping process, necessary for the preparation of cartographic databases with reduction of time for preparation of design basis large civil engineering projects, such as roads, railways, studies of urban road systems, power plants and other infrastructure systems needed to develop the country.展开更多
基金supported by the National Key Technologies R&D Program of China under Grant No. 2015BAK38B01the National Natural Science Foundation of China under Grant Nos. 61174103 and 61603032+4 种基金the National Key Research and Development Program of China under Grant Nos. 2016YFB0700502, 2016YFB1001404, and 2017YFB0702300the China Postdoctoral Science Foundation under Grant No. 2016M590048the Fundamental Research Funds for the Central Universities under Grant No. 06500025the University of Science and Technology Beijing - Taipei University of Technology Joint Research Program under Grant No. TW201610the Foundation from the Taipei University of Technology of Taiwan under Grant No. NTUT-USTB-105-4
文摘Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.
文摘This study aims to analyze the quality of positioning orthophoto generated through the technique of direct georeferencing using metric digital camera system coupled to the laser system, and both systems were aided inertial navigation platform. For this, we compared the coordinates obtained in 16 control points collected in the field with dual-frequency GNSS （global navigation satellite system） receiver with those obtained in the orthophoto, flight conducted in June 2009 on the campus of the State University of Campinas--UNICAMP, using a medium format digital camera and laser system, with which we obtained images with a spatial resolution of 0.15 m. Taking into account the pattern accuracy cartographic used in Brazil, it is concluded that the products produced have a standard accuracy ＂A＂ to 1：2,000 scale, which represents the best quality level, both for planimetric as for altimetry, and that the procedure reached results consistent with cartographic products in 1：2,000 scale, representing a reduction of steps in the mapping process, necessary for the preparation of cartographic databases with reduction of time for preparation of design basis large civil engineering projects, such as roads, railways, studies of urban road systems, power plants and other infrastructure systems needed to develop the country.
