A novel mobile self-reconfigurable robot is presented. This robot consists of several independent units. Each unit is composed of modular components including ultrasonic sensor, camera, communication, computation, and...A novel mobile self-reconfigurable robot is presented. This robot consists of several independent units. Each unit is composed of modular components including ultrasonic sensor, camera, communication, computation, and mobility parts, and is capable of simple self-reconfiguring to enhance its mobility by expanding itself. Several units can not only link into a train or other shapes autonomously via camera and sensors to be a united whole robot for obstacle clearing, but also disjoin to be separate units under control after missions. To achieve small overall size, compact mechanical structures are adopted in modular components design, and a miniature advanced RISC machines （ARM） based embedded controller is developed for minimal power consumption and efficient global control. The docking experiment between two units has also been implemented.展开更多
Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of ...Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches （e.g., agglomera- tive hierarchical clustering） for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software mod- ularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.展开更多
基金Supported by the National High Technology Research and Development Programme of China ( No. 2004AA420110)Heilongjiang Province Technology Foundation (No. GB04A502)
文摘A novel mobile self-reconfigurable robot is presented. This robot consists of several independent units. Each unit is composed of modular components including ultrasonic sensor, camera, communication, computation, and mobility parts, and is capable of simple self-reconfiguring to enhance its mobility by expanding itself. Several units can not only link into a train or other shapes autonomously via camera and sensors to be a united whole robot for obstacle clearing, but also disjoin to be separate units under control after missions. To achieve small overall size, compact mechanical structures are adopted in modular components design, and a miniature advanced RISC machines （ARM） based embedded controller is developed for minimal power consumption and efficient global control. The docking experiment between two units has also been implemented.
基金supported by the Office of Research,Innovation,Commercialization and Consultancy(ORICC)Universiti Tun Hussein Onn Malaysia(UTHM),Malaysia(No.U063)
文摘Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches （e.g., agglomera- tive hierarchical clustering） for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software mod- ularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.
