This paper proposes a new type of tri-sectional wheel-based cable climbing robot which is able to climb up vertical cylindrical cables of a cable-stayed bridge. The robot is composed of three pairs of wheels equally s...This paper proposes a new type of tri-sectional wheel-based cable climbing robot which is able to climb up vertical cylindrical cables of a cable-stayed bridge. The robot is composed of three pairs of wheels equally spaced circularly which are joined by six connecting boards to form a whole closed hexagonal body to clasp a cable. The whole design is entirely modular to enable to assenably the robot on-siteeasy eaoily. To analyze the static features of the robot, a mathematical model of climbing is deduced. Furthermore, taking a cable with a diameter of 80mm as an example, we calculate the design parameters of the robot. For safly landing in the case of electrical accident, a centrifugal speed regulator is proposed and applied to consume useless energy generated when the robot is slipping down along the cables. A simplified mathematical model of the landing mechanism is deduced. Finally, several experiments on the climbing mechanism demonstrate that the robot can carry payloads less than 2.2kg to climb up a cable with diameters varying from 65mm to 205mm.展开更多
Microblogs,such as facebook and twitter,have much attention among the users and organizations.Nowadays,twitter is more popular because of its real-time nature.People often interacted with real-time events such as eart...Microblogs,such as facebook and twitter,have much attention among the users and organizations.Nowadays,twitter is more popular because of its real-time nature.People often interacted with real-time events such as earthquakes and floods through twitter.During a disaster,the number of posts or tweets is drastically increased in twitter.At the time of the disaster,detecting a target event is a challenging task.In this paper,a framework is proposed for observing the tweets and to detect the target event.For detecting the target event,a classifier is devised based on different combinations of statistical features such as the position of the keyword in a tweet,length of a tweet,the frequency of hashtag,and frequency of user mentions and the URL.From the result,it is evident that the combination of frequency of hashtag and position of keyword features provides good classification results than the other combinations of features.Hence,usage of two features,namely,frequency of hashtag and position of the earthquake keyword reduces the event’s detection time.And also these two features are further helpful for detecting the sub-events which are used for filtering the tweets related to the disaster.Additionally,different classifiers such as Artificial Neural Networks(ANN),decision tree,and K-Nearest Neighbor(KNN)are compared by using these two features.However,Support Vector Machine(SVM)with linear kernel by using the combination of position of earthquake keyword and frequency of hashtag outperforms state-of-the-art methods.Therefore,SVM(linear kernel)with proposed features is applied for detecting the earthquake during disaster.The proposed algorithm is tested on Nepal earthquake and landslide datasets,2015.展开更多
基金Supported by the National High Technology Research and Development Programme of China （No. 2006AA04Z234） and the China Postdoctoral Science Foundation （ No. 20090461051 ）
文摘This paper proposes a new type of tri-sectional wheel-based cable climbing robot which is able to climb up vertical cylindrical cables of a cable-stayed bridge. The robot is composed of three pairs of wheels equally spaced circularly which are joined by six connecting boards to form a whole closed hexagonal body to clasp a cable. The whole design is entirely modular to enable to assenably the robot on-siteeasy eaoily. To analyze the static features of the robot, a mathematical model of climbing is deduced. Furthermore, taking a cable with a diameter of 80mm as an example, we calculate the design parameters of the robot. For safly landing in the case of electrical accident, a centrifugal speed regulator is proposed and applied to consume useless energy generated when the robot is slipping down along the cables. A simplified mathematical model of the landing mechanism is deduced. Finally, several experiments on the climbing mechanism demonstrate that the robot can carry payloads less than 2.2kg to climb up a cable with diameters varying from 65mm to 205mm.
文摘Microblogs,such as facebook and twitter,have much attention among the users and organizations.Nowadays,twitter is more popular because of its real-time nature.People often interacted with real-time events such as earthquakes and floods through twitter.During a disaster,the number of posts or tweets is drastically increased in twitter.At the time of the disaster,detecting a target event is a challenging task.In this paper,a framework is proposed for observing the tweets and to detect the target event.For detecting the target event,a classifier is devised based on different combinations of statistical features such as the position of the keyword in a tweet,length of a tweet,the frequency of hashtag,and frequency of user mentions and the URL.From the result,it is evident that the combination of frequency of hashtag and position of keyword features provides good classification results than the other combinations of features.Hence,usage of two features,namely,frequency of hashtag and position of the earthquake keyword reduces the event’s detection time.And also these two features are further helpful for detecting the sub-events which are used for filtering the tweets related to the disaster.Additionally,different classifiers such as Artificial Neural Networks(ANN),decision tree,and K-Nearest Neighbor(KNN)are compared by using these two features.However,Support Vector Machine(SVM)with linear kernel by using the combination of position of earthquake keyword and frequency of hashtag outperforms state-of-the-art methods.Therefore,SVM(linear kernel)with proposed features is applied for detecting the earthquake during disaster.The proposed algorithm is tested on Nepal earthquake and landslide datasets,2015.
