In order to investigate the aggregation mechanism of green macroalgae (Enteromorpha prolifera) on the Qingdao coastline,the macroalgal drift characteristics in the Yellow Sea during June and July in 2008 and 2010 were...In order to investigate the aggregation mechanism of green macroalgae (Enteromorpha prolifera) on the Qingdao coastline,the macroalgal drift characteristics in the Yellow Sea during June and July in 2008 and 2010 were simulated using a three-dimensional wave-tide-circulation coupled model.In June 2008,the monthly-mean surface current flowed onshore and its direction was almost perpendicular to the Qingdao coastline,which was identified as the main reason for a huge accumulation of algae in the coastal waters off Qingdao.The current became parallel to the coastline in July 2008;this shift in current direction led to little accumulation of algae near Qingdao and thus relieved the environmental pressure on the Olympic sailing events.By using the coupled model,we predicted that there would be no serious algal accumulation at Qingdao in late June 2010,which was later confirmed by observations.This study demonstrated that the drift path of macroalgae near Qingdao is mainly controlled by the surface current,which is primarily driven by wind.Regional climate change is therefore one of the means by which physical processes affect marine ecosystems.展开更多
Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks. Recently, many algorithms have been proposed based on various node-si...Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks. Recently, many algorithms have been proposed based on various node-similarity measures. Among these measures, the common neighbour index, the resource allocation index, and the local path index, stemming from different source, have been proved to have relatively high accuracy and low computational effort. In this paper, we propose a similarity index by combining the resource allocation index and the local path index. Simulation results on six unweighted networks show that the accuracy of the proposed index is higher than that of the local path one. Based on the same idea of the present index, we develop its corresponding weighted version and test it on several weighted networks. It is found that, except for the USAir network, the weighted variant also performs better than both the weighted resource allocation index and the weighted local path index. Due to the improved accuracy and the still low computational complexity, the indices may be useful for link prediction.展开更多
While cable-driven snake robots are promising in exploring confined spaces, their hyper-redundancy makes the collision-free motion planning difficult. In this paper, by combining the prediction lookup and interpolatio...While cable-driven snake robots are promising in exploring confined spaces, their hyper-redundancy makes the collision-free motion planning difficult. In this paper, by combining the prediction lookup and interpolation algorithms, we present a new path following method for cable-driven snake robots to high-efficiently slither into complex terrains along a desired path. In our method, we first discretize the desired path into points, and develop the prediction lookup algorithm to efficiently find the points matched with joints of the robot. According to geometric relations between the prediction lookup results and link length of the robot, we develop the interpolation algorithm to reduce the tracking errors caused by the discretization. Finally, simulations and experiments of inspections in two confined spaces including the obstacle array and pipe tank system are performed on our custom-built 25 degree of freedoms(DOFs) cable-driven snake robot. The results demonstrate that the presented method can successfully navigate our snake robot into confined spaces with high computational efficiency and good accuracy, which well verifies effectiveness of our development.展开更多
Currently, simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge. Existing compensating methods are mainly focusing on 3-ai...Currently, simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge. Existing compensating methods are mainly focusing on 3-aixs machining, which sometimes only take one given point as the compensative point at each given cutter location. This paper presents a redesigned surface based machining strategy for peripheral milling of thin-walled parts. Based on an improved cutting force/heat model and finite element method（FEM） simulation environment, a deflection error prediction model, which takes sequence of cutter contact lines as compensation targets, is established. And an iterative algorithm is presented to determine feasible cutter axis positions. The final redesigned surface is subsequently generated by skinning all discrete cutter axis vectors after compensating by using the proposed algorithm. The proposed machining strategy incorporates the thermo-mechanical coupled effect in deflection prediction, and is also validated with flank milling experiment by using five-axis machine tool. At the same time, the deformation error is detected by using three-coordinate measuring machine. Error prediction values and experimental results indicate that they have a good consistency and the proposed approach is able to significantly reduce the dimension error under the same machining conditions compared with conventional methods. The proposed machining strategy has potential in high-efficiency precision machining of thin-walled parts.展开更多
Traditional methods for plan path prediction have low accuracy and stability. In this paper, we propose a novel approach for plan path prediction based on relative motion between positions(RMBP) by mining historical f...Traditional methods for plan path prediction have low accuracy and stability. In this paper, we propose a novel approach for plan path prediction based on relative motion between positions(RMBP) by mining historical flight trajectories. A probability statistical model is introduced to model the stochastic factors during the whole flight process. The model object is the sequence of velocity vectors in the three-dimensional Earth space. First, we model the moving trend of aircraft including the speed(constant, acceleration, or deceleration), yaw(left, right, or straight), and pitch(climb, descent, or cruise) using a hidden Markov model(HMM) under the restrictions of aircraft performance parameters. Then, several Gaussian mixture models(GMMs) are used to describe the conditional distribution of each moving trend. Once the models are built, machine learning algorithms are applied to obtain the optimal parameters of the model from the historical training data. After completing the learning process, the velocity vector sequence of the flight is predicted by the proposed model under the Bayesian framework, so that we can use kinematic equations, depending on the moving patterns, to calculate the flight position at every radar acquisition cycle. To obtain higher prediction accuracy, a uniform interpolation method is used to correct the predicted position each second. Finally, a plan trajectory is concatenated by the predicted discrete points. Results of simulations with collected data demonstrate that this approach not only fulfils the goals of traditional methods, such as the prediction of fly-over time and altitude of waypoints along the planned route, but also can be used to plan a complete path for an aircraft with high accuracy. Experiments are conducted to demonstrate the superiority of this approach to some existing methods.展开更多
基金supported by the National 908 Project of China (908-02-01-04)the National Key Technology R&D Special Program (2008 BAC49B02)
文摘In order to investigate the aggregation mechanism of green macroalgae (Enteromorpha prolifera) on the Qingdao coastline,the macroalgal drift characteristics in the Yellow Sea during June and July in 2008 and 2010 were simulated using a three-dimensional wave-tide-circulation coupled model.In June 2008,the monthly-mean surface current flowed onshore and its direction was almost perpendicular to the Qingdao coastline,which was identified as the main reason for a huge accumulation of algae in the coastal waters off Qingdao.The current became parallel to the coastline in July 2008;this shift in current direction led to little accumulation of algae near Qingdao and thus relieved the environmental pressure on the Olympic sailing events.By using the coupled model,we predicted that there would be no serious algal accumulation at Qingdao in late June 2010,which was later confirmed by observations.This study demonstrated that the drift path of macroalgae near Qingdao is mainly controlled by the surface current,which is primarily driven by wind.Regional climate change is therefore one of the means by which physical processes affect marine ecosystems.
基金Project supported by the National Natural Science Foundation of China (Grant No. 30570432)the Young Research Foundation of Education Department of Hunan Province of China (Grant No. 11B128)partly by the Doctor Startup Project of Xiangtan University (Grant No. 10QDZ20)
文摘Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks. Recently, many algorithms have been proposed based on various node-similarity measures. Among these measures, the common neighbour index, the resource allocation index, and the local path index, stemming from different source, have been proved to have relatively high accuracy and low computational effort. In this paper, we propose a similarity index by combining the resource allocation index and the local path index. Simulation results on six unweighted networks show that the accuracy of the proposed index is higher than that of the local path one. Based on the same idea of the present index, we develop its corresponding weighted version and test it on several weighted networks. It is found that, except for the USAir network, the weighted variant also performs better than both the weighted resource allocation index and the weighted local path index. Due to the improved accuracy and the still low computational complexity, the indices may be useful for link prediction.
基金supported by the National Natural Science Foundation of China(Grant Nos.51435010,and 91848204)。
文摘While cable-driven snake robots are promising in exploring confined spaces, their hyper-redundancy makes the collision-free motion planning difficult. In this paper, by combining the prediction lookup and interpolation algorithms, we present a new path following method for cable-driven snake robots to high-efficiently slither into complex terrains along a desired path. In our method, we first discretize the desired path into points, and develop the prediction lookup algorithm to efficiently find the points matched with joints of the robot. According to geometric relations between the prediction lookup results and link length of the robot, we develop the interpolation algorithm to reduce the tracking errors caused by the discretization. Finally, simulations and experiments of inspections in two confined spaces including the obstacle array and pipe tank system are performed on our custom-built 25 degree of freedoms(DOFs) cable-driven snake robot. The results demonstrate that the presented method can successfully navigate our snake robot into confined spaces with high computational efficiency and good accuracy, which well verifies effectiveness of our development.
基金supported by Key Program of National Natural Science Foundation of China (Grant No. 50835001) General Program of National Natural Science Foundation of China (Grant No. 50775023)Program for New Century Excellent Talents of Ministry of Education of China (Grant No. NCET-08-081)
文摘Currently, simultaneously ensuring the machining accuracy and efficiency of thin-walled structures especially high performance parts still remains a challenge. Existing compensating methods are mainly focusing on 3-aixs machining, which sometimes only take one given point as the compensative point at each given cutter location. This paper presents a redesigned surface based machining strategy for peripheral milling of thin-walled parts. Based on an improved cutting force/heat model and finite element method（FEM） simulation environment, a deflection error prediction model, which takes sequence of cutter contact lines as compensation targets, is established. And an iterative algorithm is presented to determine feasible cutter axis positions. The final redesigned surface is subsequently generated by skinning all discrete cutter axis vectors after compensating by using the proposed algorithm. The proposed machining strategy incorporates the thermo-mechanical coupled effect in deflection prediction, and is also validated with flank milling experiment by using five-axis machine tool. At the same time, the deformation error is detected by using three-coordinate measuring machine. Error prediction values and experimental results indicate that they have a good consistency and the proposed approach is able to significantly reduce the dimension error under the same machining conditions compared with conventional methods. The proposed machining strategy has potential in high-efficiency precision machining of thin-walled parts.
基金Project supported by the National Natural Science Foundation of China(No.71573184)the National Key Scientific Instrument and Equipment Development Project(No.2013YQ490879)the Special Program of Office of China Air Traffic Control Commission(No.GKG201403004)
文摘Traditional methods for plan path prediction have low accuracy and stability. In this paper, we propose a novel approach for plan path prediction based on relative motion between positions(RMBP) by mining historical flight trajectories. A probability statistical model is introduced to model the stochastic factors during the whole flight process. The model object is the sequence of velocity vectors in the three-dimensional Earth space. First, we model the moving trend of aircraft including the speed(constant, acceleration, or deceleration), yaw(left, right, or straight), and pitch(climb, descent, or cruise) using a hidden Markov model(HMM) under the restrictions of aircraft performance parameters. Then, several Gaussian mixture models(GMMs) are used to describe the conditional distribution of each moving trend. Once the models are built, machine learning algorithms are applied to obtain the optimal parameters of the model from the historical training data. After completing the learning process, the velocity vector sequence of the flight is predicted by the proposed model under the Bayesian framework, so that we can use kinematic equations, depending on the moving patterns, to calculate the flight position at every radar acquisition cycle. To obtain higher prediction accuracy, a uniform interpolation method is used to correct the predicted position each second. Finally, a plan trajectory is concatenated by the predicted discrete points. Results of simulations with collected data demonstrate that this approach not only fulfils the goals of traditional methods, such as the prediction of fly-over time and altitude of waypoints along the planned route, but also can be used to plan a complete path for an aircraft with high accuracy. Experiments are conducted to demonstrate the superiority of this approach to some existing methods.
