In this work, a systematic approach is presented to obtain the input-output equations of a single loop 4-bar spatial mechanisms. The dialytic method along with Denavit-Hartenberg parameters can be used to obtain these...In this work, a systematic approach is presented to obtain the input-output equations of a single loop 4-bar spatial mechanisms. The dialytic method along with Denavit-Hartenberg parameters can be used to obtain these equations efficiently. A genetic algorithm （GA） has been used to solve the problem of spatial mechanisms synthesis. Two types of mechanisms, e.g., RSCR and RSPC （R： revolute; S： spherical; C： cylindrical; P： prismatic）, have illustrated the application of the GA to solve the problem of function generation and path generation. In some cases, the GA method becomes trapped in a local minimum. A combined GA-fuzzy logic （GA-FL） method is then used to improve the final result. The results show that GAs, combined with an adequate description of the mechanism, are well suited for spatial mechanism synthesis problems and have neither difficulties inherent to the choice of the initial feasible guess, nor a problem of convergence, as it is the case for deterministic methods.展开更多
When the classical constant false-alarm rate （CFAR） combined with fuzzy C-means （FCM） algorithm is applied to target detection in synthetic aperture radar （SAR） images with complex background, CFAR requires bloc...When the classical constant false-alarm rate （CFAR） combined with fuzzy C-means （FCM） algorithm is applied to target detection in synthetic aperture radar （SAR） images with complex background, CFAR requires block-by-block estimation of clutter models and FCM clustering converges to local optimum. To address these problems, this paper pro-poses a new detection algorithm： knowledge-based combined with improved genetic algorithm-fuzzy C-means （GA-FCM） algorithm. Firstly, the algorithm takes target region＇s maximum and average intensity, area, length of long axis and long-to-short axis ratio of the external ellipse as factors which influence the target appearing probabil- ity. The knowledge-based detection algorithm can produce preprocess results without the need of estimation of clutter models as CFAR does. Afterward the GA-FCM algorithm is improved to cluster pre-process results. It has advantages of incorporating global optimizing ability of GA and local optimizing ability of FCM, which will further eliminate false alarms and get better results. The effectiveness of the proposed technique is experimentally validated with real SAR images.展开更多
基金Project supported by the CPER (Contrats de Projets Etat Région) Poitou-Charentes 2007-2013 (Program Project 10 "Imageset interactivités")the Tunisian Secretary of State of Scientific Research and Technology (SERST) through the contract LAB-MA 05
文摘In this work, a systematic approach is presented to obtain the input-output equations of a single loop 4-bar spatial mechanisms. The dialytic method along with Denavit-Hartenberg parameters can be used to obtain these equations efficiently. A genetic algorithm （GA） has been used to solve the problem of spatial mechanisms synthesis. Two types of mechanisms, e.g., RSCR and RSPC （R： revolute; S： spherical; C： cylindrical; P： prismatic）, have illustrated the application of the GA to solve the problem of function generation and path generation. In some cases, the GA method becomes trapped in a local minimum. A combined GA-fuzzy logic （GA-FL） method is then used to improve the final result. The results show that GAs, combined with an adequate description of the mechanism, are well suited for spatial mechanism synthesis problems and have neither difficulties inherent to the choice of the initial feasible guess, nor a problem of convergence, as it is the case for deterministic methods.
基金supported by the National Natural Science Foundation of China(6107113961171122)+1 种基金the Fundamental Research Funds for the Central Universities"New Star in Blue Sky" Program Foundation the Foundation of ATR Key Lab
文摘When the classical constant false-alarm rate （CFAR） combined with fuzzy C-means （FCM） algorithm is applied to target detection in synthetic aperture radar （SAR） images with complex background, CFAR requires block-by-block estimation of clutter models and FCM clustering converges to local optimum. To address these problems, this paper pro-poses a new detection algorithm： knowledge-based combined with improved genetic algorithm-fuzzy C-means （GA-FCM） algorithm. Firstly, the algorithm takes target region＇s maximum and average intensity, area, length of long axis and long-to-short axis ratio of the external ellipse as factors which influence the target appearing probabil- ity. The knowledge-based detection algorithm can produce preprocess results without the need of estimation of clutter models as CFAR does. Afterward the GA-FCM algorithm is improved to cluster pre-process results. It has advantages of incorporating global optimizing ability of GA and local optimizing ability of FCM, which will further eliminate false alarms and get better results. The effectiveness of the proposed technique is experimentally validated with real SAR images.
