Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an ex...Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification （FMLC） as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search （DFPS）, interactive trial and error （T＆E）, and 3x3-pixel kernel window （PKW）. Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA overimproved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.展开更多
Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniq...Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV （multi-threshold voltage） In this paper, the exact and optimal value of threshold voltage （Vth） for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI （artificial intelligence） search algorithm. The proposed algorithm is called LOAIS （leakage optimization using AI search）. LOAIS exploits the total slack time of each transistor＇s location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit.展开更多
通过优化汽轮机叶片的安装顺序,来减少安装后的残余不平衡量。对此提出一种阈值式迭代局部搜索(threshold iterative local search,TILS)算法,该算法在迭代局部搜索(iterative local search,ILS)算法基础上,采用阈值限定扰动与随机扰动...通过优化汽轮机叶片的安装顺序,来减少安装后的残余不平衡量。对此提出一种阈值式迭代局部搜索(threshold iterative local search,TILS)算法,该算法在迭代局部搜索(iterative local search,ILS)算法基础上,采用阈值限定扰动与随机扰动相结合的方法来跳出局部最优解,减少了平均到达局部最优解所需的迭代步数。实验证明,该方法可以在短时间内找到一个近似最优叶片排序组合,相对于ILS算法,搜索效率提高了20%以上。计算得到的合成质径积的近似最优解,相对于现有分组排序法、遗传算法、云自适应遗传算法(CAGA)等方法,分别减小到其最优解的0.33%~31%,且计算时间也大幅度减小。展开更多
文摘Abstract： Change detection is a standard tool to extract and analyze the earth＇s surface features from remotely sensed data. Among the different change detection techniques, change vector analysis （CVA） have an exceptional advantage of discriminating change in terms of change magnitude and vector direction from multispectral bands. The estimation of precise threshold is one of the most crucial task in CVA to separate the change pixels from unchanged pixels because overall assessment of change detection method is highly dependent on selected threshold value. In recent years, integration of fuzzy clustering and remotely sensed data have become appropriate and realistic choice for change detection applications. The novelty of the proposed model lies within use of fuzzy maximum likelihood classification （FMLC） as fuzzy clustering in CVA. The FMLC based CVA is implemented using diverse threshold determination algorithms such as double-window flexible pace search （DFPS）, interactive trial and error （T＆E）, and 3x3-pixel kernel window （PKW）. Unlike existing CVA techniques, addition of fuzzy clustering in CVA permits each pixel to have multiple class categories and offers ease in threshold determination process. In present work, the comparative analysis has highlighted the performance of FMLC based CVA overimproved SCVA both in terms of accuracy assessment and operational complexity. Among all the examined threshold searching algorithms, FMLC based CVA using DFPS algorithm is found to be the most efficient method.
文摘Leakage power is the dominant source of power dissipation for Sub-100 nm VLSI （very large scale integration） circuits. Various techniques were proposed to reduce the leakage power at nano-scale; one of these techniques is MTV （multi-threshold voltage） In this paper, the exact and optimal value of threshold voltage （Vth） for each transistor in any sequential circuit in the design is found, so that the value of the total leakage current in the design is at the minimum. This could be achieved by applying AI （artificial intelligence） search algorithm. The proposed algorithm is called LOAIS （leakage optimization using AI search）. LOAIS exploits the total slack time of each transistor＇s location and their contributions in the leakage current. It is introduced by AI heuristic search algorithms under 22 nm BSIM4 predictive technology model. The proposed approach saves around 80% of the sub-threshold leakage current without degrading the performance of the circuit.
文摘通过优化汽轮机叶片的安装顺序,来减少安装后的残余不平衡量。对此提出一种阈值式迭代局部搜索(threshold iterative local search,TILS)算法,该算法在迭代局部搜索(iterative local search,ILS)算法基础上,采用阈值限定扰动与随机扰动相结合的方法来跳出局部最优解,减少了平均到达局部最优解所需的迭代步数。实验证明,该方法可以在短时间内找到一个近似最优叶片排序组合,相对于ILS算法,搜索效率提高了20%以上。计算得到的合成质径积的近似最优解,相对于现有分组排序法、遗传算法、云自适应遗传算法(CAGA)等方法,分别减小到其最优解的0.33%~31%,且计算时间也大幅度减小。
