多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考...多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space,DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。展开更多
A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOV...A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.展开更多
This paper concerns a decoding strategy to improve the throughput in NAND flash memory using lowdensity parity-check(LDPC) codes. As the reliability of NAND flash memory continues degrading, conventional error correct...This paper concerns a decoding strategy to improve the throughput in NAND flash memory using lowdensity parity-check(LDPC) codes. As the reliability of NAND flash memory continues degrading, conventional error correction codes have become increasingly inadequate.LDPC code is highly desirable, due to its powerful correction strength. However, in order to maximize the correction strength, LDPC codes demand fine-grained memory sensing,leading to a significant read latency penalty. To address the drawbacks caused by soft-decision LDPC decoding, this paper proposes a hybrid hard-/soft-decision LDPC decoding strategy. Simulation results show that the proposed approach could reduce the read latency penalty and hence improve the decoding throughput up to 30 %, especially in early lifetime of NAND flash memory, compared with the conventional decoding with equivalent area.展开更多
文摘多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption,MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space,DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。
基金supported by the National Natural Science Foundation of China under Grants No.61173100,No.61173101the Fundamental Research Funds for the Central Universities under Grant No.DUT10RW202
文摘A new joint decoding strategy that combines the character-based and word-based conditional random field model is proposed.In this segmentation framework,fragments are used to generate candidate Out-of-Vocabularies(OOVs).After the initial segmentation,the segmentation fragments are divided into two classes as "combination"(combining several fragments as an unknown word) and "segregation"(segregating to some words).So,more OOVs can be recalled.Moreover,for the characteristics of the cross-domain segmentation,context information is reasonably used to guide Chinese Word Segmentation(CWS).This method is proved to be effective through several experiments on the test data from Sighan Bakeoffs 2007 and Bakeoffs 2010.The rates of OOV recall obtain better performance and the overall segmentation performances achieve a good effect.
基金supported partly by the National Natural Science Foundation of China(61274028)the National High-tech R&D Program of China(2011AA010405)
文摘This paper concerns a decoding strategy to improve the throughput in NAND flash memory using lowdensity parity-check(LDPC) codes. As the reliability of NAND flash memory continues degrading, conventional error correction codes have become increasingly inadequate.LDPC code is highly desirable, due to its powerful correction strength. However, in order to maximize the correction strength, LDPC codes demand fine-grained memory sensing,leading to a significant read latency penalty. To address the drawbacks caused by soft-decision LDPC decoding, this paper proposes a hybrid hard-/soft-decision LDPC decoding strategy. Simulation results show that the proposed approach could reduce the read latency penalty and hence improve the decoding throughput up to 30 %, especially in early lifetime of NAND flash memory, compared with the conventional decoding with equivalent area.
