A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified sce...A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol(BP).However,the research results are not applicable to communications in Complex Deep Space Networks(CDSNs)without the custody mechanism of BP that are more suitable for deep space communications with LTP.In this paper,we propose a model of file delivery time for LTP in CDSNs.Based on the model,we propose a Parameter Optimization Design Algorithm for LTP(LTP-PODA)of configuring reasonable parameters for LTP.The results show that the accuracy of the proposed model is at least 6.47%higher than that of the previously established models based on simple scenarios,and the proposed model is more suitable for CDSNs.Moreover,the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan.Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77%compared with configuring the other parameter configuration plans.展开更多
With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an i...With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.展开更多
Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper ...Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper investigated the unique features of deep space communica-tions in detail, discussed the key technologies and its development trends for deep space communica-tions.展开更多
针对工业缺陷检测系统在资源受限环境下难以兼顾检测精度和计算效率的问题,提出了一种基于混合状态空间模型的轻量化工业缺陷检测网络架构。该架构通过设计C2f_EfficientVIM_CGLU模块,将视觉状态空间模型的全局序列建模能力与卷积门控...针对工业缺陷检测系统在资源受限环境下难以兼顾检测精度和计算效率的问题,提出了一种基于混合状态空间模型的轻量化工业缺陷检测网络架构。该架构通过设计C2f_EfficientVIM_CGLU模块,将视觉状态空间模型的全局序列建模能力与卷积门控线性单元的局部特征增强机制深度融合,构建了高效的缺陷特征表征学习框架。引入HSM-SSD(Hidden State Mixer based State Space Duality)机制,采用线性时间复杂度的状态空间建模方法处理长距离依赖关系,显著提升了对不规则形状和稀疏分布缺陷的识别能力。设计Slimneck轻量化特征金字塔网络,通过GSConv(Ghost Shuffle Convolution)稀疏卷积和VoV-GSCSP(Variance of Variance Ghost Shuffle Cross Stage Partial)高效特征融合模块,在保持检测精度的前提下实现了网络参数的大幅压缩。在NEU-DET和APDDD数据集上的大量实验表明,所提网络架构在NEU-DET数据集上的mAP50达到92.13%,相比基线模型YOLOv8n提升9.77个百分点,参数量仅为2.9 M,计算复杂度为7.7 GFLOPs,相比传统的Faster-RCNN方法参数量减少93%以上。在APDDD数据集上的mAP50达到89.68%,验证了方法的良好泛化性能和快速检测能力。该研究为工业4.0智能制造环境下的高效质量检测系统提供了理论基础和技术支撑。展开更多
基金supported by the Strategic Leading Project of the Chinese Academy of Sciences(No.XDA15014603).
文摘A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol(BP).However,the research results are not applicable to communications in Complex Deep Space Networks(CDSNs)without the custody mechanism of BP that are more suitable for deep space communications with LTP.In this paper,we propose a model of file delivery time for LTP in CDSNs.Based on the model,we propose a Parameter Optimization Design Algorithm for LTP(LTP-PODA)of configuring reasonable parameters for LTP.The results show that the accuracy of the proposed model is at least 6.47%higher than that of the previously established models based on simple scenarios,and the proposed model is more suitable for CDSNs.Moreover,the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan.Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77%compared with configuring the other parameter configuration plans.
基金supported by National Natural Science Foundation of China (61471109, 61501104 and 91438110)Fundamental Research Funds for the Central Universities ( N140405005 , N150401002 and N150404002)Open Fund of IPOC (BUPT, IPOC2015B006)
文摘With the development of science, economy and society, the needs for research and exploration of deep space have entered a rapid and stable development stage. Deep Space Optical Network(DSON) is expected to become an important foundation and inevitable development trend of future deepspace communication. In this paper, we design a deep space node model which is capable of combining the space division multiplexing with frequency division multiplexing. Furthermore, we propose the directional flooding routing algorithm(DFRA) for DSON based on our node model. This scheme selectively forwards the data packets in the routing, so that the energy consumption can be reduced effectively because only a portion of nodes will participate the flooding routing. Simulation results show that, compared with traditional flooding routing algorithm(TFRA), the DFRA can avoid the non-directional and blind transmission. Therefore, the energy consumption in message routing will be reduced and the lifespan of DSON can also be prolonged effectively. Although the complexity of routing implementation is slightly increased compared with TFRA, the energy of nodes can be saved and the transmission rate is obviously improved in DFRA. Thus the overall performance of DSON can be significantly improved.
基金Supported by the National Natural Science Foundation of China (No. 60972061,60972062,and 61032004)the National High Technology Research and Development Program of China ("863" Program) (No. 2008AA12A204)
文摘Deep space communications has played an important role in deep space exploration. Compared with common satellite and terrestrial communications, deep space communications faces more challenging environment. The paper investigated the unique features of deep space communica-tions in detail, discussed the key technologies and its development trends for deep space communica-tions.
文摘针对工业缺陷检测系统在资源受限环境下难以兼顾检测精度和计算效率的问题,提出了一种基于混合状态空间模型的轻量化工业缺陷检测网络架构。该架构通过设计C2f_EfficientVIM_CGLU模块,将视觉状态空间模型的全局序列建模能力与卷积门控线性单元的局部特征增强机制深度融合,构建了高效的缺陷特征表征学习框架。引入HSM-SSD(Hidden State Mixer based State Space Duality)机制,采用线性时间复杂度的状态空间建模方法处理长距离依赖关系,显著提升了对不规则形状和稀疏分布缺陷的识别能力。设计Slimneck轻量化特征金字塔网络,通过GSConv(Ghost Shuffle Convolution)稀疏卷积和VoV-GSCSP(Variance of Variance Ghost Shuffle Cross Stage Partial)高效特征融合模块,在保持检测精度的前提下实现了网络参数的大幅压缩。在NEU-DET和APDDD数据集上的大量实验表明,所提网络架构在NEU-DET数据集上的mAP50达到92.13%,相比基线模型YOLOv8n提升9.77个百分点,参数量仅为2.9 M,计算复杂度为7.7 GFLOPs,相比传统的Faster-RCNN方法参数量减少93%以上。在APDDD数据集上的mAP50达到89.68%,验证了方法的良好泛化性能和快速检测能力。该研究为工业4.0智能制造环境下的高效质量检测系统提供了理论基础和技术支撑。
