Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete diction...Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.展开更多
Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environ...Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environments.In this paper,compressed sensing(CS)theory based on dictionary learning is introduced to the inverse problem of ECT,and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space.Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT,it is not necessary to rely on the sparsity of coefficient vector to solve the nonlinear mapping as most algorithms based on CS theory.Two-phase flow distribution in a cylindrical pipe was modeled and simulated,and three variations without sparse constraint based on Landweber,Tikhonov,and Newton-Raphson algorithms were used to rapidly reconstruct a 2-D image.展开更多
Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms us...Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.展开更多
This paper proposes an adaptive sparsity-based direct position determination (DPD) appoach to locate multiple targets in the case of time-varying channels. The novel feature of this method is to dynamically adjust bot...This paper proposes an adaptive sparsity-based direct position determination (DPD) appoach to locate multiple targets in the case of time-varying channels. The novel feature of this method is to dynamically adjust both the overcomplete basis and the sparse solution based on a two-step dictionary learning (DL) framework. The method first performs supervised offline DL by using the quadratic programming approach, and then the dictionary is continuously updated in an incremental fashion to adapt to the time-varying channel during the online stage. Furthermore, the method does not need the number of emitters a prior. Simulation results demonstrate the performance of the proposed algorithm on the location estimation accuracy.展开更多
To apply the compression-type anchor to deep roadways, this paper analyzes and summaries the bearing mechanism, shear stress distribution of the grout body and engineering application of the compression-type anchor. T...To apply the compression-type anchor to deep roadways, this paper analyzes and summaries the bearing mechanism, shear stress distribution of the grout body and engineering application of the compression-type anchor. The analysis shows that the compression-type anchor has the advantages of reasonable grout body stress form, high loading capacity, good corrosion resistance and durability. The analysis also indicates that further study on the stress distribution of the compression-type anchor’s grout body and the design of a new compression-type anchor with high strength initial support, simple structure, and small diameter that is suitable for the surrounding rock in deep roadway engineering is necessary to apply compression-type anchor supporting technology to the deep roadway.展开更多
Massive machine type communication aims to support the connection of massive devices,which is still an important scenario in 6G.In this paper,a novel cluster-based massive access method is proposed for massive multipl...Massive machine type communication aims to support the connection of massive devices,which is still an important scenario in 6G.In this paper,a novel cluster-based massive access method is proposed for massive multiple input multiple output systems.By exploiting the angular domain characteristics,devices are separated into multiple clusters with a learned cluster-specific dictionary,which enhances the identification of active devices.For detected active devices whose data recovery fails,power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via re-transmission.Simulation results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.展开更多
随着新型负荷和分布式电源(distributed generations,DGs)的广泛接入,电力系统中谐波问题日渐凸显,对各并网点进行谐波监测是电网谐波污染责任划分、溯源和治理的前提。针对谐波信号随机性强、特征不明显导致监测困难的问题,该文提出一...随着新型负荷和分布式电源(distributed generations,DGs)的广泛接入,电力系统中谐波问题日渐凸显,对各并网点进行谐波监测是电网谐波污染责任划分、溯源和治理的前提。针对谐波信号随机性强、特征不明显导致监测困难的问题,该文提出一种基于字典原子共享的电力系统谐波动态监测方法。首先,对电网谐波特性进行分析,提出一种基于压缩感知稀疏字典原子共享和复用的谐波动态监测架构,实现电网运行数据的连续动态采样;然后在此框架下,提出一种基于残差能量的稀疏度自适应匹配追踪(residual energy based sparsity adaptive matching pursuit,REB-SAMP)算法,通过计算每次迭代后的残差能量来表征原始数据被稀疏分解程度,并基于此制定算法的迭代终止判别和变步长策略;此外,将Gabor过完备稀疏字典与傅里叶稀疏字典级联构建超完备合成字典,提升算法对谐波监测数据的稀疏表示性能;最后,基于PSCAD/EMTDC仿真平台搭建分布式电源并网系统,验证所提算法的合理性和有效性。仿真结果表明:所提算法更易感知并网点谐波情况,具有重构精度高、抗噪性强、收敛性好的优点。展开更多
基金Acknowledgements This work was supported by the National Science Foundation of China under Grant No. 60976065. The authors would like to thank the anonymous reviewers for comments that helped improve the paper.
文摘Compressed Sensing （CS） offers a method to solve the channel estimation problems for an underwater acoustic system, based on the existence of a sparse representation of the treated signal and an overcomplete dictionary with a set of non-orthogonal bases. In this paper, we proposed a new approach to optimize dictionaries by decreasing the average measure of the mutual coherence of the effective dictionary. A fixed link between the average mutual coherence and the CS perforrmnce is indicated by designing three factors： operating bandwidth, the number of pilot subcarriers, and coherence bandwidth. Both the Orthogonal Matching Pursuit （OMP） and the Basis Pursuit De-Noising （BPDN） are compared to the Dantzig Selector （DS） for different Signal Noise Ratio （SNR） and shown to benefit from the newly designed dictionary. Nurnerical sinmlations and experimental data of an OFDM receiver are used to evaluate the proposed method in comparison with the conventional LeastSquare （LS） estirmtor. The results show that the dictionary with a better condition considerably improves the perforrmnce of the channel estimation.
基金This research was supported by the National Natural Science Foundation of China(No.51704229)Outstanding Youth Science Fund of Xi’an University of Science and Technology(No.2018YQ2-01).
文摘Electrical capacitance tomography(ECT)has great application potential inmultiphase processmonitoring,and its visualization results are of great significance for studying the changes in two-phase flow in closed environments.In this paper,compressed sensing(CS)theory based on dictionary learning is introduced to the inverse problem of ECT,and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space.Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT,it is not necessary to rely on the sparsity of coefficient vector to solve the nonlinear mapping as most algorithms based on CS theory.Two-phase flow distribution in a cylindrical pipe was modeled and simulated,and three variations without sparse constraint based on Landweber,Tikhonov,and Newton-Raphson algorithms were used to rapidly reconstruct a 2-D image.
基金supported by the National Natural Science Foundation of China(61773202,71874081)the Special Financial Grant from China Postdoctoral Science Foundation(2017T100366)+2 种基金the Key Laboratory of Avionics System Integrated Technology for National Defense Science and Technology,China Institute of Avionics Radio Electronics(6142505180407)the Open Fund of CAAC Key laboratory of General Aviation Operation,Civil Aviation Management Institute of China(CAMICKFJJ-2019-04)the Innovation Project of the Civil Aviation Administration of China(EAB19001)。
文摘Signal reconstruction is a significantly important theoretical issue for compressed sensing.Considering the situation of signal reconstruction with unknown sparsity,the conventional signal reconstruction algorithms usually perform low accuracy.In this work,a sparsity adaptive signal reconstruction algorithm using sensing dictionary is proposed to achieve a lower reconstruction error.The sparsity estimation method is combined with the construction of the support set based on sensing dictionary.Using the adaptive sparsity method,an iterative signal reconstruction algorithm is proposed.The sufficient conditions for the exact signal reconstruction of the algorithm also is proved by theory.According to a series of simulations,the results show that the proposed method has higher precision compared with other state-of-the-art signal reconstruction algorithms especially in a high compression ratio scenarios.
文摘This paper proposes an adaptive sparsity-based direct position determination (DPD) appoach to locate multiple targets in the case of time-varying channels. The novel feature of this method is to dynamically adjust both the overcomplete basis and the sparse solution based on a two-step dictionary learning (DL) framework. The method first performs supervised offline DL by using the quadratic programming approach, and then the dictionary is continuously updated in an incremental fashion to adapt to the time-varying channel during the online stage. Furthermore, the method does not need the number of emitters a prior. Simulation results demonstrate the performance of the proposed algorithm on the location estimation accuracy.
文摘To apply the compression-type anchor to deep roadways, this paper analyzes and summaries the bearing mechanism, shear stress distribution of the grout body and engineering application of the compression-type anchor. The analysis shows that the compression-type anchor has the advantages of reasonable grout body stress form, high loading capacity, good corrosion resistance and durability. The analysis also indicates that further study on the stress distribution of the compression-type anchor’s grout body and the design of a new compression-type anchor with high strength initial support, simple structure, and small diameter that is suitable for the surrounding rock in deep roadway engineering is necessary to apply compression-type anchor supporting technology to the deep roadway.
文摘地震数据去噪在地震资料处理中扮演着关键的角色,提升地震数据的信噪比将为后续高质量处理和精确解释奠定坚实基础。目前,地震数据去噪的方法已经得到了广泛发展,其中字典学习方法具有独特的优势。当前经典的K-奇异值分解(K-singular value decomposition,K-SVD)字典学习算法存在去噪结果损失了部分原始信号和计算效率不太理想等问题,为了将这些问题进一步优化,提出了一种基于顺序广义K-均值算法(sequential generalized K-means,SGK)的字典学习方法用于地震数据去噪。首先,从样本数据中提取随机位置的块,并移除空白块,以初始化字典。接着,在字典学习阶段,通过地震数据本身的特征自适应地构造出最新的稀疏表示字典。随后,利用学得的字典对包含噪声的地震数据分块进行去噪处理,将去噪后的块进行平均处理,并重新构建图像块,最终实现地震数据的去噪。通过合成数据和实际数据的实验,从信噪比、计算效率以及对有效信号的保护方面验证本文方法的去噪性能。
基金supported by Natural Science Foundation of China(62122012,62221001)the Beijing Natural Science Foundation(L202019,L211012)the Fundamental Research Funds for the Central Universities(2022JBQY004)。
文摘Massive machine type communication aims to support the connection of massive devices,which is still an important scenario in 6G.In this paper,a novel cluster-based massive access method is proposed for massive multiple input multiple output systems.By exploiting the angular domain characteristics,devices are separated into multiple clusters with a learned cluster-specific dictionary,which enhances the identification of active devices.For detected active devices whose data recovery fails,power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via re-transmission.Simulation results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.
文摘随着新型负荷和分布式电源(distributed generations,DGs)的广泛接入,电力系统中谐波问题日渐凸显,对各并网点进行谐波监测是电网谐波污染责任划分、溯源和治理的前提。针对谐波信号随机性强、特征不明显导致监测困难的问题,该文提出一种基于字典原子共享的电力系统谐波动态监测方法。首先,对电网谐波特性进行分析,提出一种基于压缩感知稀疏字典原子共享和复用的谐波动态监测架构,实现电网运行数据的连续动态采样;然后在此框架下,提出一种基于残差能量的稀疏度自适应匹配追踪(residual energy based sparsity adaptive matching pursuit,REB-SAMP)算法,通过计算每次迭代后的残差能量来表征原始数据被稀疏分解程度,并基于此制定算法的迭代终止判别和变步长策略;此外,将Gabor过完备稀疏字典与傅里叶稀疏字典级联构建超完备合成字典,提升算法对谐波监测数据的稀疏表示性能;最后,基于PSCAD/EMTDC仿真平台搭建分布式电源并网系统,验证所提算法的合理性和有效性。仿真结果表明:所提算法更易感知并网点谐波情况,具有重构精度高、抗噪性强、收敛性好的优点。
