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Ocean alkalinity enhancement (OAE) has the potential to mitigate ocean acidification (OA) and induce atmospheric carbon dioxide (CO2) removal (CDR). We evaluate the CDR and OA mitigation impacts of a sustained point‐source OAE of 1.67 × 1010 mol total alkalinity (TA) yr−1 (equivalent to 667,950 metric tons NaOH yr−1) in Unimak Pass, Alaska. We find the alkalinity elevation initially mitigates OA by decreasing pCO2 and increasing aragonite saturation state and pH. Then, enhanced air‐to‐sea CO2 exchange follows with an approximate e‐folding time scale of 5 weeks. Meaningful modeled OA mitigation with reductions of >10 μatm pCO2 (or just under 0.02 pH units) extends 100–100,000 km2 around the TA addition site. The CDR efficiency (i.e., the experimental seawater dissolved inorganic carbon (DIC) increase divided by the maximum DIC increase expected from the added TA) after the first 3 years is 0.96 ± 0.01, reflecting essentially complete air‐sea CO2 adjustment to the additional TA. This high efficiency is potentially a unique feature of the Bering Sea related to the shallow depths and mixed layer depths. The ratio of DIC increase to the TA added is also high (≥0.85) due to the high dissolved carbon content of seawater in the Bering Sea. The air‐sea gas exchange adjustment requires 3.6 months to become (>95%) complete, so the signal in dissolved carbon concentrations will likely be undetectable amid natural variability after dilution by ocean mixing. We therefore argue that modeling, on a range of scales, will need to play a major role in assessing the impacts of OAE interventions. Plain Language Summary The Intergovernmental Panel on Climate Change suggests that carbon dioxide (CO2) removal (CDR) approaches will be required to stabilize the global temperature increase at 1.5–2°C. In this study, we simulated the climate mitigation impacts of adding alkalinity (equivalent to 667,950 metric ton NaOH yr−1) in Unimak Pass on the southern boundary of the Bering Sea. We found that adding alkalinity can accelerate the ocean CO2 uptake and storage and mitigate ocean acidification near the alkalinity addition. It takes about 3.6 months for the Ocean alkalinity enhancement impacted area to take up the extra CO2. The naturally cold and carbon rich water in the Bering Sea and the tendency of Bering Sea surface waters to linger near the ocean surface without mixing into the subsurface ocean both lead to high CDR efficiencies (>96%) from alkalinity additions in the Bering Sea. However, even with high efficiency, it would take >8,000 alkalinity additions of the kind we simulated to be operating by the year 2100 to meet the target to stabilize global temperatures within the targeted range. Key Points We used regional ocean model to simulate single point‐source ocean alkalinity enhancement in the Bering Sea The steady state carbon dioxide removal efficiency was near one in years 3+ of the simulation The meaningful modeled ocean acidification mitigation is confined to the region near the alkalinity addition

【Objective】 Water infiltration in soil is an important process for designing drip irrigation and is impacted by soil properties. The purpose of this paper is to investigate the effect of soil hydraulic properties on water infiltration from a single-point source in membrane-hole irrigation. 【Method】 The studies were based on the HYDRUS-2D software, with the soil hydraulic properties described by the van Genuchten formula. 【Result】 ① The cumulative infiltration amount was negatively correlated with the parameter α, positively correlated with the parameter n, and was independent of the residual water content parameter θr. The horizontal advancement of the wetting front was negatively correlated to the parameter α and was independent of the parameter θr. The vertical advancement of the wetting front was negatively correlated to the parameter θr and positively correlated to the parameter n. At the end of infiltration, the volume of the wetted zone was positively correlated to the parameters θrand n. Change in the parameter n had the greatest effect on the size of the wetted zone.② The relative sensitivity of the cumulative infiltration gradually decreased with the increase in α; The positive perturbation of α affected the cumulative infiltration per unit membrane pore area to a weaker extent than the negative perturbation. α occurred in the negative perturbation, the relative sensitivity of the transport distance of the horizontal wetting front increased with the increase of the perturbation amplitude, and α occurred in the positive perturbation, the relative sensitivity of the transport distance of the horizontal wetting front decreased with the increase of the perturbation amplitude.③ When n was negatively perturbed, the relative sensitivities of cumulative infiltration per unit membrane pore area and vertical wetting front transport distance both showed a tendency to increase and then decrease with the increase of perturbation amplitude; when n was positively perturbed, the relative sensitivities of cumulative infiltration per unit membrane pore area showed a tendency to decrease and then increase and then decrease with the increase of perturbation amplitude. 【Conclusion】 Disturbing the parameter n has the most significant effect on water infiltration from the single-point source in membrane hole irrigation, while disturbing the parameter θr had the least effect.

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.

Recovering M sources from N mixtures in underdetermined cases, i.e., M > N, is a great challenge, especially for insufficiently sparse sources in noisy cases. To solve this problem, an improved underdetermined blind source separation (UBSS) method is proposed based on single source points (SSPs) identification and l0-norm. Firstly, we present a mixing matrix estimation method based on SSPs that is identified by directly searching the identical normalized time–frequency (TF) vectors of mixed signals. This method considers the linear representation relations among these TF vectors and therefore could achieve more accurate SSPs identification even in noisy cases. Then, we prove that a non-SSP will be misjudged as SSP with probability zero under some assumptions, which guarantees the stability and effectiveness of the proposed method. Secondly, SSPs are only searched in some optimal frequency bins so that all SSPs in these frequency bins can be identified at one time. Then, the mixing matrix is estimated using hierarchical clustering technique. Thirdly, to recover source signals with real number of active sources, a l0-norm-based source recovery method is proposed which would be transformed to find the submatrix with the least column of the mixing matrix that can linearly represent TF vectors of mixed signals. Therefore, source signals can be recovered with the real number of active sources, which improves the estimation accuracy of source signals. Some experiments are carried out to show the effectiveness of the proposed method. The present research could improve the estimation accuracy of sources for insufficiently sparse sources with noise in underdetermined cases.

Underdetermined blind source separation is a signal processing technique that is more suitable for practical applications and aims to separate the source signals from the mixed signals. The mixing matrix estimation is a major step in the underdetermined blind source separation. Since the current methods for estimating the mixing matrix have the disadvantages of insufficient accuracy or weak noise immunity, a two-stage single-source point screening that combines the cosine angle algorithm and the L1-norm optimization algorithm is proposed. During the first stage, the first-stage single-source points are extracted from the original mixed signals using the cosine angle algorithm. During the second stage, based on the L1-norm optimization algorithm, the reference single-source points are extracted from the original mixed signals. The reference single-source points are then clustered to obtain the clustering center, which is defined as the reference center. In combination with the reference center, the deviation and interference points in the first-stage single-source points are eliminated by the cosine distance. The remaining signal points are considered as the second-stage single-source points, which are clustered to obtain the mixing matrix estimation. Experiments on simulated and speech signals show that the proposed method can obtain more accurate and robust mixing matrix estimation, leading to better separation of the source signals.

Phase wraparound due to large inter-sensor spacings in multi-channel demixing renders the DUET and AdRess source separation algorithms—known for their low computational complexity and effective speech demixing performance—unsuitable for hearing-assisted living applications, where such configurations are needed. DUET is limited to relative delays of up to 7 samples, given a sampling rate of Fs=16 kHz in anechoic scenarios, while the AdRess algorithm is constrained to instantaneous mixing problems. The task of this paper is to improve the performance of DUET-type time–frequency (TF) masks when microphones are placed far apart. A significant challenge in assistive hearing scenarios is phase wraparound caused by large relative delays. We evaluate the performance of a large relative delay estimation method, called the Elevatogram, in the presence of significant phase wraparound. We present extensions of DUET and AdRess, termed Elevato-DUET and Elevato-AdRess, which are effective in scenarios with relative delays of up to 200 samples. The findings demonstrate that Elevato-AdRess not only outperforms Elevato-DUET in terms of objective separation quality metrics—BSS_Eval and PEASS—but also achieves higher intelligibility scores, as measured by the Perceptual Evaluation of Speech Quality (PESQ) Mean Opinion Score (MOS) scores. These findings suggest that the phase wraparound limitations of DUET and AdRess algorithms in assistive hearing scenarios involving large inter-microphone spacing can be addressed by introducing the Elevatogram-based Elevato-DUET and Elevato-AdRess algorithms. These algorithms improve separation quality and intelligibility, with Elevato-AdRess demonstrating the best overall performance.

In underdetermined blind source separation (UBSS), the estimation of the mixing matrix is crucial because it directly affects the performance of UBSS. To improve the estimation accuracy, this paper proposes a joint clustering analysis method based on density based spatial clustering of applications with noise (DBSCAN) and clustering by fast search and find of density peaks (CFSFDP). In the reprocessing, the observed signals in the time domain are transformed into sparse signals in the frequency domain through a short time Fourier transform (STFT), and single-source-point (SSP) detection is used to enhance the linear clustering characteristic of signals. In addition, to facilitate the use of density-based clustering analysis, mirroring mapping is used to transform the linear clustering into compact clustering on the positive half unit circle (or sphere). For the estimation of the underdetermined mixing matrix (UMM), the DBSCAN algorithm is first used to search for high-density data points, and automatically find the number of clusters and the cluster centers; then, the CFSFDP algorithm is used to search the density peaks of the data clusters, so as to further modify the cluster centers. Because each cluster center corresponds to a column vector of the mixing matrix, the proposed algorithm can estimate the UMM through cluster analysis. The simulation results show that the proposed algorithm can not only improve the estimation accuracy of the UMM, but also provide a more robust estimator. In addition, the joint clustering method also makes up for the shortcomings of the CFSFDP algorithm that requires human intervention.

This paper considers mixing matrix estimation for underdetermined blind source separation. First, we propose an effective detection algorithm to identify single source points where only one source occurs. The detection algorithm finds single source points by utilizing the time–frequency coefficients of mixed signals and the complex conjugates of the coefficients. Then, a method based on probability density is proposed in order to find more reliable single source points and cluster them. Finally, the mixing matrix is obtained through re-selecting and clustering single source points. The experimental results indicate that the algorithm can accurately estimate the mixing matrix when there are fewer sensors than sources.

Sparse component analysis (SCA) has been widely used for blind source separation(BSS) for many years. Recently, SCA has been applied to operational modal analysis (OMA), which is also known as output-only modal identification. This paper considers the sparsity of sources’ time-frequency (TF) representation and proposes a new TF-domain SCA under the OMA framework. First, the measurements from the sensors are transformed to the TF domain to get a sparse representation. Then, single-source-points (SSPs) are detected to better reveal the hyperlines which correspond to the columns of the mixing matrix. The K-hyperline clustering algorithm is used to identify the direction vectors of the hyperlines and then the mixing matrix is calculated. Finally, basis pursuit de-noising technique is used to recover the modal responses, from which the modal parameters are computed. The proposed method is valid even if the number of active modes exceed the number of sensors. Numerical simulation and experimental verification demonstrate the good performance of the proposed method.

TN911.7%TP18; 欠定盲源分离是盲信号处理领域的难题,其中的混合矩阵估计是决定超完备独立分量分析和欠定盲分离成功与否的关键步骤.为了提高欠定混合矩阵的估计精度,在单源点检测的基础上,提出一种人工蜂群搜索策略优化的线性聚类分析方法.首先,利用短时傅里叶变换将时域中的观测信号变换到时-频域,并做单源点检测以增强信号的线性聚类特性.然后,基于稀疏的时-频信号,通过对蜂群食物源进行矩阵编码使人工蜂群算法与欠定盲分离问题无缝地契合;将随机性与确定性的搜索策略相结合以协调蜂群的多样性与聚类算法的收敛速度;在蜂群的局部搜索中引入Levy飞行策略,进一步探索当前最优解的邻域以提高聚类的精度.最后,采用人工蜂群算法产生线性聚类的直线方向向量估计混合矩阵的列向量.通过对音频信号的仿真实验,结果表明:本文所提出的改进人工蜂群搜索策略不仅可提供有效的线性聚类分析,而且极大地提高了欠定混合矩阵的估计精度.