The increasing penetration of power electronics, such as grid-connected inverters and active loads may cause power quality issues, which reduce the sensitivity of monitoring and control systems due to measurement noises. This article presents an optimal singular value decomposition (SVD) filtering method for grid-connected inverters to improve sampling accuracy against measurement noises. First, the principle of this proposed method is based on the Hankel matrix theory, and then the implementation process is explained, during which the relationship between the Hankel matrix dimension and noise reduction is discussed. Furthermore, the optimal singular value is analyzed and proposed to determine the reconstruction order. Then, the comparative analysis of the proposed optimal SVD filtering method and difference spectrum method is given to explain the optimal reconstruction order. Finally, simulation verifications are implemented to validate the effectiveness of the proposed filtering method, considering the Hankel matrix dimension, reconstruction order, and different signal–noise ratio (SNR). The verification results show that the proposed optimal SVD filtering method can accurately identify the sampling current of grid-connected inverters, even if severe harmonic noises and oscillation happen. The proposed method can reduce the effects of harmonic disturbance on measurement accuracy and control performance of grid-connected inverters, which can improve the robustness of grid-connected inverters.

Utilising a cross-modal attention mechanism for multimodal feature integration may engender confounding effects, resulting indetrimental biases during modal interaction, and consequently impacting the outcomes of emotion classification. To addressthis issue, a cross-modal fusion network based on causal gating attention mechanism was proposed. First, a feature-maskingtext embedding module is utilised to enhance the semantic representation capability of both the audio and video modalities.Subsequently, a cross-modal attention fusion module is employed to complementarily merge the audio and video modalities,obtaining the fused audio-video modality features. Next, a causal gating cross-modal fusion network is used to fully integratethe heterogeneous data of text, audio, and video modalities. Finally, the sentiment analysis results are classified using SoftMax.The proposed cross-modal fusion network demonstrated superior performance in sentiment classification when compared tobaseline techniques on the CMU-MOSEI dataset. It effectively associated and combined pertinent multi-modal information.

This article provides a comprehensive overview of the potential challenges and solutions of second-life batteries. First, safety issues of second-life batteries are investigated, which is highly related to the thermal runaway of battery systems. The critical solutions for the thermal runaway problem are discussed, including structural optimization, parameter identification, advanced BMS, and artificial intelligence (AI)-based control strategies. Furthermore, the cell inhomogeneity problem of second-life battery systems is analyzed, where the passive balancing strategy and active balancing strategy are reviewed, respectively. Then, the compatibility issue of second-life batteries is investigated to determine whether electrical dynamic characteristics of a second-life battery can meet the performance requirements for energy storage. In addition, date security and protection methods are reviewed, including digital passport, smart meters and Internet of Things (IoT). The future trends and solutions of key challenges for second-life battery utilization are discussed.

In Open-domain Chinese Knowledge Base Question Answering (ODCKBQA), most common simple questions can be answered by a single relational fact in the knowledge base (KB). The abbreviations, aliases, and nesting of entities in Chinese question sentences, and the gap between them and the structured semantics in the knowledge base, make it difficult for the system to accurately return answers. This study proposes a semantic union model (SUM), which concatenates candidate entities and candidate relationships, using a contrastive learning algorithm to learn the semantic vector representation of question and candidate entity-relation pairs, and perform cosine similarity calculations to simultaneously complete entity disambiguation and relation matching tasks. It can provide information for entity disambiguation through the relationships between entities, avoid error propagation, and improve the system performance. The experimental results show that the system achieves a good average F1 of 85.94% on the dataset provided by the NLPCC-ICCPOL 2016 KBQA task.

激发源启动时间的设计是高效混叠采集的关键技术之一.高效混叠采集是随高密度地震勘探而产生的采集作业新模式,它能显著缩短地震勘探周期、降低采集成本,但激发源启动时间的随机程度直接影响采集资料的品质.为此,文中创新性地提出一种地震勘探随机激发时间产生算法.首先,利用卫星授时技术和压控晶振在较短时间内的电压变化与振荡频率呈近似线性关系的特点,以固定时间间隔方式进行本地时钟的校准和卫星时间同步;然后,根据激发源位置、准备好的炮点激发顺序、时距规则参数等信息,计算出偏差不大于10μs的预计激发时间;最后,基于该预计激发时间,以实时采集的振动传感数据作为种子,随机产生一个指定窗口内小抖动的随机数作为炮点激发的整毫秒时间,确保生成激发时间的随机性.对测试结果的分析表明,所提方法计算得到的随机激发时间的抖动值具有数值大小与范围可控、随机性强的特征,满足工业应用的实际需求.

To address the issue of inadequate accuracy and real-time performance of infrared ship target detection methods on coastal defense scenarios, a novel lightweight ship detection algorithm based on improved YOLOv7 framework is proposed. This framework incorporates several enhancements to augment its capabilities. First, to achieve model lightweight processing, the algorithm integrates the MobileNetv3 network into the architecture of the Backbone network. This addition contributes to efficient computation and model size reduction. Second, an attention mechanism is introduced within the Neck network to mitigate noise and interference, thereby improving the network's feature extraction capability. In addition, we employ a bidirectional weighted feature pyramid to enhance feature fusion within the network, promoting more effective information integration. Finally, the algorithm incorporates Wise IoU to optimize the loss function, improving convergence speed and model accuracy. Experimental evaluations on the Arrow dataset demonstrate noteworthy improvements over the standard YOLOv7 approach. Specifically, the proposed enhanced algorithm exhibits a 0. 9 percentage points increase in accuracy, 2. 5 percentage points increase in recall, and 1. 2 percentage points increase in mean average precision (mAP) at IoU thresholds of 0. 5 and 0. 5: 0. 95. In addition, it achieves approximately 38. 4% reduction in model parameters and a 65. 5% reduction in floating point operations per second (FLOPs). This enhanced algorithm delivers superior inspection accuracy while meeting the speed requirements for efficient ship inspection. Consequently, it effectively enables high-speed and high-precision ship detection.

基于泰勒级数的P波旅行时方程的计算精度受到具有垂直对称轴的横向各向同性(Transverse Isotropy Medium with Vertical Symmetry Axis,VTI)介质的影响,为了改善这种情况,提出一种基干平方处理与系数匹配的计算方法.首先,利用平方处理将非平方形式的基于泰勒级数的旅行时公式转换为包含高阶项的平方形式;再使用系数匹配法处理旅行时平方公式的高阶项,保持方程炮检距的最高阶为常用的4阶;然后,在简化参数形式与优化旅行时平方公式计算的基础上,得到与各向异性参数相关的新系数y;最后,形成了含有y的基于平方处理与系数匹配的4阶P波旅行时计算方法.基于水平层状VTI介质模型的实验结果表明,与有理近似算法、三射线广义时差近似计算法和扩展广义时差计算法相比,该方法计算误差更小,同时远炮检距处的计算能力得到一定提高,从而给以旅行时方程为基础的应用提供了更多选择.

Currently, using MATLAB Web App Server to deploy MATLAB Web applications for hosting and sharing interactive Web applications involves the following problems: slow loading of applications, incompatibility with some browser versions, and various shortcomings of deployment tools, such as poor scalability and difficulty in optimizing black boxes. These problems adversely affect the user experience. To address this situation, we propose the use of front-end technology to design application layouts and build an application hosting platform with front-end and back-end separation using Nginx and Python. This method is shown to be simple and efficient, and it can successfully overcome the aforementioned problems while providing new ideas for hosting and sharing.

In actual exploration, the demand for 3D seismic data collection is increasing, and the requirements for data are becoming higher and higher. Accordingly, the collection cost and data volume also increase. Aiming at this problem, we make use of the nature of data sparse expression, based on the theory of compressed sensing, to carry out the research on the efficient collection method of seismic data. It combines the collection of seismic data and the compression in data processing in practical work, breaking through the limitation of the traditional sampling frequency, and the sparse characteristics of the seismic signal are utilized to reconstruct the missing data. We focus on the key elements of the sampling matrix in the theory of compressed sensing, and study the methods of seismic data acquisition. According to the conditions that the compressed sensing sampling matrix needs to meet, we introduce a new random acquisition scheme, which introduces the widely used Low-density Parity-check (LDPC) sampling matrix in image processing into seismic exploration acquisition. Firstly, its properties are discussed and its conditions for satisfying the sampling matrix in compressed sensing are verified. Then the LDPC sampling method and the conventional data acquisition method are used to synthesize seismic data reconstruction experiments. The reconstruction results, signal-to-noise ratio and reconstruction error are compared to verify the seismic data based on sparse constraints. The LDPC sampling method improves the current seismic data reconstruction efficiency, reduces the exploration cost and the effectiveness and feasibility of the method.

Compressive sensing theory mainly includes the sparsely of signal processing, the structure of the measurement matrix and reconstruction algorithm. Reconstruction algorithm is the core content of CS theory, that is, through the low dimensional sparse signal recovers the original signal accurately. This thesis based on the theory of CS to study further on seismic data reconstruction algorithm. We select Orthogonal Matching Pursuit algorithm as a base reconstruction algorithm. Then do the specific research for the implementation principle, the structure of the algorithm of AOMP and make the signal simulation at the same time. In view of the OMP algorithm reconstruction speed is slow and the problems need to be a given number of iterations, which developed an improved scheme. We combine the optimized OMP algorithm of constraint the optimal matching of item selection strategy, the backwards gradient projection ideas of adaptive variance step gradient projection method and the original algorithm to improve it. Simulation experiments show that improved OMP algorithm is superior to traditional OMP algorithm of improvement in the reconstruction time and effect under the same condition.

Pre-stack Reverse Time migration (RTM) is a full wave field imaging method. There are lots of data calculation tasks and data storage tasks in RTM. This requires powerful data analysis and processing capabilities to meet the needs of actual production. In this paper, we first discuss the seismic data regularization related technology and the impact of the regularized seismic data on the RTM imaging results. Then, in order to improve the efficiency of RTM data processing, cloud computing technology is used. We research the data parallel processing method of RTM in cloud computing environment. We apply the parallel computation of CPU and GPU to RTM, and design a MapReduce parallel computing model C-GMR suitable for multi-GPU nodes. The experimental results show that the technical measures can greatly improve the computational efficiency on the basis of guaranteeing the advantages of RTM high-precision imaging, and provide good technical support for the application of RTM to process massive seismic data.

As a mechanical and electrical integration product, brushless DC motor has better controllability and wide speed range. According to its characteristics, a brushless DC motor control system based on STM32F103ZET6 MCU is designed and realized, and then the control system was analyzed and discussed. Adopted GPIO module, PWM module, timer module, etc., coupled with efficient PID algorithm, three main functions of this system have been implemented, such as start stop control, position detection and closed-loop speed regulation. The experimental results show that this control system works well with low cost and high price-performance ratio.

The detection of speech endpoint is an important application for speech signal processing.Although there are variance methods, the endpoint can't be detected accurately in low SNR (Signal to Noise Ratio).The paper pointes out an endpoint detection algorithm combining two methods together: the one is improved spectral subtraction based on multitaper spectral estimation, and the other is BARK subband variance in frequency domain.Firstly, the noisy speech signal is processed though the improved spectral subtraction based on multitaper spectral estimation.It can achieve the purpose of noise reduction through this step.Then the noisy speech signal is detected using the method of BARK subband variance in frequency domain.Compared with the common endpoint detection algorithm, it is concluded that endpoint detection accuracy by new method can be improved in low SNR.

For those who have access to seismic exploration principles and seismic data processing initially, there are essential difficulties. The difficulties include the great amount of seismic data, complicated processing procedure, long processing period as well as costly software and facilities for data processing. It is an important issue for how to make the beginners understand and master the fundamental procedures of seismic data processing easily, and how to make the processing procedures just look like to be done with microcomputer. This article is emphasized on virtual simulating seismic forward data processing based on LabVIEW platform and LabSQL. LabSQL is LabVIEW’s database kit, which is supplied by the 3rd party for accessing the database. It is beneficial for beginners to control processing procedure efficiently. This method is worth being promoted.

Faced with the massive information and data, signal and information processing need to solve the problem of removing redundant information and obtain useful information. The PCA method is introduced, which can detect the effective information in noise. Taking the simulated seismic data as targets, the principle component is used to analysis the seismic data in different speeds and slopes in the first place. And then using the principle component to recover the original signals is studied. After the signal simulation experiment, the principle component effectiveness is verified further after comparing the original signals and the recovered signals not adding noise.

Seismic data is generated by a sharp pulse, which transforms into to Earth and is reflected by the layer status in the Earth. The Data is 3D or 2D. Because of transforming through the Earth, Seismic data has wide main lobe and strong side lobe, which is different from that of the sharp pulse. In order to recover the character that is similar to that of sharp pulse, the 2D and 3D seismic data is usually processed by the deconvolution. The data includes down going data and up going data mainly. The deconvolution is done by down going data in the general procedure. In this paper, it is done by up going data statistical distinctively. The new methods can get stable deconvolution operator and deconvolution result. It can be used to process the data that is contaminated high-frequency noise. The new approach can get well data to be used in the following process flow and bring a better result to interpretation and application.

With various kinds of data, such as Zero Vertical Seismic Profile (VSP), 3D VSP, multiple directional Walkaway VSP and full azimuth seismic data, the Vertical Transverse Isotropy (VTI) and Horizontal Transverse Isotropy (HTI) characteristics of plutonic igneous rock are studied, and an approach to estimate the pseudo anisotropy for HTI media is presented. It is concluded that the target zone has a little HTI characteristics and spatial variation of HTI can reduce the difference between forward moveout and first break observation of reflection. The relative variant HTI parameter by single direction respectively can describe the transversely anisotropy of media. Spatial variation of HTI estimated is consistent with the real fracture orientation and can predict fracture reservoirs.

As well known, the amount of data is enormous in seismic signal processing while the FFT is for complex signal usually. Using complex sequences FFT arithmetic to deal with seismic signal in simple will double the amount of data and increase the amount of calculation. Based on the symmetrical character of real signal, this paper discussed methods of FFT and IFFT to treat with real signal, and the quantitative analysis has been done at the same time. Finally, the field seismic data were calculated with the method. It is clearly that the method can reduce about half amount of calculation and save the computer memory space.

Routine seismic imaging technique and pre-stack time/depth migration imaging technique in isotropic media have been widely applied in structure exploration and lithology exploration,great application effects have been achieved.However the seismic resolution still can not meet the need of exploration for thin reservoir and oilfield development.Based on the data gathered by integrated seismic of zero-offset VSP,8-direction Walkaway VSP,3D VSP and full azimuth 3D ground seismic,the influence of VTI medium,HTI medium and residual static correction to seismic exploration were discussed for thin reservoir and fractured reservoir in stable deposition environment.For the reservoir and fractured reservoir in stable deposition environment the study results show that the VTI medium is the main influence factor for seismic imaging resolution,it could cause 100 ms time difference within 3000M offset,for HTI medium(including non-uniform velocity and structure dip) the orientation influence is generally less than ±20 ms,but it could also affects the resolution of seismic imaging.Meantime the solution of HTI medium relative space information could help to predict fractured reservoir.In addition VTI and HTI media could directly affect the solution accuracy of residual static correction,which is based on surface consistency theory.Therefore for thin reservoir and fractured reservoir in stable deposition environment,fully considering the influence of VTI and HTI media will help us raise seismic exploration resolution and reservoir prediction ability.