Data Acquisition Approach Research Articles

Do Geographical Indication Products Promote the Growth of the Agricultural Economy? An Empirical Study Based on Meta-Analysis

Do geographical indication products help facilitate the development of the agricultural economy? This problem is a point of controversy in the field of global agricultural intellectual property. For a long time, there have been different viewpoints on this problem; that is, there is a positive correlation, negative correlation, U-shape correlation, or no correlation between the geographical indication products and the development of the agricultural economy in the context of different studies. To clarify the influence mechanism between the two and explain why there are these disputes, this study used the meta-analysis method to statistically reanalyze 405 observation values provided in 64 independent research samples from the context of different regions around the world. The study results show that geographical indications not only generate more economic benefits than ordinary products but also contribute to the growth of the agricultural economy by effectively promoting the development of agricultural product trade and the enhancement of agricultural product price. There exists a low positive correlation between the geographical indication products and the agricultural economy (r = 0.176, 95% CI = [0.126, 0.225]). In addition, the promotion effect of geographical indication products on the agricultural economy is regulated by the country of origin of the samples, sample level, publication journal, data type, data acquisition approach, and research method. Our research findings further revealed the internal relationship mechanism between the geographical indication products and the agricultural economy and lay a foundation for better protecting and developing geographical indication products.

MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images

The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different categories may lead to the long-tail problem during the training. Given that similar sensors, data acquisition approaches, and data structures could make the targets in different categories possess certain similarities, those categories can be modeled together within a subspace rather than the entire space to leverage the amounts of sample data in different subspaces. To this end, a subspace-dividing strategy and a subspace-based multi-branch network is proposed for object detection in remotely sensed images. Specifically, a combination index is defined to depict this kind of similarity, a generalized category consisting of similar categories is proposed to represent the subspace, and a new subspace-based loss function is devised to address the relationship between targets in one subspace and across different subspaces to integrate the sample data from similar categories within a subspace and to balance the amounts of sample data between different subspaces. Furthermore, a subspace-based multi-branch network is constructed to ensure the subspace-aware regression. Experiments on the DOTA and HRSC2016 datasets demonstrated the superiority of our proposed method.

Photoion Mass-Selected Threshold Photoelectron Spectroscopy to Detect Reactive Intermediates in Catalysis: From Instrumentation and Examples to Peculiarities and a Database.

Photoion mass-selected threshold photoelectron spectroscopy (ms-TPES) is a synchrotron-based, universal, sensitive, and multiplexed detection tool applied in the areas of catalysis, combustion, and gas-phase reactions. Isomer-selective vibrational fingerprints in the ms-TPES of stable and reactive intermediates allow for unequivocal assignment of spectral carriers. Case studies are presented on heterogeneous catalysis, revealing the role of ketenes in the methanol-to-olefins process, the catalytic pyrolysis mechanism of lignin model compounds, and the radical chemistry upon C-H activation in oxyhalogenation. These studies demonstrate the potential of ms-TPES as an analytical technique for elucidating complex reaction mechanisms. We examine the robustness of ms-TPES assignments and address sampling effects, especially the temperature dependence of ms-TPES due to rovibrational broadening. Data acquisition approaches and the Stark shift from the extraction field are also considered to arrive at general recommendations. Finally, the PhotoElectron PhotoIon Spectral Compendium (https://pepisco.psi.ch), a spectral database hosted at Paul Scherrer Institute to support assignment, is introduced.

An efficient approach addressing the chemical complexity of Jiawei Fangji Huangqi decoction by integrating ultra-high-performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry and intelligent data processing workflows.

A challenge in the quality control of traditional Chinese medicine is the systematic multicomponent characterization of the compound formulae. Jiawei Fangji Huangqi, a modified form of Fangji Huangqi, is a prescription comprising seven herbal medicines. To address the chemical complexity of the Jiawei Fangji Huangqi decoction, we integrated ion mobility-quadrupole time-of-flight high-definition MSE coupled to ultra-high-performance liquid chromatography and intelligent data processing workflows available in the UNIFI software package. Good chromatographic separation was achieved on CORTECS UPLC T3 column within 52min, and high-accuracy MS2 data were acquired using high-definition MSE in the negative and positive modes. A chemical library of 1250 compounds was created and incorporated into the UNIFI software to enable automatic peak annotation of the high-definition MSE data. We identified or tentatively characterize 430 compounds in the Jiawei Fangji Huangqi decoction. The potential superiority of high-definition MSE over conventional MS data acquisition approaches was revealed in its spectral quality (MS2 ), differentiation of isomers, separation of coeluting compounds, and target mass coverage. The multiple components of the Jiawei Fangji Huangqi decoction were elucidated, offering insight into its improved pharmacological action compared with that of the Fangji Huangqi formula.

Rapid Chemical Screening of Microplastics and Nanoplastics by Thermal Desorption and Pyrolysis Mass Spectrometry with Unsupervised Fuzzy Clustering.

The transport and chemical identification of microplastics and nanoplastics (MNPs) are critical to the concerns over plastic accumulation in the environment. Chemically and physically transient MNP species present unique challenges for isolation and analysis due to many factors such as their size, color, surface properties, morphology, and potential for chemical change. These factors contribute to the eventual environmental and toxicological impact of MNPs. As analytical methods and instrumentation continue to be developed for this application, analytical test materials will play an important role. Here, a direct mass spectrometry screening method was developed to rapidly characterize manufactured and weathered MNPs, complementing lengthy pyrolysis-gas chromatography-mass spectrometry analysis. The chromatography-free measurements took advantage of Kendrick mass defect analysis, in-source collision-induced dissociation, and advancements in machine learning approaches for the data analysis of complex mass spectra. In this study, we applied Gaussian mixture models and fuzzy c-means clustering for the unsupervised analysis of MNP sample spectra, incorporating clustering stability and information criterion measurements to determine latent dimensionality. These models provided insight into the composition of mixed and weathered MNP samples. The multiparametric data acquisition and machine learning approach presented improved confidence in polymer identification and differentiation.

Digital-twin-enabled JIT design of rock tunnel: Methodology and application

An accurate and timely design of individual processes in drill and blast tunneling is essential for robust and low-risk construction of rock tunnels. Conventional dynamic design method of rock tunnel focuses on the modification of preliminary design schemes using exposed on-site data. A novel conceptual model, just-in-time (JIT) design of rock tunnel, is proposed in this study to denote the intrinsic requirement that tunnel support parameters should be determined in a JIT way when the rock mass are excavated. Specifically, implementation of JIT design of rock tunnel can be divided into three steps, i.e., acquisition of on-site raw data, interpretation of on-site raw data, and aggregation of the interpreted results to obtain a revised design scheme. Time consumption, accuracy and degree of automation are selected as three main indicators to measure the performance of such conceptual model. Moreover, to further improve the efficiency and accuracy, digital twin (DT) is integrated into the implementation of JIT design of rock tunnel, where Scene-Entity-Relationship-Incident-Control (SERIC) modelling method is proposed as a new DT application mode for share and reuse across the domain. The proposed DT-enabled JIT design of rock tunnel is applied into a practical engineering project, where several advanced data acquisition, interpretation and aggregation approaches are integrated using DT. Results show that the design efficiency and accuracy have been largely improved and the proposed method could facilitate information sharing between end-users and stakeholders.

Magnetic resonance imaging using a nonuniform Bo (NuBo) field-cycling magnet.

Magnetic resonance imaging (MRI) is a powerful noninvasive diagnostic tool with superior soft tissue contrast. However, access to MRI is limited since current systems depend on homogeneous, high field strength main magnets (B0-fields), with strong switchable gradients which are expensive to install and maintain. In this work we propose a new approach to MRI where imaging is performed in an inhomogeneous field using radiofrequency spatial encoding, thereby eliminating the need for uniform B0-fields and conventional cylindrical gradient coils. The proposed technology uses an innovative data acquisition and reconstruction approach by integrating developments in field cycling, parallel imaging and non-Fourier based algebraic reconstruction. The scanner uses field cycling to image in an inhomogeneous B0-field; in this way magnetization is maximized during the high field polarization phase, and B0 inhomogeneity effects are minimized by using a low field during image acquisition. In addition to presenting the concept, this work provides experimental verification of a long-lived spin echo signal, spatially varying resolution, as well as both simulated and experimental 2D images. Our initial design creates an open MR system that can be installed in a patient examination table for body imaging (e.g., breast or liver) or built into a wall for weighted-spine imaging. The proposed system introduces a new class of inexpensive, open, silent MRIs that could be housed in doctor's offices much like ultrasound is today, making MRI more widely accessible.

Recent advances of data‐independent acquisition mass spectrometry‐based proteomics

Recent advances of data‐independent acquisition mass spectrometry‐based proteomics

BIM–GIS Integrated Utilization in Urban Disaster Management: The Contributions, Challenges, and Future Directions

In the 21st Century, disasters have severe negative impacts on cities worldwide. Given the significant casualties and property damage caused by disasters, it is necessary for disaster management organizations and the public to enhance urban disaster management. As an effective method, BIM (Building Information Modeling)–GIS (Geographic Information System) integration can significantly improve urban disaster management. Despite the significance of BIM–GIS integration, there is rarely the adoption of BIM–GIS integration in urban disaster management, which significantly hinders the development of the quality and efficiency of urban disaster management. To enhance urban disaster management and reduce the negative impact caused by disasters, this study is developed to perform a systematic review of the utilization of BIM–GIS integration in urban disaster management. Through the systematic review, the capabilities of BIM–GIS integration in disaster prevention and mitigation, disaster response, and post-disaster recovery are reviewed and analyzed. Moreover, the data acquisition approaches, interoperability, data utilization and analysis methods, and future directions of BIM–GIS integrated utilization in the disaster management process are also discussed and analyzed. Through this study, the public and urban disaster managers can effectively familiarize themselves with and utilize the capabilities of BIM–GIS integration in urban disaster management, thereby improving the urban disaster management efficiency and the survival rate of disaster victims worldwide. For BIM and GIS software developers, this study can support them to familiarize themselves with the methods and trends of BIM–GIS integrated utilization in urban disaster management and thus optimize the development of software for BIM and GIS.

Time-frequency fusion features-based incremental network for smartphone measured structural seismic response classification

Smartphone as a portable device can be used for public participatory structural seismic response acquisition. This data acquisition approach not only reduces the acquisition cost but also facilitates the possibility to obtain city-scale building seismic responses. In this paper, we propose a novel structural seismic response classification method using time–frequency fusion features-based incremental network, namely SeismicNet, that can automatically classify the signals acquired through smartphones into structural response under normal operation conditions (structural normal response) or structural response under strong ground motion (structural seismic response). In this method, the time-domain signals of structural response are converted into time–frequency maps, which will be fed into a lightweight convolutional network for the extraction of the initial fusion features. Subsequently, these features are mapped by random weights to dynamic nodes. This not only improves the capability of feature expression, but also allows incremental updating of the model. Finally, a simulated structural response dataset and some real structural seismic responses are used to verify the accuracy and practicability of the proposed method. Furthermore, the results showed that the proposed method is more than 84 times in training speed and 2 times in inference speed than some well-known deep learning networks (VGG16 and ResNet50).

The Plant Phenomics and Genomics Research Data Repository: An On-Premise Approach for FAIR-Compliant Data Acquisition.

The FAIR data principle as a commitment to support long-term research data management is widely accepted in the scientific community. However, although many established infrastructures provide comprehensive and long-term stable services and platforms, a large quantity of research data is still hidden. Currently, high-throughput plant genomics and phenomics technologies are producing research data in abundance, the storage of which is not covered by established core databases. This concerns the data volume, for example, time series of images or high-resolution hyperspectral data; the quality of data formatting and annotation, e.g., with regard to structure and annotation specifications of core databases; uncovered data domains; or organizational constraints prohibiting primary data storage outside institutional boundaries. To share these potentially dark data in a FAIR way and master these challenges the ELIXIR Germany/de.NBI service Plant Genomic and Phenomics Research Data Repository (PGP) implements an on-premise approach, which allows research data to be kept in place and wrapped in FAIR-aware software infrastructure. In this chapter, the e!DAL infrastructure software and the PGP repository are presented as best practice on how to easily setup FAIR-compliant and intuitive research data services.

SMART PUBLIC OPINION MONITORING AND ANALYSIS SYSTEM FOR ETHNIC REGIONS: A MULTI-MODAL APPROACH USING MACHINE LEARNING

In ethnic regions, public opinion plays a vital role in shaping social stability and harmony. However, monitoring and analyzing public opinion in these regions can be challenging due to the complex linguistic and cultural factors involved. To address this issue, this study proposes a Smart Public Opinion Monitoring and Analysis System for Ethnic Regions, which utilizes a multi-modal approach for data acquisition and machine learning techniques for analysis. The system collects public opinion data from multiple modalities, including text, speech, image, and video, and uses intelligent speech processing and text translation to analyze non-textual data. The sentiment analysis module evaluates the polarity of public opinion, while the topic modeling module identifies key topics and keywords of public interest. The system also includes an early warning and risk detection module that uses machine learning algorithms to detect potential risks and generate alerts in real-time. Finally, the data visualization module presents the analysis results in an intuitive and user-friendly manner. The proposed system has been tested on a dataset of public opinion data from ethnic regions, and the results demonstrate its effectiveness in monitoring and analyzing public opinion in different modalities. The system can help government agencies and relevant stakeholders to respond quickly to potential risks and maintain social stability in ethnic regions.

Label-free Chemical Characterization of Polarized Immune Cells in vitro and Host Response to Implanted Bio-instructive Polymers in vivo Using 3D OrbiSIMS.

The Three-dimensional OrbiTrap Secondary Ion Mass Spectrometry (3D OrbiSIMS) is a secondary ion mass spectrometry instrument, a combination of a Time of Flight (ToF) instrument with an Orbitrap analyzer. The 3D OrbiSIMS technique is a powerful tool for metabolic profiling in biological samples. This can be achieved at subcellular spatial resolution, high sensitivity, and high mass-resolving power coupled with MS/MS analysis. Characterizing the metabolic signature of macrophage subsets within tissue sections offers great potential to understand the response of the human immune system to implanted biomaterials. Here, we describe a protocol for direct analysis of individual cells after in vitro differentiation of naïve monocytes into M1 and M2 phenotypes using cytokines. As a first step in vivo, we investigate explanted silicon catheter sections as a medical device in a rodent model of foreign body response. Protocols are presented to allow the host response to different immune instructive materials to be compared. The first demonstration of this capability illustrates the great potential of direct cell and tissue section analysis for in situ metabolite profiling to probe functional phenotypes using molecular signatures. Details of the in vitro cell approach, materials, sample preparation, and explant handling are presented, in addition to the data acquisition approaches and the data analysis pipelines required to achieve useful interpretation of these complex spectra. This method is useful for in situ characterization of both in vitro single cells and ex vivo tissue sections. This will aid the understanding of the immune response to medical implants by informing the design of immune-instructive biomaterials with positive interactions. It can also be used to investigate a broad range of other clinically relevant therapeutics and immune dysregulations. Graphical overview.

An experimental and theoretical approach to assessing the structure of fine-grained modified concretes

Introduction. The development of approaches to assessing structural characteristics of multicomponent modified concretes using non-destructive testing methods at all stages of hydration is a relevant means focused on ensuring optimal rheological and strength characteristics of concretes.
 Materials and methods. Modified fine-grained concrete was analyzed as the material under study, since its composition meets the requirements of additive technologies applied in the construction industry. Modification was implemented by introducing additives and mechanically activating the initial cement-sand composition. The strength of resulting specimens was controlled as of the onset of curing. Structure formation was studied using a computed tomography scanner.
 Results. A tomography data processing approach was developed to evaluate the modified concrete’s structure. Key aspects of the stage-by-stage processing of specimens’ images were identified and a solution was obtained for determining the threshold values of correspondence between image shades having different density structures. In the course of compa­ring the results of the strength and structure study, the value of the percentage ratio of inclusions was obtained for various densities during the curing process.
 Conclusions. The proposed data acquisition approach contributes to development of further research in this area; it can become a stage in developing a new concrete structure analysis method. The study of specimens at early curing stages in combination with non-destructive control methods contributes to (1) development of rapid assessment techniques, applied to the quality of materials, and (2) an efficiency increase in the process of development.

The impact of Word of Mouth on employee turnover rate

In the general environment, many companies have employees leaving or jumping ship en masse is a tremendous challenge for company culture, brand reputation, and team management. A large part of the reason for this is related to the reasons why employees leave. This paper presents the impact of negative emotions and turnover rates due to unfulfilled personal needs (according to Maslow's theory of individual needs). The paper uses questionnaires, in-depth personal interviews, and anonymous back surveys, emphasizing sample collection, primary data acquisition, sampling, and other practical approaches to conclude that negative emotions and word-of-mouth communication are strongly correlated and influence turnover rates and corporate management risks some extent.

UAV LiDAR Metrics for Monitoring Crop Height, Biomass and Nitrogen Uptake: A Case Study on a Winter Wheat Field Trial

Efficient monitoring of crop traits such as biomass and nitrogen uptake is essential for an optimal application of nitrogen fertilisers. However, currently available remote sensing approaches suffer from technical shortcomings, such as poor area efficiency, long postprocessing requirements and the inability to capture ground and canopy from a single acquisition. To overcome such shortcomings, LiDAR scanners mounted on unmanned aerial vehicles (UAV LiDAR) represent a promising sensor technology. To test the potential of this technology for crop monitoring, we used a RIEGL Mini-VUX-1 LiDAR scanner mounted on a DJI Matrice 600 pro UAV to acquire a point cloud from a winter wheat field trial. To analyse the UAV-derived LiDAR point cloud, we adopted LiDAR metrics, widely used for monitoring forests based on LiDAR data acquisition approaches. Of the 57 investigated UAV LiDAR metrics, the 95th percentile of the height of normalised LiDAR points was strongly correlated with manually measured crop heights (R2 = 0.88) and with crop heights derived by monitoring using a UAV system with optical imaging (R2 = 0.92). In addition, we applied existing models that employ crop height to approximate dry biomass (DBM) and nitrogen uptake. Analysis of 18 destructively sampled areas further demonstrated the high potential of the UAV LiDAR metrics for estimating crop traits. We found that the bincentile 60 and the 90th percentile of the reflectance best revealed the relevant characteristics of the vertical structure of the winter wheat plants to be used as proxies for nitrogen uptake and DBM. We conclude that UAV LiDAR metrics provide relevant characteristics not only of the vertical structure of winter wheat plants, but also of crops in general and are, therefore, promising proxies for monitoring crop traits, with potential use in the context of Precision Agriculture.

Neuro-Inspired Autonomous Data Acquisition for Energy-Constrained IoT Sensors

The unprecedented pervasiveness of the Internet of Things (IoT) has unleashed an urgent need for autonomous IoT sensors that do not only autonomously operate, but more importantly autonomously also make intelligent decisions, including when and what data to acquire. Inspired by the autonomous nervous system (ANS) and its rapid de-centralized response to sensory stimuli, this article proposes an autonomous data acquisition approach for energy-constrained IoT sensors. The proposed approach achieves autonomy through rapid real-time event detection in the analog domain, which is then used to instantaneously trigger data acquisition from the sensor, without needing to consult the processor in making such a decision. Accordingly, the analog event-detection circuit would be the only circuit that is continuously ON, while all other system blocks remain in the sleep mode until an event is detected, significantly reducing the operation time of the overall system and the amount of redundant data it produces. A proof-of-concept circuit is designed and implemented, and its performance is verified and analyzed through extensive simulations and experiments, demonstrating event-detection speeds at the order of microseconds; orders of magnitude faster than the required limit for lossless data acquisition in many IoT applications. A case study on an Industrial IoT (IIoT) application is investigated through circuit-level implementation and simulations on real seismic data. The presented results demonstrate the feasibility of lossless autonomous active seismic data acquisition with a 95% reduction in the overall operation time of the sensor node as well as in the amount of data it produces compared to conventional data-acquisition approaches.

A comparison of high-throughput imaging methods for quantifying plant growth traits and estimating above-ground biomass accumulation

Image-based estimation of above-ground biomass accumulation is recognized as the predominant asset of breeding programs for accelerating gains in crop adaptation and productivity. High-throughput phenotyping (HTP) has the potential to greatly facilitate genetic improvements by dissecting morphological traits which can serve as accurate predictors of optically sensed plant biomass. Thus, various high-throughput data acquisition methods have been recently developed to quantify desirable phenotypes from images. Novel insights are essential to provide helpful guidelines to breeders for the optimal selection of phenotyping approaches aimed at estimating plant biomass. In this study, three representative HTP data acquisition methods based on two-dimensional (2D) image analysis, Multi View Stereo (MVS)-Structure from Motion (SfM) three-dimensional (3D)-reconstruction and Structured Light (SL) 3D-scanning were compared for estimating fresh (FAGB) and dry above-ground biomass (DAGB) weight of potted plants at early growth stages. Two crop species with contrasting canopy shapes and architectures, namely maize (Zea mais L.) and tomato (Solanum lycopersicum L.), were used as model plants. First, the performances of each sensing approach were tested in the accurate reproduction of the major phenotypic traits and, secondly, in the reliable fresh/dry AGB estimation from the relevant allometric equations calibrated according multi- (six sampling dates, once a week) and mono-temporal (one sampling date at harvest time) datasets. The overall results demonstrated the effectiveness of the tested methods in reproducing the salient features of canopies with increasing architectural complexity, including plants’ height (R2̅ = 0.98, rRMSE̅ = 7.73 % and AIC̅ = 475.07), shoot area (R2̅ = 0.91, rRMSE̅ = 29.53 % and AIC̅ = 1369.77) and convex hull volume (R2̅ = 0.88, rRMSE̅ = 27.32 % and AIC̅ = 818.19). In this context, the shoot area associated with the age of the plant was found to be the most indicative phenotypic determinant for an accurate estimation of FAGB and DAGB. Accordingly, the greater ability of the 2D image analysis in quantifying canopies of elongated plants characterized by thin organs ensured the best estimates of fresh/dry biomass accumulation in maize (0.98 ≤ R2̅ ≤ 0.99 % and 8.98 % ≤ rRMSE̅ ≤ 16.03 %, considering multi- and mono-temporal calibrated models). Contrariwise, the MVS-SfM 3D-reconstruction of more complex canopies with compact habit was advantageous for the accurate prediction of above-ground DAGB and FAGB dynamics in tomato (R2̅ = 0.99 % and 6.70 % ≤ rRMSE̅ ≤ 15.82 %, considering multi- and mono-temporal calibrated models). These findings provide references to carefully select the best suited HTP data acquisition approach for the accurate estimation of biomass accumulation across plants of different canopy complexity, thereby paving the way to break through current phenotyping bottlenecks in breeding applications for current and future food security.

Applications of ion mobility-mass spectrometry in the chemical analysis in traditional Chinese medicines

离子淌度质谱(IM-MS)是一种将离子淌度分离与质谱分析相结合的新型分析技术。IM-MS的主要优势不仅是在质谱检测前提供了基于气相离子形状、大小、电荷数等因素的多一维分离,而且能够提供碰撞截面积、漂移时间等质谱信息进而辅助化合物鉴定。近年来,随着IM-MS技术的不断发展,该技术在中药化学成分分析中受到越来越多的关注。首先,IM-MS已成功应用于改善中药复杂成分尤其是同分异构体或等量异位素等成分的分离;其次,IM-MS可通过多重碎裂模式辅助高质量中药小分子质谱信息的获取;此外,IM-MS提供的高维质谱数据信息还可促进中药复杂体系多成分的整合分析。该文在对IM-MS分类和基本原理进行概述的基础上,从分离能力及分离策略、多重碎裂模式、多维质谱数据处理策略3个方面,重点综述了IM-MS在中药化学成分分析中的应用,以期为IM-MS在中药化学成分研究提供参考。

Lamb Wave Frequency Diverse Array.

The imaging method based on the conventional phased array (PA) can only realize angle focusing. To break through the above limitations, frequency diverse array (FDA) is first introduced from radar to Lamb wave, which opens up a possibility to realize angle-range focusing in damage localization. The energy pattern of FDA without considering the features of radar signals is re-derived, and a corresponding FDA focusing imaging method for Lamb wave is developed. In the presented method, by appending various frequency offsets to the redesigned anti-dispersive excitations, the desired FDA focusing and the dispersion elimination for Lamb wave are achieved simultaneously. Furthermore, based on full matrix capture being the data acquisition approach, a novel total focusing is realized with comprehensive consideration of positive and negative frequency offsets. Benefiting from FDA focusing, compared with the PA methods, the proposed method exhibits superior adjacent defects' identification ability and preferable interference suppression capability with excellent location accuracy and strong parameter robustness. Its superiority is validated by the experiment on aluminum plate with multiple adjoining surface and inner defects.