Pattern Recognition Methods Research Articles

Laparoscopic Image-based Critical Action Recognition and Anticipation with Explainable Features.

Surgical workflow analysis integrates perception, comprehension, and prediction of the surgical workflow, which helps real-time surgical support systems provide proper guidance and assistance for surgeons. This paper promotes the idea of critical actions, which refer to the essential surgical actions that progress towards the fulfillment of the operation. Fine-grained workflow analysis involves recognizing current critical actions and previewing the moving tendency of instruments in the early stage of critical actions. Aiming at this, we propose a framework that incorporates operational experience to improve the robustness and interpretability of action recognition in in-vivo situations. High-dimensional images are mapped into an experience-based explainable feature space with low-dimension to achieve critical action recognition through a hierarchical classification structure. To forecast the instrument's motion tendency, we model the motion primitives in the polar coordinate system (PCS) to represent patterns of complex trajectories. Given the laparoscopy variance, the adaptive pattern recognition (APR) method, which adapts to uncertain trajectories by modifying model parameters, is designed to improve prediction accuracy. The in-vivo dataset validations show that our framework fulfilled the surgical awareness tasks with exceptional accuracy and real-time performance.

Pewarnaan Titik Ketakteraturan Lokal pada Hasil Operasi Amalgamasi Titik Graf Lintasan

Definition of graph is set pair (𝑉(𝐺),𝐸(𝐺)) where 𝑉(𝐺) is vertex set and 𝐸(𝐺) is edge set. A maping 𝐼 : 𝑉(𝐺)→{1,2, … ,𝑘} as label function and weight function 𝑤 : 𝑉(𝐺)→𝑁 is desined as 𝑤(𝑢)=Σ𝑣∈𝑁(𝑢)𝑙(𝑣). The function 𝑤 is called local irregularity vertex coloring if: (i) 𝑜𝑝𝑡(𝑙)=𝑚𝑖𝑛 (𝑚𝑎𝑘𝑠 (𝑙𝑖) ;𝑙𝑖 𝑖𝑠 𝑙𝑎𝑏𝑒𝑙 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛) and (ii) for every 𝑢𝑣 ∈ 𝐸(𝐺),𝑤(𝑢) ≠ 𝑤(𝑣). The chromatic number of local irregularity vertex coloring denoted by 𝜒𝑙𝑖𝑠(𝐺) is defined as 𝜒𝑙𝑖𝑠(𝐺)=𝑚𝑖𝑛{|𝑤(𝑉(𝐺))|;𝑤 𝑖𝑠 𝑙𝑜𝑐𝑎𝑙 𝑖𝑟𝑟𝑒𝑔𝑢𝑙𝑎𝑟𝑖𝑡𝑦 𝑣𝑒𝑟𝑡𝑒𝑥 𝑐𝑜𝑙𝑜𝑟𝑖𝑛𝑔}. The method used in this paper is pattern recognition and axiomatic deductive method. In this paper, we learn local irregularity vertex coloring of vertex amalgamation of path graph and determine the chromatic number on local irregularity vertex coloring of vertex amalgamation of path graph. This paper use vertex amalgamation of path graph (𝑎𝑚𝑎𝑙(𝑃𝑛 ,𝑣,𝑚)). The result of this study are expected to be used as basic studies and science development as well as applications related to local irregularity vertex coloring of vertex amalgamation of path graph.

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules.

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules. Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.

Sustainable development, eco-tourism carrying capacity and fuzzy algorithm-a study on Kanas in Belt and Road

In this paper, the method of fuzzy pattern recognition is adopted in more precisely evaluating the actual state of eco-tourism development regarding a given tourist destination in comparison with the three standard patterns (saturated/optimal/deficient) of development degree. The research process is as follows: Firstly, the indictors of carrying capacity of a tourist destination and the corresponding measuring factors are established; secondly, an assessment group is recruited to work out the most constraining factors among the measuring factors; thirdly, by means of field survey, numerical values of the actual state are acquired; fourthly, there comes out the membership vectors and the membership matrix of the standard patterns corresponding to the vectors of three standard patterns, threshold and the actual state; Finally, it could be identified which standard pattern that the actual state is closest to via the lattice degrees of proximity. An exemplary case study on Kanas National Nature Reserves is attached to the logic calculus. This paper is contributed to dynamically monitor the threshold of tourism carrying capacity and precisely identify which carrying capacity (spatial resource/ecological environment/economic resources/people’s psychology/socio-culture) with potential risks.

Machine learning applied to fMRI patterns of brain activation in response to mutilation pictures predicts PTSD symptoms

BackgroundThe present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues.MethodsTrauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious (“safe context”) or real-life scenes (“real context”). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts.ResultsThe GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms.ConclusionThis study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.

Generator Stator Partial Discharge Pattern Recognition Based on PRPD-Grabcut and DSC-GoogLeNet Deep Learning

Partial discharge (PD) pattern recognition of typical defects from generator stator bars is the basis of generator condition monitoring and fault diagnosis. A PD pattern recognition method for generator stator bars based on a phase-resolved partial discharge graph cut (PRPD-Grabcut) and Depthwise Separable Convolution GoogLeNet (DSC-GoogLeNet) deep learning neural network is proposed in this paper. First, five typical defects are designed on stator bars in the laboratory. A long-term high voltage test is carried out, and 37500 PRPD original graphs are obtained. Second, a PRPD-Grabcut method based on image segmentation is proposed, which is designed to extract key components of the PRPD map. Finally, a DSC-GoogLeNet based PD pattern recognition method is proposed, which combines the GoogLeNet inception module and depth-wise separable convolution. Experimental results show that the PRPD-Grabcut contributes to certain improvements in the recognition accuracy and training efficiency of the neural network. The DSC-GoogLeNet shows superiority in recognition accuracy, cross-entropy loss, and training time compared to a variety of existing lightweight neural networks. In addition, compared with other networks, the DSC-GoogLeNet greatly improves the recognition accuracy of PD types with similar but different risk levels for stator insulation.

Intelligent perception of tandem flapping wings' flow field environment with sparse detectors

Bionic flapping wing vehicles have great potential for civil and defense applications due to their flexibility and concealment at low Reynolds numbers. Since traditional flow field pattern recognition methods are difficult to identify effective information from the measured local flow field and deduce the state information of the moving body, this study uses an artificial intelligence method to establish the internal correlation between flow field pattern and state information. Specifically, a fully connected neural network is adopted to recognize the tandem flapping wings' flow field pattern by using different data acquisition methods and detector array distribution methods. Compared with the neural network based on time series data, the neural network based on spatial distribution data can realize the real-time judgment of flow field environment, which is closer to the real-time requirements in practical applications. In the paper, the intelligent perception of multi-flapping wings' flow field environment with sparse detectors is carried out and lays the theoretical foundation for autonomous navigation and obstacle avoidance of flapping wing aircrafts.

An Improved Data-Driven Method for Steering Feedback Torque of Driving Simulator

Traditional modeling methods of steering feedback torque (SFT) widely used in driving simulator are mainly physics-based, but suffers from high model complexity and low model accuracy due to the inaccurate or unknown structure, dynamics, and related parameters of the steering system, such as backlash or dry friction. For these problems, data-driven methods were considered to have advantageous in modeling such a complex yet dynamic system. However, due to the limitation of the data collected for model training from real vehicle test, the commonly used artificial neural network (ANN) cannot fully reflect the steering dynamics, and does not work well in many cases where collected data do not cover, and thus lead to poor generalization performance. In this article, a long short-term memory (LSTM) model is proposed and proved to simulate well on the dynamics of SFT, and works well in most driving conditions. A driving simulator is developed to verify the performance of different models in terms of generalization of working conditions. Based on the driving simulator data, we found that ANN failure generally arises in the transition between conventional and unconventional operating conditions, which is recognized by the proposed driving pattern recognition method. The LSTM-based modeling method can effectively solve this problem. The comparative analysis reveals that the improved LSTM model can simulate the SFT with higher accuracy and broader adaptability to driving simulator conditions.

Building Multiple Classifier Systems Using Linear Combinations of Reduced Graphs

Despite great efforts done in research in the last decades, the classification of general graphs, i.e., graphs with unconstrained labeling and structure, remains a challenging task. Due to the inherent relational structure of graphs it is difficult, or even impossible, to apply standard pattern recognition methods to graphs to achieve high recognition accuracies. Common methods to solve the non-trivial problem of graph classification employ graph matching in conjunction with a distance-based classifier or a kernel machine. In the present paper, we address the specific task of graph classification by means of a novel framework that uses information acquired from a broad range of reduced graph subspaces. Our novel approach can be roughly divided into three successive steps. In the first step, differently reduced graphs are created out of the original graphs relying on node centrality measures. In the second step, we compute the graph edit distance between each reduced graph and all the other graphs of the corresponding graph subspace. Finally, we linearly combine the distances in the third step and feed them into a distance-based classifier to obtain the final classification result. On six graph data sets, we empirically confirm that the proposed multiple classifier system directly benefits from the combined distances computed in the various graph subspaces.

Exploring energetic, exergetic, economic and environmental (4E) performance of waste heat power generation in nuclear power plant systems: A perspective of pattern recognition

Combined cogeneration systems are employed to recover waste heat from the sCO2 cycle in nuclear power generation systems. However, dealing with their multidimensional and nonlinear design and optimization spaces is a complex challenge for designers. In this study, the combined cogeneration system is parameterized and modeled: 4E models including energetic, exergetic, economic and environmental are established for sCO2 combined Organic Rankine Cycle (sCO2/ORC) and trans-critical CO2 Cycle (sCO2/tCO2) respectively. Several decision variables and five performance indicators are selected. To gain a deeper understanding of the mechanisms and optimize the combined cogeneration system, pattern recognition methods including the Self-Organizing Map (SOM), an unsupervised competitive learning approach, and Statistical Sobol Global Sensitivity Analysis methods are proposed. These techniques help identify key parameters and system patterns within the design space. Then, the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) is adopted to build a Pareto-optimal decision space, and the SOM and PCPs are proposed to visualize and mining based on pattern recognition and graph visualization interaction. The design parameters of the top cycle, P2, PRc, and x, have been confirmed as having a substantial impact on the system's performance. The multiple attributes, criterions and objectives problems in the combined cogeneration system are ultimately simplified to “Economy-Environment”, double objectives problem, and three trade-offs’ solutions are discussed: prioritizing economics, prioritizing the environment, and finding a balanced economic-environmental compromise. Pattern recognition method provides decision-makers with a fresh perspective for gaining a deeper understanding of the multi-criteria objectives space and potential trade-offs. The integration of pattern recognition into the thermodynamic, economic, and environmental models outlined in this paper represents a substantial and valuable advancement in the analysis and optimization of combined cogeneration cycles.

Flexibility management and decision making in cyber-physical systems utilizing digital lean principles with Brain-inspired computing pattern recognition in Industry 4.0

ABSTRACT Industry 4.0 and its accompanying Cyber-Physical Manufacturing Systems in digitized have recently made new approaches to optimising production operations in manufacturing. The objective of this article is to evaluate how digital lean principles can support Industry 4.0 in pursuit of reducing non-value-added tasks from production processes, flexibility management and decision-making process with machine learning techniques. This research study comes under three levels, such as data level, information level, and knowledge level. Initially, the data from the CNC machine are collected via a Wireless Sensor Network (WSN). The collected data are stored in the cloud platform where the unwanted wastes of 7 Muda are removed by using the Value Stream Mapping (VSM 4.0) tool. It is critical in the big data world to have a systematic method for gathering, managing, and analysing data to gain valuable insights from it. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related types of machinery. Accordingly, Big Data Analytics empowers the lean principle technique of Total Productive Maintenance (TPM) is suggested for avoiding potential failures to predict maintenance by allowing KPIs to be calculated in real-time. This approach requires high-performance procedures and adaptable manufacturing systems in the current digitalized lean. The information is then processed in the knowledge layer, utilizing the algorithm, rule and lean knowledge bases. The flexibility of manufacturing firms is determined by the adaptability of their shop floor processes. To meet these requirements the article developed pull control techniques of capacity slack CONWIP (CSC) control in digital lean production systems to guide the CPS deployment to offer flexible production systems. Reliable software systems are hoped to facilitate data analysis and autonomous decision-making. Finally, in the decision-making process, the article proposed the Brain-Inspired Computing of Structural and Syntactic (BIC-SS) pattern recognition method. The performance analysis of these findings is simulated in MATLAB software. Simulation can be done with the identification of ideal Kanban parameters like cycle time, lead time, delivery frequency and lot size. Furthermore, lean manufacturing improves company quality and productivity by decreasing waste and production costs, as well as adapting well to the many innovative systems that encourage the culture of change and quality inside organizations.

Research on Pattern Recognition Method for φ-OTDR System Based on Dendrite Net

The phase-sensitive optical time-domain reflectometer (φ-OTDR) is commonly used in various industries such as oil and gas pipelines, power communication networks, safety maintenance, and perimeter security. However, one challenge faced by the φ-OTDR system is low pattern recognition accuracy. To overcome this issue, a Dendrite Net (DD)-based pattern recognition method is proposed to differentiate the vibration signals detected by the φ-OTDR system, and normalize the differential signals with the original signals for feature extraction. These features serve as input for the pattern recognition task. To optimize the DD for the pattern recognition of the feature vectors, the Variable Three-Term Conjugate Gradient (VTTCG) is employed. The experimental results demonstrate the effectiveness of the proposed method. The classification accuracy achieved using this method is 98.6%, which represents a significant improvement compared to other techniques. Specifically, the proposed method outperforms the DD, Support Vector Machine (SVM), and Extreme Learning Machine (ELM) by 7.5%, 8.6%, and 1.5% respectively. The findings of this research paper indicate that the pattern recognition method based on DD and optimized using the VTTCG can greatly enhance the accuracy of the φ-OTDR system. This improvement has important implications for various applications in industries such as pipeline monitoring, power communication networks, safety maintenance, and perimeter security.

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event patterns, this research proposes the AVOA-LightGBM method for optical cable fault event pattern recognition based on wavelet packet decomposition. Initially, a three-layer wavelet packet decomposition is performed on different fault events, resulting in eight characteristic signals. These signals are then normalized and used as input for each recognition model. The Light Gradient Boosting Machine (LightGBM) is optimized using the African vulture optimization algorithm (AVOA) for pattern recognition. The experimental results demonstrate that this method achieves a recognition accuracy of 98.24%. It outperforms LightGBM, support vector machine (SVM), and extreme learning machine (ELM) by 3.7%, 19.15%, and 5.67%, respectively, in terms of accuracy. Moreover, it shows a 1.8% improvement compared with the combined model PSO-LightGBM.

Different sized gold nanoparticles for array-based sensing of pesticides and its application for strawberry pollution monitoring

The use of pesticides plays an essential role in improving crop quality and yield, however, it causes air, water, and soil pollution and the residue of these pesticides in agricultural products threatens the ecosystem and human life. Therefore, it is highly desirable to develop rapid, simple, and cost-effective methods for regular monitoring of pesticide residues in agricultural products especially strawberry that is consumed fresh and unpeeled. In this study, gold nanoparticles (AuNPs) of varying sizes have been exploited as sensing units to design a non-enzymatic colorimetric sensor array for the detection and discrimination of various pesticides including; bifenazate (BF), paraquat (PQ), diazinon (DZ), thiometon (TM), and carbendazim (CD) and chlorpyrifos (CP). Because of their strong size- and environmentally-dependent properties, AuNPs with different sizes produced distinguished plasmonic patterns in the presence of pesticides at a vast range of concentrations (25–800 ng mL−1). Plasmonic patterns of sensor units have been analyzed by various data visualization (bar plots and heat maps) and pattern recognition methods (linear discriminant analysis (LDA)). The multivariate calibrations showed linear responses ranging from 50 to 800 ng mL−1 for carbendazim, chlorpyrifos, paraquat, and bifenazate and 25–800 ng mL−1 for diazinon and thiometon. The limit of detection (LOD) was calculated to be 17.7, 22.8, 22.4, 9.7, 7.4, and 23.8 ng mL−1 for carbendazim, chlorpyrifos, paraquat, diazinon, thiometon, and bifenazate respectively. Finally, the applicability of the designed sensor was evaluated in real samples comprising tap water, well water, soil, and fruit, leave, drainage water, and culture substrate of strawberry.

Metabolic profile changes of kidney aging and protective effects of Polygonatum sibiricum polysaccharides on D-galactose-induced aging mice

ObjectiveTo investigate the metabolic trajectory of kidney aging and the effects of Polygonatum sibiricum polysaccharides (PSP) against kidney aging in D-galactose (D-gal)-induced aging mice, based on ultra-performance liquid chromatography/Q-Exactive Orbitrap mass spectrometry (UPLC-Q-Exactive MS/MS). MethodsA total of 36 C57 BL/6J mice were randomly allocated to six groups: control (CON), model (MOD), PSP low-dose (PSP-L), PSP medium-dose (PSP-M), PSP high-dose (PSP-H), and positive drug ascorbic acid (VC) groups. To create models of aging mice, D-gal was intraperitoneally administered to all other groups of mice except the CON group. After modeling, the appropriate Chinese medicine [PSP-L: 150 mg/(kg·d), PSP-M: 300 mg/(kg·d), PSP-H: 600 mg/(kg·d)] or positive drug [ascorbic acid, 300 mg/(kg·d)] was administered for intervention. Key markers of renal function in urine and serum of mice in each group, such as creatinine (Crea), urea nitrogen (BUN), and uric acid (UA) levels, as well as key indicators of oxidative stress in serum and kidney, including superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), and glutathione peroxidase (GSH-Px) were determined to validate the successful establishment of kidney aging models and to estimate the effects of PSP. Hematoxylin and eosin (HE), periodic acid Schiff (PAS), and β-galactosidase staining were used to assess the renal pathological changes. The metabolic profiles of serum, kidney, and urine samples from CON, MOD, and PSP-H groups were analyzed by UPLC-Q-Exactive MS/MS, and pattern recognition methods were used to outline the metabolic trajectory of kidney aging and to identify the characteristic metabolites. ResultsAge-related alterations in renal histopathology and impaired renal function in mice were also associated with oxidative stress indicators. Following the injection of PSP [PSP-H: 600 mg/(kg·d)], the pathological indices associated with aging were adjusted to normal levels, renal function and oxidative stress were improved in aging mice, and renal pathological damage was markedly improved. Meanwhile, the potential biomarkers were identified by UPLC-Q-Exactive MS/MS analysis and were further analyzed to form related metabolic pathways, with P < 0.05 as a threshold. The results showed that purine, sphingolipid, glycerophospholipid, tryptophan, and riboflavin metabolisms were the main metabolic pathways associated with aging. After administration of PSP, these pathological indices returned to normal levels, and biomarkers related to the aging process, such as adenosine monophosphate (AMP), tryptophan, and 5-hydroxytryptophan, also demonstrated, to some degree, reverse regulation (promoting synthesis). ConclusionMetabolomics methods based on UPLC-Q-Exactive MS/MS and multivariate statistical analysis can be adopted to establish metabolic profiles in aging mice. PSP has been shown to protect against kidney aging by interfering with the purine, sphingolipid, glycerophospholipid, tryptophan, and riboflavin metabolisms in the kidney.

Artificial Intelligence and Computational Pathology: A comprehensive review of advancements and applications

Abstract Conventional pathology is essential for disease diagnosis, but challenges like inter-observer variability can impact treatment decisions. Immunohistochemistry assays improve patient identification, but advanced quantitative tools are needed for accurate marker quantification and spatial analysis. AI enhances accuracy in pathology by contextualizing spatial data and revolutionizing medicine through advanced data processing and machine learning. Clinical informatics and AI integration advance patient care and open new horizons in pathology. Artificial intelligence (AI) is crucial in pathology, leveraging deep learning techniques to integrate pathological images with radiological, clinical, and genomic data. These pattern recognition methods enhance disease diagnosis and prognosis assessment. This review article provides an overview of AI in pathology, recent advancements, and future prospects. It emphasizes digital pathology, image acquisition, data preprocessing, and feature extraction for AI-driven pathology analysis. A comprehensive analysis of the key findings and outcomes from recent studies incorporating AI in pathology is done. A focus on various applications, including but not limited to cancer diagnosis, grading, and prognosis, as well as the identification of specific tissue patterns and rare diseases. The impact of AI on workflow optimization, quality assurance, and predictive analytics in pathology is also discussed. This section explores the implications and challenges of AI adoption in pathology. We discuss benefits like enhanced accuracy and resource allocation but also cover limitations such as dataset requirements, ethics, interpretability, and regulations. Ongoing research and collaborations aim to address these concerns and ensure responsible AI implementation in pathology.

“Agentic” cluster aggression: KIBS auditors and law firms as key tax haven drivers

PurposeThe purpose here is to show how the “shadow” economy has grown in scale and impetus in recent years, though even before modern times it has been present (e.g. the City of London, Shaxson, 2011) since at least the middle ages. The reasons for this have become complicated, but we can identify some “deep structures” that are common. Firstly, “globalisation” made it easier for multinationals to escape national regulatory regimes. Secondly, one of the ways neoliberal trading regulations allowed such actors to augment their assets was by means of what they initially called “transfer-pricing” but which now is officially known as “profit shifting” through tax havens. Thirdly, the growth in international trade in legal and illegal ways caused money laundering – even by otherwise respectable banks – to grow across borders. Conversely, from the supply-side, tax haven status was increasingly accessed by jurisdictions that sought to achieve economic growth by supplying tax haven services, both Delaware and Ireland as exemplars of a “developmental” fiscal policy.Design/methodology/approachThis paper adopts a “pattern recognition” design, an approach that is abductive, meaning interpretive, as shown in the observation that explanation can be valid or reliable without direct observation. This is shown in the indirect observation that “rain fell because the terrace has puddles” or “ancient glaciers once carved this valley”.FindingsReviewing the European Union’s (EU) list of non-co-operating jurisdictions in support of the OECD’s review of base erosion and profit-shifting activity, Collin concluded the EU’s listing “moved the needle” somewhat but was only a modest success. This is because of its reluctance to sanction its own members or large economies like the USA. Data on foreign direct investment and offshore banking assets suggest listed jurisdictions did not suffer notably from being named and shamed. In all cases studied, this contribution found legally damaging, fraudulent, conflict of interest and corrupt practice activities everywhere.Originality/valueThe originality is found in three spheres. Firstly, the pattern recognition method was vindicated in yielding hard to research results. Secondly, the “assemblage-thirdspace” theory was found advantageous in demonstrating the uneven geography of tax haven clusters and their common history in turbocharging economic development. Finally, the empirics showed the ruses executed by cluster members in tax havens to circumvent the law from global management consultancies to micro-firms consisting of tax lawyers and other experts interacting in knowledge supply chains of dubious morality.

Review of sEMG for Robot Control: Techniques and Applications

Surface electromyography (sEMG) is a promising technology that can capture muscle activation signals to control robots through novel human–machine interfaces (HMIs). This technology has already been applied in scenarios such as prosthetic design, assisted robot control, and rehabilitation training. This article provides an overview of sEMG-based robot control, covering two important aspects: (1) sEMG signal processing and classification methods and (2) robot control strategies and methods based on sEMG. First, the article outlines the general steps in sEMG signal processing and summarizes the commonly used methods for data acquisition, pre-processing, and feature extraction. In addition, machine-learning-based pattern recognition methods have been introduced for sEMG signal classification. Subsequently, user intent-based robot control strategies are classified into three categories: full-human continuous control, semi-autonomous continuous control, and discrete control, and their control methods and applicable scenarios are compared. Finally, this article discusses the advantages, disadvantages, and future development prospects of sEMG-based robot control. This review provides a comprehensive overview of sEMG-based robot control, from signal processing and classification methods to robot control strategies and methods, aiming to guide future research on selecting filters, feature sets, and pattern recognition methods and to assist in establishing sEMG-driven robot control frameworks.

Two-Step Identification Method and Experimental Verification of Weld Damage at Joints in Spatial Grid Structures

Welded joints in grid structures are susceptible to damage and destruction when exposed to random excitation. The complexity of the grid structure poses challenges for realizing the damage recognition of welded joints. In this study, a two-step method is proposed specifically for damage identification of welded joints in grid structures, combining wavelet analysis and fuzzy pattern recognition to accurately identify the location and extent of damage in welded joints. Firstly, the structure is divided based on the analysis of the influence range of joint damage. Key joints are selected within the sub-regions where sensors are installed, and the acceleration response of these key joints is measured. Wavelet analysis is then applied to identify the sub-regions where weld damage occurs. Secondly, an equivalent finite element model is established for joints with varying degrees of damage. The damage index, calculated as the ratio of the absolute value of the difference in the first-order element strain mode of the members, increases with the degree of damage during joint weld damage. By monitoring the changes in the damage index of sensitive members, which exhibit significant changes with varying weld damage degrees, a damage pattern database is constructed for each sub-region. The membership degree between joint damage and the patterns in the pattern database is then calculated to determine the location and degree of weld damage. To validate the effectiveness of the proposed method, an experiment was conducted using a grid structure model with replaceable members. Highly sensitive FBG sensors were designed to measure the acceleration response of the joints, resulting in accurate identification of damaged sub-regions solely through the measurement of key joint acceleration responses. Furthermore, within the damaged sub-regions, the fuzzy pattern recognition method precisely determined the location and degree of weld damage in the joints. The experimental results demonstrate that the proposed method effectively reduces the complexity of the structure by dividing the grid structure into sub-regions, and enables the two-step identification method to achieve successful damage identification for the joints in the grid structure with high efficiency and accuracy.

EEG signal recognition algorithm with sample entropy and pattern recognition

Brain-computer interface (BCI) is an emerging paradigm to achieve communication between external devices and the human brain. Due to the low signal-to-noise ratio of the original electroencephalograph (EEG) signals, it is different to achieve feature extraction and feature selection, and further high classification accuracy cannot be obtained. To address the above problems, this paper proposes a pattern recognition method that takes into account sample entropy combined with a batch-normalized convolutional neural network. In addition, the sample entropy is used to extract features from the EEG signal data processed by wavelet transform and independent component analysis, and then the extracted data are fed into the convolutional neural network structure to recognize the EEG signal. Based on the comparison of experimental results, it is found that the method proposed in this paper has a high recognition rate.