Similarity Matching Method Research Articles

A feature matching and compensation method based on importance weighting for occluded human pose estimation

Human pose estimation is a hot research in the field of computer vision. Existing algorithms perform well in normal scenarios but struggle in occlusion environments. In order to address occlusion problems, we firstly design a multi-scale feature reinforcement backbone (MFRI) to obtain accurate features, which contains three sub-modules. The first sub-module reinforces detailed keypoint features and eliminates redundant background, The second sub-module assigns different weights to different scale information based on importance. The fourth sub-module integrates different scale feature information using a cascaded method. In addition, we design an occlusion handling method. To begin with, we design a multi-scale feature extractor (MSFE) to construct human topological feature, and propose a feature similarity matching method between keypoint detail features and topological features to identify occluded keypoints and eliminate obstacle features. Based on similarity, we design a feature compensation method to extract common attributes among all keypoints and unique features of each keypoint from multiple aspects to compensate for occluded features. Finally, we propose an adjacency matrix improvement method to enhance its ability for describing node relationships. We conduct comparative experiments on the COCO2017 dataset, COCO-Wholebody dataset, and CrowdPose dataset, achieving accuracy of 78.8%, 66.3%, and 77.8%, respectively. Additionally, we design a series of ablation experiments and visualizations. The results show that our method has better performance in handling occlusions.

Browsing target extraction and spatiotemporal preference mining from the complex virtual trajectories

Public Map Service Platforms (PMSPs) aggregate and disseminate the earth observation data. Leveraging spatiotemporal preference patterns derived from browsing targets within complex virtual trajectories on PMSPs aids in constructing user-profiles and comprehending their intentions. However, complex virtual trajectories, characterized by numerous trajectory points and overlapping pyramidal spatial structures, introduce inefficiencies and inaccuracies during browsing target extraction. To mitigate this, we propose an Optimized Spatial Structure Segmentation (OSSS) method that divides complex virtual trajectories into sub-trajectories with simplified spatial structures, enhancing the efficiency of browsing target extraction. Spatiotemporal reconstruction of these sub-trajectories establishes sequences of browsing targets, revealing patterns of interest transitions. Moreover, recognizing the spatial uncertainty inherent in complex virtual trajectories, which results in imprecise matching between browsing targets and spatial features, we introduce a spatial co-occurrence semantic modeling approach. This involves constructing a POI semantic space and introducing a semantic similarity matching method to reduce spatial uncertainty and refine the accuracy of mining spatiotemporal preference patterns. We evaluate these methods using real-world data from Tianditu. Results demonstrate that the OSSS improves extraction efficiency by 3.45 times and accuracy by 18.84%. Additionally, the semantic similarity approach combined with spatial co-occurrence effectively mitigates spatial uncertainty. This research contributes to advancing the intelligence of PMSPs.

GEOSatDB: global civil earth observation satellite semantic database

ABSTRACT Satellite remote sensing, characterized by extensive coverage, frequent revisits, and continuous monitoring, provides essential data support for addressing global challenges. Over the past six decades, thousands of Earth observation satellites and sensors have been deployed worldwide. These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies. This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential. In this paper, we describe the development of GEOSatDB, the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model. A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information. The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors. The global Earth observation capabilities of 195 countries worldwide have been analyzed in detail, and a concrete use case along with an associated query demonstration is presented. This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observation resources.

Statistical similarity matching and filtering for clinical image retrieval by machine learning approach

Clinical image retrieval plays a pivotal role in modern healthcare for diagnostics and research, but prior research has grappled with the challenge of achieving high accuracy due to limited filtering techniques. The proposed method includes statistical distance measurements for similarity comparison and a machine learning technique for image filtering. Throughout this framework, the search area for similarity matching is reduced by first filtering away irrelevant images using the probabilistic outcomes of the Support Vector Machine (SVM) classification as class predictions of search and database images. Resizing is done as part of the preprocessing. Then, using Principal Component Analysis (PCA), the preprocessed data’s textural features, visual characteristics, and low-level features are extracted. The study also suggested an adaptive similarity matching method centered on a linear integration of feature-level similarities on the individual-level level. The precision and ranking order details of the most appropriate images retrieved and predicted by SVMs are considered when calculating the feature weights. The system continually alters weights for every distinctive search to generate beneficial outcomes. The supervised and unsupervised learning strategies are studied to link low-level global image features in the generated PCA-based Eigen Space using their high-level semantic and visual classifications to reduce the semantic gap and enhance retrieval effectiveness. The ground-truth database used in experiments has 1594 unique medical images with 3 different databases. Our method significantly improves the precision and recall rates in image retrieval tasks by combining sophisticated feature extraction, data-driven algorithms, and deep learning models. Research obtained an impressive accuracy of 0.99, demonstrating the effectiveness of our approach. This novel methodology addresses the limitations of prior research and provides a robust and reliable solution for clinicians and researchers in the medical field seeking to access and analyze relevant clinical images.

A non-contact vehicle weighing approach based on bridge weigh-in-motion framework and computer vision techniques

Bridge weigh-in-motion (BWIM) system plays increasingly important role in vehicle load monitoring and bridge maintenance due to advantages of high efficiency and little impact on traffic. However, the contact-type sensors in a traditional BWIM system should be installed in advance and is environment-sensitive, which hinders its application. To this end, a vision-based contactless BWIM (cBWIM) system is proposed. The core idea of the current method is to replace the traditional contact-type sensors by two consumer cameras to realize real-time monitoring of bridge displacement and vehicle trajectory. The cBWIM system consists of three modules, including the axle weight identification algorithm, the bridge displacement monitoring system and the vehicle trajectory tracking system. The influence line-based Moses algorithm is utilized for identification of axle weight. For bridge displacement monitoring, the minimum bounding rectangle-based similarity matching method is proposed to achieve high accuracy measurement of displacement and real-time data processing. For vehicle trajectory tracking, a deep learning-based YOLOv5s algorithm is adopted as a detector, combined with DeepSort algorithm to realize real-time tracking of vehicle position. The cBWIM system is applicable for non-uniform motion and vehicle occlusion cases. Laboratory tests covering various cases (i.e. uniform motion, non-uniform motion and visual occlusion) and field measurement on a full-scale bridge have demonstrated that the proposed method is effective and reliable and can provide alternative tool to the current vehicle weighing methods.

CNN-Based Vessel Meeting Knowledge Discovery From AIS Vessel Trajectories

How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance.

Shadow invariant classification for high-resolution images

Shadows are one of the most commonly observed optical phenomena in real-world environments. Often they are encountered in a target-scene area depending on the angle of view, scene structure, scene illumination, and scene geometry. In practice, the performance of several image-driven analysis techniques, such as classification tasks, becomes unstable in the presence of shadows. Specifically, shadow regions are profoundly difficult for image classification applications resulting in false predictions because they are inherently complex and are quite unlike anything seen during training. This paper introduces a novel algorithm named shadow invariant classifier system (SICS) to mitigate the challenges posed by shadows in various classification tasks, as such studies are currently lacking. Also, we propose a general framework for automatically selecting an optimal threshold for the proposed similarity matching methods (SMMs). The proposed algorithms use spectral information alone and require no pre-knowledge of shadows and their environmental settings. They are resilient to the complexities associated with the presence of shadows. To validate the performance, we conducted several classification experiments using six different remote sensing images from multi-sensor and multiplatform modalities having a varying scale of shadows and lighting effects. A thorough evaluation using the high spatial, spectral resolution multispectral and hyperspectral imagery demonstrates the effective shadow insensitive classification performance obtained by the proposed algorithms

Similarity matching method of power distribution system operating data based on neural information retrieval

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy. Therefore, improvement of the ability of data-driven operation management, intelligent analysis, and mining is urgently required. To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation, maintenance experience, and knowledge by rule and line, a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology. Based on the processing flow of the operating data of the power distribution system, a technical framework of neural information retrieval is established. Combined with the natural graph characteristics of the power distribution system, a unified graph data structure and a data fusion method of data access, data complement, and multi-source data are constructed. Further, a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed. The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set. The model is verified on the operating section of the power distribution system of a provincial grid area. The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

A hybrid algorithm for clinical decision support in precision medicine based on machine learning

PurposeThe objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger.Design/methodology/approachIn the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track.FindingsThe experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature.Originality/valueThis research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.

An AVMD-DBN-ELM Model for Bearing Fault Diagnosis

Rotating machinery often works under complex and variable working conditions; the vibration signals that are widely used for the health monitoring of rotating machinery show extremely complicated dynamic frequency characteristics. It is unlikely that a few certain frequency components are used as the representative fault signatures for all working conditions. Aiming at a general solution, this paper proposes an intelligent bearing fault diagnosis method that integrates adaptive variational mode decomposition (AVMD), mode sorting based deep belief network (DBN) and extreme learning machine (ELM). It can adaptively decompose non-stationery vibration signals into temporary frequency components and sort out a set of effective frequency components for online fault diagnosis. For online implementation, a similarity matching method is proposed, which can match the online-obtained frequency-domain fault signatures with the historical fault signatures, and the parameters of AVMD-DBN-ELM model are set to be the same as the most similar case. The proposed method can decompose vibration signals into different modes adaptively and retain effective modes, and it can learn from the idea of an attention mechanism and fuse the results according to the weight of MIV. It also can improve the timeliness of the fault diagnosis. For comprehensive verification of the proposed method, the bearing dataset from the University of Ottawa is used, and some recent methods are repeated for comparative analysis. The results can prove that our proposed method has higher reliability, higher accuracy and higher efficiency.

Revisiting the challenges and surveys in text similarity matching and detection methods

The massive amount of information from the internet has revolutionized the field of natural language processing. One of the challenges was estimating the similarity between texts. This has been an open research problem although various studies have proposed new methods over the years. This paper surveyed and traced the primary studies in the field of text similarity. The aim was to give a broad overview of existing issues, applications, and methods of text similarity research. This paper identified four issues and several applications of text similarity matching. It classified current studies based on intrinsic, extrinsic, and hybrid approaches. Then, we identified the methods and classified them into lexical-similarity, syntactic-similarity, semantic-similarity, structural-similarity, and hybrid. Furthermore, this study also analyzed and discussed method improvement, current limitations, and open challenges on this topic for future research directions.

Matching method based on similarity of working trajectories

To identify whether the actual work trajectory of workers in the factory meets the predetermined work trajectory requirements, we proposed an efficient and accurate work trajectory similarity matching method. We comprehensively considered the similarity between the actual work track and the predetermined track from the two characteristics of track angle and track distance. Among them, the similarity of track rotation angle is calculated using the improved longest common subsequence algorithm, and the similarity of track distance is calculated using the improved dynamic time warping (DTW) algorithm. Then the results of the similarity calculation of these two features are weighted. Finally, the weighted results are used to evaluate the similarity between the actual work track and the predetermined track, so as to judge whether the actual work track meets the requirements of the predetermined track. Experimental data show that the trajectory similarity matching algorithm in this paper has higher accuracy and efficiency than traditional DTW and other algorithms, and has higher ability to resist the interference of trajectory point evacuation than traditional DTW and other algorithms.

A life-cycle integrated model for product eco-design in the conceptual design phase

Eco-design that addresses the consumption of resources and energy, as well as pollution emissions from a product life-cycle perspective, is an effective way to solve environmental problems. However, the optimal eco-design solution is hardly generated in the conceptual design stage due to the complex association of the life cycle design information. To this end, this paper proposes a new eco-design model based on a life cycle integrated framework to effectively manage life cycle information in the conceptual design phase . In this work, (1) The life cycle-oriented model is proposed to integrate the product design information in terms of a systematic association mechanism between function, structure, material and process to construct the design space. (2) A life cycle design scenario-based similarity matching method is developed to support the environmental impact assessment of design options in the design space. (3) A hope tree-based heuristic search algorithm with the principle of minimizing environmental impact is proposed to efficiently obtain the eco-design solution. An application for eco-design of the lift equipment is given to demonstrate the capability of the proposed method. The results show that the environmental impact of the generated eco-design solution is improved by 19.48%, and the material and use stage has the highest environmental impact which accounts for 39.59% and 47.74% respectively. This work provides a targeted method for guiding designers to implement eco-design in the conceptual design phase through the life cycle. • An integrated life cycle model is proposed to express the design information of function, structure, material and process. • A scenario-based similarity matching method is proposed to acquire the LCI for the design options. • Hope tree-based algorithm is used to efficiently obtain the optimal eco-design solution.

Index similarity assisted particle filter for early failure time prediction with applications to turbofan engines and compressors

The particle filter (PF) has been widely studied in the prognostics’ field due to its ability to deal with nonlinear and non-stationary systems. However, there is no update of the model parameters during the prediction, preventing PF to work in its traditional way to generate accurate long-term predictions. In order to solve this problem, we put forward an improved PF that is based on a novel health index (HI) similarity matching method. This method is employed to search for similar HIs in the training library and construct an optimal “similar HI” for the system under study. Finally, the obtained HI is consistently fed into the PF to deliver precise state-of-health (SoH) estimates. The effectiveness of the proposed PF was validated on the C-MAPSS datasets as well as data collected from an operational reciprocating compressor. We observed that the new similarity matching method demonstrated excellent performance in finding suitable HIs for failure time prediction. We also observed that the proposed PF framework had a superior prognostics performance over the standard PF. We obtained an averaged predictive accuracy of 96% (C-MAPSS data) and 92% (compressor data) when only the first 10% of the degradation data were used. This work highlights the promise of combining index similarity, Procrustes analysis and PF for complementing existing prognostic methods.

Mechanical health indicator construction and similarity remaining useful life prediction based on natural language processing model

Remaining useful life (RUL) prediction, as an essential task for prediction and health management, can improve the reliability of degradation systems. In this paper, multi-dimensional mechanical monitoring readings are treated as natural language sequences and inputted to a natural language processing model, Transformer, to extract semantic compression vectors. The health indicators (HIs) are constructed from the changes of compressed semantic vectors during the mechanical system operation, reflecting the mechanical health status at each monitoring point. Subsequently, combined with the similarity matching method, the RUL prediction is achieved by weighted summation. Transformer mainly consists of attention layers and residual connection layers, which contain no recurrence and convolution. The attention layer can highlight the critical time step information during the HI construction. Moreover, the output of different time steps can be obtained simultaneously in a parallel computation manner. The proposed scheme is conducted on two publicly available datasets for experimental and evaluation metrics comparison. The experimental results show that the proposed scheme can achieve competitive prediction results, with some of the metrics reaching the highest. An autoencoder only containing attention mechanism can effectively extract degradation feature representations for the adaptive construction of unsupervised HIs.

Research on Specific Long-term Single Object Tracking Algorithms in the Context of Traffic

A robust object tracking algorithm based on a three-channel Siamese network is proposed for the visual object tracking problem in the context of traffic. By adding the prediction box in the previous frame as the second template, our network obtained the second template to perform secondary prediction on the current frame. Then, in order to make sure whether the model to accurately regress the correct prediction box by the template frame or the previous frame, we design a similarity matching method based on Structure Similarity Index Measure(SSIM). We have also designed a template update method to ensure that the network selects the correct matching box. Our algorithm has a major advantage in robustness over other real-time target tracking algorithms such as SiamRPN.

Classification of hand‐wrist maturity level based on similarity matching

Judging the maturity level of each hand-wrist reference bone is the core issue in bone age assessment. Relying on the superiority of convolutional neural networks in feature representation, deep learning is widely studied for the automatic bone age assessment. However, an efficient but complex deep learning network requests a large dataset with bone-maturity-level labels for training, restricting its large-scale application in bone maturity classification. For this reason, we transform the bone-maturity-level classification problem into the similarity matching problem. Also, we propose a general structure based on Siamese network by merging two inputs into a two-channel input and introducing a dual attention mechanism, to create an Attentional Two-Channel Network (ATC-Net). This paper takes the intermediate phalanges III as an example to assess the performance of the similarity matching method and the ATC-Net. Experiments show that our method can perform better on small datasets, which effectively makes up for the data shortage problem. The ATC-Net used for classification significantly reduces the evaluation time compared with other classical networks. It reduces the time of assessing one sample by about 49% as compared to VGG-16. And more importantly, it achieves the highest classification accuracy of 92.74% among all investigated networks.

Detecting Falsified Financial Statements Using a Hybrid SM-UTADIS Approach : Empirical Analysis of Listed Traditional Chinese Medicine Companies in China

By combining the similarity matching (SM) method with the utilities additives discriminates (UTADIS) method, we propose a hybrid SM-UTADIS approach to detect falsified financial statements (FFS) of listed companies. To evaluate the performance of this hybrid approach, we conduct experiments using the annual financial ratios of listed traditional Chinese medicine (TCM) companies in China. There are three stages in the detection procedure. First, we use the cosine similarity matching method to select matched companies for each considered company, derive the deviation data of each considered company as a sample dataset to capture the intrinsic law of the financial data, and further divide these into training and testing datasets for the next two stages. Second, we put the training dataset into the UTADIS to train the SM-UTADIS model. Finally, we use the trained SM-UTADIS model to classify the testing dataset and evaluate the performance of the proposed method. Furthermore, we use other approaches, such as single UTADIS and logistic and SM-logistic regression models, to detect FFS. By comparing these results to those of the hybrid SM-UTADIS approach, we find that the proposed hybrid approach greatly improves the accuracy of FFS detection.

Wind Power Prediction for Wind Farm Clusters Based on the Multifeature Similarity Matching Method

Regional wind power prediction (WPP) is conducive to achieving scientific dispatch of the power system as well as improving its reliability. Data mining based on a large number of samples is an effective way to improve the accuracy of WPP. A new regional power forecasting method in the context of data mining is proposed in the article. First, a previously described regional WPP method, single feature similarity matching (SFSM) method, is analyzed and a new multifeature similarity matching (MFSM) method is proposed. Second, the optimization and analysis processes of four key parameters in the MFSM method are proposed and the impact of each key parameter on prediction error and the applicability of the method under different regional scales is analyzed. Finally, the new method and the optimization and analysis processes are verified by wind farm clusters, composed of 52 wind farms in northeast China, and the new method is compared with the conventional method, such as additive method, statistical up-scaling method, and the SFSM method. The results show that the improved MFSM method is superior to the conventional prediction method, whereby the root mean square error (RMSE) of 0 to 2 h is reduced by 3.65% and the RMSE of 0 to 4 h is reduced by 1.56%. The new method has been proved to be an effective WPP method for wind farm clusters and has potential industrial application prospects.

The Effect of Database Type on Face Recognition Performance for Surveillance Applications

Face recognition is one of the most important biometric approaches due to its potential applications in surveillance monitoring and access control. This paper presents a PCA and SVM based face recognition system for surveillance application. A proposed training database selection criteria suitable for surveillance application which consist of 1 mean image per distance class from all the available database sessions is also used for the face recognition system. In this study, the ChokePoint database, specifically the grayscale (PPG) and colored (MPCI) versions of the ChokePoint database, were selected for this work. The objectives of this work is to investigate the effect of the using different training data as well as using different similarity matching method on face recognition for surveillance application. It was found that regardless of the type of databases used, the recognition output pattern on different training data selection criteria was found to be similar. It was also found that regardless of the similarity matching method used, the face recognition system also shows the same recognition performance pattern. The experiment suggests that the proposed training database selection criteria will give similar recognition performance regardless of databases type or face recognition technique used. Overall, the ChokePoint colour database (MPCI) gives better recognition performance than the ChokePoint grayscale database (PPG). Finally, it can be concluded that using 1 mean image per class from all the available database sessions (Case-6) is better compared to using 1 image per class that are randomly selected from all the database sessions (Case-4). Even though a straight comparison between this work proposed system and several published system is not meaningful as different face recognition approaches and experiment criteria are used, nevertheless, this work proposed method performs with 100% recall and reject recognition rate.