Pattern Recognition Technology Research Articles

Anomaly detection of bridge health monitoring data based on KNN algorithm

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

Pattern Recognition of Construction Bidding System Based on Image Processing

Bidding for construction projects is a very important and representative field in the industry. The system of bidding for construction projects has made considerable progress over the years due to the accumulation of experience and many contributions to the field. Nowadays, with the rapid development of information technology and the intellectualization of the bidding system for construction projects, the accumulation and processing of data has become an essential element of its development. In order to manage the bidding system of construction engineering reasonably, this paper proposes a system based on image processing and pattern recognition technology, which can recognize patterns in the bidding process of construction engineering. Firstly, through binarization and other methods, the bidding project documents of construction projects are preprocessed in order to extract the main information from the documents; secondly, information is obtained through pattern recognition and processing; finally, the results of pattern recognition are imported into the bidding system, and the bidding information is quickly added to the database. This paper proposes a bidding system for construction projects based on image processing and pattern recognition. The system provides a quick and convenient means of importing the bidding information and allows personnel to efficiently process the bidding documents, particularly when there are numerous documents and and too much information. Finally, the user survey shows that the proposed bidding system based on image processing and pattern recognition can reduce the workload of construction project personnel, greatly facilitating the progress of the project.

A SURVEY ON BIOMETRIC AUTHENTICATION SYSTEMS IN CLOUD TO COMBAT IDENTITY THEFT

Biometrics has by far proven to be an effective solution for many of the security challenges that is prevalent in most of the technical fields in this Digital era. It is the measurement of physical or behavioral traits in humans used for unique identification. Biometricsuses the pattern recognition technology of Digital Image Processing for identifying unique features in humans. Most commonly applied or considered biometric modalities comprise fingerprint impression, facial landmarks, iris anatomy, speech recognition, hand writing detection, hand geometry recognition, Finger vein detection and signature identification. Biometric technology is applied in various fields, Security systems being one among them. This paper presents a thorough survey on how biometrics can be effectively applied to eliminate oneof the cloud security issue, identitytheft. Awide range of biometric authentication system protocols and implementations in cloud environment, especially to combat identity theft have been proposed earlier.

Research on quick extraction method for integrated information of intelligent transportation system scheduling based on internet of things

In order to improve the remote monitoring capability for the integrated information of intelligent transportation system (ITS) scheduling, a quick extraction method for integrated information of ITS scheduling based on tracking and recognition of the distributed internet of things (IoT) sensor information fusion is proposed. Using the comprehensive information of IoT node scheduling, the adaptive distributed optimisation positioning design of the IoT node is extracted. Combining pattern recognition technology for scheduling information processing; the integrated information mining and scheduling feature extraction is implemented by quantitative fusion tracking method, which realises the automatic mining of intelligent transportation system integration information. The simulation results show that the proposed method has better automatic clustering for extracting integrated information of ITS scheduling, as well as faster extraction speed and higher matching ability. It also has a good application value in the integrated information of ITS scheduling.

Research on quick extraction method for integrated information of intelligent transportation system scheduling based on internet of things

In order to improve the remote monitoring capability for the integrated information of intelligent transportation system (ITS) scheduling, a quick extraction method for integrated information of ITS scheduling based on tracking and recognition of the distributed internet of things (IoT) sensor information fusion is proposed. Using the comprehensive information of IoT node scheduling, the adaptive distributed optimisation positioning design of the IoT node is extracted. Combining pattern recognition technology for scheduling information processing; the integrated information mining and scheduling feature extraction is implemented by quantitative fusion tracking method, which realises the automatic mining of intelligent transportation system integration information. The simulation results show that the proposed method has better automatic clustering for extracting integrated information of ITS scheduling, as well as faster extraction speed and higher matching ability. It also has a good application value in the integrated information of ITS scheduling.

Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems

With the popularization of video detection and recognition systems and the advancement of video image processing technology, the application research of intelligent transportation systems based on computer vision technology has received more and more attention. It comprehensively utilizes image processing, pattern recognition, artificial intelligence and other technologies. It also involves processing and analyzing the video image sequence collected by the detection system, intelligently understanding the video content and making processing, and dealing with various problems such as accident information judgment, pedestrian and vehicle classification, traffic flow parameter detection, and moving target tracking. It promotes intelligent transportation systems to be more intelligent and practical, and provides comprehensive, real-time traffic status information for traffic management and control. Therefore, the research on the method of traffic information detection based on computer vision has important theoretical and practical significance. The detection and recognition of video targets is an important research direction in the field of intelligent transportation and computer vision. However, due to the background complexity, illumination changes, target occlusion and other factors in the detection and recognition environment, the application still faces many difficulties, and the robustness and accuracy of detection and recognition need to be further improved. In this paper, several key problems in video object detection and recognition are studied, including accurate segmentation of target and background, shadow in complex scenes; accurate classification of extracted foreground targets; and target recognition in complex background. In response to these problems, this paper proposes a corresponding solution.

Infrared Thermography in Exercise Physiology: The Dawning of Exercise Radiomics.

Infrared thermography (IRT) is a non-invasive tool to measure the body surface radiation temperature (Tsr). IRT is an upcoming technology as a result of recent advancements in camera lenses, detector technique and data processing capabilities. The purpose of this review is to determine the potential and applicability of IRT in the context of dynamic measurements in exercise physiology. We searched PubMed and Google Scholar to identify appropriate articles, and conducted six case experiments with a high-resolution IRT camera (640×480 pixels) for complementary illustration. Ten articles for endurance exercise, 12 articles for incremental exercise testing and 11 articles for resistance exercise were identified. Specific Tsr changes were detected for different exercise types. Close to physical exertion or during prolonged exercise six recent studies described "tree-shaped" or "hyper-thermal" surface radiation pattern (Psr) without further specification. For the first time, we describe the Tsr and Psr dynamics and how these may relate to physiological adaptations during exercise and illustrate the differential responsiveness of Psr to resistance or endurance exercise. We discuss how bias related to individual factors, such as skin blood flow, or related to environmental factors could be resolved by innovative technological approaches. We specify why IRT seems to be increasingly capable of differentiating physiological traits relevant for exercise physiologists from various forms of environmental, technical and individual bias. For refined analysis, it will be necessary to develop and implement standardized and accurate pattern recognition technology capable of differentiating exercise modalities to support the evaluation of thermographic data by means of radiomics.

Research on Key Technologies of 3D Dynamic Scene Reconstruction in Virtual Reality

Word In the process of reconstructing dynamic scenes, the traditional 3D reconstruction will generate certain data interference and data transition problems due to the certain continuity of the collected data. A three-dimensional dynamic scene reconstruction scheme based on virtual reality is proposed. Virtual reality technology is an emerging technology. The key is to model the actual object to obtain a virtual image and present it to people. Three-dimensional reconstruction is one of the core technologies, including monocular vision technology, pattern recognition technology, support vector machine computing technology and sensor technology.

Cross flow and heat transfer of hollow-fiber tube banks with complex distribution patterns and various baffle designs

The pressure drop and heat transfer of hollow-fiber tube banks with various packing patterns and baffle designs are studied by combining pattern recognition technology and computational fluid dynamics. The fluid passes across the fiber bank. The inlet flow Reynolds number ranges from 20 to 180. The pressure drop and heat transfer coefficients of the contactors are highly correlated to the topological characteristics of the fringe of the fiber bank. A single parameter ψ, which summarizes this information, is calculated using pattern recognition technology. The pressure drop and Nusselt number of a contactor exhibit power law relations with ψ. Interestingly, this phenomenon is independent of the baffle design, which affects only the magnitude of the pressure drop and the heat transfer coefficient. Generally, a higher ψ results in contactors with worse performance. For ψ > 0.6, the wall effect is negligible, and all contactors show similar performance regardless of the fiber pattern. The comprehensive performance parameter ∅ is approximately 0.6 for all contactors with various patterns. An ‘‘insert’’ strategy that is used to reduce ψ is demonstrated. The results show how to optimize the structure of contactor for industrial production.

Intelligent and automatic laser frequency locking system using pattern recognition technology

This paper reports on an experimental realization of an automatic laser locking scheme using the artificial intelligence algorithm. We design a classifier based on the support vector machine to recognize the pattern of the saturated absorption spectroscopy signal of a laser and to locate its lockable points. We experimentally investigated our intelligent locking scheme on an external-cavity-diode-laser (ECDL) at 780 nm through the rubidium (Rb) cell and Rb saturated absorption lines. Test results show that the laser frequency can be automatically locked or relocked to the desired point within 11.6 s, either from free running state or from unlocked state, where the entire locking and relocking process is managed by the machine itself rather than human operations. The intelligent method reported in this paper provides a robust and smart solution for laser locking in unmanned conditions, e.g., space-borne experiments.

Research on Data Mining Algorithm Based on Pattern Recognition

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.

Research on Pedestrian Recognition Based on DM642 Driver Assistance System on Vehicle

Road pedestrian recognition is a core issue in the field of automotive safety, the pedestrians captured by the video cameras installation on the road ahead of the vehicle. To determine and mark pedestrians and estimate the potential risks according to the image processing and pattern recognition technology. Taking measures to protect pedestrian in the vehicle have important applications in driver assistance. The paper collected road ahead of the vehicle video information in the DM642 hardware platform, extracted the lane edge line by improved Hough transform method, and the extraction of candidate regions according to the features of pedestrians itself, using the Adaboost method of training the classifier. The results show that the classification accuracy rate reached 92%, conform to the real-time performance of the system requirement. With the development of DSP technology, pedestrian’s collection, processing, recognition as one of the embedded pedestrian recognition system has a broad research value and business prospects.

Advanced geological prediction technology of tunnel based on image recognition

The purpose of this study is to solve the problem that the image obtained by the advanced geological prediction technology of tunnel in the tunnel engineering construction project is difficult to identify, the identification result of the original image is too dependent on the personal habits and experience of the forecaster, and the identification result is not accurate enough. In this study, the geological radar image is taken as the research object, and the method of the advanced geological prediction technology of the tunnel is studied and summarized. The image pattern recognition technology is introduced into the advanced geological prediction of the tunnel, and the image recognition of the geological radar image is carried out. After preprocessing of filtering, binarization, and refinement, it is found that the image can be more accurate, and the impact of noise on the image is also alleviated. In addition, neural network pattern recognition technology is adopted after image preprocessing to establish a neural network model. After recognition, curve targets can be successfully detected, indicating the feasibility of image recognition technology. Therefore, the image recognition technology can make up for the deficiency of the observation personnel’s understanding of the geological radar image due to personal habits, and it provides a new way to identify the advanced geological prediction technology of the tunnel.

Design of Traffic Monitoring System Based on Video Detection

Traffic monitoring system is an important means to ensure the safe operation of traffic. According to the characteristics of road traffic monitoring environment, a real-time traffic monitoring system is designed by using image processing, computer vision, pattern recognition and other technologies. Experiments show that the system can effectively detect various typical traffic incidents and traffic operation parameters, and provide a favorable guarantee for traffic safety.

Application of graphics processing unit parallel computing in pattern recognition for vibration events based on a phase-sensitive optical time domain reflectometer.

The technology of pattern recognition for vibration events based on the phase-sensitive optical time domain reflectometer (Φ-OTDR) has been significantly improved, thanks to plenty of valuable research in recent years. However, it remains challenging to develop an efficient algorithm for it with computing resources that are simpler to achieve at lower cost. To the best of our knowledge, this paper, for the first time, analyzes the superiority of using graphics processing unit (GPU) parallel computing to improve time-consuming performance in pattern recognition for vibration events based on Φ-OTDR. And the pattern-recognition algorithm, including spectral subtraction and artificial neural networks, is implemented by CPU and GPU, respectively. Then, the time consumption of the CPU-based method and the time consumption of the GPU-based method are, respectively, recorded and compared. As a result of our experiments, we concluded that using GPU parallel computing can develop an efficient algorithm with a computing resource that is simpler to achieve at a lower cost.

Representation-based classification methods with enhanced linear reconstruction measures for face recognition

Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure (LRM) can be well used for classifying objects. In this article, we propose two enhanced linear reconstruction measure-based classification methods based on the sparsity-augmented collaborative representation-based classification method (SA-CRC). One is the weighted enhancement linear reconstruction measure-based classification method (WELRMC) that introduces data localities into SA-CRC. Another is the two-phase weighted enhancement linear reconstruction measure-based classification method (TPWELRMC) that integrates both the coarse and fine representations into SA-CRC. To demonstrate the effectiveness of the proposed methods, experiments are conducted on several public face databases in comparison with the state-of-the-art representation-based classification methods. The experimental results show that the proposed methods significantly outperform the competing RBC methods.

A Method of True and Fake Objects Pattern Recognition Integrating Image Information and Spectral Information

At present, pattern recognition technology has been widely used in the fields of objects, faces, fingerprints, military target recognition, etc. However, pattern recognition method still has obvious shortcomings when applied to the above fields. It is currently restricted to the use of image information for identification. When the image features of the research object are highly similar, the accuracy of pattern recognition is low and cannot meet the actual application requirements. For example, in the case of mixed true and false targets, it is difficult to obtain satisfactory recognition results using only image information. Aiming at the above problems, a pattern recognition method integrating image information and spectral information is proposed in this paper. Firstly, the image recognition model based on the convolutional neural network model is built to identify object categories based on the semantic features of objects and obtain preliminary recognition results. Then, on the basis of the preliminary recognition results, the measured spectral data of the object (spectrum range 400-1 000 nm, spectral resolution 2 nm) is used to perform true and fake identification of the object based on the back propagation (BP) neural network model. The principle of true and false recognition is that the true and false targets are different in material, causing significant difference in their hyperspectral information. Finally, recognition results are obtained. In order to verify the accuracy of the proposed method, true and fake apples and grapes are used as experimental subjects and the result is that:The recognition accuracy obtained by using only image information is 38.50%, and the recognition accuracy obtained by using only spectral information is 63.00%, however, the recognition accuracy obtained by the method proposed in this paper is 95.00%. Compared with the existing pattern recognition method without spectral information participation, the pattern recognition method using image information and spectral information proposed in this paper improves the pattern recognition accuracy under the mixed condition of true and false targets, and can be widely applied to object recognition, face recognition, fingerprint recognition, military target recognition and other fields.

Recognition of Beam's Music Notation Patterns Using Artificial Neural Networks with The Backpropagation Method

The beam notations is officially used as the standard of international music notation, and is often found in scores for both musical instruments and vocals. In Indonesia, the use of numerical notation is more widely used and understood, because the learning process of notation beams is not easy, and takes time for the introduction of each symbol and its meaning. The pattern recognition technology makes it possible to recognize the pattern of the beam notations. The software used for system development is Matlab, utilizing artificial neural network using backpropagation method to recognize the pattern of beam notation. Backpropagation is a supervised learning method, where the system will be given the training first, and then the system can understand and identify patterns based on the knowledge gained. The final result shows that the system is able to recognize patterns from notations that have been previously studied with the highest percentage of 91.20%.

An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.