Fuzzy Pattern Recognition System for Detection of Alga Distribution

To realize the on-line measurement and make analysis on the density of algae and their cluster distribution, the fluorescent detection and fuzzy pattern recognition techniques are used. The principle of fluorescent fiber-optic detection is given as well as the method of fuzzy feature extraction using a class of neural network.

Ultrasonic Testing Combined with Pattern Recognition for the Detection of Kissing Bonds

Kissing bonds are defects in the adhesive bonds with intimate contact of touching surface but considerably lowered shear strength. Their detection specifically in the aerospace area is so not satisfactory. Usually, kissing bonds are inconspicuous in ultrasonic C-scans. However, the determination of attributes in the time domain and the frequency domain of an ultrasound signal provides the opportunity to derive a pattern for bonded area. Deviations from the pattern found in inconspicuous bonding areas indicate kissing bonds. The survey described here deals with the manufacturing of adhesively joint samples that purposefully include kissing bonds, as well as potential solutions for detecting them through ultrasonic testing combined with pattern recognition. The properties of the epoxy-based adhesive were varied by changing the mixing ratios between resin and hardener. Samples with a mixing ratio far apart from the manufacturer’s recommendation with an inconspicuous appearance in a C-scan, but low shear strength values were taken for further evaluation. After a definition and learning phase, a 100 percent hit rate to separate good bondings from kissing bonds could be derived in a blind test. The discriminating feature found is due to the frequency shift between good and kissing bonds as well as the relative amplitude of the second peak.

Value of serum pattern recognition receptors Collectin, Dectin and CD14 detection for disease evaluation in children with hand, foot and mouth disease

Objective:To study the value of serum pattern recognition receptors Collectin, Dectin and CD14 detection for disease evaluation in children with hand, foot and mouth disease (HFMD). Methods:76 children diagnosed with hand, foot and mouth disease in our hospital between May 2013 and March 2016 were selected as the HFMD group of the research, 80 healthy children receiving physical examination in our hospital during the same period were selected as the control group of the research, and the serum was collected to determine the levels of pattern recognition receptors, immunoglobulins, complements, inflammatory media and target organ damage indexes. Results:Serum Collectin, Dectin and CD14 levels of HFMD group were significantly higher than those of control group (P<0.05);serum immunoglobulin G (IgG) IgM, IgA, C3 and C4 levels of HFMD group were significantly lower than those of control group (P<0.05) and negatively correlated with Collectin, Dectin and CD14 levels;serum PCT, C-reactive protein (CRP), interleukin-6 (IL-6), IL-10, tumor necrosis factor-α(TNF-α), N-terminal pro-brain natriuretic peptide (NT-proBNP), neuron-specific enolase (NSE), S100βand surfactant protein A (SP-A) levels of HFMD group were significantly higher than those of control group (P<0.05) and positively correlated with Collectin, Dectin and CD14 levels. Conclusions:High expression of serum pattern recognition receptors Collectin, Dectin and CD14 in children with hand, foot and mouth disease is closely related to the immune response, inflammatory response and target organ function damage during the disease progression.

Spatial pattern recognition for near-surface high temperature increases in mountain areas using MODIS and SRTM DEM

Abrupt near-surface temperature changes in mountainous areas are a special component of the mountain climate system.Fast and accurate measurements of the locations,intensity,and width of the near-surface changes are necessary but highly difficult due to the complicated environmental conditions and instrumental issues.This paper develops a spatial pattern recognition method to measure the near-surface high temperature increase(NSHTI),one of the lesser-attended changes.First,raster window measurement was proposed to calculate the temperature lapse rate using MODIS land surface temperature and SRTM DEM data.It fully considers the terrain heights of two neighboring cells on opposite or adjacent slopes with a moving window of 3×3 cell size.Second,a threshold selection was performed to identify the NSHTI cells using a threshold of-0.65℃/100 m.Then,the NSHTI strips were parameterized through raster vectorization and spatial analysis.Taking Yunnan,a mountainous province in southwestern China,as the study area,the results indicate that the NSHTI cells concentrate in a strip-like pattern along the mountains and valleys,and the strips are almost parallel to the altitude contours with a slight northward uplift.Also,they are located mostly at a 3/5 height of high mountains or within 400 m from the valley floors,where the controlling topographic index is the altitude of the terrain trend surface but not the absolute elevation and the topographic uplift height and cutting depth.Additionally,the NSHTI intensity varies with the geographic locations and the proportions increase with an exponential trend,and the horizontal width has a mean of about 1000 m and a maximum of over 5000 m.The result demonstrates that the proposed method can effectively recognize NSHTI boundaries over mountains,providing support for the modeling of weather and climate systems and the development of mountain resources.

Color Thresholding, Detection and Recognition of the Road Signs Using the Information Set Theory

In this paper, approaches are presented for the thresholding, detection, tracking and recognition of the road signs as part of an Advanced Driver Assistance System (ADAS). In all these approaches, feature extraction is the backbone, whereas detection and recognition require the use of detectors and classifiers, respectively. In this, two issues are dominant: 1) Tackling the variability involved in the lighting conditions, sizes, and shapes of the road signs after segregating them from a world scene, and 2) Focusing on inaccurate fuzzy modeling arising due to the improper distribution of pixel intensities. The variability is overcome with the uncertainty representation using the information sets, an extension of fuzzy sets, whereas the incorrect fuzzy modeling is rectified using the pervasive information sets, an extension of intuitionistic fuzzy sets. The development of the intuitionistic fuzzy transform paralleling the fuzzy entropy function paves the way for the formulation of different hesitancy features by cashing in on the non-membership function. Next, promulgation of the Hanman law prescribes the fuzzy gradient/divergent values for different tasks. The notable landmarks of this work are the creation of a Color-Based Detector (CBD), derivation of the incremental hesitancy features accrued from the color histograms and the formulation of a variant of the Hanman Transform Classifier using Convolutional Neural Network (CNN) features. We have used the Belgium dataset to vindicate the efficacy of the proposed methods.

Traffic light detection and recognition in intersections based on intelligent vehicle

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

Expression and clinical significance of pattern recognition receptor-associated genes in hand, foot and mouth disease

Objective:To explore which pattern recognition receptors(PRRs)play a key role in the development of hand,foot,and mouth disease(HFMD)by analyzing PRR-associated genes.Methods:We conducted a comparative analysis of PRR-associated gene expression in human peripheral blood mononuclear cells(PBMCs)infected with enterovirus 71(EV-A71)which were derived from patients with HFMD of different severities and at different stages.A total of 30 PRR-associated genes were identified as significantly upregulated both over time and across different EV-A71 isolates.Subsequently,ELISA was employed to quantify the expression of the six most prominent genes among these 30 identified genes,specifically,BST2,IRF7,IFI16,TRIM21,MX1,and DDX58.Results:Compared with those at the recovery stage,the expression levels of BST2(P=0.027),IFI16(P=0.016),MX1(P=0.046)and DDX58(P=0.008)in the acute stage of infection were significantly upregulated,while no significant difference in the expression levels of IRF7(P=0.495)and TRIM21(P=0.071)was found between different stages of the disease.The expression levels of BST2,IRF7,IFI16 and MX1 were significantly higher in children infected with single pathogen than those infected with mixed pathogens,and BST2,IRF7,IFI16 and MX1 expression levels were significantly lower in coxsackie B virus(COXB)positive patients than the negative patients.Expression levels of one or more of BST2,IRF7,IFI16,TRIM21,MX1 and DDX58 genes were correlated with PCT levels,various white blood cell counts,and serum antibody levels that reflect disease course of HFMD.Aspartate aminotransferase was correlated with BST2,MX1 and DDX58 expression levels.Conclusions:PRR-associated genes likely initiate the immune response in patients at the acute stage of HFMD.

An entropy-based unsupervised anomaly detection pattern learning algorithm

Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. Focusing on this problem, in this paper, firstly a new anomaly detection measurement is proposed according to the probability characteristics of intrusion instances and normal instances. Secondly, on the basis of anomaly detection measure, we present a clustering-based unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage above. Finally, some experiments are conducted to verify the proposed algorithm is valid.

Intent Pattern Recognition of Lower-limb Motion Based on Mechanical Sensors

Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.

Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects

In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.

On similarity measures of interval-valued intuitionistic fuzzy sets and their application to pattern recognitions

The concept of the degree of similarity between interval-valued intuitionistic fuzzy sets （IVIFSs） is introduced, and some distance measures between IVIFSs are defined based on the Hamming distance, the normalized Hamming distance, the weighted Hamming distance, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance, etc. Then, by combining the Hausdorff metric with the Hamming distance, the Euclidean distance and their weighted versions, two other similarity measures between IVIFSs, i. e., the weighted Hamming distance based on the Hausdorff metric and the weighted Euclidean distance based on the Hausdorff metric, are defined, and then some of their properties are studied. Finally, based on these distance measures, some similarity measures between IVIFSs are defined, and the similarity measures are applied to pattern recognitions with interval-valued intuitionistic fuzzy information.

Application of the new pattern recognition system in the new e-nose to detecting Chinese spirits

We present a new pattern recognition system based on moving average and linear discriminant analysis （LDA）, which can be used to process the original signal of the new polymer quartz piezoelectric crystal air-sensitive sensor system we designed, called the new e-nose. Using the new e-nose, we obtain the template datum of Chinese spirits via a new pattern recognition system. To verify the effectiveness of the new pattern recognition system, we select three kinds of Chinese spirits to test, our results confirm that the new pattern recognition system can perfectly identify and distinguish between the Chinese spirits.

Group Decision Making Based Fuzzy Pattern Recognition Model for Lectotype Optimization of Offshore Platforms 1

This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for criteria are the main cause of uncertainty in the evaluation process, therefore it is necessary to integrate the judgments from different decision makers with different experience, knowledge and preference. This paper first uses a complementary principle based pairwise comparison method to obtain the subjective weight of the criteria from each decision maker. A fuzzy pattern recognition model is then developed to integrate the judgments from all the decision makers and the information from the criteria, under the supervision of the subjective weights. Finally a case study is given to show the efficiency and robustness of the proposed model.

Detection of design patterns by combining static and dynamic analyses

Design patterns are micro architectures that have proved to be reliable, robust and easy to implement. Detecting design pattern from source code of object-oriented system can help a designer, a developer or a maintainer to understand the software system. In this paper, a new method is provided which can detect design patterns from source code combining both static and dynamic analysis. To acquire the run-time dynamic information of software systems, a code instrumentation method is adopted. At the same time, all static and dynamic information is presented in UML diagrams format. The pattern detection process and its detection results are visual and interactive. This method is tested on a call center and a traffic simulation system. Experimental results prove that the method is effective in design patterns detection.

New pattern recognition system in the e-nose for Chinese spirit identification

This paper presents a new pattern recognition system for Chinese spirit identification by using the polymer quartz piezoelectric crystal sensor based e-nose. The sensors are designed based on quartz crystal microbalance（QCM） principle,and they could capture different vibration frequency signal values for Chinese spirit identification. For each sensor in an8-channel sensor array, seven characteristic values of the original vibration frequency signal values, i.e., average value（A）,root-mean-square value（RMS）, shape factor value（S_f）, crest factor value（C_f）, impulse factor value（I_f）, clearance factor value（CL_f）, kurtosis factor value（K_v） are first extracted. Then the dimension of the characteristic values is reduced by the principle components analysis（PCA） method. Finally the back propagation（BP） neutral network algorithm is used to recognize Chinese spirits. The experimental results show that the recognition rate of six kinds of Chinese spirits is 93.33% and our proposed new pattern recognition system can identify Chinese spirits effectively.

In-pit coal mine personnel uniqueness detection technology based on personnel positioning and face recognition

Since the coal mine in-pit personnel positioning system neither can effectively achieve the function to detect the uniqueness of in-pit coal-mine personnel nor can identify and eliminate violations in attendance management such as multiple cards for one person, and swiping one's cards by others in China at present. Therefore, the research introduces a uniqueness detection system and method for in-pit coal-mine personnel integrated into the in-pit coal mine personnel positioning system, establishing a system mode based on face recognition + recognition of personnel positioning card + release by automatic detection. Aiming at the facts that the in-pit personnel are wearing helmets and faces are prone to be stained during the face recognition, the study proposes the ideas that pre-process face images using the 2D-wavelet-transformation-based Mallat algorithm and extracts three face features: miner light, eyes and mouths, using the generalized symmetry transformation-based algorithm. This research carried out test with 40 clean face images with no helmets and 40 lightly-stained face images, and then compared with results with the one using the face feature extraction method based on grey-scale transformation and edge detection. The results show that the method described in the paper can detect accurately face features in the above-mentioned two cases, and the accuracy to detect face features is 97.5% in the case of wearing helmets and lightly-stained faces.

Improved K-means Algorithm for Manufacturing Process Anomaly Detection and Recognition

Anomaly detection and recognition are of prime importance in process industries.Faults are usually rare,and, therefore,predicting them is difficult.In this paper,a new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques.The new initialization method tries to choose suitable initial points,which are well separated and have the potential to form high-quality clusters.Based on the clustering result of historical disqualification product data in manufacturing process which generated by the Improved-K-means algorithm,a prediction model which is used to detect and recognize the abnormal trend of the quality problems is constructed.This simple and robust alarm-system architecture for predicting incoming faults realizes the transition of quality problems from diagnosis afterward to prevention beforehand indeed.In the end,the alarm model was applied for prediction and avoidance of gear-wheel assembly faults at a gear-plant.