Quantitative Identification Method Research Articles

Dynamic identification of coupler force of heavy haul locomotive: An effective and long-term intelligent measurement method

Longitudinal impulse, as a common dynamic problem of heavy haul trains, could induce the lateral instability of the coupler and the separation of the locomotive and wagon connection and thus seriously hinder the train running safety. The coupler force undergoes complex changes during longitudinal impulse transmission and exhibits strong nonlinear characteristics, which poses significant challenges to the identification of the coupler force. To address these issues, this paper reports a novel quantitative method for the coupler force identification of heavy haul locomotives. Firstly, the moving average filtering method is used to preprocess the signal of longitudinal relative displacement between the coupler and carbody (LRDCC), and the approximate relative velocity (ARV) feature is also extracted from the preprocessed signal. Then, a Convolutional Neural Network (CNN) model is constructed to extract high-level features from LRDCC and ARV. The model with randomly initialized weight parameters does not require additional training and thus reduces the computational cost. Finally, an Extreme Learning Machine (ELM) is used as the regression layer to process the extracted high-level features and further improve the identification performance. Additionally, the dung beetle algorithm is used to obtain the optimal model parameters to improve the coupler force identification performance. Not only a dynamics simulation but a field test is carried out to show that the proposed method could accurately detect the coupler force of heavy haul locomotives. Furthermore, the proposed method shows promising prospects in replacing the traditional monitoring method under the actual long-term working conditions of trains.

Analysis method and experimental study of ice accumulation detection signal based on Lamb waves

A quantitative identification method for in-flight icing has the capability to significantly enhance the safety of aircraft operations. Ultrasonic guided waves have the unique advantage of detecting icing in a relatively large area, but quantitative identification of ice layers is a challenge. In this paper, a quantitative identification method of ice accumulation based on ultrasonic guided waves is proposed. Firstly, a simulation model for the wave dynamics of piezoelectric coupling in three dimensions is established to analyze the propagation characteristics of Lamb waves in a structure consisting of an aluminum plate and an ice layer. The wavelet transform method is utilized to extract the Time of Flight (ToF) or Time of Delay (ToD) of S0/B1 mode waves, which serves as a characteristic parameter to precisely determine and assess the level of ice accumulation. Then, an experimental system is developed to evaluate the feasibility of Lamb waves-based icing real-time detection in the presence of spray conditions. Finally, a combination of the Hampel median filter and the moving average filter is developed to analyze ToF/ToD signals. Numerical simulation results reveal a positive correlation between geometric dimensions (length, width, thickness) of the ice layer and ToF/ToD of B1 mode waves, indicating their potential as indicators for quantifying ice accumulation. Experimental results of real-time icing detection indicate that ToF/ToD will reach greater peak values with the growth of the arbitrary-shaped ice layer until saturation to effectively predict the simulation results. This study lays a foundation for the practical application of quantitative icing detection via ultrasonic guided waves.

Research on quantitative identification method for wire rope wire breakage damage signals based on multi-decomposition information fusion

Steel wire ropes are widely used in various fields, such as mining, elevators, and cable cars. However, their long-term use can lead to wire breakage, posing safety risks. The detection of wire breakages in steel wire ropes is crucial. This study addresses the shortcomings of existing quantitative identification methods for steel wire rope damage detection and proposes a novel model for fusion-based classification and recognition of wire rope damage. This model first combines the continuous wavelet transform and variational mode decomposition for feature extraction. Subsequently, it utilized convolutional neural networks to learn data features and introduced an attention mechanism to weigh and select the fused data. The final output provides the classification results, aiming to enhance the classification accuracy. Comparative experiments and ablation studies were conducted using the memory networks, autoencoder, and support vector machine models. The experimental results demonstrate the superiority of the proposed model regarding feature extraction, classification accuracy, and automation. The model achieved an accuracy rate of 94.44 % when classifying the nine types of wire breakages. This study presents an effective approach for signal processing and damage classification in steel wire rope damage detection, which is crucial for improving the reliability of wire breakage detection in steel wire ropes.

Quantitative Evaluation of Paleocene Reservoir Diagenetic Facies by Logging in Lishui West Sag, East China Sea Basin

Exploration in the Lishui West Sag of the East China Sea Basin is limited by the scarcity of offshore drilling sites, and the prediction of deep, high-quality reservoirs is challenging using only geophysical methods. This study introduces a quantitative approach to diagenetic facies division in individual wells based on logging data, providing a new method and perspective for the prediction of deep, high-quality reservoirs. We employed comprehensive data from core, logging, thin-section casting, cathodoluminescence, scanning electron microscopy, and X-ray diffraction analyses from five wells to study the petrology, physical properties, diagenetic types and strength, and diagenetic minerals of the Paleocene sandstone reservoirs in the Lishui West Sag. Apparent compaction rate, apparent cementation rate, and other quantitative characterization parameters were used to calculate the comprehensive diagenetic coefficient (Cg), and the diagenetic facies were divided into compaction, cementation, and dissolution facies. A logging calculation model for the comprehensive diagenetic coefficient (Cg) and a quantitative identification method for diagenetic facies in individual well reservoirs were established through a fitting analysis between Cg and logging curve parameters. Continuous quantitative identification of vertical diagenetic facies in the five wells in the study area showed that the high-quality reservoirs in wells L1, L2, and L3 within the L1 gas field are characterized by extensive development of dissolution facies, while wells L4 and L5 are dominated by compaction and cementation facies, with poor reservoir properties and no industrial gas flow output. The results demonstrate the reliability of the model method. The establishment of this quantitative characterization method for diagenetic facies using logging data provides guidance for the prediction of favorable reservoirs.

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data

The study of urban functional area identification is of great significance for urban function cognition, spatial planning, and economic development. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Aiming at the above problems, this paper proposes a multi-scale recursive recognition method based on interactive validation for urban functional areas using taxi trajectory data and point of interest (POI) data as the main data sources. First, the dynamic time warping (DTW) algorithm generates a time series similarity matrix, a CA-RFM model combining the clustering algorithm and random forest model is constructed. The model extracts significant feature regions as inputs through a K-medoid clustering algorithm, which are imported into the random forest model for urban functional zone (UFZ) identification. Then, to overcome the shortcomings of a single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area, the results show that the combination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the study demonstrates the superiority of the proposed method in two aspects, namely, faster delineation and improved accuracy in urban functional area identification.

Development of SYBR Green I-based quantitative PCR assay for identification of porcine circovirus 1, 2 and 3

Porcine Circovirus (PCV) includes Porcine Circovirus 1(PCV1), Porcine Circovirus 2 (PCV2) and Porcine Circovirus 3 (PCV3). In recent years, co-infection exists between PCV1, PCV2 and PCV3 serotypes. Therefore, it is particularly necessary to establish a fast, specific and sensitive SYBR Green I real-time quantitative PCR detection method for PCV1, PCV2 and PCV3. In this experiment, specific primers were selected and the reaction conditions were optimized. A real-time quantitative PCR identification method was established. The results showed the detection limits of this assay were 40.3 copies/μl for PCV1, 25.2 copies/μl for PCV2 and22.4 copies/ μl for PCV3. There was no cross-reactivity with swine fever virus (CSFV), porcine reproductive and respiratory syndrome virus (PRRSV), porcine pseudorabies virus (PRV) and porcine parvovirus (PPV). The intra-assay and inter-assay coefficients of variation were less than 1%. The test results of 100 PCV suspected positive samples revealed that the PCV1, PCV2 and PCV3 singular infection rate was 10% (10/100), 64% (64/100) and 52% (52/100), respectively. The PCV1 and PCV2 co-infection rate was 8% (8/100), the PCV1 and PCV3 co-infection rate was 7% (7/100), the PCV2 and PCV3 co-infection rate was 26% (26/100), and the PCV1, PCV2 and PCV3 co-infection rate was 7% (7/100). This method has good specificity, sensitivity and stability. It provides a promising tool for rapid differential detection of PCV1, PCV2 and PCV3.

Qualitative identification of lonicerae japonicae flos in traditional chinese medicine using metabarcoding combined with specific mini-barcodes.

Lonicerae japonicae flos, also known as Jinyinhua (JYH), is an important component of traditional Chinese patent medicine (TCPM) products. However, the potential for adulteration and substitution with low-quality materials highlights the need for a reliable and sensitive approach to identify the species composition of TCPM products for consumer safety. We used universal ITS2 primers to amplify TCPMs containing JYH. However, the results were inconclusive, as only one operational taxonomic unit (OTU) was identified as Lonicera sp., which could not be identified at the species level. To confirm the species identification of Lonicera sp. in TCPM, we developed a short mini-barcode primer based on the psbA-trnH region, which, in combination with DNA metabarcoding technology, allowed for qualitative and quantitative analysis of artificially mixed samples. We applied the mini-barcode to distinguish TCPMs containing JYH and demonstrated its relatively accurate quantitative ability in identifying two Lonicera species. Our study presents a method for qualitative and quantitative identification of JYH, providing a promising application of DNA metabarcoding technology in the quality control of TCPM products.

A new method for the characterization of microcracks based on seepage characteristics

Microcracks are the main seepage channels and reservoir space for oil and gas in dense sandstone reservoirs, and the degree of development dominates the reservoir’s high and stable production capacity. A new method has been devised to address the lack of quantitative identification and characterization methods for microcrack networks. The method is based on core stress sensitivity, permeability anisotropy, and two-phase seepage rule testing. By improving upon the traditional black oil model, this method can accurately calculate the impact that microcracks of varying degrees of development have on the capacity of tight oil reservoirs. The study shows that 1) the higher the degree of microcrack development, the stronger the reservoir stress sensitivity and the greater the permeability anisotropy. As the degree of microcrack development increases, the irreducible water saturation decreases, the residual oil saturation gradually increases, and the oil–water two-phase co-infiltration zone becomes more extensive and smaller. The degree of microcrack development in tight reservoirs can be characterized based on the seepage characteristic parameters; 2) a microcrack characterization method and classification criteria have been established. It is based on stress sensitivity coefficients, permeability anisotropy parameters, and phase seepage characteristics in cores with different microcrack development degrees. For the first time, the method enables a macroscopic-level description of microcrack seepage; 3) numerical calculations show that the degree of microcrack development significantly affects the reservoir’s oil production and water production. The higher the degree of microcrack development, the higher the reservoir’s initial oil production and cumulative oil production. However, when the degree of microcrack development is too high, the microcracks are connected, thus exhibiting the nature of large fractures. This strengthens the bypassing communication effect and causes the microscopic inhomogeneity to strengthen, the oil production decreases rapidly, and water production increases quickly at the later stage. This research result enriches the reservoir microcrack characterization and evaluation system, which has essential theoretical guidance and practical significance for the rational and effective development of tight oil and tight sandstone gas.

Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling.

Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.

Triplex-colored nucleic acid lateral flow strip and multiplex polymerase chain reaction coupled method for quantitative identification of beef, pork and chicken

Meat adulteration is one of the worldwide challenges for food quality control. Now, we developed a multiplex PCR and nucleic acid lateral flow strip (mPCR-NALFS) integrated method that employs triplex-color latex microspheres (LMs) as visual labels for simultaneous identification of beef, pork and chicken DNA. First, three primer pairs for beef, pork and chicken DNA are tailed with specific oligonucleotide sequences (anti-recognition/anti-detection/anti-control probes) as swing tags. Then, double-stranded amplicons obtained after mPCR amplification were flanked by tags. Finally, these amplicons can accurately hybridize with the pre-immobilized recognition probes on the conjugate pad, detection probes on the test lines and control probes on the control line, respectively. The LMs and recognition probe modified LMs were characterized by scanning electron microscopy, ultraviolet-visible spectroscopy and Fourier Transform Infrared spectroscopy. This strip can quantitatively detect beef, pork and chicken DNA within 10 min without compromising on accuracy compared to the electrophoresis. Specificity analysis revealed no cross-reactivity with meat derived from beef, pork, chicken, duck, mutton, rabbit, goose, ostrich, camel and horse meat. As low as 0.1 % (wt%) of beef, pork and chicken in the simulated adulterated meats were co-detected. This assay was successfully employed for adulteration identification in 28 commercial beef and lamb products.

Modified Maximum Likelihood Estimation Metal Magnetic Memory Quantitative Identifying Model of Weld Defect Levels Based on Dempster–Shafer Theory

Metal magnetic memory (MMM) is a nondestructive testing technology based on the magnetomechanical effect, which is widely used in the qualitative detection of stress concentration zones for welded joints. However, there is inevitable residual stress after welding, which brings the bottleneck of quantitative identification between the weld residual stress concentration and the early hidden damage. In order to overcome the bottleneck of quantitative identification of weld defect levels with MMM technology, a modified maximum likelihood estimation (MLE) MMM quantitative identifying model is first proposed. The experimental materials are Q235B welded plate specimens. Fatigue tension experiments were operated to find the MMM feature laws of critical hidden crack by comparing with synchronous X-ray detection results. Six MMM characteristic parameters, which are, ΔHp(x), Gxmax, Zxmax, ΔHp(y), Gymax and Zymax, are extracted corresponding to the normal state, the hidden crack state and the macroscopic crack, respectively. The MLE values of the six parameters are obtained by the kernel density functions with optimized bandwidth from the view of mathematical statistics. Furthermore, the modified MLE MMM quantitative identifying model is established based on D–S theory to overcome the partial overlap of MLE values among different defect levels, of which the uncertainty is as low as 0.3%. The verification result from scanning electron microscopy (SEM) is consistent with the prediction of the modified MLE MMM model, which provides a new method for quantitative identification of weld defect levels.

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network.

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

Quantitative Identification of Water Sources of Coalbed Methane Wells, Based on the Hydrogen and Oxygen Isotopes of Produced Water—A Case of the Zhijin Block, South China

The quantitative identification of water sources is an important prerequisite for objectively evaluating the degree of aquifer interference and predicting the production potential of coalbed methane (CBM) wells. However, this issue has not been solved yet, and water sources are far from being completely understood. Stable water isotopes are important carriers of water source information, which can be used to identify the water sources for CBM wells. Taking the Zhijin block in the Western Guizhou Province as an example, the produced water samples were collected from CBM wells. The relationships between the stable isotopic compositions of the produced water samples and the production data were quantitatively analyzed. The following main conclusions were obtained. (1) The δD and δ18O values of the produced water samples were between −73.37‰ and −27.56‰ (average −56.30‰) and between −11.04‰ and −5.93‰ (average −9.23‰), respectively. The water samples have D-drift characteristics, showing the dual properties of atmospheric precipitation genesis and water–rock interaction modification of the produced water. An index d was constructed to enable the quantitative characterization of the degree of D-drift of the produced water. (2) The stable isotopic compositions of produced water showed the control of the water sources on the CBM productivity. The probability of being susceptible to aquifer interference increased with the increasing span of the producing seam combination, reflected in the lowering δD and δ18O values and the decreasing gas productivity. (3) Three types of water, namely, static water, dynamic water, and mixed water, were identified. The characteristic values of the isotopic compositions of the static and dynamic water were determined. Accordingly, a quantitative identification method for the produced water sources was constructed, based on their stable isotopic compositions. The identification results have a clear correlation with the gas production, and the output of the static water contributes to the efficient CBM production. The method for the quantitative identification of the water sources proposed in this study, can help to improve the CBM development efficiency and optimize the drainage technology.

Using composite system index to identify China's ecological and socio-economic transition zone.

Regions with synthetic geographical gradients tend to exhibit distinct ecological transitions. As a compound ecosystem, transition zone can provide a basis for decision-making in the sustainable ecological management by investigating its boundary and complexity. To determine the characteristics of the transition zone where natural ecological and socio-economic factors interact, a conceptual framework and a quantitative identification method for the ecotone of coupled human and natural systems have been proposed. The composite system index can be used to ascertain the coupling intensity, coupling direction, and ecological transition of the system. Taking China as an example, this study showed evidence of the existence of a tremendous amount of ecological and socio-economic transition zone (complex coupled areas) between the east and west of China, and sporadic ecotone in other regions of the country. This transition zone accounted for about 1/4 of China's land surface area, and had a fragile environment that faced challenges of environmental protection and economic development. In the area across the Hu Line, human and natural factors jointly explain a low proportion of the variance in ecological and socio-economic transition zone (the complexity of coupled systems, with 62.01% of unexplained proportion higher than that in other regions). In this region, the topographic position index was the critical element associated with the transition zone, and accounted for nearly 20% of the variation of composite system index. The discovery and characterization of the ecological and socio-economic transition zone is crucial for understanding its uncertainty and diversity and the complex of coupled ecosystems.

YoloXT: A object detection algorithm for marine benthos

In recent years, the marine economy has developed rapidly, and human demand for marine resources has increased greatly. At present, target detection technology has a wide range of applications and prospects in seabed observation and ocean engineering. However, the accuracy and robustness of existing target detection methods are low due to the complex underwater environment, poor lighting, and poor quality of undersea images and videos. To solve these problems, this paper proposes YoloXT, a new quantitative identification method for marine benthos. YoloXT introduces the DECA (Deformable Coordinate Attention) module, which expands the spatial awareness in feature extraction and can learn image features more effectively. Meanwhile FPST-PAN (Feature Pyramid S2win Transformer, Improved Path Aggregation Network) was proposed to deal with the problem of marine benthic target diversity. It further integrates deep and shallow features through multi-scale skip-connection and Transformer and improves the model's ability to deal with complex and changeable marine environments. Finally, the positive and negative sample assignment strategy OAA (Optimal Anchor Assignment) applied to the detection head is proposed. It effectively avoids the problem of unbalanced distribution of positive and negative samples caused by traditional sample assignment methods and marine benthos image noise. Experiments on the IOC-URPC dataset show that the mAP of YoloXT is 3.9% higher than that of YoloX, reaching 70.9%. YoloXT has demonstrated excellent performance in quantitative identification task of marine organisms, which can effectively contribute to the exploitation and conservation of marine resources. The source code is publicly available at https://github.com/F1veZhang/YOLOXT.

A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes

The detection of broken wires in steel wire ropes is of great significance for the production safety. However, the existing identification techniques mainly focus on the external broken wires problem. Here, the artificial feature extraction is one of the most important method, while only the prior knowledge of the artificial feature extraction method is adequate, the identification precision can be satisfied. Therefore, it is still a challenge to realize intelligent diagnosis for the broken wires. Besides, the identification of internal broken wires problem is still not well solved. In this paper, a quantitative identification method based on continuous wavelet transform (CWT) and convolutional neural network (CNN) is proposed to solve the internal and external broken wires identification problem. The key technology of this research is that the fault information from the time-frequency images converted by the magnetic flux leakage (MFL) signals can be automatically extracted through a designed CNN. The main innovation is that the complex signal processing work can be eliminated and the internal and external broken wires can be accurately identified simultaneously by combining CWT and CNN. The experimental results of a steel wire rope test rig are compared with the traditional recognition method, which shows that the proposed method achieved significant improvement on detection accuracy and recognition performance.

Identification of mixed mode damage types on rock-concrete interface under cyclic loading

The study of the mixed mode Ⅰ-Ⅱ fracture damage characteristics of the rock-concrete interface under cyclic loading is essential to grasp the operation of gravity dams under seismic loading. To reveal the mixed mode fracture damage characteristics of the rock-concrete interface under cyclic loading, a four-point shear (FPS) test for rock-concrete specimens was carried out at first. The entire fracture damage process of rock-concrete specimens was monitored in real-time by acoustic emission (AE) and digital image correlation (DIC), the widely used nondestructive monitoring techniques. The mechanical test results show that the modal angle is related to the stiffness decay of rock-concrete specimens. The quantitative identification method of tensile and shear damage based on machine learning algorithm can quantitatively identify the damage types at the interface of rock-concrete specimens. The influence of rock type and modal angle on the interface damage mechanism was quantified by the damage identification method, and the spatial and temporal evolution of tensile and shear damage with cyclic loading was determined. The spatial and temporal evolution characteristics of the damage were compared with the DIC strain contours of the corresponding stages to reveal the mechanism of the influence of the damage evolution law on the crack expansion path.

Rock thin-section analysis and identification based on artificial intelligent technique

Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth. It is also an important evaluation method for oil and gas exploration and development. It can be used to identify the petrological characteristics of reservoirs, determine the type of diagenesis, and distinguish the characteristics of reservoir space and pore structure. It is necessary to understand the physical properties and sedimentary environment of the reservoir, obtain the relevant parameters of the reservoir, formulate the oil and gas development plan, and reserve calculation. The traditional thin-section identification method has a history of more than one hundred years, which mainly depends on the geological experts' visual observation with the optical microscope, and is bothered by the problems of strong subjectivity, high dependence on experience, heavy workload, long identification cycle, and incapability to achieve complete and accurate quantification. In this paper, the models of particle segmentation, mineralogy identification, and pore type intelligent identification are constructed by using deep learning, computer vision, and other technologies, and the intelligent thin-section identification is realized. This paper overcomes the problem of multi-target recognition in the image sequence, constructs a fine-grained classification network under the multi-mode and multi-light source, and proposes a modeling scheme of data annotation while building models, forming a scientific, quantitative and efficient slice identification method. The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency, but also realizes the intuitive, accurate and quantitative identification results, which is a subversive innovation and change to the traditional thin-section identification practice.

Three-dimensional ice shape detection based on flash pulse infrared thermal wave testing

Ice detection is an important issue in the field of icing prevention and de-icing. In this study, an experimental platform was built for ice detection using flash pulse infrared thermal wave detection, followed by a quantitative recognition approach for identifying the three-dimensional shape of ice. The new method was combined the edge recognition with thickness calculation of inverse heat transfer problem. And the edge of the ice was based on the Principal Component Analysis (PCA) and the Canny algorithm. Thus, by processing the ice edges and giving an initial thickness, the finite element model of the ice was established to numerically simulate the temperature distribution for ice thickness inversion based on the forward heat transfer problem. Meanwhile, the thickness of the ice was inversed by the Levenberg-Marquardt (LM) method based on the inverse heat transfer problem. The resulting edges and thickness of the ice were found to be in good agreement with the experimental results, demonstrating the feasibility of the proposed methods. This paves the way to explore an effective accurate and quantitative identification method for three-dimensional ice shape detection in infrared thermal wave detection.