Recognition Efficiency Research Articles

Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm

AbstractNuclear power turbine fault diagnosis is an important issue in the field of nuclear power safety. The numerous state parameters in the operation and maintenance of nuclear power turbines are collected, forming a complex high‐dimensional feature space. These high‐dimensional feature spaces contain redundant information, which increases the training cost and reduces the recognition accuracy and efficiency of the fault diagnosis model. To address the aforementioned challenges, a vibration fault diagnosis algorithm in nuclear power turbines is proposed. First, a long short‐term memory‐based denoising autoencoder (LDAE) is designed to enhance the capability of uncertainty awareness. Then, a feature extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective mode selection, and sample entropy is devised to extract the latent features from the complex high‐dimensional feature space. Furthermore, using extreme gradient boosting (XGBoost) as the classifier, LDAE‐VMD‐XGBoost model is constructed for fault diagnosis of nuclear power turbines. Considering the impact of multiple hyperparameters of LDAE‐VMD‐XGBoost model on the performance, the pathfinder algorithm is used to optimise the model hyperparameter settings and improve the fault diagnosis accuracy. Experimental results demonstrate the performance of the proposed improved LDAE‐VMD‐XGBoost in accurate nuclear power turbine vibration fault diagnosis.

Defect identification method for stainless steel welded pipes based on eddy current testing

Abstract A defect identification and classification method for 316 stainless steel welded pipes is proposed based on eddy current testing technology. Initially, this approach acquires eddy current signals from steel pipes, applies Empirical Mode Decomposition (EMD) to derive Intrinsic Mode Functions (IMFs), chooses the principal IMF based on interrelationships, and extracts time-frequency domain characteristic parameters from the selected IMF. To enhance model recognition efficiency, Principal Component Analysis (PCA) is employed to reduce the dimensionality of the feature vector set. Ultimately, a Support Vector Machine (SVM) is utilized to identify and classify weld defects. The results indicate that this method is highly accurate in identifying defects in 316 stainless steel welded pipes.

An element perception and state recognition method oriented to complex assembled product

Quality inspection during assembly process has an important impact on the performance and service life of the final product. Automated inspection solutions based on machine vision and artificial intelligence greatly improve the efficiency of inspection. However, the current mainstream large-scene inspection solutions, such as mobile vision platforms, are difficult to apply widely due to cost issues. Therefore, portable vision inspection solutions have gradually gained attention. However, the bottleneck technologies that limit this solution are full-element perception and occlusion judgment under free viewing angle, robust object matching method. To address the above problems, This paper proposes a free-viewpoint-based element perception and state recognition scheme. An improved structure estimation model is adopted to establish a bridge between virtual and actual mapping under the support of small-sample data, and all elements are located and occlusion analyzed by a 3D model-constrained view evolution algorithm. In addition, a hierarchy matching and optimization method is established to complete visual analysis. Experimental results show that this scheme performs well in structure estimation, element localization, occlusion judgment, state recognition, and work efficiency, demonstrating high application value.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

An Intelligent Detection System for Wheat Appearance Quality

In the realm of commercial trade, the appearance quality of wheat is a crucial metric for assessing its value and grading. Traditionally, evaluating wheat appearance quality is a manual process conducted by inspectors, which is time-consuming, laborious, and error-prone. In this research, we developed an intelligent detection system for wheat appearance quality, leveraging state-of-the-art neural network technology for the efficient and standardized assessment of wheat appearance quality. Our system was meticulously crafted, integrating high-performance hardware components and sophisticated software solutions. Central to its functionality is a detection model built upon multi-grained convolutional neural networks. This innovative setup allows for the swift and precise evaluation and categorization of wheat quality. Remarkably, our system achieved an exceptional overall recognition accuracy rate of 99.45% for wheat grain categories, boasting a recognition efficiency that was approximately five times faster than manual recognition processes. This groundbreaking system serves as a valuable tool for assisting inspectors, offering technical support for customs quarantine, grain reserves, and food safety.

Deep recurrent neural networks distributed on a Hadoop/Spark cluster for fall detection

Falls detection approaches struggle with both Big Data scalability and upholding individual privacy, this research work proposed a novel approach for posture recognition followed by fall detection, taking advantage of the synergy between Random Forests and Uniform Local Binary Patterns (uLBP) histograms for an accurate and fast posture identification while respecting privacy. Additionally, it referred to deep recurrent neural networks distributed on a Hadoop and Spark platform for time series analysis in fall detection. This combination of methods allowed us to achieve acceptable real-time monitoring precision. This study, therefore addressed two objectives simultaneously: efficiency and scalability in posture recognition using Random Forests and uLBP, and fall detection relying on the recurrent neural network (RNN) for time series processing. The suggested solution is designed for home telemonitoring, where scalability and effective data management are supported through Hadoop/Spark. The integration of these technologies promotes reliable detection without any privacy violation, paving the way for a wider adoption of home monitoring systems for an increasing population of dependent individuals.

Understanding the Interactions of Ubiquitin-Specific Protease 7 with Its Substrates through Molecular Dynamics Simulations: Insights into the Role of Its C-Terminal Domains in Substrate Recognition.

Ubiquitin-specific protease 7 (USP7) is a deubiquitinase enzyme that plays a critical role in regulating various cellular processes by cleaving ubiquitin molecules from target proteins. The C-terminal loop (CTL) motif is a specific region at the C-terminal end of the USP7 enzyme. Recent experiments suggest that the CTL motif plays a role in USP7's catalytic activity by contributing to the enzyme's structural stability, substrate recognition, and catalytic efficiency. The objective of this work is to elucidate these roles through the utilization of computational methods for molecular simulations. For this, we conducted extensive molecular dynamics (MD) simulations to investigate the conformational dynamics and protein-protein interactions within the USP7 enzyme-substrate complex with the substrate consisting of the ubiquitin tagged with the fluorescent label rhodamine 110-gly (Ub-Rho). Our results demonstrate that the CTL motif plays a crucial role in stabilizing the Ubl domains' conformation and augmenting the stability of active conformations within the enzyme-substrate complex. Conversely, the absence of the CTL motif results in increased flexibility and variability in Ubl domains' motion, leading to a reduced percentage of active conformations. Furthermore, our analysis of protein-protein interactions highlights the significance of the CTL motif in anchoring the Ubl45 domains to the catalytic domain (CD), thereby facilitating stable interactions with the substrate. Overall, our findings provide valuable insights into the conformational dynamics and protein-protein interactions inherent in the USP7 enzyme-substrate complex. These insights shed light on some mechanistic details of USP7 concerning the substrate's recognition before its catalytic action.

Deep learning models for atypical serotonergic cells recognition

Background:The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of “typical” features of their activity, i.e. slow regular firing and relatively long duration of action potentials. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with “atypical” features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied. New Methods: We propose deep learning models capable of discriminating typical and atypical serotonergic neurons from non-serotonergic cells with high accuracy. The research utilized electrophysiological in vitro recordings from serotonergic neurons identified by the expression of fluorescent proteins specific to the serotonergic system and non-serotonergic cells. These recordings formed the basis of the training, validation, and testing data for the deep learning models. The study employed convolutional neural networks (CNNs), known for their efficiency in pattern recognition, to classify neurons based on the specific characteristics of their action potentials. Results:The models were trained on a dataset comprising 27,108 original action potential samples, alongside an extensive set of 12 million synthetic action potential samples, designed to mitigate the risk of overfitting the background noise in the recordings, a potential source of bias. Results show that the models achieved high accuracy and were further validated on ”non-homogeneous” data, i.e., data unknown to the model and collected on different days from those used for the training of the model, to confirm their robustness and reliability in real-world experimental conditions. Comparison with existing methods: Conventional methods for identifying serotonergic neurons allow recognition of serotonergic neurons defined as typical. Our model based on the analysis of the sole action potential reliably recognizes over 94% of serotonergic neurons including those with atypical features of spike and activity. Conclusion:The model is ready for use in experiments conducted with the here described recording parameters. We release the codes and procedures allowing to adapt the model to different acquisition parameters or for identification of other classes of spontaneously active neurons.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

Object Detection of BISINDO Sign Language Letters Using Residual Network

Indonesian Sign Language or BISINDO is an alternative language used by people who suffer from disabilities, especially those who have hearing impairments. This language grew and developed from the deaf community, so its use is based on the visual aspect. This research aims to apply Residual Networks to detect objects in the context of Bisindo Letter Sign Language, with the hope of increasing accuracy and efficiency in letter recognition. Object detection goes through 2 stages, namely feature extraction and model training. ResNet is a type of Convolutional Neural Network (CNN) architecture that utilizes models that have been previously trained, so it can save the time required in the model development process. In this research, Residual Network (ResNet) was used for feature extraction to recognize important aspects in the Bisindo letter sign image, such as hand position, finger shape characteristics, and direction of movement. The research results show that the new dataset used as training data and test data has a fairly good ability to detect with a division of 70% train set, 20% valid set and 10% test set with size 640x640 with 300 epochs for the training model.

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Fingerprint analysis has been an essential component of forensic science and biometric authentication systems. The precise recognition of fingerprint patterns is critical for solving crimes, confirming identities and assuring the security of numerous applications. In recent years, the convergence of learning approaches, particularly machine learning and deep learning, has played an essential part in improving the accuracy and efficiency of fingerprint pattern recognition. In this paper, we discuss recent advancements in physiological-based biometric multimodalities, with a particular focus on the field of precise pattern identification in fingerprint analysis. Additionally, we assume the task of summarizing and examining a range of physiological-based biometric modalities, encompassing both traditional and deep learning approaches. A detailed review of several biometric measurements across many modalities is presented, encompassing various phases, including preprocessing, feature extraction and classification, which are thoroughly discussed. We provide a comprehensive analysis of the challenges and future developments associated with conventional and deep learning methodologies. The objective is to enable investigators to recognize these problems. A review is conducted to evaluate the standard and deep learning approaches employed in different physiological-based biometric systems. The comparative analysis of this review suggests that more advancement is necessary for the development of a reliable physiological-based approach to enhance and optimize the functionality of the fingerprint system.

Research on Road Sign Recognition Method for Autonomous Vehicles Based on Convolutional Neural Networks

With the rapid development of autonomous driving technology, road sign recognition has become an important link to ensure the safety of autonomous vehicles. This paper proposes a road sign recognition method for autonomous vehicles based on Convolutional Neural Networks (CNN), aiming to improve the accuracy and efficiency of road sign recognition by using CNN. Firstly, the background and significance of this issue were introduced, including the development of autonomous vehicles and their demand for road sign recognition technology. The focus was on the application of convolutional neural networks in road sign recognition. This article provides a detailed introduction to the dataset used, the designed convolutional neural network model, and data preprocessing methods. In order to further improve accuracy, methods such as data augmentation and image enhancement were adopted. The research results of this paper indicate that the road sign recognition method for autonomous vehicles based on convolutional neural networks has high accuracy and efficiency, providing important support for the practical application of autonomous driving technology.

Efficient railway kilometer marker recognition via spatio-temporal slimming and multi-view fusion

Efficiently recognizing kilometer markers in railway systems is crucial for ensuring the safety and reliability of train operations, particularly within the Industrial Internet of Things (IIoT) framework where edge devices are often resource-constrained. This paper highlights the significance of a real-time AI-driven approach to railway kilometer marker recognition. We introduce an innovative method that employs spatio-temporal slimming and multi-view fusion techniques, elevating both precision and computational efficiency for real-time analytics in IIoT. Our approach begins with the implementation of an Adaptive Region of Interest (AROI) and a spatio-temporal calibration mechanism for effective marker detection in real-time. Furthermore, a multi-view fusion method addresses challenges such as occlusion, blurriness, and low-light conditions, common in real-world industrial environments, which includes a variation-aware memory bank for constructing informative views and a fusion network. Experimental results demonstrate the effectiveness of our method in a real-world rail transportation setting, significantly enhancing the accuracy and efficiency of kilometer marker recognition, thereby contributing to the safety and operational efficiency of rail systems.

Understanding Relations Between Product Icon Type, Feature Type, and Abstraction: Evidence From ERPs and Eye-Tracking Studys

The representation and recognition of icons play a crucial role in interface interaction efficiency and user experience within human–computer interaction. However, the intricate relationship between product icon types, feature types, and abstraction in cognitive contexts has yet to be clarified. This study aimed to delve into the cognitive mechanisms concerning practical and hedonic product icons across varying abstraction levels using EEG analysis. Moreover, it investigated how the explicitness and implicitness of these icons and their abstraction levels influence recognition efficiency via eye-tracking studies. In Experiment A, a high abstraction level led to prolonged reaction times (RTs), reduced accuracy rate (ACC), more negative N400, and decreased late positive potential (LPP) amplitudes. These outcomes suggested increased effort, heightened semantic conflict, and negative emotional engagement. Moreover, at middle abstraction levels, RTs were consistently longer for hedonic product icons compared to practical ones. Additionally, both N400 and LPP amplitudes were notably larger for hedonic product icons. In Experiment B, eye-tracking results revealed that compared with implicit features, the change of abstraction degree of explicit features is more likely to increase the number of fixation points and RTs of users. Specifically, functional explicit features wielded the greatest impact on overall icon cognitive efficiency. Synthesizing both experiments revealed significant differences in abstraction requirements for practical vs. hedonic product icons. A practical implication arises: moderate overall abstraction suits practical product icons, while higher abstraction suits hedonic ones. Among features, it’s essential to avoid excessive abstraction in functional and symbolic aspects, while simplifying or removing area line features and chamfered elements can be beneficial. This study contributes to Internet of Things (IoT) icon research, offering potential guidance for graphic designers in crafting user-friendly icons.

Videoautopsy-A Minimally Invasive Autopsy Method Using Endoscopic Techniques in Forensic Medicine: Clinical Features.

In light of falling global autopsy rates, one of the causes of which is the resulting body disfigurement, it has become crucial to search for new, minimally invasive post-mortem diagnostic tools. One of these methods is videoautopsy, a minimally invasive autopsy technique using endoscopic methods. In the years 2020-2023, 15 videoautopsies were conducted at the Department of Forensic Medicine of the Poznan University of Medical Sciences in order to determine the usefulness of the method in forensic approaches. Each post-mortem examination included laparoscopy and thoracoscopy, followed by a classic autopsy to assess the effectiveness of the method. In total, the endoscopic examination allowed for determining the cause of death in 53.3% of cases, and when the cause of death was located in the abdominal cavity or chest, the percentage increased to 80%. Traumatic lesions had good recognition efficiency. In addition, it was also possible to collect material for histopathological and toxicological tests. Retroperitoneal organs were difficult to assess. The main limitation of the method is the inability to assess the inside of the skull and the structures of the central nervous system. Videoautopsy may become an important tool in post-mortem diagnostics and in forensic cases, especially when the alternative is to not perform an autopsy. Further research is necessary to standardise the examination protocol, optimise the instrumentation, and assess the potential synergistic effect with other methods of minimally and non-invasive post-mortem examination.

4-Oxo-2-Nonenal- and Agitation-Induced Aggregates of α-Synuclein and Phosphorylated α-Synuclein with Distinct Biophysical Properties and Biomedical Applications.

α-Synuclein (α-syn) can form oligomers, protofibrils, and fibrils, which are associated with the pathogenesis of Parkinson's disease and other synucleinopathies. Both the lipid peroxidation product 4-oxo-2-nonenal (ONE) and agitation can induce aggregation of α-syn and phosphorylated α-syn. Thus, clarification of the characteristics of different α-syn species could help to select suitable aggregates for diagnosis and elucidate the pathogenesis of diseases. Here, we characterized ONE-induced wild-type (WT) α-syn aggregates (OW), ONE-induced phosphorylated α-syn (p-α-syn) aggregates (OP), agitation-induced α-syn preformed fibrils (PFF), and agitation-induced p-α-syn preformed fibrils (pPFF). Thioflavin T (ThT) dying demonstrated that OW and OP had fewer fibrils than the PFF and pPFF. Transmission electron microscopy revealed that the lengths of PFF and pPFF were similar, but the diameters differed. OW and OP had more compact structures than PFF and pPFF. Aggregation of p-α-syn was significantly faster than WT α-syn. Furthermore, OW and OP were more sodium dodecyl sulfate-stable and proteinase K-resistant, suggesting greater stability and compactness, while aggregates of PFF and pPFF were more sensitive to proteinase K treatment. Both ONE- and agitation-induced aggregates were cytotoxic when added exogenously to SH-SY5Y cells with increasing incubation times, but the agitation-induced aggregates caused cell toxicity in a shorter time and more p-α-syn inclusions. Similarly, p-proteins were more cytotoxic than non-p-proteins. Finally, all four aggregates were used as standard antigens to establish sandwich enzyme-linked immunosorbent assay (ELISA). The results showed that the recognition efficiency of OW and OP was more sensitive than that of PFF and pPFF. The OW- and OP-specific ELISA for detection of p-α-syn and α-syn in plasma samples of Thy1-α-syn transgenic mice showed that the content of aggregates could reflect the extent of disease. ONE and agitation induced the formation of α-syn aggregates with distinct biophysical properties and biomedical applications.

LGD: A fast place recognition method based on the fusion of local and global descriptors

Place recognition, which is also known as loop detection, has been one of the main problems in the field of robot navigation, and it is crucial in realizing high-performance simultaneous localization and mapping (SLAM). Most of the existing methods directly construct local or global descriptors with point clouds, ignoring the offset of different visits in the same place. To overcome the current limitations, this study emphasizes the importance of point cloud features in improving the environment recognition precision and proposes an innovative framework that combines local features that are spatially invariant and prominent descriptive global features as a representation of a laser scanner frame. The feature center of the laser point cloud is obtained by computing local feature points with spatial invariance, which improves the offset between the original point cloud and the feature center. Furthermore, a coarse-to-fine hierarchical recognition strategy is developed to improve the efficiency of place recognition. An evaluation with different offsets and synthesis is conducted on the publicly available KITTI and self-developed CHDloop datasets. The experimental results show that the proposed method can improve time efficiency by more than 18% compared to the SC and ISC, and there are also significant improvements in the recall rate and accuracy in slight lane-change scenarios. Moreover, the proposed method can obtain rich lane-change loops in moderate and large lane-change scenarios where the SC and ISC fail to obtain effective loops. The proposed method can be used as a lightweight and interpretable approach for place recognition, which not only can be quickly deployed in any SLAM framework but can also efficiently handle more complex place recognition issues in urban environments.

Application of Stacking Ensemble Learning in Predicting Copper’s Flotation Concentrate Grade

Addressing issues such as a low operational condition recognition efficiency, strong subjectivity, and significant fluctuations in Outotec X fluorescence analysis data in copper flotation production, a copper concentrate grade classification model is constructed based on image processing technology and the Stacking ensemble learning algorithm. Firstly, a feature extraction model for copper concentration flotation foam images is established, extracting color, texture, and size statistical features to build a feature dataset. Secondly, to avoid redundancy in the feature data, which could reduce model accuracy, a combined correlation feature selection is employed for dimensionality reduction, with the filtered feature subset being used as the model input. Finally, to fully leverage the strengths of each model, a Stacking ensemble learning copper concentrate grade classification model is constructed with support vector machine (SVM), random forest (RF), and adaptive boosting (AdaBoost) as base models and logistic regression (LR) as the meta-model. The experimental results show that this ensemble model achieves good recognition for different grade categories, with a precision, recall, and F1 score of 90.01%, 89.85%, and 89.93%, respectively. The accuracy of this Stacking ensemble model, with a 7% improvement over Outotec X fluorescence analysis, demonstrates a potential to meet the daily production needs of beneficiation plants.

Dual miRNA-Triggered DNA Walker Assisted by APE1 for Specific Recognition of Tumor Cells.

The identification of a specific tumor cell is crucial for the early diagnosis and treatment of cancer. However, it remains a challenge due to the limited sensitivity and accuracy, long response time, and low contrast of the recent approaches. In this study, we develop a dual miRNA-triggered DNA walker (DMTDW) assisted by APE1 for the specific recognition of tumor cells. miR-10b and miR-155 were selected as the research models. Without miR-10b and miR-155 presence, the DNA walker remains inactive as its walking strand of W is locked by L1 and L2. After miR-10b and miR-155 are input, the DNA walker is triggered as miR-10b and miR-155 bind to L1 and L2 of W-L1-L2, respectively, unlocking W. The DNA walker is driven by endogenous APE1 that is highly catalytic and is highly expressed in the cytoplasm of tumor cells but barely expressed in normal cells, ensuring high contrast and reaction efficiency for specific recognition of tumor cells. Dual miRNA input is required to trigger the DNA walker, making this strategy with a high accuracy. The DMTDW strategy exhibited high sensitivity for miRNA analysis with a detection limit of 44.05 pM. Living cell-imaging experiments confirmed that the DMTDW could effectively respond to the fluctuation of miRNA and specifically identified MDA-MB-231 cells from different cell lines. The proposed DMTDW is sensitive, rapid, and accurate for specific tumor cell recognition. We believe that the DMTDW strategy can become a powerful diagnostic tool for the specific recognition of tumor cells.