Discrimination Scheme Research Articles

Low noise InGaAs/InP single-photon detector with DC to 1 GHz tunable gate frequency

An InGaAs/InP single-photon detector (SPD) typically operates in gated mode, but the capacitive response of a single-photon avalanche diode introduces spike noise, obscuring the avalanche signal. Most avalanche signal discrimination schemes cannot completely eliminate spike noise, resulting in residual noise. Limited by residual noise, these schemes have large noise and a limited frequency tuning range. However, for applications like quantum key distribution and laser ranging, a low noise, frequency-tunable InGaAs/InP SPD is crucial for enhancing system performance. Here, we propose a (residual noise assisted) discrimination method that aligns the maximum amplitude of the avalanche signal with the peak of the residual noise. This method turns the residual noise from adversity into an advantage for signal discrimination. With this method, we achieve tunable gating frequency from DC to 1 GHz. Additionally, this method enables the discrimination of weak avalanche signals, allowing effective single-photon detection at low avalanche gain. Across the entire tuning range, at a 20% detection efficiency, the dark count rate is approximately 5.0×10−7 per gate, and the afterpulse probability is less than 1.0%, significantly lower than in previous experiments. The proposed SPD exhibits low noise and a wide tunable gating frequency range, providing a reliable foundation for various applications.

The mediating role of psychological distress in the association between migration experiences and stigma and coping strategies: a cross-sectional study on Ethiopian migrant returnees

BackgroundEthiopian migrants to the Middle East and South Africa are exposed to dangerous traveling and working conditions and their experiences are mostly tragic. They are unwelcomed not only by the situation in the destination but also by the community at home which is an important indicator of stigma and discrimination. However, there is lack of evidence on how psychological distress is associated with migration experiences, stigma and coping strategies. Therefore, it was aimed to determine the mediating effect of psychological distress in the association between migration experiences and stigma and discrimination and coping strategies.MethodsA total of 739 Ethiopian migrant returnees from the Middle East and South Africa were included in the study. A cross-sectional study was conducted in five migration hotspot areas in Ethiopia, namely Addis Ababa, Dessie, Shashemene, Hossana, and Gondar. Data related to socio-demographic, economic, migration experiences, psychological distress, coping strategies, and stigma and discrimination were collected. Psychological distress was measured using the 21- item version of the Depression, Anxiety and Stress Scale and coping strategies were measured with the Brief-COPE. Structural equation modeling was employed to estimate the relationship among variables.ResultsAbout 395 (57.33%) of the participants reported symptoms of depression, 428 (59.86%) anxiety symptoms, and 313 (45.21%) stress symptoms. The mediated association between physical violence and coping strategy through psychological distress was 0.29 (adjusted β = 0.29, 95%CI: 0.15, 0.44). Denial of salary had positive direct (adjusted β = 1.00, 95%CI: 0.50, 1.50) and mediated (adjusted β = 1.20, 95%CI: 0.71, 1.68) associations with stigma and discrimination through psychological distress. Restricted freedom had positive mediated (adjusted β = 0.39, 95%CI: 0.13, 0.65) and total (adjusted β = 0.94, 95%CI: 0.59, 1.29) associations with stigma and discrimination through psychological distress.ConclusionDepression, anxiety, and stress symptoms are common among migrant returnees. Religious practice is the most commonly used coping mechanism. Psychological distress has positive mediating association with the relationships between denial of salary by employers and stigma and discrimination, physical violence and coping strategy, and restricted freedom and stigma and discrimination. Psychological interventions to deal with the psychological distress and stigma and discrimination of migrant returnees and to encourage the practice of positive coping strategies are warranted.

Tuning the Sensitivity of MoS2 Nanopores: From Labeling to Labeling-Free Detection of DNA Methylation.

DNA methylation discrimination is often challenged by complicated pretreatment, insufficient sensitivity, and suboptimal accuracy. Here, single-molecule readout of DNA methylation is reported using single-layer MoS2 nanopores. By tuning pore dimension, the sensitivity of MoS2 nanopores is manipulated, empowering both labeling and labeling-free strategies for DNA methylation discrimination. With methyl-CpG-binding domain protein 1 (MBD1)-labeled methylated DNA translocation in customized nanopores, multiple methylated sites with distance as short as 70bp in double strand DNA can be resolved. To further improve spatial resolution, small MoS2 nanopores are engineered with single-nucleotide sensitivity, realizing labeling-free methylation detection with single-nucleotide resolution to recognize two nucleotides with only one methyl difference. This study demonstrates the availability of engineered MoS2 nanopores in DNA methylation detection, underscoring their potential for epigenetic alteration research at the single-molecule level.

Exploring Collaboration and Social Dynamics in the Paediatric Morbidity and Mortality Meeting, A Qualitative Case Study.

To explore collaboration and social dynamics within paediatric Morbidity and Mortality meetings. Qualitative Exploratory Case Study Methodology incorporating semi-structured interviews and qualitative observations. Data were collected in a large quaternary paediatric hospital in Melbourne, Australia. Semi-structured interviews with meeting attendees were conducted after observing and documenting meeting conduct. Reflexive thematic analysis was used to interpret data and generate a thematic map of findings. Forty-four interviews and 32 meeting observations were conducted between July 2019 and January 2020. Participants valued collaboration between attendees in Morbidity and Mortality meetings, however there were barriers to this. They included differing views about roles and negative impacts of hierarchies and authority. Senior doctors dominated discussion, and participants described this occasionally signalling reverence and respect, but sometimes signified intimidation and feeling unsure about how to contribute. Because of this complexity, successfully achieving positive social dynamics in a meeting required active promotion and management. Morbidity and Mortality meetings mirror the complexity and richness of the clinical environment. Descriptions of how meeting conduct can shape positive workplace culture and address hierarchical obstructions to safe clinical care highlight their far-reaching potential. Effective collaboration is an intrinsic part of realising this value. This paper addresses a paucity in the literature in understanding how social dynamics and collaboration in Morbidity and Mortality meetings are interpreted and experienced. These findings illuminate challenges and obstacles to achieving a productive and equitable social dynamic in meetings. They also illustrate positive discrimination strategies that may improve participation and widespread engagement of nurses, junior doctors, and allied health professionals. Importantly, fostering constructive social dynamics in the Morbidity and Mortality meeting could positively impact patient safety culture and therefore patient care. No patient or public contribution.

DBSF-Net: Infrared Image Colorization Based on the Generative Adversarial Model with Dual-Branch Feature Extraction and Spatial-Frequency-Domain Discrimination

Thermal infrared cameras can image stably in complex scenes such as night, rain, snow, and dense fog. Still, humans are more sensitive to visual colors, so there is an urgent need to convert infrared images into color images in areas such as assisted driving. This paper studies a colorization method for infrared images based on a generative adversarial model. The proposed dual-branch feature extraction network ensures the stability of the content and structure of the generated visible light image; the proposed discrimination strategy combining spatial and frequency domain hybrid constraints effectively improves the problem of undersaturated coloring and the loss of texture details in the edge area of the generated visible light image. The comparative experiment of the public infrared visible light paired data set shows that the algorithm proposed in this paper has achieved the best performance in maintaining the consistency of the content structure of the generated image, restoring the image color distribution, and restoring the image texture details.

WSSS-CRAM: precise segmentation of histopathological images via class region activation mapping.

Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data. In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data. Specifically, we use a discriminative activation strategy to generate category-specific image activation maps via class labels. The category-specific activation maps are then post-processed using conditional random fields to obtain reliable regions that are directly used as ground-truth labels for the segmentation branch. Critically, the two steps of the pseudo-label acquisition and training segmentation model are integrated into an end-to-end model for joint training in this method. Through quantitative evaluation and visualization results, we demonstrate that the framework can predict pixel-level labels from image-level labels, and also perform well when testing images without image-level annotations. Future, we consider extending the algorithm to different pathological datasets and types of tissue images to validate its generalization capability.

Shallow water source depth discrimination based on a vertical linear array using deep learning

This study aims to explore source depth discrimination in shallow water waveguides using a vertical line array. Due to the similarity in physical characteristics of features and environmental parameters, the feature distributions of shallow and deep sources overlap in feature space. This overlap is further exacerbated by strong background noise, reducing detection reliability. Therefore, a deep learning-based source depth discrimination (DL-SDD) scheme is proposed. Specifically, this scheme is based on a residual structure, embedding channel attention mechanisms into the deep structure to eliminate noise-related information gradually. Furthermore, a specially designed loss function considers inter-class and intra-class distances to achieve compact and mutually distant distributions of source features. When this loss function is applied, the overlap of source feature distributions is suppressed in end-to-end feature learning, leading to a high detection probability. The numerical simulations demonstrate that the proposed DL-SDD outperforms traditional method, achieving a 7 % increase in detection probability and a 15 % decrease in false alarm rate, even near the discrimination depth. Furthermore, the discrimination depth is reduced by nearly half. Experimental results from the South China Sea validate the effectiveness of the proposed DL-SDD.

Perceived Weight Discrimination and General Coping Strategies.

Perceived weight discrimination is associated consistently with worse health outcomes. Coping strategies may be one mechanism of this association. The present research examined the association between perceived weight discrimination and strategies used to cope with general stress (not weight-specific) and whether these strategies accounted for part of the association with markers of health. Participants (N = 1882) completed a cross-sectional survey with a comprehensive measure of coping strategies and reported on their perceived experience of weight discrimination and markers of physical, mental, and social health. Perceived weight discrimination was associated with greater use of disengaged coping strategies (β = .19, p < .01) and was unrelated to active and support coping strategies. Disengaged coping mediated the association between weight discrimination and worse physical, mental, and social health (proportion of mediation ranged from 14 to 47%). This pathway was independent of body mass index (BMI). Individuals in the obesity weight category (BMI ≥ 30kg/m2) were less likely to use active (β = - .11, p < .01) and support (β = - .09, p < .01) coping strategies, which did not consistently mediate the association with health. Perceived experiences of weight discrimination are associated with disengaged coping strategies to manage stressful experiences, and these strategies are one mechanism that may contribute to the worse health associated with unfair treatment due to weight.

The relationship between gendered racism and binge eating symptoms among young adult U.S. Black women: Identity shifting as a mediator

Although research has illustrated that racial disparities in access to treatment for binge-eating disorder (BED) among Black women exist, little is known about the psychosocial related experiences of binge eating behaviors among Black women. Binge eating disorder is characterized by the recurrent consumption of large amounts of food within a brief period, accompanied by a loss of sense of control over the eating and distress over the eating behaviors. Past research has shown that race and gender related stressors are positively associated with emotional eating among Black young adult women and that they may engage in problem-solving coping strategies like identity shifting (conscious and unconscious alterations of thoughts, behaviors, perspective, and appearances) to manage these stressors. Considering the literature, the present study was developed to examine the mediating role of identity shifting in the relationship between gendered racism and binge eating symptoms among young adult Black women. To test this assertion, we administered an online survey to 239 Black women (Mage = 27.32). The results indicated that gender racism significantly predicted both identity shifting and binge eating and identity shifting significantly predicted binge eating. Additionally, identity shifting accounted for 34.5 % of the associations between gendered racism and binge eating symptoms. These findings highlight the importance of exploring experiences of discrimination and coping strategies when considering ways to reduce mental health concerns, such as binge eating symptoms, among young adult Black women.

A novel weighted sparse classification framework with extended discriminative dictionary for data-driven bearing fault diagnosis

Dictionary learning has emerged as an effective approach for data-driven fault diagnosis due to its strong sparse representation ability. Nevertheless, the gathered vibration signals always exhibit a time-shift phenomenon, greatly diminishing the representation capability of dictionary learning methods. Besides, in real-world industrial scenarios, where heavy noise as common features inevitably cover the discriminative features, the decline in the recognition performance of data-driven fault diagnosis methods is a critical challenge. In this paper, a novel weighted sparse classification framework with extended discriminative dictionary (WSC-EDD) is proposed. First, an extended discriminative dictionary design strategy is developed to construct generalized-domain extended dictionary model with strong representation and discrimination ability, in which a novel generalized-domain dictionary fusion method is developed to reduce the effect of time-shift phenomenon and enhance the representation ability of the dictionary. Further, generalized-domain discriminative sub-dictionaries are optimized by the K-singular value decomposition in a data-driven fashion and then the whole extension dictionary are designed adaptively. Second, a weighted optimization method is developed to highlight the contribution of non-zero elements in the correct projection region in sparse codes, in which weighted diagonal matrices are designed. Finally, a sparse recognition method is established for bearing fault classification. The experiment results on three challenging bearing datasets demonstrate that the proposed framework yields average diagnosis accuracies of 99.75%, 99.93% and 100%, respectively. Moreover, the superiority and robustness of WSC-EDD are further confirmed by comparing with several competing methods.

Occupational Health, Psychosocial Risks and Prevention Factors in Lesbian, Gay, Bisexual, Trans, Queer, Intersex, Asexual, and Other Populations: A Narrative Review

Despite advances in legislation and social attitudes, workplace discrimination against sexual and gender minority populations remains prevalent, posing significant psychosocial risks. This study aims to explore the occupational health challenges faced by LGBTQIA+ populations, examining factors of discrimination, support, and prevention strategies. Methods: A narrative review of articles from the Scopus, PubMed, and Web of Science databases was carried out, focusing on studies published between 2011 and 2024. Articles in English, Spanish, and Portuguese were considered. Results: 359 studies were identified, and 11 articles were selected for analysis. The findings reveal pervasive discrimination experienced by LGBTQIA+ workers in the workplace, impacting their mental health and job satisfaction. Factors influencing disclosure of sexual or gender identity at work were identified, including workplace characteristics and anti-discrimination policies. Conclusions: Promoting inclusive and supportive work environments is crucial to the well-being of LGBTQIA+ employees. Recommendations include strengthening anti-discrimination protections, improving education and training on LGBTQIA+ issues, and promoting explicit communication about support and equality. Despite progress, more research is needed to address gaps in understanding and effectively promote the occupational health of sexual and gender minority populations.

Robust discriminator of chiral molecules via a topological invariant.

We propose a scheme for chirality discrimination via a topological invariant. The physical model is based on a three-level subspace of a molecule. By modulating the components of the control field with proper frequencies, two different two-level effective Hamiltonians are derived for the left-handed and the right-handed molecules. We parameterize the effective Hamiltonians with two angles and demonstrate that a topological quantum phase transition can be induced by tuning the effective Rabi frequency if the molecule is right-handed. This phenomenon provides a method to discriminate the chirality of the molecule by measuring a topological invariant, i.e., the Chern number, of the parametric manifold. Since the Chern number is robust against perturbations to the system, the scheme is insensitive to the systematic errors of the control fields, the deviations of the modulation frequencies, and decoherence of the molecule. Therefore, the scheme may provide useful perspectives to construct a robust discriminator of chiral molecules.

Fluconazole-resistant Candida parapsilosis: fast detection of the Y132F ERG11p substitution, and a proposed microsatellite genotyping scheme

ObjectivesWe propose fast and accurate molecular detection of the Y132F ERG11p substitution directly on pure-cultured Candida parapsilosis isolates. We also assessed a discriminative genotyping scheme to track circulating genotypes. MethodsA total of 223 C. parapsilosis isolates (one patient each) from 20 hospitals, located in Spain and Italy were selected. Isolates were fluconazole-resistant (n = 94; harbouring the Y132F ERG11p substitution [n = 85], the G458S substitution [n = 6], the R398I substitution [n = 2], or the wild-type ERG11 gene sequence) or fluconazole-susceptible (n = 129). Two targeted-A395T-mutation PCR formats (conventional and real-time) were engineered and optimized on fluconazole-susceptible and fluconazole-resistant pure-cultured isolates, thus skipping DNA extraction. Two genotyping schemes were compared: Scheme 1 (CP1, CP4a, CP6, and B markers), and Scheme 2 (6A, 6B, 6C, CP1, CP4a, and CP6 markers). ResultsThe screening performed using both PCR formats showed 100% specificity (fluconazole-susceptible isolates; n = 129/129) and sensitivity (Y132F isolates; n = 85/85) values; however, results were available in 3 and 1.5 hours with the conventional and real-time PCR formats, respectively. Overall, Scheme 1 showed higher genetic diversity than Scheme 2, as shown by the number of alleles detected (n = 98; mean 23, range 13–38), the significantly higher observed and expected heterozygosity, and the probability of identity index (2.5 × 10−6). Scheme 2 markers did not provide further genotypic discrimination of Y132F fluconazole-resistant genotypes. ConclusionBoth proposed PCR formats allow us to speed up the accurate detection of substitution Y132F ERG11p in C. parapsilosis isolates with 100% specificity and sensitivity. In addition, we recommend CP1, CP4a, CP6, and B microsatellite markers for genotyping fluconazole-resistant isolates.

Co-creation in a duopoly considering the effect of product return

Co-creation has become popular in experience good markets as it can resolve the uncertainty of the mismatch between product attributes and customer preferences. This paper delves into an examination of how firms strategize product co-creation with their customers in a competitive duopoly context, all the while taking into account the potential product return stemming from mismatch uncertainty. Results show that when the launching cost is moderate and the customer’s participation cost is relatively small, an asymmetric equilibrium with one firm launching the project and the other not is reached; otherwise, both firms adopt co-creation or not, conditional on the launching cost. Besides, firms may fall into a prisoner’s dilemma in which both firms launch the project when the launching cost is moderate. Furthermore, it is shown that co-creation functions as an effective way for firms to escape the Bertrand trap and reap the benefits of the product’s high valuation. Additionally, there is a mutual influence between co-creation and product return. A higher product return cost incentivizes firms to embrace co-creation, but meanwhile, it makes them more susceptible to falling into a prisoner’s dilemma scenario. In a state of equilibrium, co-creation consistently diminishes the product return for the firm engaging in it. Particularly, the firm not providing co-creation can take a free ride on the competitor’s co-creation benefits in reducing product return when the return cost is relatively large. Lastly, the value of co-creation can be amplified through the implementation of a price discrimination strategy, particularly when the participation cost is relatively large.

RADDA-Net: Residual attention-based dual discriminator adversarial network for surface defect detection

Surface defect detection is a critical yet challenging task in the product production process, mainly because of the complex background texture on the material surface, the large intra-class defect differences, and the weak texture defect regions. To address this problem, a Residual Attention-based Dual Discriminator Adversarial Network (RADDA-Net) is developed to accurately detect various types of texture defect regions. Specifically, a residual network-based feature extraction module integrating multi-channel attention mechanism and multi-scale convolutional layers is designed to improve the detection ability of abnormal defects. Besides, to deeply excavate material surface texture information, the pixel attention-based up-sampling blocks are also incorporated into RADDA-Net to enhance the extraction of shallow features. To further improve the detection performance of RADDA-Net, a dual discriminant adversarial training strategy and a comprehensive measurement method are introduced to assist the developed network to identify defects from both macroscopic structure and microscopic edges. Finally, the six evaluation indexes of our proposed method outperformed other state-of-the-art methods on the four defect datasets, among which the highest detection precision, recall, and F-measure are 0.926, 0.916, 0.920, respectively. Therefore, the experimental results consistently verify the detection accuracy and effectiveness of RADDA-Net.

The Distributed Multi Constrained Channel Access Mechanism By Using Framework For Mobile Adhoc Network

The Mobile Ad hoc Network (MANET) is an autonomous wireless communication system, and does not have any fixed infrastructure. The Network loads which arise due to application evolving using MANET environment has to balance using routing protocol. Networks are increasingly resource intensive to efficiently manage the bandwidth &amp; Energy in the group communication on basis of addressing the routing issues to provide the enhanced functionality of the network on basis of channel utilization and channel conditioning against load balancing. In this research, proposed a novel unified framework for Distributed multi constrained Channel Access Mechanism for mobile Adhoc Network on inclusion of Scheduled Throughput Discriminant Multi-Data synchronization Strategies for Channel Allocation in Mobile Adhoc Network on various traffic patterns, Multi Trajectory Particle Swarm Optimization towards Optimal Velocity Control against load balancing and band width controlling and finally Self Adaptive Redundancy Elimination Clustering and Distributed Load Bandwidth Management Constraints for Mobile Ad Hoc Network.

Towards a Novel Generative Adversarial Network-Based Framework for Remote Sensing Image Demosaicking

During satellite remote sensing imaging, the use of Bayer mode sensors holds significant importance in saving airborne computing resources and reducing the burden of satellite transmission systems. The demosaicing techniques play a key role in this process. The integration of Generative Adversarial Networks (GANs) has garnered significant interest in the realm of image demosaicking, owing to their ability to generate intricate details. However, when demosaicing mosaic images in remote sensing techniques, GANs, although capable of generating rich details, often introduce unpleasant artifacts while generating content. To address this challenge and differentiate between undesirable artifacts and realistic details, we have devised a novel framework based on a Progressive Discrimination Strategy within a Generative Adversarial Network architecture for image demosaicking. Our approach incorporates an artifact-weighted Location Map refinement technique to guide the optimization process towards generating authentic details in a stable and precise manner. Furthermore, our framework integrates a global attention mechanism to boost the interaction of spatial-channel information across different dimensions, thereby enhancing the performance of the generator network. Moreover, we conduct a comparative analysis of various prevalent attention mechanisms in the context of remote sensing image demosaicking. The experimental findings unequivocally demonstrate that our proposed methodology not only achieves superior reconstruction accuracy on the dataset but also enhances the perceptual quality of the generated images. By effectively mitigating artifacts and emphasizing the generation of true details, our approach represents a significant advancement in the field of remote sensing image demosaicking, promising enhanced visual fidelity and realism in reconstructed images.

Accurately recognizing chromium species with multi-functionalized nano Au-based sensor array

High-resolution identification of chromium (Cr) species, especially various organic-Cr complexes, in a convenient and economically-feasible manner is the prerequisite for achieving the advanced treatment of chromium wastewater. To this end, a colorimetric nano-Au sensor array was developed by taking advantage of the UV-spectra shift of gold nanoparticles (Au NPs) upon interaction with Cr species; specifically, four molecular modifiers [i.e., iminodiacetic acid (IDA), tripolyphosphate (TPP), cetyltrimethylammonium bromide (CTAB), and 1,5-diphenylcarbazide (DPC)] were intentionally employed for assembling nano-Au array receptors, which showed respective responses toward different Cr species through the formation of coordination, hydrophobic interaction, electrostatic attraction, and redox reaction, respectively; the “fingerprint” differences of the unique optical properties were then integrated for semi-quantitatively recognizing Cr species by pattern recognition techniques. Eleven ubiquitous Cr species [i.e., Cr(III), Cr(VI), and various Cr(III)-organic complexes] served as the model samples, which could be sensitively identified, no matter in individual or mixture mode, by the developed nano-Au sensor array on the basis of the colorimetric responses resulted from diverse nano-Au-aggregation behaviors, with excellent anti-interference ability in the simulated or actual water scenario. Attractively, the nano-Au sensor array can achieve very sensitive detection limit of the quantitative analyses of Cr species in a prompt in-situ manner, which usually requires a two-step process of separation and detection for the conventional analytical methods. Such a convenient strategy of Cr species discrimination conduces to rationally designing specific protocols for the advanced treatment of chromium wastewater.

Detection of sheep butter adulteration with cow butter and margarine by employing Raman spectroscopy and multivariate data analysis

The adulteration of sheep butter with cow butter and margarine is a significant challenge in the food industry, impacting product authenticity and consumer confidence. In this research article, a comprehensive and accurate approach is proposed for detecting sheep butter adulteration by employing Raman spectroscopy in conjunction with the combined method of DD-SIMCA and PLS-DA.Initially, Raman spectroscopy employed to collect spectral data from pure butter and margarine samples, enabling the modeling of target objects by DD-SIMCA to exclude adulterated samples. Subsequently, PLS-DA was utilized for the discrimination of sample classes.Raman spectroscopy, combined with the classification and discrimination strategy, is a powerful tool for quickly and non-destructively detecting sheep butter adulteration, ensuring food quality control. The combined strategy successfully discriminated between pure samples, achieving high performances based on the figures of merit (sensitivity: 77.78–100% and specificity: 88.23–100%) for detecting and identifying the type of adulteration in sheep butter. These outcomes underscore the efficacy of the proposed approach in not only detecting but also identifying the specific type of adulteration present in sheep butter, thereby addressing a critical need in the food industry. This research contributes significantly to advancing the field of food quality assurance and holds great implications for support consumer confidence in product authenticity.

Two-stage residual networks for damage identification and location of stiffened composite panel based on guided waves

The damage detection of the stiffened composite panel, as a typical aircraft structure, is a research hotspot in Structural Health Monitoring (SHM). where guided waves propagate with multi-modal and dispersion characteristics. The traditional damage detection method manually extracts the potential discriminative features of the signal to achieve damage identification, depending on expert experience. In this paper, we propose a two-stage residual networks (ResNets) framework based on guided waves to locate damage in the stiffened composite panel, which automatically mines the high-dimensional features with sensitive discriminant information. The guided wave signal acquisition system collects four types of data: health data, stringer damage data, damage data on the skin of the stringer-side, and damage data on the skin-side. The first-stage utilizes a ResNet to classify the structure condition, while in the second-stage, three separate ResNets are employed to locate the damage according to the classification results of the first-stage. The experimental results show that the accuracy of the first-stage damage classification and the damage localization of the stringer and the skin of the stringer-side in the second-stage has reached 100%, and that of the skin-side is 99.13%, which significantly outperforms single-stage methods. This strategy of inter-class discrimination and intra-class precise localization of damage can not only identify the damaged regions but also determine the specific location of the damage, which greatly increases the performance of SHM. The present two-stage method is a potential solution for future SHM strategies and further investigation is warranted.