Search Results (29)

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article

Background: Malnutrition may increase the risk of frailty in individuals with musculoskeletal pain. However, this scenario has not been explored in detail. As such, the present study was conducted to examine the effects of malnutrition on the risk of incident and worsening frailty in community-dwelling older adults with musculoskeletal pain. Methods: Data from 895 community-dwelling older adults participating in the Toledo Study of Healthy Ageing who reported experiencing musculoskeletal pain during the month preceding data collection (mean age: 74.9 ± 5.6 years) were analyzed. Pain characteristics (i.e., intensity, locations, and treatment) were assessed based on self-reported information regarding the last month. Malnutrition was operationalized according to the GLIM criteria. Frailty status was assessed at baseline and at follow-up (~2.99 years), according to the Frailty Phenotype paradigm, operationalized through the Frailty Trait Scale 5. Associations between the variables were tested using logistic regression analyses adjusted for many covariates established a priori. Results: Malnutrition increased the risk of frailty (odds ratio [OR] = 4.41) and worsening of frailty status (OR = 6.25) in the participants who used ≥2 groups of painkillers in comparison to their non-undernourished peers. Conclusions: The findings of the present study indicate that malnutrition increases the risk of both developing and worsening frailty in older adults with musculoskeletal disorders. In particular, an increased risk of incident frailty and worsening frailty status was found in undernourished individuals using ≥2 analgesic drugs. Our results suggest that nutritional assessment should be included in the evaluation of old people living with musculoskeletal pain. Full article

The Transformer model has received significant attention in Human Activity Recognition (HAR) due to its self-attention mechanism that captures long dependencies in time series. However, for Inertial Measurement Unit (IMU) sensor time-series signals, the Transformer model does not effectively utilize the a priori information of strong complex temporal correlations. Therefore, we proposed using multi-layer convolutional layers as a Convolutional Feature Extractor Block (CFEB). CFEB enables the Transformer model to leverage both local and global time series features for activity classification. Meanwhile, the absolute position embedding (APE) in existing Transformer models cannot accurately represent the distance relationship between individuals at different time points. To further explore positional correlations in temporal signals, this paper introduces the Vector-based Relative Position Embedding (vRPE), aiming to provide more relative temporal position information within sensor signals for the Transformer model. Combining these innovations, we conduct extensive experiments on three HAR benchmark datasets: KU-HAR, UniMiB SHAR, and USC-HAD. Experimental results demonstrate that our proposed enhancement scheme substantially elevates the performance of the Transformer model in HAR. Full article

ARINC 653 Part 1 Supplement 5 (ARINC 653P1-5) provides temporal partitioning capabilities for real-time applications running on the multicore processors in Integrated Modular Avionics (IMAs) systems. However, it is difficult to schedule a set of ARINC 653 multicore partitions to achieve a minimum processor occupancy. This paper proposes a model-based optimization method for ARINC 653 multicore partition scheduling. The IMA multicore processing system is modeled as a network of timed automata in UPPAAL. A parallel genetic algorithm is employed to explore the solution space of the IMA system. Owing to a lack of priori information for the system model, the configuration of genetic operators is self-adaptively controlled by a Q-learning algorithm. During the evolution, each individual in a population is evaluated independently by compositional model checking, which verifies each partition in the IMA system and combines all the schedulability results to form a global fitness evaluation. The experiments show that our model-based method outperforms the traditional analytical methods when handling the same task loads in the ARINC 653 multicore partitions, while alleviating the state space explosion of model checking via parallelization acceleration. Full article

Shared market opinions and beliefs by market participants generate a set of constraints that mediate information through a not-so-unstable system of expected target prices. Price trajectories, within these sets of constraints, confirm or disprove the likelihood of participant expectations and cannot, de facto, be considered permutable, as literature has shown, since their inner structure is dynamically affected by their own progress, suggesting per se the presence of both heat and cycles. This study described and discussed how trajectories are built using different alphabets and suggests that prices follow an ergodic course within structurally similar tessellation classes. It is reported that the courses of price moves are self-similar due to their a priori structure, and they do not need to be complete in order to create the conditions, in resembling conditions, for the appearance of the well-known and commonly used Fibonacci ratios between price trajectories. To date, financial models and engineering are mostly based on the mathematics of randomness. If these theoretical findings need empirical validation, such a potential infrastructure of ratios would suggest the possibility for a superstructure to exist, in other words, the emergence of exploitable patterns. Full article

Currently, the integration of the Global Navigation Satellite System (GNSS), Ultra-Wideband (UWB), and Inertial Navigation System (INS) has become a reliable positioning method for outdoor dynamic vehicular and airborne applications, enabling high-precision and continuous positioning in complex environments. However, environmental interference and limitations of single positioning sources pose challenges. Especially in areas with limited access to satellites and UWB base stations, loosely coupled frameworks for GNSS/INS and UWB/INS are insufficient to support robust estimation. Furthermore, within a tightly coupled framework, parameter estimations from different sources can interfere with each other, and errors in computation can easily contaminate the entire positioning estimator. To balance robustness and stability in integrated positioning, this paper proposes a comprehensive quality control method. This method is based on the semi-tightly coupled concept, utilizing the INS position information and considering the dilution of precision (DOP) skillfully to achieve complementary advantages in GNSS/UWB/INS integrated positioning. In this research, reliable position and variance information obtained by INS are utilized to provide a priori references for a robust estimation of the original data from GNSS and UWB, achieving finer robustness without increasing system coupling, which fully demonstrates the advantages of semi-tight integration. Based on self-collected data, the effectiveness and superiority of the proposed quality control strategy are validated under severely occluded environments. The experimental results demonstrate that the semi-tightly coupled robust estimation method proposed in this paper is capable of accurately identifying gross errors in GNSS and UWB observation data, and it has a significant effect on improving positioning accuracy and smoothing trajectories. Additionally, based on the judgment of the DOP, this method can ensure the output of continuous and reliable positioning results in complex and variable environments. Verified by actual data, under the conditions of severe sky occlusion and NLOS (Non-Line-of-Sight), compared with the loosely coupled GNSS/INS, the positioning accuracy in the E, N, U directions of the semi-tight coupled GNSS/INS proposed in this paper has improved by 37%, 46%, and 28%. Compared with the loosely coupled UWB/INS, the accuracy in the E and N directions of the semi-tight coupled UWB/INS has improved by 60% and 34%. In such environments, GNSS employs the RTD (Real-Time Differential) algorithm, UWB utilizes the two-dimensional plane-positioning algorithm, and the positioning accuracy of the semi-tight coupled robust model of GNSS/UWB/INS in the E, N, U directions is 0.42 m, 0.55 m, and 3.20 m respectively. Full article

Background: More is to be explored between dietary patterns and sleep quality in the Chinese adult population. Methods: A cross-sectional study including 7987 Shanghai suburban adults aged 20–74 years was conducted. Dietary information was obtained using a validated food frequency questionnaire. Adherence to a priori dietary patterns, such as the Chinese Healthy Eating Index (CHEI), Dietary Approaches to Stop Hypertension (DASH) diet and Mediterranean diet (MD), was assessed. Sleep quality was assessed from self-reported responses to the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Logistic regression models adjusting for confounders were employed to examine the associations. Results: The overall prevalence of poor sleep (PSQI score ≥ 5) was 28.46%. Factor analysis demonstrated four a posteriori dietary patterns. Participants with a higher CHEI (OR_{Q4 vs. Q1}: 0.81, 95% CI: 0.70–0.95), DASH (OR_{Q4 vs. Q1}: 0.70, 95% CI: 0.60–0.82) or MD (OR_{Q4 vs. Q1}: 0.75, 95% CI: 0.64–0.87) had a lower poor sleep prevalence, while participants with a higher “Beverages” score had a higher poor sleep prevalence (OR_{Q4 vs. Q1}: 1.18, 95% CI: 1.02–1.27). Conclusions: In Shanghai suburban adults, healthier dietary patterns and lower consumption of beverages were associated with better sleep quality. Full article

Hispanic cancer survivors face unique barriers to meeting American Cancer Society (ACS) nutrition and physical activity guidelines, which reduce the risk of cancer recurrence and mortality and improve quality of life. This pilot intervention trial evaluated the feasibility and acceptability of a two-week ACS guideline-based nutrition and physical activity text message intervention in a predominantly Hispanic sample of cancer survivors and their informal caregivers. A mixed methods approach was used to assess feasibility and acceptability of the intervention. Feasibility and acceptability were measured by meeting a-priori cut-offs of >80% for recruitment, retention, and text message response rate. Participants also completed a semi-structured exit interview by telephone that assessed intervention components. Thirteen cancer survivors and six caregivers (n = 19) participated in this pilot study; 78% self-identified as Hispanic. Mean time since treatment completion for survivors was 11.9 years (SD 8.4), and 67% had breast cancer. Cancer survivors had a higher acceptability rate for physical activity (94%) compared to nutrition messages (86%), whereas equal acceptability rates were observed for both types of messages among caregivers (91%). Texting interventions are a feasible, acceptable, and a cost-effective strategy that have the potential to promote lifestyle behavior change among Hispanic cancer survivors and caregivers. Full article

Background: Management of cleft lip and palate is interdisciplinary. An evidence-mapping approach was envisaged to highlight the existing gaps in this field, using only the highest level of evidence. Objectives: To conduct evidence mapping and quality analysis of systematic reviews and meta-analyses related to any aspect of cleft lip and palate. Search Methods: The cleft lip and palate field was divided into 9 domains and 50 subdomains and a method of categorization of systematic reviews was established. A comprehensive search strategy was carried out in seven databases along with the search of gray literature and references of included articles. Selection criteria: Systematic reviews related to any aspect of cleft lip and palate, conducted by a minimum of two reviewers, with a comprehensive search strategy and adequate quality analysis were included. Data collection and analysis: A self-designed, pre-piloted data-extraction sheet was used to collect information that was analyzed through an expert group discussion. Quality analysis was performed using ROBIS-I, AMSTAR 2, and the PRISMA checklist. Results: A total of 144 systematic reviews published between 2008 and 2022 were included. The largest number of these could be categorized in the therapeutic domain (n = 58). A total of 27% of the studies were categorized as inconclusive, 40% as partially conclusive, and 33% as conclusive. As per ROBIS-I, 77% of reviews had high risk of bias while 58% were graded as critically low in quality as per AMSTAR 2. The majority of systematic reviews showed low reporting errors. Conclusions: The majority of systematic reviews related to cleft lip and palate relate to therapeutic and prognostic domains and show high risk of bias and critically low quality regardless of the source journal. The results of this paper might serve as a starting point encouraging authors to carry out high-quality research where evidence is lacking. Registration: A multidisciplinary expert-group formulated an a priori protocol, registered in Open Science Framework (DOI 10.17605/OSF.IO/NQDV2). Full article

To solve the problem of anomaly detection in annular metal turning surfaces, this paper develops an anomaly detection algorithm based on a priori information and a multi-scale self-referencing template by combining the imaging characteristics of annular workpieces. First, the annular metal turning surface is unfolded into a rectangular expanded image using bilinear interpolation to facilitate subsequent algorithm development. Second, the grayscale information from the positive samples is used to obtain the a priori information, and a multi-scale self-referencing template method is used to obtain its own multi-scale information. Then, the phase error and large-size anomaly interference problems of the self-referencing method are overcome by combining the a priori information with its own information, and an accurate response to anomalous regions of various sizes is realized. Finally, the segmentation completeness of the anomalous region is improved by utilizing the region growing method. The experimental results show that the proposed method achieves a mean pixel AUROC of 0.977, and the mean M_IOU of segmentation reaches 0.788. In terms of efficiency, this method is also much more efficient than the commonly used anomaly detection algorithms. The proposed method can achieve rapid and accurate detection of defects in annular metal turning surfaces and has good industrial application value. Full article

The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to accurately identify the foreign objects of underground belt conveyors due to the influence of fog, high-speed operation, and obscuration, the coal mine belt conveyor foreign object recognition method of improved YOLOv5 algorithm with defogging and deblurring was proposed. In order to improve the clarity of the monitoring video of the belt conveyor, the dark channel priori defogging algorithm is applied to reduce the impact of fog on the clarity of the monitoring video, and the image is sharpened by user-defined convolution method to reduce the blurring effect on the image in high-speed operation condition. In order to improve the precision of foreign object identification, the convolution block attention module is used to improve the feature expression ability of the foreign object in the complex background. Through adaptive spatial feature fusion, the multi-layer feature information of the foreign object image is more fully fused so as to achieve the goal of accurate recognition of foreign objects. In order to verify the recognition effect of the improved YOLOv5 algorithm, a comparative test is conducted with self-built data set and a public data set. The results show that the performance of the improved YOLOv5 algorithm is better than SSD, YOLOv3, and YOLOv5. The belt conveyor monitoring video of resolution for 1920 × 1080 in Huangling Coal Mine is used for identification verification, the recognition accuracy can reach 95.09%, and the recognition frame rate is 56.50 FPS. The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment. Full article

The diversity and non-uniformity of the positioning systems available in maritime navigation systems often impede the watchkeeping officer in the selection of the appropriate positioning system, in particular, in restricted basins. Thus, it is necessary to introduce a mathematical apparatus to suggest, in an automated manner, which of the available systems should be used at the given moment of a sea trip. Proper selection of the positioning system is particularly important in integrated navigation systems, in which the excess of navigation information may impede the final determinations. In this article, the authors propose the use of the decision-robustness function to assist in the process of selecting the appropriate positioning system and reduce the impact of navigation observations encumbered with large errors in self-positioning accuracy. The authors present a mathematical apparatus describing the decision function (a priori object), with the determination of decision-assistance criteria, and the robustness function (a posteriori object), with different types of attenuation function. In addition, the authors present a computer application integrating both objects in the decision-robustness function. The study was concluded by a test showing the practical application of the decision-robustness function proposed in the title. Full article

Subjective neighborhood perceptions (such as attachment or satisfaction) have been linked to demographic factors and self-reported living conditions. There has been less success to include census-based variables. One explanation is the frequent a priori application of rigid neighborhood definitions. We assessed subjective neighborhood relations, demographic information, and self-defined neighborhoods via a postcard-based, participatory GIS approach. We linked several census-based variables (e.g., the proportion of seniors or the average members per household) to four different neighborhood definitions. We found that census-based variables allowed no prediction of neighborhood relations when adjusted to statistical districts, and a limited prediction when adjusted to two different-sized buffers. We found the best prediction of neighborhood relations through census-based variables when they were adjusted to self-defined neighborhoods. Larger households, fewer households per building, and a higher proportion of seniors benefited neighborhood relations. Our findings underline the importance of adjusting the definition of ‘neighborhood’ to that of the residents when studying neighborhood attachment or sense of community. Full article

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar has received a lot of attention due to the advantages of waveform diversity. Suppression of mainlobe deceptive jamming can be effectively achieved with the degree of freedom (DOF) in the range domain. However, the existing research mainly focuses on non-coherent signals. The echo signal of VHF-FDA-MIMO radar for low elevation has its own unique characteristics. False targets cannot be suppressed with conventional beamforming methods. Thus, a signal model for VHF-FDA-MIMO radar subjected to deceptive jamming is established. The reconstruction of the covariance matrix and the estimation of the steering vector are implemented with the generalized MUSIC algorithm. In addition, a matching Capon reconstruction method is proposed to finish the robust suppression of false targets for the problem of self-cancellation in the absence of a priori information. Finally, the beampattern and performance curves of different methods are compared. The simulation results show that the methods can be effectively applied to the suppression of deceptive jamming in VHF-FDA-MIMO radar under the multipath effect. Full article

In recent years, the development of deep learning-based remaining useful life (RUL) prediction methods of bearings has flourished because of their high accuracy, easy implementation, and lack of reliance on a priori knowledge. However, there are two challenging issues concerning the prediction accuracy of existing methods. The run-to-failure sequential data and its RUL labels are almost inaccessible in real-world scenarios. Meanwhile, the existing models usually capture the general degradation trend of bearings while ignoring the local information, which restricts the model performance. To tackle the aforementioned problems, we propose a novel health indicator derived from the original vibration signals by combining principal components analysis with Euclidean distance metric, which was motivated by the desire to resolve the dependency on RUL labels. Then, we design a novel self-attention augmented convolution GRU network (SACGNet) to predict the RUL. Combining a self-attention mechanism with a convolution framework can both adaptively assign greater weights to more important information and focus on local information. Furthermore, Gated Recurrent Units are used to parse the long-term dependencies in weighted features such that SACGNet can utilize the important weighted features and focus on local features to improve the prognostic accuracy. The experimental results on the PHM 2012 Challenge dataset and the XJTU-SY bearing dataset have demonstrated that our proposed method is superior to the state of the art. Full article