Difficulties in recognizing objects in underwater video recordings stem from the subpar quality of the videos, specifically the presence of blurriness and low contrast. The application of Yolo series models to the detection of objects in underwater video has seen substantial growth in recent years. These models, while effective in other contexts, underperform on underwater video footage that lacks clarity and contrast. Additionally, the frame-level results' interdependencies are not taken into consideration in these analyses. For the purpose of resolving these problems, we present a video object detection model, UWV-Yolox. To bolster underwater video, the Contrast Limited Adaptive Histogram Equalization method is implemented, firstly. Subsequently, a novel CSP CA module is introduced, integrating Coordinate Attention into the model's core architecture to enhance the representations of targeted objects. Next, a loss function is proposed that incorporates regression and jitter losses. To conclude, a frame-level optimization module is introduced, leveraging the relationship between consecutive frames in video data to enhance the precision of object detection in video streams. Our model's performance is assessed by constructing experiments on the UVODD dataset, the details of which are given in the corresponding paper, and mAP@0.05 is chosen as the assessment measure. The original Yolox model is outperformed by the UWV-Yolox model, the latter having an mAP@05 score of 890%, an improvement of 32%. Compared to other object detection models, the UWV-Yolox model exhibits more reliable object predictions, and our modifications are readily adaptable to other models as well.
Optic fiber sensors, with their strengths in high sensitivity, superior spatial resolution, and small size, have contributed significantly to the growing field of distributed structure health monitoring. Still, the practical limitations in installing and maintaining the dependability of fiber optic components have become a critical issue for this technology's widespread application. A textile-based fiber optic sensing system, along with a novel installation procedure for bridge girders, is introduced in this paper to mitigate deficiencies in existing fiber optic sensing technologies. Biofilter salt acclimatization To monitor the distribution of strain within the Grist Mill Bridge, situated in Maine, a sensing textile was employed, relying on Brillouin Optical Time Domain Analysis (BOTDA). Installation in tight bridge girders was streamlined by the creation of a modified slider, improving efficiency. Loading tests, utilizing four trucks on the bridge, yielded a successful strain response recording of the bridge girder's strain by the sensing textile. Quarfloxin inhibitor The fabric sensor displayed a capacity to differentiate between various loading points. These findings unveil a novel method for installing fiber optic sensors, highlighting the potential of fiber optic sensing textiles in structural health monitoring applications.
The use of off-the-shelf CMOS cameras in cosmic ray detection is a subject examined in this paper. The constraints of current hardware and software are discussed and shown in their application to this objective. A hardware solution for sustained testing of algorithms, intended for the detection of potential cosmic rays, is presented. A novel algorithm, which we have developed, implemented, and rigorously tested, facilitates real-time image frame processing from CMOS cameras, thereby enabling the detection of potential particle tracks. We contrasted our outcomes with previously reported results and obtained acceptable outcomes, effectively overcoming some restrictions of existing algorithms. Access to both the source code and the data is available for download.
Work productivity and well-being are inextricably linked to thermal comfort. Thermal comfort for humans indoors is mostly governed by the performance of the HVAC (heating, ventilation, and air conditioning) systems. Frequently, the thermal comfort control metrics and measurements in HVAC systems are insufficiently detailed and use limited parameters, thereby preventing accurate regulation of thermal comfort in indoor environments. The responsiveness of traditional comfort models to individual demands and sensory nuances is significantly constrained. This research's data-driven thermal comfort model was developed to improve the overall thermal comfort for occupants currently present in office buildings. An architecture structured on the principles of cyber-physical systems (CPS) is employed to achieve these targets. The construction of a simulation model aids in simulating the behaviors of multiple occupants in an open-plan office building. Computational time is reasonable, according to the results, for a hybrid model accurately predicting occupants' thermal comfort levels. This model effectively increases occupant thermal comfort by an impressive 4341% to 6993%, yet maintains or reduces energy use slightly, from 101% to 363%. The potential for implementing this strategy in real-world building automation systems is dependent upon the strategic placement of sensors in modern buildings.
Peripheral nerve tension, a factor in neuropathy's pathophysiology, presents a challenge for clinical assessment. To automatically assess tibial nerve tension via B-mode ultrasound imaging, we aimed to develop a novel deep learning algorithm in this study. DNA-based medicine The algorithm was constructed using a dataset of 204 ultrasound images of the tibial nerve in three positions, encompassing maximum dorsiflexion, -10 and -20 degrees of plantar flexion from the maximum dorsiflexion position. Sixty-eight healthy volunteers, without any abnormalities in their lower limbs during the testing phase, had their images captured. The U-Net model was used to automatically extract 163 cases from the dataset, which had undergone prior manual segmentation of the tibial nerve in all images. Furthermore, a convolutional neural network (CNN) classification procedure was undertaken to ascertain each ankle's position. The testing dataset of 41 data points underwent five-fold cross-validation to validate the automatic classification process. The most accurate mean segmentation, at 0.92, was accomplished via manual methods. Five-fold cross-validation revealed that the mean accuracy of automatic tibial nerve identification at differing ankle locations was over 0.77. Employing ultrasound imaging analysis with U-Net and CNN algorithms, the tension of the tibial nerve can be accurately evaluated at different dorsiflexion angles.
When reconstructing single images at a higher resolution, GANs yield image textures that are congruent with human visual sensibilities. Nevertheless, the process of reconstruction frequently introduces spurious textures, artificial details, and substantial discrepancies in fine-grained features between the recreated image and the original data. To achieve higher visual quality, we explore the feature correlation patterns between adjacent layers, and present a differential value dense residual network as a remedy. Using a deconvolution layer, we first enlarge the features, then we extract the features using a convolution layer, and finally we calculate the difference between the expanded and extracted features, which will highlight the regions of interest. Differential value accuracy is improved by using dense residual connections in each layer of the extraction process, yielding more complete magnified features. Introducing the joint loss function next, high-frequency and low-frequency information are fused, contributing to a certain improvement in the visual characteristics of the reconstructed image. The Set5, Set14, BSD100, and Urban datasets reveal that our DVDR-SRGAN model surpasses Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models in terms of PSNR, SSIM, and LPIPS metrics.
Smart factories and the industrial Internet of Things (IIoT) now leverage intelligence and big data analytics for their extensive decision-making processes. Nonetheless, this technique encounters crucial obstacles in computation and data processing, brought about by the complexity and heterogeneity within large datasets. To ensure optimal production, predict future market outlooks, and successfully avert and handle risks, smart factory systems predominantly depend on the results of analysis. Nevertheless, the application of conventional solutions, including machine learning, cloud computing, and artificial intelligence, has proven insufficient. Novel solutions are essential for the long-term viability of smart factory systems and industries. Differently, the accelerating growth of quantum information systems (QISs) is motivating multiple sectors to study the advantages and disadvantages of implementing quantum-based processing solutions, aiming for exponentially faster and more efficient processing times. We investigate, within this paper, the utilization of quantum methods for dependable and sustainable IIoT-driven smart factory advancement. We spotlight various IIoT applications, demonstrating the potential for quantum algorithms to optimize scalability and productivity. Significantly, a universal system model is conceived for smart factories. In this model, quantum computers are not required. Quantum cloud servers, supplemented by quantum terminals at the edge layer, execute the desired quantum algorithms without requiring expertise. Two case studies drawn from real-world situations were used to evaluate and confirm the efficacy of our model. The analysis spotlights the beneficial application of quantum solutions throughout various smart factory sectors.
Tower cranes, while vital for large-scale construction projects, can pose significant safety risks due to the potential for collisions with nearby equipment or personnel on the site. A crucial step in mitigating these issues is gaining immediate and precise knowledge of the location and orientation of both tower cranes and their lifting hooks. Computer vision-based (CVB) technology, being a non-invasive sensing method, is widely deployed on construction sites for the purpose of object detection and the precise determination of their three-dimensional (3D) locations.