KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin

Whole-genome sequencing of <em>Oryza brachyantha</em> reveals mechanisms underlying <em>Oryza</em> genome evolution

The wild species of the genus Oryza contain a largely untapped reservoir of agronomically important genes for rice improvement. Here we report the 261-Mb de novo assembled genome sequence of Oryza brachyantha. Low activity of long-terminal repeat retrotransposons and massive internal deletions of ancient long-terminal repeat elements lead to the compact genome of Oryza brachyantha. We model 32,038 protein-coding genes in the Oryza brachyantha genome, of which only 70% are located in collinear positions in comparison with the rice genome. Analysing breakpoints of non-collinear genes suggests that double-strand break repair through non-homologous end joining has an important role in gene movement and erosion of collinearity in the Oryza genomes. Transition of euchromatin to heterochromatin in the rice genome is accompanied by segmental and tandem duplications, further expanded by transposable element insertions. The high-quality reference genome sequence of Oryza brachyantha provides an important resource for functional and evolutionary studies in the genus Oryza

Multidirectional effects of Sr-, Mg-, and Si-containing bioceramic coatings with high bonding strength on inflammation, osteoclastogenesis, and osteogenesis

Ideal coating materials for implants should be able to induce excellent osseointegration, which requires several important parameters, such as good bonding strength, limited inflammatory reaction, balanced osteoclastogenesis and osteogenesis, to gain well-functioning coated implants with long-term life span after implantation. Bioactive elements, like Sr, Mg and Si, have been found to play important roles in regulating the biological responses. It is of great interest to combine bioactive elements for developing bioactive coatings on Ti-6Al-4V orthopedic implants to elicit multidirectional effects on the osseointegration. In this study, Sr, Mg and Si-containing bioactive Sr2MgSi2O7 (SMS) ceramic coatings on Ti-6Al-4V were successfully prepared by plasma-spray coating method. The prepared SMS coatings have significantly higher bonding strength (~37MPa) than conventional pure hydroxyapatite (HA) coatings (mostly in the range of 15-25 MPa). It was also found that the prepared SMS coatings switch the macrophage phenotype into M2 extreme, inhibiting the inflammatory reaction via the inhibition of Wnt5A/Ca2+ and Toll-like receptor (TLR) pathways of macrophages. In addition, the osteoclastic activities were also inhibited by SMS coatings. The expression of osteoclastogenesis related genes (RANKL and MCSF) in bone marrow derived mesenchymal cells (BMSCs) with the involvement of macrophages was decreased, while OPG expression was enhanced on SMS coatings compared to HA coatings, indicating that SMS coatings also downregulated the osteoclastogenesis. However, the osteogenic differentiation of BMSCs with the involvement of macrophages was comparable between SMS and HA coatings. Therefore, the prepared SMS coatings showed multidirectional effects, such as improving bonding strength, reducing inflammatory reaction and downregulating osteoclastic activities, but maintaining a comparable osteogenesis, as compared with HA coatings. The combination of bioactive elements of Sr, Mg and Si into bioceramic coatings can be a promising method to develop bioactive implants with multifunctional properties for orthopaedic application

Nutrient element-based bioceramic coatings on titanium alloy stimulating osteogenesis by inducing beneficial osteoimmmunomodulation

A paradigm shift has taken place in which bone implant materials has gone from being relatively inert to having immunomodulatory properties, indicating the importance of immune response when these materials interact with the host tissues. It has therefore become important to endow the implant materials with immunomodulatory properties favouring osteogenesis and osseointegration. Strontium, zinc and silicon are bioactive elements that have important roles in bone metabolism and that also elicit significant immune responses. In this study, Sr-, Zn- and Si-containing bioactive Sr2ZnSi2O7 (SZS) ceramic coatings on Ti–6Al–4V were successfully prepared by a plasma-spray coating method. The SZS coatings exhibited slow release of the bioactive ions with significantly higher bonding strength than hydroxyapatite (HA) coatings. SZS-coated Ti–6Al–4V elicited significant effects on the immune cells, inhibiting the release of pro-inflammatory cytokines and fibrosis-enhancing factors, while upregulating the expression of osteogenic factors of macrophages; moreover, it could also inhibit the osteoclastic activities. The RANKL/RANK pathway, which enhances osteoclastogenesis, was inhibited by the SZS coatings, whereas the osteogenic differentiation of bone marrow mesenchymal stromal cells (BMSCs) was significantly enhanced by the SZS coatings/macrophages conditioned medium, probably via the activation of BMP2 pathway. SZS coatings are, therefore, a promising material for orthopaedic applications, and the strategy of manipulating the immune response by a combination of bioactive elements with controlled release has the potential to endow biomaterials with beneficial immunomodulatory properties

Design of Line Loss Rate Calculation Method for Low-Voltage Desk Area Based on GA-LMBP Neural Network Model

The distribution network line loss computation method needs to be enhanced in light of the ongoing growth of the national power grid. The study classifies and segments the station data using a decision tree model and a multi-feature volume weighted station clustering algorithm. It then uses a back propagation neural network as a substrate, along with the Levenberg-Marquard algorithm and genetic algorithm for optimization. Collect and organize relevant data on line loss rates in low-voltage substation areas, including information on energy meters, meter boxes, and lines. Next, construct a genetic algorithm neural network model and use the backpropagation algorithm for training. Evaluate the accuracy and stability of various models by comparing the error between predicted and actual line loss rates through experiments. Finally, optimize the neural network parameters and network structure to improve the model&#x2019;s prediction accuracy and robustness. The experimental data showed that compared to the density-based spatial clustering algorithm for noisy applications, the contour coefficient metrics of the proposed multi-feature volume weighted station clustering algorithm improved by 0.05 and the average consumption time of the algorithm was reduced by 75&#x0025;. Compared to the back-propagation neural network model optimized by the Levenberg-Marquard algorithm, the root-mean-square error of the neural network model optimized by the addition of the genetic algorithm for the calculation of the line loss rate of the four station samples was reduced by 72&#x0025;, 55&#x0025;, 53&#x0025; and 37&#x0025;, and the values of R2 were improved by 8.72&#x0025;, 13.59&#x0025;, 7.91&#x0025; and 11.69&#x0025;, respectively. The testing results demonstrated that the neural network model has good generalization capabilities and a high degree of curve fitting. Also, the relative errors of the calculation of the station area&#x2019;s line loss rate are mainly within the range of 0&#x0025; and 10&#x0025;. For the growth of energy conservation in the country, this innovative technology offers a new way to determine and manage line loss of the station area

SU2GE-Net: a saliency-based approach for non-specific class foreground segmentation

Abstract Salient object detection is vital for non-specific class subject segmentation in computer vision applications. However, accurately segmenting foreground subjects with complex backgrounds and intricate boundaries remains a challenge for existing methods. To address these limitations, our study proposes SU2GE-Net, which introduces several novel improvements. We replace the traditional CNN-based backbone with the transformer-based Swin-TransformerV2, known for its effectiveness in capturing long-range dependencies and rich contextual information. To tackle under and over-attention phenomena, we introduce Gated Channel Transformation (GCT). Furthermore, we adopted an edge-based loss (Edge Loss) for network training to capture spatial-wise structural details. Additionally, we propose Training-only Augmentation Loss (TTA Loss) to enhance spatial stability using augmented data. Our method is evaluated using six common datasets, achieving an impressive $$F_{\beta }$$ F β score of 0.883 on DUTS-TE. Compared with other models, SU2GE-Net demonstrates excellent performance in various segmentation scenarios

Active-Current Control of Large-Scale Wind Turbines for Power System Transient Stability Improvement Based on Perturbation Estimation Approach

This paper proposes an active-current control strategy for large-scale wind turbines (WTs) to improve the transient stability of power systems based on a perturbation estimation (PE) approach. The main idea of this control strategy is to mitigate the generator imbalance of mechanical and electrical powers by controlling the active-current of WTs. The effective mutual couplings of synchronous generators and WTs are identified using a Kron-reduction technique first. Then, the control object of each WT is assigned based on the identified mutual couplings. Finally, an individual controller is developed for each WT using a PE approach. In the control algorithm, a perturbation state (PS) is introduced for each WT to represent the comprehensive effect of the nonlinearities and parameter variations of the power system, and then it is estimated by a designed perturbation observer. The estimated PS is employed to compensate the actual perturbation, and to finally achieve the adaptive control design without requiring an accurate system model. The effectiveness of the proposed control approach on improving the system transient stability is validated in the modified IEEE 39-bus system

Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector

Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image. However, because the patch shrinks during preprocessing, most existing approaches that employ adversarial patches to attack object detectors would diminish the attack success rate on small and medium targets. This paper proposes a Frequency Module(FRAN), a frequency-domain attention module for guiding patch generation. This is the first study to introduce frequency domain attention to optimize the attack capabilities of adversarial patches. Our method increases the attack success rates of small and medium targets by 4.18% and 3.89%, respectively, over the state-of-the-art attack method for fooling the human detector while assaulting YOLOv3 without reducing the attack success rate of big targets.Comment: 10pages, 4 figure

STDC-MA Network for Semantic Segmentation

Semantic segmentation is applied extensively in autonomous driving and intelligent transportation with methods that highly demand spatial and semantic information. Here, an STDC-MA network is proposed to meet these demands. First, the STDC-Seg structure is employed in STDC-MA to ensure a lightweight and efficient structure. Subsequently, the feature alignment module (FAM) is applied to understand the offset between high-level and low-level features, solving the problem of pixel offset related to upsampling on the high-level feature map. Our approach implements the effective fusion between high-level features and low-level features. A hierarchical multiscale attention mechanism is adopted to reveal the relationship among attention regions from two different input sizes of one image. Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilization of multiscale features. STDC- MA maintains the segmentation speed as an STDC-Seg network while improving the segmentation accuracy of small objects. STDC-MA was verified on the verification set of Cityscapes. The segmentation result of STDC-MA attained 76.81% mIOU with the input of 0.5x scale, 3.61% higher than STDC-Seg.Comment: 10 pages, 5 figure