Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Image Segmentation Based on Mathematical Morphological Operator

Image segmentation is the process of partitioning a digital image into multiple regions (sets of pixels); the pixels in each region have similar attributes. It is often used to separate an image into regions in terms of surfaces, objects, and scenes, especially for object location and boundary extraction. Until now, many general-purpose algorithms and techniques have been proposed for image segmentation. Typical and traditional methods are: (1) threshold-based method; (2) edge-based method; and (3) region-based method. In this chapter, we propose an approach of image segmentation based on mathematical morphology operator: toggle operator. The experimental result shows that the proposed method can segment natural scene images into homogeneous regions effectively

Lack of association between the CALM1 core promoter polymorphism (-16C/T) and susceptibility to knee osteoarthritis in a Chinese Han population

<p>Abstract</p> <p>Background</p> <p><it>CALM1 </it>gene encodes calmodulin (CaM), an important and ubiquitous eukaryotic Ca²⁺-binding protein. Several studies have indicated that a deficient CaM function is likely to be involved in the pathogenesis of osteoarthritis (OA). Using a convincing genome-wide association study, a Japanese group has recently demonstrated a genetic association between the <it>CALM1 </it>core promoter polymorphism (-16C/T transition SNP, rs12885713) and OA susceptibility. However, the subsequent association studies failed to provide consistent results in OA patients of differently selected populations. The present study is to evaluate the association of the -16C/T polymorphism with knee OA in a Chinese Han population.</p> <p>Methods</p> <p>A case-control association study was conducted. The polymorphism was genotyped in 183 patients who had primary symptomatic knee OA with radiographic confirmation and in 210 matched controls. Allelic and genotypic frequencies were compared between patients and control subjects.</p> <p>Results</p> <p>No significant difference was detected in genotype or allele distribution between knee OA and control groups (all <it>P </it>> 0.05). The association was also negative even after stratification by sex. Furthermore, no association between the -16C/T SNP genotype and the clinical variables age, sex, BMI (body mass index) and K/L (Kellgren/Lawrence) score was observed in OA patients.</p> <p>Conclusion</p> <p>The present study suggests that the CALM1 core promoter polymorphism -16C/T is not a risk factor for knee OA susceptibility in the Chinese Han population. Further studies are needed to give a global view of this polymorphism in pathogenesis of OA.</p

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

Existence and Global Stability of a Periodic Solution for Discrete-Time Cellular Neural Networks

A novel sufficient condition is developed to obtain the discrete-time analogues of cellular neural network (CNN) with periodic coefficients in the three-dimensional space. Existence and global stability of a periodic solution for the discrete-time cellular neural network (DT-CNN) are analysed by utilizing continuation theorem of coincidence degree theory and Lyapunov stability theory, respectively. In addition, an illustrative numerical example is presented to verify the effectiveness of the proposed results

Spread of Neisseria meningitidis Serogroup W Clone, China

During February 2011–June 2012, invasive infection with Neisseria meningitidis serogroup W was identified in 11 persons in southeastern China. All isolates tested had matching or near-matching pulsed-field gel electrophoresis patterns and belonged to multilocus sequence type 11. The epidemiologic investigation suggested recent transmission of this clonal complex in southeastern China

Melatonin Ameliorates Hemorrhagic Transformation via Suppression of ROS-Induced NLRP3 Activation after Cerebral Ischemia in Hyperglycemic Rats

Melatonin is a strong antioxidant which beneficially protects against middle cerebral artery occlusion (MCAO) followed by hemorrhagic transformation in rats; protection includes the reduction of neurological deficits, infarction, and hematoma volume. The molecular mechanisms underlying these neuroprotective effects in the MCAO model have not been clearly identified. This study examined the influence and involved mechanism of melatonin on inflammation in hemorrhagic transformation following hyperglycemia MCAO rat model. Compared with the MCAO group, MCAO+dextrose (DX) group showed worse neurological function and higher infarction and hematoma volume. Interestingly, the protein expression of Nod-like receptor protein 3 (NLRP3) inflammasome increased in the MCAO+DX group compared with the MCAO group, which indicated that NLRP3 inflammasome may be involved in the DX-induced hemorrhagic transformation following MCAO. Then, three dosages of melatonin were intraperitoneally injected 2 h after MCAO induction. Melatonin treatment attenuated inflammatory response by inhibiting the reactive oxygen species (ROS) and NLRP3 inflammasome, alleviating neuronal injury, and reducing infarction and hematoma volume, finally improving neurological score. Melatonin also repressed cortical levels of proinflammatory cytokine IL-1β, which were increased 24 h after hyperglycemia MCAO. In order to identify the potential mechanisms, we further revealed that nigericin administration reversed the neuroprotective effect of melatonin by promoting NLRP3 inflammasome activation. In general, this present study reveals that melatonin prevents the occurrence of hyperglycemia-enhanced hemorrhagic transformation, and this effect might be beneficial to attenuate neurological dysfunction via suppressing the inflammatory response after MCAO which possibly associated with the inhibition of the ROS/NLRP3 inflammasome pathway