Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model

Power load forecasting plays an important role in power systems, and the accuracy of load forecasting is of vital importance to power system planning as well as economic efficiency. Power load data are nonsmooth, nonlinear time-series and “noisy” data. Traditional load forecasting has low accuracy and curves not fitting the load variation. It is not well predicted by a single forecasting model. In this paper, we propose a novel model based on the combination of data mining and deep learning to improve the prediction accuracy. First, data preprocessing is performed. Second, identification and correction of anomalous data, normalization of continuous sequences, and one-hot encoding of discrete sequences are performed. The load data are decomposed and denoised using the double decomposition modal (LVMD) strategy, the load curves are clustered using the double weighted fuzzy C-means (DBFCM) algorithm, and the typical curves obtained are used as load patterns. In addition, data feature analysis is performed. A convolutional neural network (CNN) is used to extract data features. A bidirectional long short-term memory (BLSTM) network is used for prediction, in which the number of hidden layer neurons, the number of training epochs, the learning rate, the regularization coefficient, and other relevant parameters in the BLSTM network are optimized using the influenza virus immunity optimization algorithm (IVIA). Finally, the historical data of City H from 1 January 2016 to 31 December 2018, are used for load forecasting. The experimental results show that the novel model based on LVMD-DBFCM load c1urve clustering combined with CNN-IVIA-BLSTM proposed in this paper has an error of only 2% for electric load forecasting

Improving the Detection Accuracy of Underwater Obstacles Based on a Novel Combined Method of Support Vector Regression and Gravity Gradient

Underwater gravity gradient detection techniques are conducive to ensuring the safety of submersible sailing. In order to improve the accuracy of underwater obstacle detection based on gravity gradient detection technology, this paper studies the gravity gradient underwater obstacle detection method based on the combined support vector regression (SVR) algorithm. First, the gravity gradient difference ratio (GGDR) equation, which is only related to the obstacle’s position, is obtained based on the gravity gradient equation by using the difference and ratio methods. Aiming at solving the shortcomings of the GGDR equation based on Newton–Raphson method (NRM), combined with SVR algorithm, a novel SVR–gravity gradient joint method (SGJM) is proposed. Second, the differential ratio dataset is constructed by simulating the gravity gradient data generated by obstacles, and the obstacle location model is trained using SVR. Four measuring lines were selected to verify the SVR-based positioning model. The verification results show that the mean absolute error of the new method in the x, y, and z directions is less than 5.39 m, the root-mean-square error is less than 7.58 m, and the relative error is less than 4% at a distance of less than 500 m. These evaluation metrics validate the reliability of the novel SGJM-based detection of underwater obstacles. Third, comparative experiments based on the novel SGJM and traditional NRM were carried out. The experimental results show that the positioning accuracy of x and z directions in the obstacle’s position calculation based on the novel SGJM is improved by 88% and 85%, respectively

Global Exponential Robust Stability of High-Order Hopfield Neural Networks with S-Type Distributed Time Delays

By employing differential inequality technique and Lyapunov functional method, some criteria of global exponential robust stability for the high-order neural networks with S-type distributed time delays are established, which are easy to be verified with a wider adaptive scope

Nitrite Degradation by a Novel Marine Bacterial Strain <i>Pseudomonas aeruginosa</i> DM6: Characterization and Metabolic Pathway Analysis

High concentrations of nitrite in marine aquaculture wastewater not only pose a threat to the survival and immune systems of aquatic organisms but also contribute to eutrophication, thereby impacting the balance of coastal ecosystems. Compared to traditional physical and chemical methods, utilizing microorganism-mediated biological denitrification is a cost-effective and efficient solution. However, the osmotic pressure changes and salt-induced enzyme precipitation in high-salinity seawater aquaculture environments may inhibit the growth and metabolism of freshwater bacterial strains, making it more suitable to select salt-tolerant marine microorganisms for treating nitrite in marine aquaculture wastewater. In this study, a salt-tolerant nitrite-degrading bacterium, designated as DM6, was isolated from the seawater (salinity of 25–30‰) of Portunus trituberculatus cultivation. The molecular identification of strain DM6 was conducted using 16S rRNA gene sequencing technology. The impacts of various environmental factors on the nitrite degradation performance of strain DM6 were investigated through single-factor and orthogonal experiments, with the selected conditions considered to be the key factors affecting the denitrification efficiency of microorganisms in actual wastewater treatment. PCR amplification of key genes involved in the nitrite metabolism pathway of strain DM6 was conducted, including denitrification pathway-related genes narG, narH, narI, nirS, and norB, as well as assimilation pathway-related genes nasC, nasD, nasE, glnA, gltB, gltD, gdhB, and gdhA. The findings indicated that strain DM6 is classified as Pseudomonas aeruginosa and exhibits efficient nitrite degradation even under a salinity of 35‰. The optimal nitrite degradation efficiency of DM6 was observed when using sodium citrate as the carbon source, a C/N ratio of 20, a salinity of 13‰, pH 8.0, and a temperature of 35 °C. Under these conditions, DM6 could completely degrade an initial nitrite concentration of 156.33 ± 1.17 mg/L within 36 h. Additionally, the successful amplification of key genes involved in the nitrite denitrification and assimilation pathways suggests that strain DM6 may possess both denitrification and assimilation pathways for nitrite degradation simultaneously. Compared to freshwater strains, strain DM6 demonstrates higher salt tolerance and exhibits strong nitrite degradation capability even at high concentrations. However, it may be more suitable for application in the treatment of wastewater from marine aquaculture systems during summer, high-temperature, or moderately alkaline conditions

Performance Evaluation of Single-Frequency Precise Point Positioning and Its Use in the Android Smartphone

The opening access of global navigation satellite system (GNSS) raw data in Android smart devices has led to numerous studies on precise point positioning on mobile phones, among which single-frequency precise point positioning (SF-PPP) has become popular because smartphone-based dual-frequency data still suffer from poor observational quality. As the ionospheric delay is a dominant factor in SF-PPP, we first evaluated two SF-PPP approaches with the MGEX (Multi-GNSS Experiment) stations, the Group and Phase Ionospheric Correction (GRAPHIC) approach and the uncombined approach, and then applied them to a Huawei P40 smartphone. For MGEX stations, both approaches achieved less than 0.1 m and 0.2 m accuracy in horizontal and vertical components, respectively. Uncombined SF-PPP manifested a significant decrease in the convergence time by 40.7%, 20.0%, and 13.8% in the east, north, and up components, respectively. For P40 data, the SF-PPP performance was analyzed using data collected with both a built-in antenna and an external geodetic antenna. The P40 data collected with the built-in antenna showed lower carrier-to-noise ratio (C/N0) values, and the pseudorange noise reached 0.67 m, which is about 67% larger than that with a geodetic antenna. Because the P40 pseudorange noise presented a strong correlation with C/N0, a C/N0-dependent weight model was constructed and used for the P40 data with the built-in antenna. The convergence of uncombined SF-PPP approach was faster than the GRAPHIC model for both the internal and external antenna datasets. The root mean square (RMS) errors for the uncombined SF-PPP solutions of P40 with an external antenna were 0.14 m, 0.15 m, and 0.33 m in the east, north, and up directions, respectively. In contrast, the P40 with an embedded antenna could only reach 0.72 m, 0.51 m, and 0.66 m, respectively, indicating severe positioning degradation due to antenna issues. The results indicate that the two SF-PPP models both can achieve sub-meter level positioning accuracy utilizing multi-GNSS single-frequency observations from mobile smartphones

GlyT1 Inhibitor NFPS Exerts Neuroprotection via GlyR Alpha1 Subunit in the Rat Model of Transient Focal Cerebral Ischaemia and Reperfusion

Background/Aims: Glycine is a strychnine-sensitive inhibitory neurotransmitter in the central nervous system (CNS), especially in the spinal cord, brainstem, and retina. The objective of the present study was to investigate the potential neuroprotective effects of GlyT1 inhibitor N [3-(4'-fluorophenyl)-3-(4'-phenylphenoxy) propyl] sarcosine (NFPS) in the rat model of experimental stroke. Methods: In vivo ischaemia was induced by transient middle cerebral artery occlusion (tMCAO). The methods of Western Blotting, Nissl Staining and Morris water maze methods were applied to analyze the anti-ischaemia mechanism. Results: The results showed that high dose of NFPS (H-NFPS) significantly reduced infarct volume, neuronal injury and the expression of cleaved caspase-3, enhanced Bcl-2/Bax, and improved spatial learning deficits which were administered three hours after transient middle cerebral artery occlusion (tMCAO) induction in rats, while, low dose of NFPS (L-NFPS) exacerbated the injury of ischaemia. These findings suggested that low and high dose of NFPS produced opposite effects. Importantly, it was demonstrated that H-NFPS-dependent neuronal protection was inverted by salicylate (Sal), a specific GlyR &#x0251;1 antagonist. Such effects could probably be attributed to the enhanced glycine level in both synaptic and extrasynaptic clefts and the subsequently altered extrasynaptic GlyRs and their subtypes. Conclusions: These data imply that GlyT1 inhibitor NFPS may be a novel target for clinical treatment of transient focal cerebral ischaemia and reperfusion which are associated with altered GlyR alpha 1 subunits