Low-frequency Components Research Articles

OII: An Orientation Information Integrating Network for Oriented Object Detection in Remote Sensing Images

Oriented object detection for remote sensing images poses formidable challenges due to arbitrary orientation, diverse scales, and densely distributed targets (e.g., across terrain). Current investigations in remote sensing object detection have primarily focused on improving the representation of oriented bounding boxes yet have neglected the significant orientation information of targets in remote sensing contexts. Recent investigations point out that the inclusion and fusion of orientation information yields substantial benefits in training an accurate oriented object system. In this paper, we propose a simple but effective orientation information integrating (OII) network comprising two main parts: the orientation information highlighting (OIH) module and orientation feature fusion (OFF) module. The OIH module extracts orientation features from those produced by the backbone by modeling the frequency information of spatial features. Given that low-frequency components in an image capture its primary content, and high-frequency components contribute to its intricate details and edges, the transformation from the spatial domain to the frequency domain can effectively emphasize the orientation information of images. Subsequently, our OFF module employs a combination of a CNN attention mechanism and self-attention to derive weights for orientation features and original features. These derived weights are adopted to adaptively enhance the original features, resulting in integrated features that contain enriched orientation information. Given the inherent limitation of the original spatial attention weights in explicitly capturing orientation nuances, the incorporation of the introduced orientation weights serves as a pivotal tool to accentuate and delineate orientation information related to targets. Without unnecessary embellishments, our OII network achieves competitive detection accuracy on two prevalent remote sensing-oriented object detection datasets: DOTA (80.82 mAP) and HRSC2016 (98.32 mAP).

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks.

Backgrounds: The validity of heart rate variability (HRV) has been substantiated in mental workload assessments. However, cognitive tasks often coincide with physical exertion in practical mental work, but their synergic effects on HRV remains insufficiently established. The study aims were to investigate the combined effects of cognitive and physical load on autonomic nerve functions. Methods: Thirty-five healthy male subjects (aged 23.5 ± 3.3years) were eligible and enrolled in the study. The subjects engaged in n-back cognitive tasks (1-back, 2-back, and 3-back) under three distinct physical conditions, involving isotonic contraction of the left upper limb with loads of 0kg, 3kg, and 5kg. Electrocardiogram signals and cognitive task performance were recorded throughout the tasks, and post-task assessment of subjective experiences were conducted using the NASA-TLX scale. Results: The execution of n-back tasks resulted in enhanced perceptions of task-load feelings and increased reaction times among subjects, accompanied by a decline in the accuracy rate (p < 0.05). These effects were synchronously intensified by the imposition of physical load. Comparative analysis with a no-physical-load scenario revealed significant alterations in the HRV of the subjects during the cognitive task under moderate and high physical conditions. The main features were a decreased power of the high frequency component (p < 0.05) and an increased low frequency component (p < 0.05), signifying an elevation in sympathetic activity. This physiological response manifested similarly at both moderate and high physical levels. In addition, a discernible linear correlation was observed between HRV and task-load feelings, as well as task performance under the influence of physical load (p < 0.05). Conclusion: HRV can serve as a viable indicator for assessing mental workload in the context of physical activities, making it suitable for real-world mental work scenarios.

Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners

Photovoltaic power generation prediction constitutes a significant research area within the realm of power system artificial intelligence. Accurate prediction of future photovoltaic output is imperative for the optimal dispatchment and secure operation of the power grid. This study introduces a photovoltaic prediction model, termed ICEEMDAN-Bagging-XGBoost, aimed at enhancing the accuracy of photovoltaic power generation predictions. In this paper, the original photovoltaic power data initially undergo decomposition utilizing the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm, with each intrinsic mode function (IMF) derived from this decomposition subsequently reconstructed into high-frequency, medium-frequency, and low-frequency components. Targeting the high-frequency and medium-frequency components of photovoltaic power, a limiting gradient boosting tree (XGBoost) is employed as the foundational learner in the Bagging parallel ensemble learning method, with the incorporation of a sparrow search algorithm (SSA) to refine the hyperparameters of XGBoost, thereby facilitating more nuanced tracking of the changes in the photovoltaic power’s high-frequency and medium-frequency components. Regarding the low-frequency components, XGBoost-Linear is utilized to enable rapid and precise prediction. In contrast with the conventional superposition reconstruction approach, this study employs XGBoost for the reconstruction of the prediction output’s high-frequency, intermediate-frequency, and low-frequency components. Ultimately, the efficacy of the proposed methodology is substantiated by the empirical operation data from a photovoltaic power station in Hebei Province, China. Relative to integrated and traditional single models, this paper’s model exhibits a markedly enhanced prediction accuracy, thereby offering greater applicational value in scenarios involving short-term photovoltaic power prediction.

Investigating Multidecadal Trends in Ice Cover and Subsurface Temperatures in the Laurentian Great Lakes Using a Coupled Hydrodynamic–Ice Model

Abstract While changing lake surface conditions have received significant research scrutiny, changes in subsurface conditions, including stratification and heat content, remain largely unexplored. In this work, we highlight changes in thermal structure, stratification dynamics, and ice characteristics in the Laurentian Great Lakes (Lakes Superior, Huron, Michigan, Erie, and Ontario) as simulated between 1979 and 2021. Three-dimensional lake hydrodynamics and ice cover were modeled using the Finite Volume Community Ocean Model (FVCOM) coupled with the Los Alamos sea ice model (CICE). Analysis revealed significant increases in surface (0.4°–0.6°C decade−1) and subsurface (0.1°–0.4°C decade−1) temperatures as well as dramatic losses in ice cover (1%–8% decade−1) and ice volume (0–3 km3 decade−1) over the last 40 years. Estimated surface heating rates were strongest during the summer and fall, while subsurface warming was most rapid during the nearly isothermal winter and spring. Intensified (decreased) summer (winter) stratification led to shifts in lake turnover dynamics, with delayed fall turnover dates (2–6 days decade−1) and earlier spring overturn dates (2–9 days decade−1). Modeled surface temperatures (LST), bottom temperatures (LBT), and annual averaged ice cover (AAIC) were used to estimate low-frequency climate signals, which were highly correlated with the Atlantic multidecadal oscillation. Warming trends fit to residual climate signals (LST: 0.1°C decade−1; LBT: 0.03°C decade−1; AAIC: −1% decade−1), calculated by removing low-frequency variability from the raw climate signal, were lower than those fit to associated low-frequency components, suggesting that recent climate change in the Great Lakes may be strongly influenced by natural multidecadal climate variability.

Analyzing the dynamical relationships between meteorological and hydrological drought of Wadi Mina basin, Algeria using a novel multiscale framework

Understanding the relationship between hydrological and meteorological drought in drought-prone regions is critical for proper reservoir management. This study presents a novel multiscale framework for investigating the associations between hydrological and meteorological drought based on the Time-Dependent Intrinsic Correlation (TDIC) method. Firstly, the characteristics of short, medium and long term standardized precipitation index (SPI) and the standardized runoff index (SRI) of the Wadi Mina basin (Algeria) have been analyzed based on data from 6 rainfall and hydrometric stations. Then an Improved Complete Ensemble Empirical Mode Decomposition with adaptive noise (ICEEMDAN) method is used to decompose the most correlated SPI and SRI series to different scales. A stronger association between the two types of droughts is evident in the low-frequency trend component regardless of the station, but their evolution pattern does not remain the same. Subsequently, a TDIC based running correlation analysis is performed between the modes to examine the SPI–SRI associations over the time domain and across the time scales. TDIC analysis has proven the dynamic behavior in the SPI–SRI associations bearing frequent alterations in nature and strength across the process scales and along the time domain. In general, at the intra-annual scales the SPI–SRI correlations are mostly weak positive with localized alterations to negative along the time domain, whereas the relationship is dominantly strong positive and long range at inter-annual scales up to 4 years. This dynamic behavior in the SPI–SRI association and the evolution pattern of trend decipher that the rainfall processes are not directly transferred to streamflow drought, but it also gets controlled by many other local meteorological processes.

High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models

With the extensive adoption of generative models across various domains, the protection of copyright for these models has become increasingly vital. Some researchers suggest embedding watermarks in the images generated by these models as a means of preserving IP rights. In this paper, we find that existing generative model watermarking introduces high-frequency artifacts in the high-frequency spectrum of the marked images, thereby compromising the imperceptibility and security of the generative model watermarking system. Given this revelation, we propose an innovative image watermarking technology that takes into account frequency-domain imperceptibility. Our approach abandons the conventional convolutional neural network (CNN) structure typically used as the watermarking embedding network in popular watermarking techniques. This helps the image watermarking system to avoid the inherent high-frequency artifacts commonly produced by CNNs. In addition, we design a frequency perturbation generation network to generate low-frequency perturbations. These perturbations are subsequently added as watermarks to the low-frequency components of the carrier image, thus minimizing the impact of the watermark embedding process on the high-frequency properties of the image. The results show that our proposed watermarking framework can effectively embed low-frequency perturbation watermarks into images and effectively suppress high-frequency artifacts in images, thus significantly improving the frequency-domain imperceptibility and security of the image watermarking system. The introduced approach enhances the average invisibility performance in the frequency domain by up to 24.9% when contrasted with prior methods. Moreover, the method attains superior image quality (&gt;50 dB) in the spatial domain, accompanied by a 100% success rate in watermark extraction in the absence of attacks. This underscores its capability to uphold the efficacy of the protected network and preserve the integrity of the watermarking process. It always maintains excellent imperceptibility and robustness. Thus, the framework shows great potential as a state-of-the-art solution for protecting intellectual property.

Analysis of Key Influencing Factors of Track Dynamic Irregularity Induced by Earthquakes in the High-Speed Railway Track–Bridge System

Stiffness degradation of track–bridge systems under earthquake excitations can lead to track dynamic irregularity, impacting the safety of train operations post-earthquake. In this paper, the nonlinear time-history analysis of CRTS II track–bridge system model established by ANSYS finite element analysis software is carried out under the action of transverse random earthquake. The train–track–bridge system model considering the stiffness degradation caused by earthquake is established by MATLAB, and the earthquake-induced track dynamic irregularity sample library is constructed. The probability distribution mode of the power spectral density sample of the earthquake-induced track dynamic irregularity is explored and the calculation method based on the probability guarantee rate is proposed. The influence of structural damping ratio, train running speed and train type on the power spectral density curve of the earthquake-induced track dynamic irregularity is analyzed. The results show that the power spectrum samples of track dynamic irregularity conform to the hypothesis test of a normal distribution. The power spectral density of earthquake-induced dynamic irregularity primarily consists of medium and low-frequency components. When the structural damping ratio increases from 0.03 to 0.04, 0.04 to 0.05, 0.05 to 0.06, and 0.06 to 0.07, the variation gradients are 0.1701, 0.1240, 0.1034 and 0.0999, respectively, which indicates that the structural damping ratio has a significant effect on the power spectral density of earthquake-induced irregularity, and the impacts of train speed and train type on the power spectral density of near-fault earthquake-induced irregularity are minimal.

Arterial Pulse Wave Velocity Signal Reconstruction Using Low Sampling Rates.

Pulse Wave Velocity (PWV) analysis is valuable for assessing arterial stiffness and cardiovascular health and potentially for estimating blood pressure cufflessly. However, conventional PWV analysis from two transducers spaced closely poses challenges in data management, battery life, and developing the device for continuous real-time applications together along an artery, which typically need data to be recorded at high sampling rates. Specifically, although a pulse signal consists of low-frequency components when used for applications such as determining heart rate, the pulse transit time for transducers near each other along an artery takes place in the millisecond range, typically needing a high sampling rate. To overcome this issue, in this study, we present a novel approach that leverages the Nyquist-Shannon sampling theorem and reconstruction techniques for signals produced by bioimpedance transducers closely spaced along a radial artery. Specifically, we recorded bioimpedance artery pulse signals at a low sampling rate, reducing the data size and subsequently algorithmically reconstructing these signals at a higher sampling rate. We were able to retain vital transit time information and achieved enhanced precision that is comparable to the traditional high-rate sampling method. Our research demonstrates the viability of the algorithmic method for enabling PWV analysis from low-sampling-rate data, overcoming the constraints of conventional approaches. This technique has the potential to contribute to the development of cardiovascular health monitoring and diagnosis using closely spaced wearable devices for real-time and low-resource PWV assessment, enhancing patient care and cardiovascular disease management.

On the Importance of Low-Frequency Signals in Functional and Molecular Photoacoustic Computed Tomography.

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

Hybrid simulation of instabilities in capacitively coupled RF CF4/Ar plasmas driven by a dual frequency source

Instabilities in capacitively coupled Ar/CF4 plasma discharges driven by dual frequency sources are investigated using a one-dimensional fluid/electron Monte Carlo hybrid model. Periodic oscillations of the electron density and temperature on the timescale of multiple low frequency (LF) periods are observed. As the electron density increases, an intense oscillation of the electron temperature within each high frequency (HF) period is initiated. This causes a fluctuation of the electron density and results in a discharge instability. This phenomenon is consistent with the discharge behavior observed in scenarios with single-frequency (SF) sources, as reported by Dong et al (2022 Plasma Sources Sci. Technol. 31 025006). However, unlike the SF case, plasma parameters such as the electron density, electric field, electron power absorption and ionization rate exhibit not only periodic fluctuations but also a spatial asymmetry under the influence of the dual-frequency source. This spatial asymmetry leads to a non-uniform distribution of the electron density between the electrodes, which is related to a spatially asymmetric electric field, electron heating, and ionization around a region of minimum electron density (inside the bulk). This region of minimum electron density is shifted back and forth through the entire plasma bulk from one electrode to the other within multiple LF period. The above phenomena are related to superposition effect between the instabilities and the dual-frequency source. Moreover, the time averaged electric field influences the spatio-temporal evolution of ion fluxes. The ion fluxes at the electrodes, which play an important role in etching processes, are affected by both the high and LF components of the driving voltage waveform as well as the observed instabilities. As the HF increases, the electronegativity and electron temperature are reduced and the electron density increases, resulting in a gradual disappearance of the instabilities.

Prediction of Lithium Oilfield Brines Based on Seismic Data: A Case Study from L Area, Northeastern Sichuan Basin, China

Lithium is an important mineral resource and a critical element in the production of lithium batteries, which are currently in high demand. Oilfield brine has significant value as a raw material for lithium extraction. However, it is often considered a byproduct of oil and gas production and is either abandoned or reinjected underground. Exploration and development of oilfield brines can enhance the economic benefits of oilfields and avoid wasting resources. Current methods for predicting brine distribution rely on geological genetic analysis, which results in low accuracy and reliability. To address this issue, we propose a workflow for lithium brine prediction that uses seismic and logging data. We introduced waveform clustering control and used the mapping relationship between seismic waveforms and well-logging curves to predict high-quality reservoirs based on the electrical and physical properties of lithium brine reservoirs. In this workflow, the seismic waveforms were first clustered using singular value decomposition. The sample sets of well-logging properties were established for the target location. The target properties were divided into high- and low-frequency components and predicted separately. The predicted results of the high-quality reservoirs in the study area were verified using elemental content test results to demonstrate the effectiveness of the method. Our study indicates that well-logging property prediction constrained by waveform clustering can predict lithium brines in a carbonate reservoir.

A personalized semi-automatic sleep spindle detection (PSASD) framework

BackgroundSleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methodsA Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. ResultsA single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methodsPSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. ConclusionsOverall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

SuperPCA and 2-D compact variational mode decomposition for feature extraction from hyperspectral images

ABSTRACT The 2-D compact variational mode decomposition (2-D-C-VMD) has potential research significance in data mining of hyperspectral images (HSIs). The paper proposes a method of concatenation of features. First, the 2-D-C-VMD technique is used to decompose the HSIs to obtain smooth and clear boundary sub-band images when the 2-D-C-VMD takes the most preferred a priori parameter K × . Second, the optimal low-frequency component features of the image and the optimal high-frequency component constructed features are concatenated first and last, and the structure of the concatenation is constructed into a new fused data. Finally, in order to avoid the redundancy of the fused data band information from affecting the classification result, combine a recently developed simple and practical superpixelwise principal component analysis (SuperPCA) dimensionality reduction method to effectively reduce the dimensionality of the fused data. By comparing the analysis with relying only on the optimal low-frequency image feature information and with the direct linear superposition fusion that has been employed, it is verified that the concatenation of features approach proposed in this paper can obtain better recognition results.

Analysis of Wheel Wear and Wheel-Rail Dynamic Characteristics of High-Speed Trains under Braking Conditions

When high-speed trains operate under braking conditions, the wheel-rail interaction increases significantly, which can lead to more serious wear problems. To analyze the evolution of wheel wear and the dynamic characteristics of wheel-rail under braking conditions, this paper first carries out long-term monitoring of the service state of wheel-rail during the operation period. The study analyzed the matching characteristics of the measured wheel-rail profile and calibrated the wear model by using the collected data. The resulting wear prediction model was then used to examine the wheel wear characteristics under braking conditions. The research results indicate that during the operation period, the wheel experiences tread concave wear within a range of ±20 mm of the rolling circle, at a rate of approximately 0.05 mm per 10,000 km. Meanwhile, the rail experiences top surface wear at a natural rate of about 0.09 mm per year. Concave wear causes the contact point of the wheel-rail to appear in two zones, resulting in a sudden change of contact geometric parameters. The concave worn wheel and rail with a 60 N profile have better matching compared to the 60 rail profile. Increasing the braking torque and wheel-rail friction coefficient will significantly increase the wheel wear depth on straight sections. On small-radius curve sections, rail side lubrication can significantly reduce high rail side wheel flange wear. A worn concave wheel can lead to unfavorable wheel-rail contact geometry characteristics and increase low-frequency components in the vehicle’s lateral dynamic response.

Impact of COVID-19-associated Dysautonomia Phenotypes on the Effectiveness of Venovenous Extracorporeal Membrane Oxygenation

RELEVANCE. It is known that patients with severe cases of the novel coronavirus infection (COVID-19) are characterized by the development of COVID-19-associated dysautonomia (COVID-19-DA). At the moment, there are no studies examining the impact of this phenomenon on the course and outcomes of the disease in the most severe cohort of patients with COVID-19, namely those requiring venovenous extracorporeal membrane oxygenation (VV ECMO). AIM OF STUDY. The purpose of the presented work is to study the effect of different COVID-19-DA phenotypes on the performance parameters and effectiveness of VV ECMO, gas exchange and hemodynamics in patients with COVID-19. MATERIAL AND METHODS. The study included 20 patients, 12 (60%) women, 8 (40%) men, with COVID-19 who underwent VV ECMO. Patients underwent 24-hour Holter monitoring with assessment of the spectral parameters of heart rate variability (HRV): the low-frequency component (LF), the high-frequency component (HF), the ratio of the low-frequency component to the high frequency component (LF / HF) on days 1, 3, 5 of the VV ECMO. Patients were divided into three groups based on the identified COVID-19-DA phenotypes. The groups were compared in terms of gas exchange, hemodynamics, and VV ECMO performance parameters. RESULTS. The level of partial pressure of carbon dioxide in arterial blood (paCO2) in the phenotype with low sympathetic tone and high tone of the parasympathetic division of the autonomic nervous system (ANS) (lShP) was significantly higher than in the phenotype with normal sympathetic tone and high tone of the parasympathetic division of the ANS (nShP) with equal VV ECMO performance parameters. The heart rate (HR) in the nShP phenotype was significantly lower than in the lShP phenotype. A significant increase in respiratory dysfunction was revealed over time in the lShP phenotype. Weaning from VV ECMO in the nShP phenotype was successful in 50 %, whereas in the lShP phenotype, weaning from VV ECMO was observed in 7,2 % of patients. No significant differences in the mortality rate were obtained. The most common cause of death in both groups was septic shock. CONCLUSIONS. The COVID-19-DA phenotype, manifested by decreased tone of the sympathetic division and increased tone of the parasympathetic division of the ANS, leads to low efficiency of VV ECMO, resulting in a statistically significantly less frequent ECMO discontinuation in those patients.

High Frequency Component Enhancement Network for Image Manipulation Detection

With the support of deep neural networks, the existing image manipulation detection (IMD) methods can detect manipulated regions within a suspicious image effectively. In general, manipulation operations (e.g., splicing, copy-move, and removal) tend to leave manipulation artifacts in the high-frequency domain of the image, which provides rich clues for locating manipulated regions. Inspired by this phenomenon, in this paper, we propose a High-Frequency Component Enhancement Network, short for HFCE-Net, for image manipulation detection, which aims to fully explore the manipulation artifacts left in the high-frequency domain to improve the localization performance in IMD tasks. Specifically, the HFCE-Net consists of two parallel branches, i.e., the main stream and high-frequency auxiliary branch (HFAB). The HFAB is introduced to fully explore high-frequency artifacts within manipulated images. To achieve this goal, the input image of the HFAB is filtered out of the low-frequency component by the Sobel filter. Furthermore, the HFEB is supervised with the edge information of the manipulated regions. The main stream branch takes the RGB image as input, and aggregates the features learned from the HFAB by the proposed multi-layer fusion (MLF) in a hierarchical manner. We conduct extensive experiments on widely used benchmarks, and the results demonstrate that our HFCE-Net exhibits a strong ability to capture high-frequency information within the manipulated image. Moreover, the proposed HFCE-Net achieves comparable performance (57.3%, 90.9%, and 73.8% F1 on CASIA, NIST, and Coverage datasets) and achieves 1.9%, 9.0%, and 1.5% improvement over the existing method.

CT synthesis from MR images using frequency attention conditional generative adversarial network

Magnetic resonance (MR) image-guided radiotherapy is widely used in the treatment planning of malignant tumors, and MR-only radiotherapy, a representative of this technique, requires synthetic computed tomography (sCT) images for effective radiotherapy planning. Convolutional neural networks (CNN) have shown remarkable performance in generating sCT images. However, CNN-based models tend to synthesize more low-frequency components and the pixel-wise loss function usually used to optimize the model can result in blurred images. To address these problems, a frequency attention conditional generative adversarial network (FACGAN) is proposed in this paper. Specifically, a frequency cycle generative model (FCGM) is designed to enhance the inter-mapping between MR and CT and extract more rich tissue structure information. Additionally, a residual frequency channel attention (RFCA) module is proposed and incorporated into the generator to enhance its ability in perceiving the high-frequency image features. Finally, high-frequency loss (HFL) and cycle consistency high-frequency loss (CHFL) are added to the objective function to optimize the model training. The effectiveness of the proposed model is validated on pelvic and brain datasets and compared with state-of-the-art deep learning models. The results show that FACGAN produces higher-quality sCT images while retaining clearer and richer high-frequency texture information.

A wavelet-LSTM model for short-term wind power forecasting using wind farm SCADA data

Supervisory Control and Data Acquisition (SCADA) system collects massive operation and environment information which directly or indirectly affects the output power in wind farms. Therefore, it becomes an imperious demand to analyze the underlying information from SCADA data for improving the performance of short-term wind power prediction. In this paper, an effective deep learning framework for short-term wind power forecasting based on SCADA data analysis is proposed. A data denoising scheme is designed based on wavelet decomposition. In this method, all SCADA signals (except the wind power signal itself) are decomposed into low-frequency component A and high-frequency component D respectively by the wavelet transform. Then, the maximum information coefficient (MIC) method is applied to choose features that have strong correlation with wind power. Finally, all the selected features and wind power are defined as input vector that are used to train long short-term memory networks. The simulation results based on real data extracted from a SCADA system installed in wind farm indicate that the designed deep learning framework can significantly improve the accuracy of short-term wind power prediction.

Hybrid energy storage system control strategy to smooth power fluctuations in microgrids containing photovoltaics

Abstract The use of a hybrid energy storage system (HESS) consisting of lithium-ion batteries and supercapacitors (SCs) to smooth the power imbalance between the photovoltaics and the load is a widespread solution, and a reasonable probabilistic allocation of the batteries and SCs affects the performance of the HESS. This paper focuses on developing a simple and effective power allocation strategy for the HESS. The moving average filtering (MAF) is considered to effectively decompose the low-frequency components as well as the high-frequency components in the signal and is simple to implement, and we spoil the low-frequency components decomposed by MAF several times to obtain more excellent low-frequency components; moreover, during HESS charging, when the state of charge (SOC) of the SC is high, we reduce the SC’s charging power by decreasing the number of times of MAF usage while ensuring the performance. During the HESS discharging, when the SOC of the SC is low, this strategy is also used to reduce the SC’s discharging power to ensure the SC’s safe operation. The proposed approach is fully validated, and it has excellent performance.

Short-range order and chemical compositions of glasses along the basaltic-rhyolite sub-alkaline join by Raman and FTIR spectroscopies

Six sub-alkaline glasses with compositions progressively shifting from the tholeiitic basalt (B100) end-member to the rhyolite (R100) end-member were investigated and analysed in the same frequency domain by both Raman and FTIR. This approach highlights spectroscopic similarities and differences such as positions, widths and intensities of Raman and FTIR bands, which may also exhibit significant overlapping. Both the Raman and FTIR spectra show several peaks grouped in three main vibrational windows: 200–650 (low frequency region, named F-I in this study), 650–850 (intermediate region, F-II) and 850–1250 cm−1 (high-frequency region, F-III). In line with previous investigations, the F-I interval can be ascribed to vibrational modes involving rings of tetrahedrally coordinated cations linked by bridging oxygens. It can be fitted with three components, whereas F-II involves the motion of Si atoms within its oxygen cage and is adequately represented by two components. Finally, F-III contains different T-O (T = tetrahedrally coordinated network-forming cation) stretching bands that can be tied to the overall degree of polymerization of the glass and are fitted with four components. In some glasses, the three and the two components within F-I and F-II are identifiable in both Raman and FTIR spectra; in cases of strong peak overlap, these peaks can be complementary to one another towards our interpretation of the molecular arrangement(s) in these glasses. Indeed, the positions of the four components in F-III are first constrained in the Raman spectra, which are more identifiable, then further refined using available Raman spectra for corresponding chemically simple silicate systems. The nine fitted components can reproduce the Raman and FTIR spectra extremely well.As a function of the amount of SiO2, the positions and intensities of the three low frequency components progressively shift in both Raman and FTIR. Similarly, the two bands in the F-II intermediate region exhibit a monotonic shifting of their positions. Indeed, the components at high frequency display less significant shifting of their positions as a function of SiO2, while their intensities change more markedly in the Raman spectrum compared to those for FTIR. The vibrational components measured in this study provide a referenced dataset of assignations of the most abundant natural volcanic glasses. Therefore, it provides a diagnostic tool based on the cross-validation of Raman and FTIR spectra to quickly identify the glass chemistry, offering the possibility to expand the applicability of remote investigations.