Primary Modulation Research Articles

Adaptive Virtual Synchronous Generator Control Strategy Based on Frequency Integral Compensation

With the increasing proportion of power electronic equipment in the power system, improving the inertia and damping characteristics of the system through virtual synchronous generator (VSG) control technology has become a hot research topic. In terms of adjusting the output frequency, traditional primary frequency modulation control cannot ensure that the output frequency is maintained within the safe operating range when the load disturbance is large, so secondary frequency modulation with the integral link is usually adopted. However, the fixed integral coefficient cannot solve the contradiction between system regulation time and frequency oscillation. In this paper, a strategy of coordinated adaptive control based on the integral coefficient, moment of inertia, and damping coefficient is proposed. According to the offset of frequency and the change rate of the frequency offset, the value of parameters is determined, and the specific parameter setting method is determined. Finally, the correctness of the proposed control strategy is verified on a simulation platform and a semi-physical experimental platform.

Enhanced semantic-positional feature fusion network via diverse pre-trained encoders for remote sensing image water-body segmentation

ABSTRACT In the era of increasingly advanced Earth Observation (EO) technologies, extracting pertinent information (such as water-bodies) from the Earth’s surface has become a crucial task. Deep Learning, especially via pre-trained models, currently offers a highly promising approach for the semantic segmentation of Remote Sensing Imagery (RSI). However, effectively adapting these pre-trained models to RSI tasks remains challenging. Typically, these models undergo fine-tuning for specialized tasks, involving modifications to their parameters or structure of the original architecture, which may impact their inherent generalization capabilities. Furthermore, robust pre-trained models on nature images are not specifically designed for RSI, presenting challenges in their direct application to RSI tasks. To alleviate these problems, our study introduces a light-weight Enhanced Semantic-positional Feature Fusion Network (ESFFNet), leveraging diverse pre-trained image encoders alongside extensive EO data. The proposed method begins by leveraging pre-trained encoders, specifically Vision Transformer (ViT)-based and Convolutional Neural Network (CNN)-based models, to extract deep semantic and precise positional features respectively, without additional training. Following this, we introduce the Enhanced Semantic-positional Feature Fusion Module (ESFFM). This module adeptly merges semantic features derived from the ViT-based encoder with spatial features extracted from the CNN-based encoder. Such integration is realized via multi-scale feature fusion, local and long-distance feature integration, and dense connectivity strategies, leading to a robust feature representation. Finally, the Primary Segmentation-guided Fine Extraction Module (PSFEM) further bolsters the precision of remote sensing image segmentation. Collectively, these two modules constitute our light-weight decoder, with a parameter size of less than 4 M. Our approach is evaluated on two distinct water-body datasets, indicating superiority over other leading segmentation techniques. In addition, our method also demonstrates exemplary efficacy in diverse remote sensing segmentation tasks, such as building extraction and land cover classification. The source codes will be available at https://github.com/zhilyzhang/ESFFNet.

Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of recurrent glioblastoma. This study addresses the need for non-invasive approaches to map heterogeneous landscape of histopathological alterations throughout the entire lesion for each patient. We developed BioNet, a biologically-informed neural network, to predict regional distributions of two primary tissue-specific gene modules: proliferating tumor (Pro) and reactive/inflammatory cells (Inf). BioNet significantly outperforms existing methods (p < 2e-26). In cross-validation, BioNet achieved AUCs of 0.80 (Pro) and 0.81 (Inf), with accuracies of 80% and 75%, respectively. In blind tests, BioNet achieved AUCs of 0.80 (Pro) and 0.76 (Inf), with accuracies of 81% and 74%. Competing methods had AUCs lower or around 0.6 and accuracies lower or around 70%. BioNet's voxel-level prediction maps reveal intratumoral heterogeneity, potentially improving biopsy targeting and treatment evaluation. This non-invasive approach facilitates regular monitoring and timely therapeutic adjustments, highlighting the role of ML in precision medicine.

Enhancing distribution system stability and efficiency through multi‐power supply startup optimization for new energy integration

AbstractThis paper addresses the challenge of maximizing power capture from new energy sources, including coal, wind, solar, and hydroelectric power, which often lack sufficient inertia support. This deficiency can lead to frequency instability and cascading failures within the power system. A cooperative optimization model for the start‐up of multiple power supplies, designed to enhance the integration of new energy sources while maintaining system stability is proposed. The model incorporates primary frequency modulation and the intrinsic inertia support capabilities of self‐synchronous voltage source field stations, considering dynamic frequency constraints. Additionally, it employs new energy units with primary frequency modulation to provide inertia support during curtailment, particularly when conventional units cannot meet frequency standards due to existing constraints. Extensive simulations and comparative analyses demonstrate that the proposed model improves new energy utilization by up to 37.5% and reduces operational costs by approximately 16%, enhancing overall operational efficiency in high energy consumption scenarios.

EENet: An effective and efficient network for single image dehazing

While numerous solutions leveraging convolutional neural networks and Transformers have been proposed for image dehazing, there remains significant potential to improve the balance between efficiency and reconstruction performance. In this paper, we introduce an efficient and effective network named EENet, designed for image dehazing through enhanced spatial–spectral learning. EENet comprises three primary modules: the frequency processing module, the spatial processing module, and the dual-domain interaction module. Specifically, the frequency processing module handles Fourier components individually based on their distinct properties for image dehazing while also modeling global dependencies according to the convolution theorem. Additionally, the spatial processing module is designed to enable multi-scale learning. Finally, the dual-domain interaction module promotes information exchange between the frequency and spatial domains. Extensive experiments demonstrate that EENet achieves state-of-the-art performance on seven synthetic and real-world datasets for image dehazing. Moreover, the network’s generalization ability is validated by extending it to image desnowing, image defocus deblurring, and low-light image enhancement.

Experience in using the «Sherpa» software package during field work and its integration with GIS INTEGRO

The article is devoted to the results of the joint use of the Sherpa software package, developed by the Karpinski Institute, with the GIS INTEGRO primary information module during the field work of FSBI VNIGNI in 2023. The creation of a modern complex of domestic software and technological products will allow for better and faster information and analytical support of oil and gas operations in the future. The possibilities provided by the developed software and technology complexes are demonstrated by examples of specific practical tasks.

ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information.

Peptide drugs have demonstrated enormous potential in treating a variety of diseases, yet toxicity prediction remains a significant challenge in drug development. Existing models for prediction of peptide toxicity largely rely on sequence information and often neglect the three-dimensional (3D) structures of peptides. This study introduced a novel model for short peptide toxicity prediction, named ToxGIN. The model utilizes Graph Isomorphism Network (GIN), integrating the underlying amino acid sequence composition and the 3D structures of peptides. ToxGIN comprises three primary modules: (i) Sequence processing module, converting peptide 3D structures and sequences into information of nodes and edges; (ii) Feature extraction module, utilizing GIN to learn discriminative features from nodes and edges; (iii) Classification module, employing a fully connected classifier for toxicity prediction. ToxGIN performed well on the independent test set with F1 score = 0.83, AUROC = 0.91, and Matthews correlation coefficient = 0.68, better than existing models for prediction of peptide toxicity. These results validated the effectiveness of integrating 3D structural information with sequence data using GIN for peptide toxicity prediction. The proposed ToxGIN and data can be freely accessible at https://github.com/cihebiyql/ToxGIN.

The Barnase-Barstar-based pre-targeting strategy for enhanced antitumor therapy in vivo

There is a great need for novel approaches to the treatment of epithelial ovarian carcinoma, which is the leading cause of mortality from gynecological malignancies. In this study, the pre-targeting technology was used to enhance the in vivo targeting of cytotoxic module composed of nanoliposomes loaded with a truncated form of Pseudomonas aeruginosa exotoxin A (PE40) to cancer cells. Pre-targeting system used in this study is composed of bacterial ribonuclease Barnase and its natural antitoxin Barstar. Barstar, genetically fused to various engineered scaffold proteins specific to tumor-associated antigens (HER2, EpCAM) serves as a primary module for precise cancer cell recognition. Barnase conjugated to a therapeutic agent serves as a cytotoxic or secondary module for malignant cell elimination. Due to strong non-covalent interaction (KD10−14 M) of Barstar and Barnase, the primary and secondary modules efficiently interact with each other on the cell surface, which has been proven by confocal microscopy and flow cytometry. Using mice with SKOV-3 ovarian cancer xenografts, we have shown that regardless of the targeting module, the pre-targeting approach is much more effective than a single-step active targeting.

Neural network architecture of a mammalian brain

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Research on Obstacle Avoidance Strategy of Automated Heavy Vehicle Platoon in High-Speed Scenarios

Amidst the rapid progression of the road transport industry, the safety and efficiency of heavy-vehicle platoons have garnered significant attention. The study tackles the challenge of obstacle avoidance presented by vehicles owing to their considerable mass, delayed response times, and line-of-sight impediments, by introducing a cooperative obstacle avoidance system for heavy-vehicle platoons based on deep reinforcement learning. The system comprises three primary modules: perception, decision-making, and control. Initially, the perception module acquires real-time environmental data. Subsequently, the decision-making module formulates obstacle avoidance decisions based on the acquired data. Specifically, it implements a two-stage braking obstacle avoidance strategy under low collision risk scenarios, while employing a fifth-degree polynomial for planning and tracking obstacle avoidance paths under high collision risk conditions suitable for steering maneuvers. The control module utilizes the local multi-agent deep deterministic policy gradient (LADDPG) algorithm to train the heavy-vehicle platoon agents, ensuring the formation’s maintenance while mitigating collisions with other vehicles and obstacles. The effectiveness of the proposed system is substantiated through simulation experiments, demonstrating its adaptability to various traffic conditions, selection of suitable obstacle avoidance strategies, and significant enhancement of obstacle avoidance performance and heavy-vehicle platoon stability.

Noise-imitation learning: unpaired speckle noise reduction for optical coherence tomography

Objective.Optical coherence tomography (OCT) is widely used in clinical practice for its non-invasive, high-resolution imaging capabilities. However, speckle noise inherent to its low coherence principle can degrade image quality and compromise diagnostic accuracy. While deep learning methods have shown promise in reducing speckle noise, obtaining well-registered image pairs remains challenging, leading to the development of unpaired methods. Despite their potential, existing unpaired methods suffer from redundancy in network structures or interaction mechanisms. Therefore, a more streamlined method for unpaired OCT denoising is essential.Approach.In this work, we propose a novel unpaired method for OCT image denoising, referred to as noise-imitation learning (NIL). NIL comprises three primary modules: the noise extraction module, which extracts noise features by denoising noisy images; the noise imitation module, which synthesizes noisy images and generates fake clean images; and the adversarial learning module, which differentiates between real and fake clean images through adversarial training. The complexity of NIL is significantly lower than that of previous unpaired methods, utilizing only one generator and one discriminator for training.Main results.By efficiently fusing unpaired images and employing adversarial training, NIL can extract more speckle noise information to enhance denoising performance. Building on NIL, we propose an OCT image denoising pipeline, NIL-NAFNet. This pipeline achieved PSNR, SSIM, and RMSE values of 31.27 dB, 0.865, and 7.00, respectively, on the PKU37 dataset. Extensive experiments suggest that our method outperforms state-of-the-art unpaired methods both qualitatively and quantitatively.Significance.These findings indicate that the proposed NIL is a simple yet effective method for unpaired OCT speckle noise reduction. The OCT denoising pipeline based on NIL demonstrates exceptional performance and efficiency. By addressing speckle noise without requiring well-registered image pairs, this method can enhance image quality and diagnostic accuracy in clinical practice.

Artificial selection of mutations in two nearby genes gave rise to shattering resistance in soybean

Resistance to pod shattering is a key domestication-related trait selected for seed production in many crops. Here, we show that the transition from shattering in wild soybeans to shattering resistance in cultivated soybeans resulted from selection of mutations within the coding sequences of two nearby genes - Sh1 and Pdh1. Sh1 encodes a C2H2-like zinc finger transcription factor that promotes shattering by repressing SHAT1-5 expression, thereby reducing the secondary wall thickness of fiber cap cells in the abscission layers of pod sutures, while Pdh1 encodes a dirigent protein that orchestrates asymmetric lignin distribution in inner sclerenchyma, creating torsion in pod walls that facilitates shattering. Integration analyses of quantitative trait locus mapping, genome-wide association studies, and allele distribution in representative soybean germplasm suggest that these two genes are primary modulators underlying this domestication trait. Our study thus provides comprehensive understanding regarding the genetic, molecular, and cellular bases of shattering resistance in soybeans.

Evaluation and Improvement of the Flexibility of Biomass Blended Burning Units in a Virtual Power Plant

Aiming at the problems of small thermal power units and biomass mixed combustion units with small generation loads and insufficient primary frequency modulation capability, which cannot be connected to the virtual power plant, this paper adopts a variety of flexibility retrofit methods for the units and explores the peak load capability of the units. Then, multiple units are coupled, and the unit coupling scheme with better economy and environmental protection is screened using comprehensive evaluation indexes. While evaluating the peaking load space of multiple unit coupling, the units’ primary frequency regulation capability and new energy consumption capability are improved. According to the calculation results, the low-pressure cylinder zero-output retrofit has the largest peaking potential among different technical paths, in which unit #3 has 27.55 MW of peaking space. The compression heat pump decoupling retrofit has the best economy, in which the daily profit of unit #3 increases from 0.93 to 1.02 million CNY with an increase of 0.09 million CNY. After the unit has been retrofitted with steam extraction, the three units can be coupled to meet the national feed-in standards. The multiple unit coupling can accommodate up to 203.44 MW of other energy sources while meeting the standard.

Joint estimation of IQ imbalance and PA nonlinearity: An iterative scheme

Power amplifier (PA) nonlinearity and in-phase and quadrature-phase (IQ) imbalance in the transmitter are of critical concern in modern communication systems due to their significant impact on the transmitter performance. In this paper, in order to eliminate their effects without increasing the complexity at the transmitter, we propose an iterative structure for IQ imbalance and PA nonlinearity joint estimation at the receiver side. The strategic design aims to mitigate the influence of one distortion before estimating the other, ultimately improving the accuracy of estimating each distortion. The proposed approach comprises two primary modules: an IQ imbalance estimation module and a PA nonlinearity estimation module. In addition, there are two supporting modules, namely the PA nonlinearity compensation module and the IQ imbalance reconstruction module, positioned before these two primary modules respectively. Furthermore, we have derived the theoretical Cramér–Rao lower bound (CRLB) for the joint distortion model of IQ imbalance and PA nonlinearity. Finally, extensive experiments are conducted to demonstrate the proposed algorithm's capability in estimating hardware distortions and mitigating them.

Optimization of such strategy as frequency modulation for grid-side energy storage control-terminal in response to frequency fluctuations in the new power system grid

Abstract This paper focuses on the refinement of the control strategy for grid-side energy storage systems as they engage in primary and secondary frequency modulation within an emerging smart grid. To attain an optimal outcome, traditional frequency modulation methods, which often require high-precision operations and extended adjustment periods, fall short of the stringent demands posed by modern power systems for swift and responsive frequency modulation. Under such circumstances, the grid-side energy storage control-terminal system presents a viable solution for managing energy storage, thereby providing rapid and precise assistance to the power system’s frequency modulation. By implementing a control mechanism that can adaptively switch between ARR signal-based and ACE signal-based frequency adjustments, a segmented parallel control approach ensures that the energy storage system can promptly and effectively modify the power within the grid system, facilitating swift frequency modulation.

Hybrid Radix-4 SESA/SEDA Adders for Medical Image Processing

Adders play a critical role as primary modules in circuit design, supporting error-tolerant applications such as machine learning, digital image processing, and signal processing. Therefore, the development of adders with low power consumption and energy efficiency is of utmost importance. Approximate adders outperform exact adders in VLSI chips. Among approximate adders, the hybridization of Single Exact and Single Approximation (SESA) and Single Exact and Dual Approximation (SEDA) techniques has emerged as a promising solution. These hybrid adders offer superior power and energy savings relative to other accurate and approximate adder designs. In this scheme, we propose the integration of hybrid radix-4 adders with approximation adders to achieve enhanced performance and pave the way for future hybrid approximate adder circuits. Extensive simulations validated the efficacy of the proposed approach. Furthermore, practical applications demonstrated the versatility and potential of hybrid adders in real-world scenarios. The proposed methodology ensures cost-effectiveness and facilitates seamless future updates and extensions. Overall, this research contributes to the advancement of efficient and low-power adder designs, addressing the evolving requirements of modern digital systems.

A smooth method for primary frequency of wind turbine based on variable power point tracking and energy storage cooperative control

AbstractThe conventional deloading control has certain problems, such as low power generation efficiency and a small speed adjustment range. To improve the system's frequency quality and enhance the power grid's stability, this study comprehensively considers the effect of random source‐load power fluctuations on the system frequency. Moreover, this study proposes a smooth primary frequency control strategy for wind turbine based on the coordinated control of the variable power point tracking and supercapacitor energy storage. The impact of wind power fluctuations on the system frequency at different timescales for wind turbine is studied based on the historical data of wind power fluctuations in a strong wind meteorological cycle of a wind farm. The method determines the capacity of the energy storage device required for frequency smoothing at the optimal timescale. In combination with the required capacity of the wind turbine to participate in the system's primary frequency regulation, the supercapacitor energy storage device is optimally configured, and a set of supercapacitor energy storage device with the lowest cost under the highest charging/discharging efficiency is designed. Simulation and experimental results show that the primary frequency adjustment capability of the control strategy proposed in this study is significantly improved compared with the conventional primary frequency modulation control.

Intake of Dark and Milk Chocolate Differently Affects Autonomic Nervous Control of the Heart During Rest and Mental Stress

Objective Chocolate is a popular food that may affect the activity of the autonomic nervous system (ANS). The aim of this study was to determine the effect of a single dose of dark or milk chocolate on ANS cardiac control during rest and mental stress induced by the Stroop test (ST). Methods Healthy participants, divided into DARK or MILK chocolate groups, ingested corresponding type of chocolate (1 g/kg body weight). They underwent measurement of ANS during relaxation and ST before and 2 h after chocolate consumption. ANS control was assessed by determination of heart rate (HR) and heart rate variability using parameters related to complex autonomic modulation (TP, SDNN) or primary vagal modulation (HFnu, RMSSD). Results HR was always increased during ST in both groups. Relaxation HR values after chocolate ingestion were higher only in the DARK chocolate group. During ST, values of TP, SDNN and HFnu decreased before and after chocolate ingestion in the DARK group, but only before chocolate ingestion in the MILK group. RMSSD values decreased during ST before and after chocolate ingestion in both groups. Relaxation TP, RMSSD and HFnu values after chocolate ingestion were lower in the DARK but not in the MILK group. Conclusion The results suggest that even a single dose of milk chocolate attenuates changes in ANS cardiac control induced by mental stress, whereas a single dose of dark chocolate has an activating effect on the heart via modification of ANS cardiac control at rest. Different levels of sugars and cocoa biologically active compounds in the two types of chocolate could explain the observed effects.

Coordinated Frequency Modulation Control Strategy of Wind Power and Energy Storage Considering Mechanical Load Optimization

When a doubly fed induction generator (DFIG) participates in primary frequency modulation by rotor kinetic energy control, the torque of the generator is changed sharply and the mechanical load pressure of the shaft increases rapidly, which aggravates the fatigue damage of shafting. In order to alleviate the fatigue load of shafting, energy storage was added in the primary frequency modulation of a wind turbine, and a coordinated frequency modulation control strategy of wind power and energy storage based on fuzzy control was proposed. The wind-storage frequency modulation power command was allocated to reduce the response speed of the wind turbine to alleviate the load pressure on the shafting by the fuzzy controller considering the rotor speed range and the state of energy storage charge, and the remaining demand power was supplemented by energy storage. Finally, the joint simulation model based on GH Bladed–Matlab was used to verify the effectiveness of the proposed control strategy. Compared with the traditional integrated control of virtual inertia, the proposed method can reduce the load pressure and fatigue damage of the shafting while satisfying the requirement of frequency modulation.

Daily standing time, dietary fiber, and intake of unsaturated fatty acids are beneficially associated with hepatic insulin sensitivity in adults with metabolic syndrome.

Obesity is associated with impaired glucose metabolism and hepatic insulin resistance. The aim was to investigate the associations of hepatic glucose uptake (HGU) and endogenous glucose production (EGP) to sedentary behavior (SB), physical activity (PA), cardiorespiratory fitness, dietary factors, and metabolic risk markers. Forty-four adults with metabolic syndrome (mean age 58 [SD 7] years, BMI ranging from 25-40kg/; 25 females) were included. HGU was measured by positron emission tomography during the hyperinsulinemic-euglycemic clamp. EGP was calculated by subtracting the glucose infusion rate during clamp from the glucose rate of disappearance. SB and PA were measured with hip-worn accelerometers (26 [SD3] days). Fitness was assessed by maximal bicycle ergometry with respiratory gas measurements and dietary intake of nutrients by 4-day food diaries. HGU was not associated with fitness or any of the SB or PA measures. When adjusted for sex, age, and body fat-%, HGU was associated with whole-body insulin sensitivity (β=0.58), water-insoluble dietary fiber (β=0.29), energy percent (E%) of carbohydrates (β=-0.32), saccharose (β=-0.32), mono- and polyunsaturated fatty acids (β=0.35, β=0.41, respectively). EGP was associated with whole-body insulin sensitivity (β=-0.53), and low-density lipoprotein cholesterol [β=-0.31], and when further adjusted for accelerometry wear time, EGP was associated with standing [β=-0.43]. (p-value for all< 0.05). Standing more, consuming a diet rich in fiber and unsaturated fatty acids, and a lower intake of carbohydrates, especially sugar, associate beneficially with hepatic insulin sensitivity. Habitual SB, PA, or fitness may not be the primary modulators of HGU and EGP. However, these associations need to be confirmed with intervention studies.