Practical Applications Research Articles

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Sequential Point Clouds: A Survey.

Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.

A Fixed-Time Noise-Tolerance ZNN Model for Time-Variant Inequality-Constrained Quaternion Matrix Least-Squares Problem.

Presently, numerical algorithms for solving quaternion least-squares problems have been intensively studied and utilized in various disciplines. However, they are unsuitable for solving the corresponding time-variant problems, and thus few studies have explored the solution to the time-variant inequality-constrained quaternion matrix least-squares problem (TVIQLS). To do so, this article designs a fixed-time noise-tolerance zeroing neural network (FTNTZNN) model to determine the solution of the TVIQLS in a complex environment by exploiting the integral structure and the improved activation function (AF). The FTNTZNN model is immune to the effects of initial values and external noise, which is much superior to the conventional zeroing neural network (CZNN) models. Besides, detailed theoretical derivations about the global stability, the fixed-time (FXT) convergence, and the robustness of the FTNTZNN model are provided. Simulation results indicate that the FTNTZNN model has a shorter convergence time and superior robustness compared to other zeroing neural network (ZNN) models activated by ordinary AFs. At last, the construction method of the FTNTZNN model is successfully applied to the synchronization of Lorenz chaotic systems (LCSs), which shows the practical application value of the FTNTZNN model.

Evaluation of serum mid-infrared spectroscopy as new prognostic marker for first-line bevacizumab-based chemotherapy in metastatic colorectal cancer

Background and aimsBevacizumab-based chemotherapy is a recommended first-line treatment for metastatic colorectal cancer (mCRC). Robust biomarkers with clinical practice applicability have not been identified for patients with this treatment. We aimed to evaluate the prognostic yield of serum mid-infrared spectroscopy (MIRS) on patients receiving first-line bevacizumab-based chemotherapy for mCRC. MethodsWe conducted an ancillary analysis from a multicentre prospective study (NCT00489697). All baseline serums were screened by attenuated total reflection method. Principal component analysis and unsupervised k-mean partitioning methods were performed blinded to all patients’ data. Endpoints were progression-free survival (PFS) and overall survival (OS). ResultsFrom the 108 included patients, MIRS discriminated two prognostic groups. First group patients had significantly lower body mass index (p = 0.026) and albumin levels (p < 0.001), and higher levels of angiogenic markers, lactate dehydrogenase and carcinoembryonic antigen (p < 0.001). In univariate analysis, their OS and PFS were shorter with respective medians: 17.6 vs 27.9 months (p = 0.02) and 8.7 vs 11.3 months (p = 0.03). In multivariate analysis, PFS was significantly shorter (HR = 1.74, p = 0.025) with a similar trend for OS (HR = 1.69, p = 0.061). ConclusionBy metabolomic fingerprinting, MIRS proves to be a promising prognostic tool for patients receiving first-line bevacizumab-based chemotherapy for mCRC.

Using Multi-Level Consistency Learning for Partial-to-Partial Point Cloud Registration.

Point cloud registration is a basic task in computer vision and computer graphics. Recently, deep learning-based end-to-end methods have made great progress in this field. One of the challenges of these methods is to deal with partial-to-partial registration tasks. In this work, we propose a novel end-to-end framework called MCLNet that makes full use of multi-level consistency for point cloud registration. First, the point-level consistency is exploited to prune points located outside overlapping regions. Second, we propose a multi-scale attention module to perform consistency learning at the correspondence-level for obtaining reliable correspondences. To further improve the accuracy of our method, we propose a novel scheme to estimate the transformation based on geometric consistency between correspondences. Compared to baseline methods, experimental results show that our method performs well on smaller-scale data, especially with exact matches. The reference time and memory footprint of our method are relatively balanced, which is more beneficial for practical applications.

Adaptability and prospects for the use of introduced representatives of the genus Weigela in different climatic conditions

The use of highly decorative and beautifully flowering shrubs in urban green spaces, especially in sharply continental climatic conditions, is extremely important. In this regard, representatives of the genus Weigela are highly valued in the in different climatic conditions. The main indicators of eco-physiological adaptability of plants, such as intensity of transpiration and photosynthetic productivity, water deficiency and the total water content, quantity of the plastids, have been studied for the representatives of highly ornamental shrubs of the genus Weigela Thumb. The object of the research was the following representatives of the genus Weigela: W. praecox, W. coraeensis, W. floribunda, W. florida and W. florida ʺVariegataʺ, which were introduced into different climatic conditions in order to climate train and replenish the dendro-collections. Physiological research was conducted, during the vigorous vegetative period (May–June months). All the measurements were carried out within the 11:00–13:00 time period, in 9 repetitions (7–10 shoots were chosen for each sample). The nature of ecological adaptability of the studied species, along with the perspective of their application in the landscaping practices in different climatic zones was revealed. At the same time it was revealed that the studied species can be used not only as solitary plants (especially on the background of green lawns) but also in groups in long blooming gardens (with flowers with different colours and blooming phases) and in decorating rock gardens.

Investigating the Endophyte Actinomycetota sp. JW0824 Strain as a Potential Bioinoculant to Enhance the Yield, Nutritive Value, and Chemical Composition of Different Cultivars of Anise (Pimpinella anisum L.) Seeds.

Anise (Pimpinella anisum L.) seeds have various nutritional and therapeutic benefits and are thus considered a valuable addition to animal and human health. Hence, in this study, we aimed to induce the nutritive and biological value of anise seeds. To this end, the potential biofortification effect of the endophytic Actinomycetota sp. JW0824 strain, isolated during the fall of 2023 from the medicinal plant Achyranthes aspera, exhibiting natural distribution in the Jazan region of Saudi Arabia, was investigated in four varieties of anise seeds from Egypt, Tunisia, Syria, and Morocco. Results revealed significant increments (p < 0.05) in the seed dry weight percentage (DW%) and oil yields. In line with increased biomass accumulation, the metabolism of the primary and secondary metabolites was increased. There were differential increases in proteins, sugars, flavonoids, alkaloids, phenols, vitamins (e.g., β-carotene, ascorbic acid), and essential oil components (e.g., phenylpropanoids and monoterpenes), along with their precursor phenylalanine. Consistently, the activity of L-phenylalanine aminolyase (PAL) was increased in the Egyptian and Tunisian varieties at 83.88% and 77.19%, respectively, while 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase (DAHPS) activity increased in all varieties, with a significant 179.31% rise in the Egyptian variety. These findings highlight the beneficial effects of Actinomycetota sp. JW0824 as a bioinoculant for anise seeds, suggesting its potential application in agricultural practices to improve seed yield and quality. Further field trials are recommended to assess the commercial viability of this endophyte for enhancing anise seed production and potentially benefiting other plant species.

Impact of a biostimulant enriched in betalain degradation products on ROS signaling, proline accumulation, and phytohormone homeostasis

This study investigates the potential of a biostimulant derived from Selenicereus undatus peel waste and enriched in betalain degradation products (BDP), to influence Arabidopsis thaliana seedling development. Notably, lower BDP concentrations enhanced seedling development, while higher dosages exhibited adverse effects. Assessment of mitochondrial activity in both seeds and purified organelles showed that the tested biostimulant did not affect mitochondrial activity or integrity, highlighting its independence from mitochondrial performance. Mechanistically, BDP-enriched biostimulant modulated ROS-signaling, diminishing H2O2 by regulating the enzymatic activity and gene expression of SOD, CAT, GPX, and GR. Particularly, analyzing their different isoform via qRT-PCR, the primary cellular compartment where detoxification occurred were identified. Furthermore, biostimulant was able to influence proline-accumulation, altering both the expression of metabolism (PC5S, P5CR and OAT) and catabolism (PDH and P5CDH) related genes. Finally, the BDP-enriched biostimulant altered phytohormone levels, mainly affecting ABA/ABA-glu, tZea/tZea-rib, and tZea/IAA. Concerning GAs, the increase in GA4 and GA7 suggested an involvement of GA13ox, a hypothesis encouraged by qRT-PCR analysis. In summary, this study underscores the potential of BDP-based biostimulant as sustainable promoters of plant growth, influencing critical regulatory pathways during germination. Further research is necessary to explore their extensive applications in agricultural practices.

What does “urgency” mean when prioritizing cancer treatment? Results from a qualitative study with German oncologists and other experts during the COVID-19 pandemic

PurposeCancer care in Germany during the COVID-19 pandemic was affected by resource scarcity and the necessity to prioritize medical measures. This study explores ethical criteria for prioritization and their application in cancer practices from the perspective of German oncologists and other experts.MethodsWe conducted fourteen semi-structured interviews with German oncologists between February and July 2021 and fed findings of interviews and additional data on prioritizing cancer care into four structured group discussions, in January and February 2022, with 22 experts from medicine, nursing, law, ethics, health services research and health insurance. Interviews and group discussions were digitally recorded, transcribed verbatim and analyzed using qualitative content analysis.ResultsNarratives of the participants focus on “urgency” as most acceptable criterion for prioritization in cancer care. Patients who are considered curable and those with a high level of suffering, were given a high degree of “urgency.” However, further analysis indicates that the “urgency” criterion needs to be further distinguished according to at least three different dimensions: “urgency” to (1) prevent imminent harm to life, (2) prevent future harm to life and (3) alleviate suffering. In addition, “urgency” is modulated by the “success,” which can be reached by means of an intervention, and the “likelihood” of reaching that success.ConclusionOur analysis indicates that while “urgency” is a well-established criterion, its operationalization in the context of oncology is challenging. We argue that combined conceptual and clinical analyses are necessary for a sound application of the “urgency” criterion to prioritization in cancer care.

Identification, Bioaccessibility, and Antioxidant Properties of Phenolic Compounds in Carob Syrup.

Carob syrup is a brown, thick syrup produced from carob pulp that can be directly consumed or used as a sweetener, which also finds applications in folk medicinal practices. In this work, the quali-quantitative phenolic profile of five different carob syrups was elucidated before and after in vitro gastro-intestinal digestion. Moreover, the anti-oxidant properties of undigested and digested carob syrups were investigated. A total of 75 phenolic compounds were identified in undigested carob syrups. The most important phenolic compound in all the samples was gallic acid, the concentration of which ranged between 54.28 and 117.73 mg/100 g. Additional compounds belonging to the classes of hydroxybenzoic acids (in particular glycosylated gallic acid derivatives), hydroxycinnamic acids, and flavonoids (especially flavonols) were also identified. During in vitro gastric digestion, gallic acid mono- and di-hexosides were diglycosylated, releasing gallic acid, which was further degraded in ellagic acid through oxidative polymerization in the intestinal phase of the digestion. Ellagic acid was the major compound detected after in vitro gastro-intestinal digestion of carob syrups. With few exceptions, the anti-oxidant properties of carob syrup were preserved even after digestion. Carob syrup can be considered an important source of phenolic compounds with demonstrated positive effects on human health.

Review and Analysis of RGBT Single Object Tracking Methods: A Fusion Perspective

Visual tracking is a fundamental task in computer vision with significant practical applications in various domains, including surveillance, security, robotics, and human-computer interaction. However, it may face limitations in visible light data, such as low-light environments, occlusion, and camouflage, which can significantly reduce its accuracy. To cope with these challenges, researchers have explored the potential of combining the visible and infrared modalities to improve tracking performance. By leveraging the complementary strengths of visible and infrared data, RGB-infrared fusion tracking has emerged as a promising approach to address these limitations and improve tracking accuracy in challenging scenarios. In this article, we present a review on RGB-infrared fusion tracking. Specifically, we categorize existing RGBT tracking methods into four categories based on their underlying architectures, feature representations, and fusion strategies, namely feature decoupling based method, feature selecting based method, collaborative graph tracking method, and traditional fusion method. Furthermore, we provide a critical analysis of their strengths, limitations, representative methods, and future research directions. To further demonstrate the advantages and disadvantages of these methods, we present a review of publicly available RGBT tracking datasets and analyze the main results on public datasets. Moreover, we discuss some limitations in RGBT tracking at present and provide some opportunities and future directions for RGBT visual tracking, such as dataset diversity, unsupervised and weakly supervised applications. In conclusion, our survey aims to serve as a useful resource for researchers and practitioners interested in the emerging field of RGBT tracking, and to promote further progress and innovation in this area.

Near surface camera informed agricultural land monitoring for climate smart agriculture

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

Dimension reduction and homogenization of composite plate with matrix pre-strain

This paper focuses on the simultaneous homogenization and dimension reduction of periodic composite plates within the framework of non-linear elasticity. The composite plate in its reference (undeformed) configuration consists of a periodic perforated plate made of stiff material with holes filled by a soft matrix material. The structure is clamped on a cylindrical part. Two cases of asymptotic analysis are considered: one without pre-strain and the other with matrix pre-strain. In both cases, the total elastic energy is in the von-Kármán (vK) regime ( ε 5 ). A new splitting of the displacements is introduced to analyze the asymptotic behavior. The displacements are decomposed using the Kirchhoff–Love (KL) plate displacement decomposition. The use of a re-scaling unfolding operator allows for deriving the asymptotic behavior of the Green St. Venant’s strain tensor in terms of displacements. The limit homogenized energy is shown to be of vK type with linear elastic cell problems, established using the Γ-convergence. Additionally, it is shown that for isotropic homogenized material, our limit vK plate is orthotropic. The derived results have practical applications in the design and analysis of composite structures.

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Nonstrict-Feedback Nonlinear Systems With Stochastic Noises

This paper discusses the problem of fault-tolerant tracking control for discrete-time nonstrict-feedback nonlinear systems in the presence of stochastic noises and actuator faults. The system is characterized by a discrete-time nonstrict-feedback structure and multiplicative stochastic noise related to the states, which poses a challenge for the design and analysis of the fault-tolerant controller. Moreover, the considered actuator faults include multiplicative and additive faults without prior information. By integrating the properties of the backstepping framework and neural networks, and by introducing an adaptive fault compensation term, a novel fault-tolerant tracking control strategy is proposed. The effects of faults are compensated and the difficulties caused by the system structure are overcome, avoiding the algebraic loop problem and overcoming the causal contradiction. Given specific parameters, the designed fault-tolerant controller can ensure that the tracking error converges to an adjustable region regarding the origin and that all system signals are uniformly bounded concerning the mean-square sense. The simulation examples illustrate the effectiveness of the designed fault-tolerant control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As system complexity has been increasing, practical systems are often affected by faults when performing tracking control. Fault-tolerant control can provide acceptable robustness and improve system safety. On the one hand, many practical systems are nonlinear, difficult to model mathematically and accurately, and affected by stochastic noises. On the other hand, with digital control, the object is a discrete-time system. In practical applications, the upper bounds of the faults and the noises are often unknown. In this paper, a novel adaptive fault-tolerant control method is proposed for the more general uncertain discrete-time nonlinear systems, i.e., nonstrict-feedback nonlinear systems, with stochastic noises and actuator faults. Neural networks and an adaptive fault compensation term are integrated into the fault-tolerant controller. By adjusting the controller parameters properly, it is ensured that the tracking error converges to a small neighborhood of zero in the event of a fault occurring. In future research, sensor faults, input constraints, optimal control design, and applications in multi-agent systems will be considered.

Image-Driven Harmonious Color Palette Generation for Diverse Information Visualization.

Color has been widely used to encode data in all types of visualizations. Effective color palettes contain discriminable and harmonious colors, which allow information from visualizations to be accurately and aesthetically conveyed. However, predefined color palettes not only lack the flexibility of custom color palette generation but also ignore the context in which the visualizations are used. Designing an effective color palette is a time-consuming and challenging process for users, even experts. In this work, we propose the generation of an image-based visualization color palette to exploit the human perception of visually appealing images while considering visualization cognition. By analyzing color palette constraints, including harmony, discrimination, and context, we propose an image-driven color generation method. We design a color clustering method in the saliency-hue plane based on visual importance detection and then select the palette based on the visualization color constraints. In addition, we design two color optimization and assignment strategies for visualizations of different data types. Evaluations through numeric indicators and user experiments demonstrate that the palettes predicted by our method are visually related to the original images and are aesthetically pleasing, supporting diverse visualization contexts and data types in practical applications.

Deep Subdomain Learning Adaptation Network: A Sensor Fault-Tolerant Soft Sensor for Industrial Processes.

Sensor faults are non-negligible issues for soft sensor modeling. However, existing deep learning-based soft sensors are fragile and sensitive when considering sensor faults. To improve the robustness against sensor faults, this article proposes a deep subdomain learning adaptation network (DSLAN) to develop a sensor fault-tolerant soft sensor, which is capable of handling both sensor degradation and sensor failure simultaneously. Primarily, domain adaptation works for process data with sensor degradation in industrial processes. Being founded on the basic structure of deep domain adaptation, a novel subdomain learner is added to automatically learn the subdomain division, enabling DSLAN adaptable to multimode industrial processes. Notably, the subdomain structure of each sample follows a categorical distribution parameterized by output of the subdomain learner. Based on the designed subdomain learner, a new probabilistic local maximum mean discrepancy (PLMMD) is presented to measure the difference in distribution between source and target features. In addition, a generator for failure data imputation is integrated in the framework, making DSLAN handle sensor failure simultaneously. Finally, the Tennessee Eastman (TE) benchmark process and two real industrial processes are used to verify the effectiveness of the proposed method. With the fault tolerance ability, soft sensing technology will take a step toward practical applications.

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.

Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation. We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing. When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%. SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data. This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.

A Multivariable, Adaptive, Robust, Primary Control Enforcing Predetermined Dynamics of Interest in Islanded Microgrids Based on Grid-Forming Inverter-Based Resources

This paper proposes a multivariable, adaptive, robust (MAR) control strategy for islanded inverter-based resources (IBRs) operating as grid-forming inverters. The proposed method is employed in the inner control loop of the primary layer in the hierarchical or decentralized structures for the islanded operation of microgrids. The MAR control scheme is responsible for stabilizing IBRs’ output voltage in autonomous operations of microgrids, considering mismatched input voltage disturbances from the grid side and a large amount of system uncertainty. The control methodology introduced in this paper does not rely on the system’s physical parameters, such as microgrid topology, load dynamics, LCL filters, and output connectors. As a result, there is no need to know the nominal values or the bounds of uncertainties in system dynamics. The MAR control method uses online adaptation rules first to identify and then adjust the control parameters of the closed-loop system based on an arbitrary dynamic model. In other words, the MAR method replaces the actual dynamics of IBRs with predetermined dynamics of interest. Simulation results in the MATLAB/Simulink environment confirm the capability of the scheme introduced for the closed-loop stabilization and voltage regulation in the presence of disturbances and a significant amount of uncertainty under various case studies; moreover, comparative simulations by comparing the presented method with other studies using sliding mode control are provided. Finally, experiments verify the effectiveness and practicality of the proposed MAR control scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Inverter-based resources are integral parts of current and especially future power and energy systems; with increasing concerns about carbon footprints, the tendency to substitute traditional synchronous generators with inverter-based resources increases. This transition towards the widespread use of power electronics devices needs careful studies regarding the stability and control of power converters. Although existing studies are addressing potential control system challenges, they suffer from complex mathematical computations and the need for the system’s preliminary information. With this in mind, this study proposes a multivariable, adaptive, robust control strategy for the inner voltage control loop of grid-forming inverters. This method utilizes online estimation algorithms to identify inverter-based resources’ parameters and tune control system parameters simultaneously, making it applicable even to cases with slow parameter variations caused by aging or environmental changes. It can compensate for potential voltage disturbances from the grid side and enable the designer to replace undesirable dynamics of inverter-based resource units with arbitrary and stable dynamics of interest. In fact, unlike traditional methods that need control parameters and gains to be tuned to achieve a proper dynamic response, the control system designer can choose reference dynamics and enforce the closed-loop system to imitate the dynamical model selected. This model is usually chosen based on established priorities, such as response time and other transient behaviors. Moreover, this method does not require complex mathematical and algebraic calculations to design and implement. It can be easily applied to inverter-based resource units after selecting the desired reference dynamics, as shown through this study’s experimental result. The above points give this method a competitive edge over the existing algorithms, especially in practical applications.

Multi-View Knowledge Ensemble With Frequency Consistency for Cross-Domain Face Translation.

Cross-domain face translation aims to transfer face images from one domain to another. It can be widely used in practical applications, such as photos/sketches in law enforcement, photos/drawings in digital entertainment, and near-infrared (NIR)/visible (VIS) images in security access control. Restricted by limited cross-domain face image pairs, the existing methods usually yield structural deformation or identity ambiguity, which leads to poor perceptual appearance. To address this challenge, we propose a multi-view knowledge (structural knowledge and identity knowledge) ensemble framework with frequency consistency (MvKE-FC) for cross-domain face translation. Due to the structural consistency of facial components, the multi-view knowledge learned from large-scale data can be appropriately transferred to limited cross-domain image pairs and significantly improve the generative performance. To better fuse multi-view knowledge, we further design an attention-based knowledge aggregation module that integrates useful information, and we also develop a frequency-consistent (FC) loss that constrains the generated images in the frequency domain. The designed FC loss consists of a multidirection Prewitt (mPrewitt) loss for high-frequency consistency and a Gaussian blur loss for low-frequency consistency. Furthermore, our FC loss can be flexibly applied to other generative models to enhance their overall performance. Extensive experiments on multiple cross-domain face datasets demonstrate the superiority of our method over state-of-the-art methods both qualitatively and quantitatively.