Network Loss Research Articles

A highly survivable X‐band low noise amplifier based on GaN HEMT technology and impact of pulse width on recovery time

AbstractGaN high electron mobility transistor (HEMT)‐based low noise amplifiers (LNAs) are an integral part of microwave receiver systems to enhance signal‐to‐noise ratio (SNR). The noise of LNA itself becomes critical for systems requiring high SNR, such as imaging and satellite communication systems. This paper discusses the design of a three‐stage LNA operating at the X‐band in the frequency range of 8.0–12.0 GHz. The amplifier's design and small signal, noise, and linearity characterizations are discussed. Stagewise analysis for gain, noise figure (NF), and matching network losses at the design stage results in achieving promising results. The proposed LNA provides a gain of 23.2 dB with dB gain ripple. Its NF is below 1.5 dB, output power at 1 dB gain compression is 16.4 dBm, and third‐order intercept point is 24.7 dBm at 10 GHz. LNA's survivability is validated to input stress as high as 42 dBm. This LNA is the best reported NF and survivability combination in the 8.0–12.0‐GHz frequency range. The reverse recovery time of LNA is measured under two different pulse conditions, and it has been shown that LNA has better recovery times for lower pulse width signals. This LNA finds its applications in radars and satellite communication systems.

Повышение энергетической эффективности работы тепловых сетей за счет утилизации тепловых потерь

The efficiency of thermal energy transfer, which is inevitably accompanied by heat losses through the insulation of pipelines, directly characterizes heating networks and centralized heat supply systems in general. The most important task in terms of current approaches to energy efficiency is to improve the efficiency of thermal energy transfer in modern heat supply systems. Currently known technical solutions to reduce heat losses through insulation of pipelines reduce heat losses, but not significantly. So, the purpose of this study is to develop a technical solution to reduce (even eliminate) standardized linear heat losses in heat supply networks of industrial enterprises. The authors have used methods of experimental research of heat transfer processes and mathematical modeling of heat and power equipment. A device for recycling heat losses is proposed. It is designed to reduce (even eliminate) normalized linear heat losses in heat supply networks of industrial enterprises. The permissible heat absorption of the device collector is determined, and a method for its regulation is described. An engineering method for calculation of the proposed device has been developed. The effectiveness of application of heat-reflecting screens in heating networks has been determined. A number of problems have been solved to improve the efficiency of heat supply systems of industrial enterprises using a device for recycling heat losses in the heating system channel.

A reliable deterministic method for multi-area economic dispatch considering power losses

This paper offers a reliable deterministic method for solving multi-area economic dispatch (MAED). The proposed method can handle nonlinear power losses in electrical networks and discrete and nonlinear characteristics in power plants, such as fuel change facilities and nonconvex cost functions. To this end, the presented method reformulates the products of two variables in the power loss terms as one-dimensional expressions. Then, it converts all nonlinear terms in the MAED into linear segments. Moreover, it handles discrete decisions in fuel selection through binary variables. We assess the performance of the proposed method by some MAED test systems. The results show that the method can reliably obtain accurate solutions in the adopted test systems. It converges to costs of 10604.7, 654.7, 121592.7, 124628.4, and 169528.9 respectively in 2-area 4-unit, 3-area 10-unit, 4-area 40-unit, 2-area 40-unit, and 10-area 130-unit. These findings reproduce or outperform results found in earlier optimization algorithms. Moreover, the computational time evaluations indicate that the method can solve the adopted MAED problems in less than one minute, showing its efficiency. Overall, the obtained results confirm the efficacy of the method’s efficacy in handling the MAED with discrete and nonlinear terms.

Discriminative feature learning based on multi-view attention network with diffusion joint loss for speech emotion recognition

In speech emotion recognition, existing models often struggle to accurately classify emotions with high similarity. In this paper, we propose a novel architecture that integrates a multi-view attention network (MVAN) and diffusion joint loss to alleviate confusion by placing a stronger focus on emotions that are challenging to classify accurately. First, we use logarithmic Mel-spectrograms (log-Mels), deltas, and delta-deltas of log-Mels as three-dimensional features to minimize external interference. Then, we design the MVAN to extract effective multi-time scale emotion features, where the channel and spatial attention are used to selectively localize the regions in the input features related to the target emotion. A Multi-time view bidirectional long and short-term memory network is used to extract the shallow edge features and deep semantic features, and multi-scale self-attention fuses these features through cross-scale attention fusion to obtain multi-time scale emotion features. Finally, a diffusion joint loss strategy is introduced to distinguish the emotional embeddings with high similarity by the generated complex emotion triplets in a diffusing fashion. We evaluated our proposed method on the Interactive Emotional Mood Binary Motion Capture (IEMOCAP), Chinese Academy of Sciences Automation Institute of Automation (CASIA), and Berlin German Emotion Speech Bank (EMODB) corpus. The results show significant improvements over existing methods, achieving 86.87% WA, 86.60% UA, and 86.82% WF1 on IEMOCAP; 70.74% WA, 70.74% UA, and 70.25% WF1 on CASIA; and 93.65% WA, 91.13% UA, and 92.26% WF1 on EMODB. These results confirm the superiority of our method. Our code and model are available at https://github.com/Littleznnz/MVAN-DiffSEG.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3-1.9 times compared to the state-of-the-art differential diagnosis systems.

ЗБІЛЬШЕННЯ ПРОПУСКНОЇ СПРОМОЖНОСТІ МАГІСТРАЛЬНИХ МЕРЕЖ УКРАЇНИ

In connection with the projected significant increase in electricity demand by 2030, as well as for the purpose of devel-oping the electricity system and effective interaction of the IPS of Ukraine with the energy systems of the European Un-ion, the article considers the need and feasibility of converting the Ukrainian power grids from 330 kV to 400 kV, com-paring the technical characteristics of grids of different voltage classes in terms of reliability, stability and efficiency of such grids. The paper provides an applied analysis of the efficiency of switching 330 kV rated voltage networks to 400 kV voltage class by the criterion of increasing the capacity of electrical equipment and reducing total power losses in the network. On the example of the Dnipro region of the IPS of Ukraine, the complex of computer programs designed for the design of power grids of power systems " Thesaurus" was used to model and analyze the modes of operation of the power grid at 330 kV and 400 kV. As a result, it was found that an increase in the voltage level will lead to a de-crease in the load of certain line sections by 17 %, while the value of total network losses will decrease by 10.8 %. Ref. 4, fig. 3.

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

The optimal scheduling method of the power grid considering the node coupling degree and the duality of mutual inverse mapping is studied to effectively establish the random dynamic scene of the power grid and improve its optimal scheduling effect. Considering the duality of the mutual inverse mapping, a random dynamic scene of the power grid is generated. The optimization model of the grid random dynamic scene partition was established considering the coupling degree of the nodes in the region. The anti-prey particle swarm optimization algorithm was used to solve the optimization model, and the scene partition results were obtained. To construct an optimization scheduling model for the regional power grid with the objective functions of minimizing energy abandonment rate and grid loss and the constraints of power flow, output, and climbing power. A predator-prey particle swarm optimization algorithm is used to solve the optimization scheduling model, and the minimum energy abandonment rate, minimum grid loss, and corresponding optimal scheduling strategies are obtained. The experimental results show that this method can effectively generate random dynamic scenes of a power grid and has a better scene partitioning effect. Under different working conditions, this method can achieve optimal dispatching of the power grid and reduce the power grid energy abandonment rate and network loss.

DDBFusion: An unified image decomposition and fusion framework based on dual decomposition and Bézier curves

Existing image fusion algorithms mostly concentrate on the design of network architecture and loss functions, and using unified feature extraction strategies while neglecting the division of redundant and effective information. However, for complementary information, unified feature extractor may not appropriate. Thus, this paper presents a unified image fusion algorithm based on Bézier curves image augmentation and hierarchical decomposition, and a self-supervised learning task is constructed to learn the meaningful information. Where Bézier curves aim to simulate different image features and constructed special self-supervised learning samples, so our method does not require task specific data and can be easily trained on public natural image datasets. Meanwhile, our dual decomposition self-supervised training method can bring redundant information filtering capability to the model. During the decomposition stage, we classify and extract different features of the images and only utilize the extracted effective information in the fusion stage, and the decomposition ability of images provides a foundation for advanced visual tasks, such as image segmentation and object detection. Finally, more detailed and comprehensive fusion images are generated, and the existence of redundant information is effectively reduced. The validity of the proposed method is verified through qualitative and quantitative analysis of multiple image fusion tasks, and our algorithm gets the state-of-the-art results on multiple datasets of different image fusion tasks. The code of our fusion method is available at https://github.com/Yukarizz/DDBFusion.

Fast power flow calculation for distribution networks based on graph models and hierarchical forward-backward sweep parallel algorithm

IntroductionIn response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm.MethodsFirstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis.Results and DiscussionResults from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing.

Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors

This study presents a multidisciplinary approach involving Explainable Artificial Intelligence (ExAI) and operational risk management to reduce Non-Technical Losses (NTL) in electricity distribution. It empirically explores how the activities of employees of utility companies contribute to NTL, a phenomenon often overlooked in existing empirical research. An ensemble classification algorithm is used to analyse utility operations data, and the SHAP explainability technique establishes the predictive significance of staff activities for NTL. Subsequently, these staff activities are mapped into risk cells using the BASEL II and III operational risk definitions, and the Three Lines of Defence (3LoD) model is developed for optimizing electricity distribution. The paper makes three original contributions to the literature: first, it empirically links staff operations to NTL; second, it maps NTL causes to Basel II/III operational risk categories; and finally, to the best of the authors’ knowledge, it is the first study to use the 3LoD model for electricity distribution optimization.

Optimal allocation of wind-based distributed generators and STATCOMs using a hierarchical stochastic programming approach

Allocating static synchronous compensators (STATCOMs) to regulate given allotted wind-based distributed generators (W-DGs) during planning stages can provide high investment returns by maximizing the installed W-DGs. Amidst rising global warming concerns, commitments to adopt renewable energy gain international momentum to curb greenhouse emissions and meet environmental targets, reducing reliance on fossil-based resources. Renewable energy's intermittency complicates distribution planning, stressing voltage devices and increasing network losses. This paper presents a new hierarchical stochastic planning model that addresses uncertainties related to W-DGs and generic loads. The model optimizes allocation with voltage constraints and reactive power while incorporating a two-stage mixed-integer nonlinear program (MINLP), maximizing net profit, and adding an economic dimension to promote renewable energy investments. Improved wind power modeling with historical data and collective evaluation metrics for selecting the best-fitted probability distribution function (PDF) enhances the accuracy of wind power integration assessment. The hierarchical approach considers relaxed voltage constraints in Stage I to allow maximum allotment of W-DGs while introducing STATCOMs and DGs' reactive power in Stage II to address voltage violations. Verification on the Canadian 41-bus network demonstrates the advantage of the hierarchical approach in allocating more W-DGs and achieving higher profits than the simultaneous planning approach. These advances significantly enhance renewable energy integration in power systems.

Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

Multifunctional bacterial cellulose‑derived carbon hybrid aerogel for ultrabroad microwave absorption and thermal insulation

Carbon aerogel has gained intense attention as one of the most promising microwave absorption materials. It can overcome severe electromagnetic pollution, thanks to its 3D macroscopic structure and superb conductive loss capacity. However, there is still a big challenge to endow multifunctionality to carbon aerogel while maintaining its good electromagnetic wave absorption (EWA) so as to adapt wide practical application. Herein, a novel carbon-based aerogel consisting of Cu and TiO2 nanoparticles dispersed on carbon nanofiber framework was derived from carbonized bacterial cellulose (CBC) decorated with its mother bacteria via freeze-drying, in situ growth and carbonization strategies. The synthesized carbon-based CBC/Cu/TiO2 aerogel achieved an excellent EWA performance with a broad effective absorption bandwidth (EAB) of 8.32 GHz. It is attributed to the synergistic loss mechanism from multiple scattering, conductive network loss, interfacial polarization loss and dipolar polarization relaxation. Meanwhile, the obtained aerogel also shows an excellent thermal insulation with a 3-mm-thick sample generating a temperature gradient of over 42 °C at 85 °C and a maximum radar cross-section (RCS) reduction of 23.88 dB m2 owing to the cellular structure and synergistic effects of multi-components. Therefore, this study proposes a feasible design approach for creating lightweight, effective, and multifunctional CBC-based EWA materials, which offer a new platform to develop ultrabroad electromagnetic wave absorber under the guidance of RCS simulation.

A New Siamese Network Loss for Cattle Facial Recognition in a Few-Shot Learning Scenario

This study explores the use of a Siamese neural network architecture to enhance classification performance in few-shot learning scenarios, with a focus on bovine facial recognition. Traditional methodologies often require large datasets, which can significantly stress animals during data collection. In contrast, the proposed method aims to reduce the number of images needed, thereby minimizing animal stress. Systematic experiments conducted on datasets representing both full and few-shot learning scenarios revealed that the Siamese network consistently outperforms traditional models, such as ResNet101. It achieved notable improvements, with mean values increasing by over 6.5% and standard deviations decreasing by at least 0.010 compared to the ResNet101 baseline. These results highlight the Siamese network’s robustness and consistency, even in resource-constrained environments, and suggest that it offers a promising solution for enhancing model performance with fewer data and reduced animal stress, despite its slower training speed.

Elevating Detection Performance in Optical Remote Sensing Image Object Detection: A Dual Strategy with Spatially Adaptive Angle-Aware Networks and Edge-Aware Skewed Bounding Box Loss Function.

In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (SA3) Network. The SA3 Network employs a hierarchical refinement approach, consisting of coarse regression, fine regression, and precise tuning, to optimize the angle parameters of rotated bounding boxes. It adapts to specific task scenarios using either class-aware or class-agnostic strategies. Experimental results demonstrate its effectiveness in significantly improving detection accuracy at high IoU thresholds. Additionally, we introduce a Gaussian transform-based IoU factor during angle regression loss calculation, leading to the development of Edge-aware Skewed Bounding Box Loss (EAS Loss). The EAS loss enhances the loss gradient at the final stage of angle regression for bounding boxes, addressing the challenge of further learning when the predicted box angle closely aligns with the real target box angle. This results in increased training efficiency and better alignment between training and evaluation metrics. Experimental results show that the proposed method substantially enhances the detection accuracy of ReDet and ReBiDet models. The SA3 Network and EAS loss not only elevate the mAP of the ReBiDet model on DOTA-v1.5 to 78.85% but also effectively improve the model's mAP under high IoU threshold conditions.

A neural network approach for speech enhancement and noise-robust bandwidth extension

When processing noisy utterances with varying frequency bandwidths using an enhancement model, the effective bandwidth of the resulting enhanced speech often remains unchanged. However, high-frequency components are crucial for perceived audio quality, underscoring the need for noise-robust bandwidth extension capabilities in speech enhancement networks. In this study, we addressed this challenge by proposing a novel network architecture and loss function based on the CAUNet, which is a state-of-the-art speech enhancement method. We introduced a multi-scale loss and implemented a coordinate embedded upsampling block to facilitate bandwidth extension while maintaining the ability of speech enhancement. Additionally, we proposed a gradient loss function to promote the neural network’s convergence, leading to significant performance improvements. Our experimental results validate these modifications and clearly demonstrate the superiority of our approach over competing methods.

Cosmetic retinoid use in photoaged skin: A review of the compounds, their use and mechanisms of action.

The inevitable attrition of skin due to ultraviolet radiation, termed photoaging, can be partially restored by treatment with retinoid compounds. Photoaged skin in lightly pigmented individuals, clinically presents with the appearance of wrinkles, increased laxity, and hyper- and hypopigmentation. Underlying these visible signs of ageing are histological features such as epidermal thinning, dermal-epidermal junction flattening, solar elastosis and loss of the dermal fibrillin microfibrillar network, fibrillar collagen and glycosaminoglycans. Retinoid compounds are comprised of three main generations with the first generation (all-trans retinoic acid, retinol, retinaldehyde and retinyl esters) primarily used for the clinical and cosmetic treatment of photoaging, with varying degrees of efficacy, tolerance and stability. All-trans retinoic acid is considered the 'gold standard' for skin rejuvenation; however, it is a prescription-only product largely confined to clinical use. Therefore, retinoid derivatives are readily incorporated into cosmeceutical formulations. The literature reported in this review suggests that retinol, retinyl esters and retinaldehyde that are used in many cosmeceutical products, are efficacious, safe and well-tolerated. Once in the skin, retinoids utilize a complex signalling pathway that promotes remodelling of photoaged epidermis and dermis and leads to the improvement of the cutaneous signs of photoaging.

The impact of different PV system grid integration approaches on voltage profile, grid losses and behaviour of PV system owners

Distribution grids in individual countries inside the European Union have different properties, while the grid operators use different approaches to integrate photovoltaic (PV) systems into the grid, which influences the voltage profile, grid losses and grid’s hosting capability. This work deals with a 0.4 kV distribution gird characteristic for the Slovenian countryside. The grid users are relatively sparsely distributed in the space. The distances between them are relatively long, while the load distribution among individual phases is unbalanced. Three different approaches to the integration of PV into the grid are discussed. The one used before 2012 with the concentrated larger PV units placed close to the transformer busbars, the one introduced with a net-metering scheme where larger PV units are randomly distributed in the grid, and the one where smaller PV units are evenly distributed throughout the grid. The case study discusses the impacts of the three approaches on the proper voltage profile provision, network losses and behaviour of the PV system owners.

Health-related quality of life for children with anterior cruciate ligament deficiency: Ensuring content validity of the new KIDS-KNEES-ACL questionnaire.

The existing knee-specific pediatric patient-reported outcome measures (PROMs) lack content and construct validity for children with anterior cruciate ligament (ACL) injury. This impairs their accuracy which can lead to false interpretations of data and inaccurate clinical guidelines. The purpose of this study was to develop a content-valid PROM for children with an ACL injury. The process adhered to the COnsensus-based Standards for the selection of health Measurement INstruments guidelines for PROM development. Informants were children with ACL deficiency and sampled based on age, sex, and treatment. Semistructured interviews were conducted exploring themes within the International Classification of Functioning, Disability and Health model. Interviews continued beyond data saturation. By thematic analysis and by probing items from the adult PROM 'KNEES-ACL', new themes and items emerged. Content coverage, relevance and understandability were continuously evaluated. All interviews were recorded and transcribed verbatim. The NVivo 12 software was used for data analysis and coding of items. A PROM of 60 items across nine subscales was formed. From cognitive interviews, 19 new items emerged. Forty-one of 55 items from KNEES-ACL were endorsed as relevant; however, all required rewording to ensure understandability. Substantial differences in the psychosocial impact between adults and children were observed. The children experienced a more considerable negative psychosocial impact caused by a loss of participation in sports, lower self-confidence and loss of social networks. This resulted in four new domains. The physical issues were similar to adults, with few exceptions. The 'KIDS-KNEES-ACL' 1.0 (qualitative version) was developed. This version will be subjected to psychometric analysis, resulting in adequate measurement properties of the final KIDS-KNEES-ACL 2.0. As the only adequate pediatric ACL-specific PROM, its use in clinical trials and databases will enhance PROM data quality, and strengthen clinical guidelines and thus the treatment of children with ACL injury. Not applicable.