VME system of the Resistive Plate Chamber (RPC) electronics for the Daya Bay Reactor Neutrino Experiment is described in this paper. A 9U VME RPC trigger module (RTM) is designed to process coincidence signals coming ...VME system of the Resistive Plate Chamber (RPC) electronics for the Daya Bay Reactor Neutrino Experiment is described in this paper. A 9U VME RPC trigger module (RTM) is designed to process coincidence signals coming from front end cards (FECs), to generate local triggers and send them to FECs to select the hit data from RPC detector, to report trigger information to a master trigger system and receive cross triggers from the master trigger system. Another 9U VME readout module is designed to collect data from all FECs, to send out configurations to FECs, and to transmit collected hit data to the data acquisition system via VME bus. Test results prove that the VME system is capable of treating a maximum data rate (2.2 MB·s-1 ), without data loss.展开更多
Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,co...Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.展开更多
Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic computing.However,most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor(CMO...Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic computing.However,most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor(CMOS)technology still suffer from substantially larger energy consumption than biological synapses,due to the slow kinetics of forming conductive paths inside the memristive units.Here we report wafer-scale Ag_(2)S-based MCAs realized using CMOS-compatible processes at temperatures below 160℃.Ag_(2)S electrolytes supply highly mobile Ag+ions,and provide the Ag/Ag_(2)S interface with low silver nucleation barrier to form silver filaments at low energy costs.By further enhancing Ag+migration in Ag_(2)S electrolytes via microstructure modulation,the integrated memristors exhibit a record low threshold of approximately−0.1 V,and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses.The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics.Moreover,the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm.An impressive accuracy of 92.6%in image recognition simulations is demonstrated with the MCAs after the compensation.The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.展开更多
Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-spe...Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.展开更多
This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss r...This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.展开更多
This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and i...This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.展开更多
Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An ...Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An operando dual lossy mode resonance(LMR)biosensor fabricated on a D-shaped single-mode fiber(SMF)is proposed for quantification of clinical indicators of inflammatory process,like in COVID-19 infection.Dual LMRs,created via two-step deposition process,yield a nanostructure with distinct SnO_(2) thicknesses on the flat surface of the fiber.Theoretical and experimental analyses confirm its feasibility,showing a sensitivity around 4500 nm/RIU for both LMRs.A novel insight in spatially-separated biofunctionalization of the sensitive fiber regions is validated through fluorescence assays,showcasing selectivity for different immunoglobulins.Real-time and label-free detection of two inflammatory markers,C-reactive protein and Ddimer,empowers the platform capability with a minimum detectable concentration below 1μg/mL for both biomolecules,which is of clinical interest.This proof-of-concept work provides an important leap in fiber-based biosensing for effective and reliable multi-analyte detection,presenting a novel,compact and multi-functional analytical tool.展开更多
Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with v...Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.展开更多
This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems,with a focus on smooth transitions between walking trot,regular trot,and flying(running)trot.First,we e...This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems,with a focus on smooth transitions between walking trot,regular trot,and flying(running)trot.First,we extend the Clock Torque Actuated Spring-Loaded Inverted Pendulum(CT-SLIP)template to three dimensions,creating a comprehensive control framework.A template-based control strategy is then developed to compute joint torques for stable locomotion,along with a detailed approach for transitioning between gaits.To enable the flight phase in the running trot,a projectile motion model is incorporated into the template.For improved turning,we implement a yaw control method that rotates the swing foot plane to enhance stability,enabling higher turning rates while maintaining steady forward motion and balance.To further enhance locomotion stability and performance,a Whole-Body Controller(WBC)is integrated.The proposed method is implemented and rigorously evaluated in the MuJoCo simulator,with experiments testing gait transitions and disturbance rejection.Additionally,comparative studies assess the impacts of both swing foot plane rotation and the WBC on overall system performance.Furthermore,the approach is validated through real hardware experiments on Unitree GO1 quadrupedal robot,successfully demonstrating smooth gait transitions,stable locomotion,and practical applicability in real-world scenarios.展开更多
The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists fac...The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.展开更多
The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the c...The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.展开更多
Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety advantages.These benefits originate from the abundan...Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety advantages.These benefits originate from the abundance of zinc and its compatibility with non-flammable aqueous electrolytes.However,the inherent instability of zinc in aqueous environments,manifested through hydrogen evolution reactions(HER)and dendritic growth,has hindered commercialization due to poor cycling stability.Enter potassium polyacrylate(PAAK)-based water-in-polymer salt electrolyte(WiPSE),a novel variant of water-in-salt electrolytes(WiSE),designed to mitigate side reactions associated with water redox processes,thereby enhancing the cyclic stability of ZnBs.In this study,WiPSE was employed in ZnBs featuring lignin and carbon composites as cathode materials.Our research highlights the crucial function of acrylate groups from WiPSE in stabilizing the ionic flux on the surface of the Zn electrode.This stabilization promotes the parallel deposition of Zn along the(002)plane,resulting in a significant reduction in dendritic growth.Notably,our sustainable Zn-lignin battery showcases remarkable cyclic stability,retaining 80%of its initial capacity after 8000 cycles at a high current rate(1 A g^(-1))and maintaining over 75%capacity retention up to 2000 cycles at a low current rate(0.2 A g^(-1)).This study showcases the practical application of WiPSE for the development of low-cost,dendrite-free,and scalable ZnBs.展开更多
Copper complexes inspired by O_(2)-activating enzymes have been widely investigated as molecular water oxidation catalysts,capable of facile and reversible O─O bond formation and cleavage under mild conditions.In thi...Copper complexes inspired by O_(2)-activating enzymes have been widely investigated as molecular water oxidation catalysts,capable of facile and reversible O─O bond formation and cleavage under mild conditions.In this study,two copper phenanthroline complexes,namely,Cu(phen)and Cu(dophen),exhibit high turnover frequencies(TOFs)of 74±13 and(5.66±0.29)×10^(3)s^(−1)for water oxidation,respectively.Moreover,amino acid-functionalized carbon dots(CDs)were used to support the adhesion of the[Cu]complexes onto the electrode,significantly enhancing the TOFs of(2.80±0.12)×10^(3)and(4.11±0.24)×10^(4)s^(−1),respectively,exceeding the activity of photosystem Ⅱ in nature.Remarkably,the amino acid-functionalized CDs provide a secondary sphere that mimics the catalytic microenvironment of the copper centre,which promotes proton-coupled electron transfer and O─O bond formation.Finally,the photovoltaic-electrolysis(PVE)system was established using CDs-supported Cu catalysts and commercial silicon solar panels,achieving a high solar-to-hydrogen efficiency of 11.59%under the illumination of AM 1.5 G.This represents the most efficient solar-driven water splitting system based on copper-based catalysts to date,introducing the biomimetic secondary sphere to a“proton-rocking”process for water oxidation catalysis and application of the PVE system.展开更多
This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension C...This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the pro- posed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound （CRB） is also derived. Simulation results verify the usefulness of the proposed algorithm.展开更多
In this paper, novel mathematical expressions are derived for the Global Positioning System (GPS) receiver interference tolerance in the presence of different types of interference signals such as: continuous wave int...In this paper, novel mathematical expressions are derived for the Global Positioning System (GPS) receiver interference tolerance in the presence of different types of interference signals such as: continuous wave interference, narrowband interference, partial band interference, broadband interference, match spectrum interference and pulse interference. Also, in this paper the mean time to loss lock is determined in order to analyse the mentioned interferences effect on the GPS receiver. These derived analytical expressions are validated with the aid of extensive simulation experiments.展开更多
Centrifugal pumps always work under steady conditions,and many researches focus on the steady operation.But transient conditions,such as sudden startup and shutdown,are inevitable.The researches on the inner flow of c...Centrifugal pumps always work under steady conditions,and many researches focus on the steady operation.But transient conditions,such as sudden startup and shutdown,are inevitable.The researches on the inner flow of centrifugal pumps under transient conditions have been done,and they show that the transient operation is different from the steady operation.In order to research the evolution of unsteady flow in a centrifugal pump under transient conditions,and to investigate the mechanism of transient effects by analyzing the unsteady flow in a centrifugal pump,the external characteristic experiment and the internal flow numerical calculation of the centrifugal pump with an open impeller during startup is presented.The relationships of the rotation speed,capacity and head between start-time are obtained by the external characteristics experiment.The numerical calculations under startup process are carried out by using the k-e model and N-S equation.The distribution of velocity and pressure in the inner channel of the tested pump was obtained by choosing fourteen start-time points and twelve geometrical points in the impeller channel during startup.The calculation results show that the velocity and the pressure increase linearly with the start-time before rotation＇s speed gets steady,then changes almost horizontally after rotation speed becomes steady,then fluctuates until being steady.The internal flow characteristics are in good agreement with the external characteristic experimental results and numerical calculation.The simulation methods and results make the basis for the diagnosis and optimization of under flow in the centrifugal pump during transient operation.展开更多
Aluminum foils having thicknesses of 10-20 μm are commonly employed as current collectors for cathode electrodes in Li-ion batteries. The effects of the surface morphology of the foil on battery performance were inve...Aluminum foils having thicknesses of 10-20 μm are commonly employed as current collectors for cathode electrodes in Li-ion batteries. The effects of the surface morphology of the foil on battery performance were investigated by using a foil with roughened surface by chemical etching and a plain foil with smooth surface on both sides. For high-conductivity LiCoO2 active materials with large particle size, there are no significant differences in battery performance between the two types of foils. But for low-conductivity LiFePO4 active materials with small particle size, high-rate discharge properties are significantly different. The possibility shows that optimizing both the surface morphology of the aluminum foil and particle size of active material leads to improvement of the battery performance.展开更多
基金Supported by National Natural Science Foundation of China (Grant No.10890091)Guangdong Province and Chinese Academy of Sciences’Comprehensive Strategic Cooperation Projects (No.2011A090100015)
文摘VME system of the Resistive Plate Chamber (RPC) electronics for the Daya Bay Reactor Neutrino Experiment is described in this paper. A 9U VME RPC trigger module (RTM) is designed to process coincidence signals coming from front end cards (FECs), to generate local triggers and send them to FECs to select the hit data from RPC detector, to report trigger information to a master trigger system and receive cross triggers from the master trigger system. Another 9U VME readout module is designed to collect data from all FECs, to send out configurations to FECs, and to transmit collected hit data to the data acquisition system via VME bus. Test results prove that the VME system is capable of treating a maximum data rate (2.2 MB·s-1 ), without data loss.
文摘Advances in software and hardware technologies have facilitated the production of quadrotor unmanned aerial vehicles(UAVs).Nowadays,people actively use quadrotor UAVs in essential missions such as search and rescue,counter-terrorism,firefighting,surveillance,and cargo transportation.While performing these tasks,quadrotors must operate in noisy environments.Therefore,a robust controller design that can control the altitude and attitude of the quadrotor in noisy environments is of great importance.Many researchers have focused only on white Gaussian noise in their studies,whereas researchers need to consider the effects of all colored noises during the operation of the quadrotor.This study aims to design a robust controller that is resistant to all colored noises.Firstly,a nonlinear quadrotormodel was created with MATLAB.Then,a backstepping controller resistant to colored noises was designed.Thedesigned backstepping controller was tested under Gaussian white,pink,brown,blue,and purple noises.PID and Lyapunov-based controller designswere also carried out,and their time responses(rise time,overshoot,settling time)were compared with those of the backstepping controller.In the simulations,time was in seconds,altitude was in meters,and roll,pitch,and yaw references were in radians.Rise and settling time values were in seconds,and overshoot value was in percent.When the obtained values are examined,simulations prove that the proposed backstepping controller has the least overshoot and the shortest settling time under all noise types.
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
基金supported by the Swedish Strategic Research Foundation(SSF FFL15-0174 to Zhen Zhang)the Swedish Research Council(VR 2018-06030 and 2019-04690 to Zhen Zhang)+1 种基金the Wallenberg Academy Fellow Extension Program(KAW 2020-0190 to Zhen Zhang)the Olle Engkvist Foundation(Postdoc grant 214-0322 to Zhen Zhang).
文摘Memristive crossbar arrays(MCAs)offer parallel data storage and processing for energy-efficient neuromorphic computing.However,most wafer-scale MCAs that are compatible with complementary metal-oxide-semiconductor(CMOS)technology still suffer from substantially larger energy consumption than biological synapses,due to the slow kinetics of forming conductive paths inside the memristive units.Here we report wafer-scale Ag_(2)S-based MCAs realized using CMOS-compatible processes at temperatures below 160℃.Ag_(2)S electrolytes supply highly mobile Ag+ions,and provide the Ag/Ag_(2)S interface with low silver nucleation barrier to form silver filaments at low energy costs.By further enhancing Ag+migration in Ag_(2)S electrolytes via microstructure modulation,the integrated memristors exhibit a record low threshold of approximately−0.1 V,and demonstrate ultra-low switching-energies reaching femtojoule values as observed in biological synapses.The low-temperature process also enables MCA integration on polyimide substrates for applications in flexible electronics.Moreover,the intrinsic nonidealities of the memristive units for deep learning can be compensated by employing an advanced training algorithm.An impressive accuracy of 92.6%in image recognition simulations is demonstrated with the MCAs after the compensation.The demonstrated MCAs provide a promising device option for neuromorphic computing with ultra-high energy-efficiency.
基金Funded by the Spanish Government and FEDER funds(AEI/FEDER,UE)under grant PID2021-124502OB-C42(PRESECREL)the predoctoral program“Concepción Arenal del Programa de Personal Investigador en formación Predoctoral”funded by Universidad de Cantabria and Cantabria’s Government(BOC 18-10-2021).
文摘Predictive maintenance often involves imbalanced multivariate time series datasets with scarce failure events,posing challenges for model training due to the high dimensionality of the data and the need for domain-specific preprocessing,which frequently leads to the development of large and complex models.Inspired by the success of Large Language Models(LLMs),transformer-based foundation models have been developed for time series(TSFM).These models have been proven to reconstruct time series in a zero-shot manner,being able to capture different patterns that effectively characterize time series.This paper proposes the use of TSFM to generate embeddings of the input data space,making them more interpretable for machine learning models.To evaluate the effectiveness of our approach,we trained three classical machine learning algorithms and one neural network using the embeddings generated by the TSFM called Moment for predicting the remaining useful life of aircraft engines.We test the models trained with both the full training dataset and only 10%of the training samples.Our results show that training simple models,such as support vector regressors or neural networks,with embeddings generated by Moment not only accelerates the training process but also enhances performance in few-shot learning scenarios,where data is scarce.This suggests a promising alternative to complex deep learning architectures,particularly in industrial contexts with limited labeled data.
基金supported by the Scientific and Technological Research Council of Turkey(TUBITAK)under Grant No.124E002(1001-Project).
文摘This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks(UPDNs),focusing on the complex 123-Bus test system.Three scenarios are investigated:(1)simultaneous power loss reduction and voltage profile improvement,(2)minimization of voltage and current unbalance indices under various operational cases,and(3)multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index,active power loss,and current unbalance index.Unlike previous research that oftensimplified system components,this work maintains all equipment,including capacitor banks,transformers,and voltage regulators,to ensure realistic results.The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem(RecPrb)in UPDNs.A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN,employing multiple performance metrics and comparative techniques.The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN.This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios,advancing the field of UPDN optimization and management.
文摘This research investigates the influence of indoor and outdoor factors on photovoltaic(PV)power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency.To predict plant efficiency,nineteen variables are analyzed,consisting of nine indoor photovoltaic panel characteristics(Open Circuit Voltage(Voc),Short Circuit Current(Isc),Maximum Power(Pmpp),Maximum Voltage(Umpp),Maximum Current(Impp),Filling Factor(FF),Parallel Resistance(Rp),Series Resistance(Rs),Module Temperature)and ten environmental factors(Air Temperature,Air Humidity,Dew Point,Air Pressure,Irradiation,Irradiation Propagation,Wind Speed,Wind Speed Propagation,Wind Direction,Wind Direction Propagation).This study provides a new perspective not previously addressed in the literature.In this study,different machine learning methods such as Multilayer Perceptron(MLP),Multivariate Adaptive Regression Spline(MARS),Multiple Linear Regression(MLR),and Random Forest(RF)models are used to predict power values using data from installed PVpanels.Panel values obtained under real field conditions were used to train the models,and the results were compared.The Multilayer Perceptron(MLP)model was achieved with the highest classification accuracy of 0.990%.The machine learning models used for solar energy forecasting show high performance and produce results close to actual values.Models like Multi-Layer Perceptron(MLP)and Random Forest(RF)can be used in diverse locations based on load demand.
基金financial support from the Spanish Agencia Estatal de Investigación (AEI) through project PID2023-149895OB-I00a predoctoral research grant from the Public University of Navarrafinancial support under the National Recovery and Resilience Plan (NRRP),Mission 4,Component 2,Investment 1.1,Call for tender No.1409 published on 14.9.2022 by the Italian Ministry of University and Research (MUR),funded by the European Union–NextGenerationEU–Project Title‘‘Fiber optics sensors as a platform for cancer diagnosis and in vitro model testing”–CUP B53D23024170001-Grant Assignment Decree No.1383 adopted on 01/09/2023 by the Italian MUR.
文摘Detecting multiple analytes simultaneously,crucial in disease diagnosis and treatment prognosis,remains challenging.While planar sensing platforms demonstrate this capability,optical fiber sensors still lag behind.An operando dual lossy mode resonance(LMR)biosensor fabricated on a D-shaped single-mode fiber(SMF)is proposed for quantification of clinical indicators of inflammatory process,like in COVID-19 infection.Dual LMRs,created via two-step deposition process,yield a nanostructure with distinct SnO_(2) thicknesses on the flat surface of the fiber.Theoretical and experimental analyses confirm its feasibility,showing a sensitivity around 4500 nm/RIU for both LMRs.A novel insight in spatially-separated biofunctionalization of the sensitive fiber regions is validated through fluorescence assays,showcasing selectivity for different immunoglobulins.Real-time and label-free detection of two inflammatory markers,C-reactive protein and Ddimer,empowers the platform capability with a minimum detectable concentration below 1μg/mL for both biomolecules,which is of clinical interest.This proof-of-concept work provides an important leap in fiber-based biosensing for effective and reliable multi-analyte detection,presenting a novel,compact and multi-functional analytical tool.
文摘Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic(PV)systems integration associated with varying loading and climate conditions.This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation,with a focus on harmonic mitigation,voltage regulation,power factor correction,and optimization techniques.The impact of passive and active filters,custom power devices such as dynamic voltage restorers(DVRs)and static synchronous compensators(STATCOMs),and grid modernization technologies on power quality is examined.Additionally,this paper specifically explores machine learning and AI-driven solutions for power quality enhancement,discussing their potential to optimize system performance and facilitate renewable energy integration.Modern optimization algorithms are also discussed as effective procedures to find the settings for power system components for optimal operation,including the allocation of distributed energy resources and the tuning of control parameters.Added to that,this paper explores the methods to maximize renewable energy hosting capacity while ensuring reliable and efficient system operation.By synthesizing existing research,this review aims to provide insights into the challenges and opportunities in distribution system operation and optimization,highlighting future research directions that enhance power quality and facilitate renewable energy integration.
基金supported by The Scientific and Technological Research Council of Türkiye(TUBITAK)1515 Frontier R&D Laboratories Support Program for Turk Telekom neXt Generation Technologies Lab(XGeNTT)under Project No.5249902supported by the Scientific Research Projects Coordination Unit of Middle East Technical University(METU)under Project No.ADEP-301-2025-11613.
文摘This paper presents a template-based control method for achieving diverse trotting motions in quadrupedal systems,with a focus on smooth transitions between walking trot,regular trot,and flying(running)trot.First,we extend the Clock Torque Actuated Spring-Loaded Inverted Pendulum(CT-SLIP)template to three dimensions,creating a comprehensive control framework.A template-based control strategy is then developed to compute joint torques for stable locomotion,along with a detailed approach for transitioning between gaits.To enable the flight phase in the running trot,a projectile motion model is incorporated into the template.For improved turning,we implement a yaw control method that rotates the swing foot plane to enhance stability,enabling higher turning rates while maintaining steady forward motion and balance.To further enhance locomotion stability and performance,a Whole-Body Controller(WBC)is integrated.The proposed method is implemented and rigorously evaluated in the MuJoCo simulator,with experiments testing gait transitions and disturbance rejection.Additionally,comparative studies assess the impacts of both swing foot plane rotation and the WBC on overall system performance.Furthermore,the approach is validated through real hardware experiments on Unitree GO1 quadrupedal robot,successfully demonstrating smooth gait transitions,stable locomotion,and practical applicability in real-world scenarios.
基金supported by NSTC 113-2221-E-155-055NSTC 113-2222-E-155-007,Taiwan.
文摘The rapid advancement of the Internet ofThings(IoT)has heightened the importance of security,with a notable increase in Distributed Denial-of-Service(DDoS)attacks targeting IoT devices.Network security specialists face the challenge of producing systems to identify and offset these attacks.This researchmanages IoT security through the emerging Software-Defined Networking(SDN)standard by developing a unified framework(RNN-RYU).We thoroughly assess multiple deep learning frameworks,including Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),Feed-Forward Convolutional Neural Network(FFCNN),and Recurrent Neural Network(RNN),and present the novel usage of Synthetic Minority Over-Sampling Technique(SMOTE)tailored for IoT-SDN contexts to manage class imbalance during training and enhance performance metrics.Our research has significant practical implications as we authenticate the approache using both the self-generated SD_IoT_Smart_City dataset and the publicly available CICIoT23 dataset.The system utilizes only eleven features to identify DDoS attacks efficiently.Results indicate that the RNN can reliably and precisely differentiate between DDoS traffic and benign traffic by easily identifying temporal relationships and sequences in the data.
文摘The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.
基金primarily supported by the Proof-of-Concept project “high-voltage aqueous electrolytes (KAW 2020.0174)”the “Wood Wal enberg Science Center” funded by Knut and Alice Wal enberg (KAW) foundation+6 种基金supported by the Swedish Research Council (2016-05990)the Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University (Faculty Grant SFO-Mat-Li U No. 2009-00971)the competence center Fun Mat-II funded by the Swedish Agency for Innovation Systems (Vinnova, grant no 2016-05156)Aforsk foundation for the project “anode free Zn-ion battery (21-130)” and “Zn-lignin battery (22-134)”Swedish Electricity Storage and Balancing Centre (SESBC) funded by Energyimyndigghetenthe Swedish Research Council VR International Postdoc Grant 2022-00213“STand UP for energy col aboration and Swedish Research Council (2020-05223)”
文摘Zinc metal batteries(ZnBs)are poised as the next-generation energy storage solution,complementing lithium-ion batteries,thanks to their costeffectiveness and safety advantages.These benefits originate from the abundance of zinc and its compatibility with non-flammable aqueous electrolytes.However,the inherent instability of zinc in aqueous environments,manifested through hydrogen evolution reactions(HER)and dendritic growth,has hindered commercialization due to poor cycling stability.Enter potassium polyacrylate(PAAK)-based water-in-polymer salt electrolyte(WiPSE),a novel variant of water-in-salt electrolytes(WiSE),designed to mitigate side reactions associated with water redox processes,thereby enhancing the cyclic stability of ZnBs.In this study,WiPSE was employed in ZnBs featuring lignin and carbon composites as cathode materials.Our research highlights the crucial function of acrylate groups from WiPSE in stabilizing the ionic flux on the surface of the Zn electrode.This stabilization promotes the parallel deposition of Zn along the(002)plane,resulting in a significant reduction in dendritic growth.Notably,our sustainable Zn-lignin battery showcases remarkable cyclic stability,retaining 80%of its initial capacity after 8000 cycles at a high current rate(1 A g^(-1))and maintaining over 75%capacity retention up to 2000 cycles at a low current rate(0.2 A g^(-1)).This study showcases the practical application of WiPSE for the development of low-cost,dendrite-free,and scalable ZnBs.
基金supported by the National Natural Science Foundation of China(No.52273187)National Key R&D Program of China(2021YFA1500800)+1 种基金Guangdong Basic and Applied Basic Research Foundation(2022A1515110372,2023A1515011306,2023A1515240077)Guangdong-Hong Kong Joint Laboratory for Preparation and Application of Ordered Structural Materials of Guangdong Province(2023B1212120011)。
文摘Copper complexes inspired by O_(2)-activating enzymes have been widely investigated as molecular water oxidation catalysts,capable of facile and reversible O─O bond formation and cleavage under mild conditions.In this study,two copper phenanthroline complexes,namely,Cu(phen)and Cu(dophen),exhibit high turnover frequencies(TOFs)of 74±13 and(5.66±0.29)×10^(3)s^(−1)for water oxidation,respectively.Moreover,amino acid-functionalized carbon dots(CDs)were used to support the adhesion of the[Cu]complexes onto the electrode,significantly enhancing the TOFs of(2.80±0.12)×10^(3)and(4.11±0.24)×10^(4)s^(−1),respectively,exceeding the activity of photosystem Ⅱ in nature.Remarkably,the amino acid-functionalized CDs provide a secondary sphere that mimics the catalytic microenvironment of the copper centre,which promotes proton-coupled electron transfer and O─O bond formation.Finally,the photovoltaic-electrolysis(PVE)system was established using CDs-supported Cu catalysts and commercial silicon solar panels,achieving a high solar-to-hydrogen efficiency of 11.59%under the illumination of AM 1.5 G.This represents the most efficient solar-driven water splitting system based on copper-based catalysts to date,introducing the biomimetic secondary sphere to a“proton-rocking”process for water oxidation catalysis and application of the PVE system.
基金supported by the National Natural Science Foundation of China(6080105261271327)+2 种基金Jiangsu Planned Projects for Postdoctoral Research Funds(1201039C)the China Postdoctoral Science Foundation (2012M521099)Hubei Key Laboratory of Intelligent Wireless Communications(IWC2012002)
文摘This paper discusses the problem of direction of departure （DOD） and direction of arrival （DOA） estimation for a bistatic multiple input multiple output （MIMO） radar, and proposes an improved reduced-dimension Capon algorithm therein. Compared with the reduced-dimension Capon algorithm which requires pair matching between the two-dimensional angle estimation, the pro- posed algorithm can obtain automatically paired DOD and DOA estimation without debasing the performance of angle estimation in bistatic MIMO radar. Furthermore, the proposed algorithm has a lower complexity than the reduced-dimension Capon algorithm, and it is suitable for non-uniform linear arrays. The complexity of the proposed algorithm is analyzed and the Cramer-Rao bound （CRB） is also derived. Simulation results verify the usefulness of the proposed algorithm.
文摘In this paper, novel mathematical expressions are derived for the Global Positioning System (GPS) receiver interference tolerance in the presence of different types of interference signals such as: continuous wave interference, narrowband interference, partial band interference, broadband interference, match spectrum interference and pulse interference. Also, in this paper the mean time to loss lock is determined in order to analyse the mentioned interferences effect on the GPS receiver. These derived analytical expressions are validated with the aid of extensive simulation experiments.
基金supported by National Natural Science Foundation of China (Grant No. 50879080, Grant No. 50609025)Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1100013,Grant No. R1100530)
文摘Centrifugal pumps always work under steady conditions,and many researches focus on the steady operation.But transient conditions,such as sudden startup and shutdown,are inevitable.The researches on the inner flow of centrifugal pumps under transient conditions have been done,and they show that the transient operation is different from the steady operation.In order to research the evolution of unsteady flow in a centrifugal pump under transient conditions,and to investigate the mechanism of transient effects by analyzing the unsteady flow in a centrifugal pump,the external characteristic experiment and the internal flow numerical calculation of the centrifugal pump with an open impeller during startup is presented.The relationships of the rotation speed,capacity and head between start-time are obtained by the external characteristics experiment.The numerical calculations under startup process are carried out by using the k-e model and N-S equation.The distribution of velocity and pressure in the inner channel of the tested pump was obtained by choosing fourteen start-time points and twelve geometrical points in the impeller channel during startup.The calculation results show that the velocity and the pressure increase linearly with the start-time before rotation＇s speed gets steady,then changes almost horizontally after rotation speed becomes steady,then fluctuates until being steady.The internal flow characteristics are in good agreement with the external characteristic experimental results and numerical calculation.The simulation methods and results make the basis for the diagnosis and optimization of under flow in the centrifugal pump during transient operation.
文摘Aluminum foils having thicknesses of 10-20 μm are commonly employed as current collectors for cathode electrodes in Li-ion batteries. The effects of the surface morphology of the foil on battery performance were investigated by using a foil with roughened surface by chemical etching and a plain foil with smooth surface on both sides. For high-conductivity LiCoO2 active materials with large particle size, there are no significant differences in battery performance between the two types of foils. But for low-conductivity LiFePO4 active materials with small particle size, high-rate discharge properties are significantly different. The possibility shows that optimizing both the surface morphology of the aluminum foil and particle size of active material leads to improvement of the battery performance.
