The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion c...The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young＇s moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square （RMS） of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films.展开更多
Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of r...Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.展开更多
Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based applications.The bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding Z-curve.Although ...Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based applications.The bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding Z-curve.Although simple,its efficiency is hindered by the step-by-step coordinate shifting and bitwise operations.To tackle this problem,we first propose the efficient encoding algorithm LTFe and the corresponding decoding algorithm LTFd,which adopt two optimization methods to boost the algorithm’s efficiency:1)we design efficient lookup tables(LT)that convert encoding and decoding operations into table-lookup operations;2)we design a bit detection mechanism that skips partial order of a coordinate or a Z-value with consecutive 0s in the front,avoiding unnecessary iterative computations.We propose order-parallel and point-parallel OpenMP-based algorithms to exploit the modern multi-core hardware.Experimental results on discrete,skewed,and real datasets indicate that our point-parallel algorithms can be up to 12.6×faster than the existing algorithms.展开更多
To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA...To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA)and the Convolutional Block Attention Module(CBAM)—to enhance detection accuracy.Additionally,Shape-IoU is employed as the loss function to refine localization precision.Our model further incorporates an adaptive feature fusion mechanism,which optimizes multi-scale object representation,ensuring robust tracking in complex aerial environments.We evaluate the performance of AVA-PRB on two benchmark datasets:Aerial Person Detection and VisDrone2019-Det.The model achieves 60.9%mAP@0.5 on the Aerial Person Detection dataset,and 51.2%mAP@0.5 on VisDrone2019-Det,demonstrating its effectiveness in aerial object detection.Beyond detection,we propose a novel trajectory estimation method that improves movement path prediction under aerial motion.Experimental results indicate that our approach reduces path deviation by up to 64%,effectively mitigating errors caused by rapid camera movements and background variations.By optimizing feature extraction and enhancing spatialtemporal coherence,our method significantly improves object tracking under aerial moving perspectives.This research addresses the limitations of fixed-camera tracking,enhancing flexibility and accuracy in aerial tracking applications.The proposed approach has broad potential for real-world applications,including surveillance,traffic monitoring,and environmental observation.展开更多
Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall.The efficiency of IIDS highly relies on the al...Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall.The efficiency of IIDS highly relies on the algorithm performance.The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms.Here,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the dimensionality.It also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised manner.Here,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers.By training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification accuracy.Thus,the model efficiency is learned linearly.The multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game theory.The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so on.The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection.Here,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.展开更多
Decoding genetic information is crucial for gene therapy and cancer diagnosis,which has attracted growing interest in the field of clinical medicine and life science.In this study,we conducted a comprehensive explorat...Decoding genetic information is crucial for gene therapy and cancer diagnosis,which has attracted growing interest in the field of clinical medicine and life science.In this study,we conducted a comprehensive exploration to obtain the detection mechanism of molecular beacons from a mechanics point of view.The potential energy function of molecular beacon/target system is established firstly,based on which the profile of molecular beacons is solved by genetic algorithm optimization.The length of stem and the total energy are further calculated when the target is hybridized with loop and stem.The results show that the hybridization between target and stem is energetically favorable compared with that between target and loop,indicating a new detection strategy.These analyses may cast light on understanding the mechanism of molecular beacons detection,and further help to design novel molecular beacons with high resolution and quantification.展开更多
To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised...To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.展开更多
Bundle recommendation offers users more holistic insights by recommending multiple compatible items at once.However,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in ...Bundle recommendation offers users more holistic insights by recommending multiple compatible items at once.However,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation algorithms.Furthermore,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a bundle.Identifying them can aid users in refining their item combinations.While the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning phase.This limitation can hinder the algorithm’s performance.To tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item detection.The encoder learns potential item correlations through a self-attention mechanism.Concurrently,the decoder garners efficient inference frameworks by directly assessing item anomalies.We have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.展开更多
Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)technologies.Despite various str...Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)technologies.Despite various strategies,critical challenges still remain in achieving broadband photoresponse,cooling-free operation,and large-scale complementary-metal-oxide-semiconductor(CMOS)-compatible manufacturability.To leap beyond these limitations,the bolometric effect–a thermal detection mechanism–is introduced into the waveguide platform.More importantly,we pursue a free-carrier absorption(FCA)process in germanium(Ge)to create an efficient light-absorbing medium,providing a pragmatic solution for full coverage of the MIR spectrum without incorporating exotic materials into CMOS.Here,we present an uncooled waveguide-integrated photodetector based on a Ge-on-insulator(Ge-OI)PIC architecture,which exploits the bolometric effect combined with FCA.Notably,our device exhibits a broadband responsivity of 28.35%/mW across 4030–4360 nm(and potentially beyond),challenging the state of the art,while achieving a noise-equivalent power of 4.03×10^(−7) W/Hz^(0.5) at 4180 nm.We further demonstrate label-free sensing of gaseous carbon dioxide(CO_(2))using our integrated photodetector and sensing waveguide on a single chip.This approach to room-temperature waveguide-integrated MIR photodetection,harnessing bolometry with FCA in Ge,not only facilitates the realization of fully integrated lab-on-a-chip systems with wavelength flexibility but also provides a blueprint for MIR PICs with CMOS-foundry-compatibility.展开更多
Distributed secondary control,depending on the sparse communication topology,excels for its flexibility and expandability in microgrids.The communication network plays an important role in microgrid control,but it is ...Distributed secondary control,depending on the sparse communication topology,excels for its flexibility and expandability in microgrids.The communication network plays an important role in microgrid control,but it is vulnerable to cyber-attacks.In this paper,the mathematical model for false data injection(FDI)attacks in AC microgrids is established,and the corresponding detection mechanism based on the morphological gradient is designed for the location of cyber-attacks in communication topology.Then,we propose a median-based resilient consensus voltage control strategy to mitigate the negative effects caused by malicious cyber-attacks and ensure the safe operation of the microgrid.Combining the detection method and resilient consensus control,a novel eventdriven mitigation scheme is derived to improve the resilience of microgrids under cyber-attacks.Finally,a tested microgrid model composed of five different distributed generation(DG)units is simulated in the MATLAB/Simulink environment.The feasibility and effectiveness of the presented detection mechanism and resilient consensus strategy are verified by simulation results applying different scenarios.展开更多
Dynamic DNA nanotechnology belongs to a larger umbrella of DNA nanotechnology that primarily uses DNA as a nano-scopic material to build mobile structures and cascaded reaction networks powered by DNA oligonucleotides...Dynamic DNA nanotechnology belongs to a larger umbrella of DNA nanotechnology that primarily uses DNA as a nano-scopic material to build mobile structures and cascaded reaction networks powered by DNA oligonucleotides.A widely used mechanism to construct a dynamic DNA system is toehold-mediated strand displacement reactions(TMSDRs).TMSDRs are easy to engineer because of the known base-pairing rules that follow the Watson–Crick model of DNA,sequence-dependent binding rates,and energies of DNAs,whose secondary structure is predictable.Due to these attributes,TMSDRs have been used to develop enzyme-free isothermal reaction networks with remarkable applications in diagnostics,therapeutics and DNA computing.In this review,we briefly introduce the working principle of TMSDRs,in silico design considerations,and diverse input and output signals that can be processed through TMSDRs.We then summarize recent applications where TMSDRs are successfully employed in detecting clinically relevant targets such as single nucleotide polymorphisms and variants,microRNAs and whole cells and to develop programmable drug delivery vehicles and regulation therapies includ-ing transcriptional and protein regulations.We also discuss TMSDRs driven biomedical applications of DNA hydrogels and DNA computing.Finally,we discuss the challenges in each of these applications and the prospects of TMSDRs in biomedical engineering.展开更多
This work presents the design of a novel static-triggered power-rail electrostatic discharge(ESD)clamp circuit. The superior transient-noise immunity of the static ESD detection mechanism over the transient one is fir...This work presents the design of a novel static-triggered power-rail electrostatic discharge(ESD)clamp circuit. The superior transient-noise immunity of the static ESD detection mechanism over the transient one is firstly discussed. Based on the discussion, a novel power-rail ESD clamp circuit utilizing the static ESD detection mechanism is proposed. By skillfully incorporating a thyristor delay stage into the trigger circuit(TC), the proposed circuit achieves the best ESD-conduction behavior while consuming the lowest leakage current(Ileak) at the normal bias voltage among all investigated circuits in this work. In addition, the proposed circuit achieves an excellent false-triggering immunity against fast power-up pulses. All investigated circuits are fabricated in a 65-nm CMOS process. Performance superiorities of the proposed circuit are fully verified by both simulation and test results. Moreover, the proposed circuit offers an efficient on-chip ESD protection scheme considering the worst discharge case in the utilized process.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.60876072)the Tianjin Research Program of Application Foundation and Advanced Technology,China(Grant No.10JCZDJC15500)
文摘The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young＇s moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square （RMS） of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films.
基金supported by the National Key Research and Development Program of China (No.2022YFE0196000)the National Natural Science Foundation of China (No.61502429)。
文摘Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.
基金funded by the Key Project of the Open Fund for Computer Technology Applications in Yunnan under Grant no.CB23031D025A.
文摘Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based applications.The bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding Z-curve.Although simple,its efficiency is hindered by the step-by-step coordinate shifting and bitwise operations.To tackle this problem,we first propose the efficient encoding algorithm LTFe and the corresponding decoding algorithm LTFd,which adopt two optimization methods to boost the algorithm’s efficiency:1)we design efficient lookup tables(LT)that convert encoding and decoding operations into table-lookup operations;2)we design a bit detection mechanism that skips partial order of a coordinate or a Z-value with consecutive 0s in the front,avoiding unnecessary iterative computations.We propose order-parallel and point-parallel OpenMP-based algorithms to exploit the modern multi-core hardware.Experimental results on discrete,skewed,and real datasets indicate that our point-parallel algorithms can be up to 12.6×faster than the existing algorithms.
基金funded by theNational Science and TechnologyCouncil(NSTC),Taiwan,under grant numbers NSTC 113-2634-F-A49-007 and NSTC 112-2634-F-A49-007.
文摘To improve small object detection and trajectory estimation from an aerial moving perspective,we propose the Aerial View Attention-PRB(AVA-PRB)model.AVA-PRB integrates two attention mechanisms—Coordinate Attention(CA)and the Convolutional Block Attention Module(CBAM)—to enhance detection accuracy.Additionally,Shape-IoU is employed as the loss function to refine localization precision.Our model further incorporates an adaptive feature fusion mechanism,which optimizes multi-scale object representation,ensuring robust tracking in complex aerial environments.We evaluate the performance of AVA-PRB on two benchmark datasets:Aerial Person Detection and VisDrone2019-Det.The model achieves 60.9%mAP@0.5 on the Aerial Person Detection dataset,and 51.2%mAP@0.5 on VisDrone2019-Det,demonstrating its effectiveness in aerial object detection.Beyond detection,we propose a novel trajectory estimation method that improves movement path prediction under aerial motion.Experimental results indicate that our approach reduces path deviation by up to 64%,effectively mitigating errors caused by rapid camera movements and background variations.By optimizing feature extraction and enhancing spatialtemporal coherence,our method significantly improves object tracking under aerial moving perspectives.This research addresses the limitations of fixed-camera tracking,enhancing flexibility and accuracy in aerial tracking applications.The proposed approach has broad potential for real-world applications,including surveillance,traffic monitoring,and environmental observation.
文摘Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall.The efficiency of IIDS highly relies on the algorithm performance.The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms.Here,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the dimensionality.It also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised manner.Here,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers.By training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification accuracy.Thus,the model efficiency is learned linearly.The multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game theory.The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so on.The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection.Here,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.
基金We are grateful for financial support from the Strategic Priority Research Program of Chinese Academy of Sciences(Grant XDB36000000)the Natural Science Foundation of Beijing(Grants 2184130 and 1202023)+1 种基金the National Natural Science Foundation of China(Grant 11672079)The computation experiment was mainly supported by the Supercomputing Center of Chinese Academy of Sciences(SCCAS).
文摘Decoding genetic information is crucial for gene therapy and cancer diagnosis,which has attracted growing interest in the field of clinical medicine and life science.In this study,we conducted a comprehensive exploration to obtain the detection mechanism of molecular beacons from a mechanics point of view.The potential energy function of molecular beacon/target system is established firstly,based on which the profile of molecular beacons is solved by genetic algorithm optimization.The length of stem and the total energy are further calculated when the target is hybridized with loop and stem.The results show that the hybridization between target and stem is energetically favorable compared with that between target and loop,indicating a new detection strategy.These analyses may cast light on understanding the mechanism of molecular beacons detection,and further help to design novel molecular beacons with high resolution and quantification.
基金Joint Funds of the National Natural Science Foundation of China(NSAF)(No.U1330130)General Program of Civil Aviation Flight University of China(No.J2015-39)
文摘To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.
基金supported in part by the Guangxi Key Laboratory of Trusted Software,China(KX202037)the Project of Guangxi Science and Technology,China(GuiKeAD 20297054)the Guangxi Natural Science Foundation Project,China(2020GXNSFBA297108)。
文摘Bundle recommendation offers users more holistic insights by recommending multiple compatible items at once.However,the intricate correlations between items,varied user preferences,and the pronounced data sparsity in combinations present significant challenges for bundle recommendation algorithms.Furthermore,current bundle recommendation methods fail to identify mismatched items within a given set,a process termed as‘‘outlier item detection’’.These outlier items are those with the weakest correlations within a bundle.Identifying them can aid users in refining their item combinations.While the correlation among items can predict the detection of such outliers,the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning phase.This limitation can hinder the algorithm’s performance.To tackle these challenges,we introduce an encoder–decoder architecture tailored for outlier item detection.The encoder learns potential item correlations through a self-attention mechanism.Concurrently,the decoder garners efficient inference frameworks by directly assessing item anomalies.We have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.
基金supported by the National Research Foundation of Korea(NRF)(2023R1A2C2002777,RS-2024-00407767)the KIST Institutional Program(2E33052)the BK21 FOUR.
文摘Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)technologies.Despite various strategies,critical challenges still remain in achieving broadband photoresponse,cooling-free operation,and large-scale complementary-metal-oxide-semiconductor(CMOS)-compatible manufacturability.To leap beyond these limitations,the bolometric effect–a thermal detection mechanism–is introduced into the waveguide platform.More importantly,we pursue a free-carrier absorption(FCA)process in germanium(Ge)to create an efficient light-absorbing medium,providing a pragmatic solution for full coverage of the MIR spectrum without incorporating exotic materials into CMOS.Here,we present an uncooled waveguide-integrated photodetector based on a Ge-on-insulator(Ge-OI)PIC architecture,which exploits the bolometric effect combined with FCA.Notably,our device exhibits a broadband responsivity of 28.35%/mW across 4030–4360 nm(and potentially beyond),challenging the state of the art,while achieving a noise-equivalent power of 4.03×10^(−7) W/Hz^(0.5) at 4180 nm.We further demonstrate label-free sensing of gaseous carbon dioxide(CO_(2))using our integrated photodetector and sensing waveguide on a single chip.This approach to room-temperature waveguide-integrated MIR photodetection,harnessing bolometry with FCA in Ge,not only facilitates the realization of fully integrated lab-on-a-chip systems with wavelength flexibility but also provides a blueprint for MIR PICs with CMOS-foundry-compatibility.
基金supported by the National Key Research and Development Program of China(2020YFE0200400)。
文摘Distributed secondary control,depending on the sparse communication topology,excels for its flexibility and expandability in microgrids.The communication network plays an important role in microgrid control,but it is vulnerable to cyber-attacks.In this paper,the mathematical model for false data injection(FDI)attacks in AC microgrids is established,and the corresponding detection mechanism based on the morphological gradient is designed for the location of cyber-attacks in communication topology.Then,we propose a median-based resilient consensus voltage control strategy to mitigate the negative effects caused by malicious cyber-attacks and ensure the safe operation of the microgrid.Combining the detection method and resilient consensus control,a novel eventdriven mitigation scheme is derived to improve the resilience of microgrids under cyber-attacks.Finally,a tested microgrid model composed of five different distributed generation(DG)units is simulated in the MATLAB/Simulink environment.The feasibility and effectiveness of the presented detection mechanism and resilient consensus strategy are verified by simulation results applying different scenarios.
基金support from the Science&Engineering Research Board,the Mathematical Research Impact Centric Support(SERB-MTR/2022/000369)the Indian Institute of Technology Delhi seed grant.
文摘Dynamic DNA nanotechnology belongs to a larger umbrella of DNA nanotechnology that primarily uses DNA as a nano-scopic material to build mobile structures and cascaded reaction networks powered by DNA oligonucleotides.A widely used mechanism to construct a dynamic DNA system is toehold-mediated strand displacement reactions(TMSDRs).TMSDRs are easy to engineer because of the known base-pairing rules that follow the Watson–Crick model of DNA,sequence-dependent binding rates,and energies of DNAs,whose secondary structure is predictable.Due to these attributes,TMSDRs have been used to develop enzyme-free isothermal reaction networks with remarkable applications in diagnostics,therapeutics and DNA computing.In this review,we briefly introduce the working principle of TMSDRs,in silico design considerations,and diverse input and output signals that can be processed through TMSDRs.We then summarize recent applications where TMSDRs are successfully employed in detecting clinically relevant targets such as single nucleotide polymorphisms and variants,microRNAs and whole cells and to develop programmable drug delivery vehicles and regulation therapies includ-ing transcriptional and protein regulations.We also discuss TMSDRs driven biomedical applications of DNA hydrogels and DNA computing.Finally,we discuss the challenges in each of these applications and the prospects of TMSDRs in biomedical engineering.
基金supported by National Science and Technology Major Project of China(Grant No.2013ZX02303002)
文摘This work presents the design of a novel static-triggered power-rail electrostatic discharge(ESD)clamp circuit. The superior transient-noise immunity of the static ESD detection mechanism over the transient one is firstly discussed. Based on the discussion, a novel power-rail ESD clamp circuit utilizing the static ESD detection mechanism is proposed. By skillfully incorporating a thyristor delay stage into the trigger circuit(TC), the proposed circuit achieves the best ESD-conduction behavior while consuming the lowest leakage current(Ileak) at the normal bias voltage among all investigated circuits in this work. In addition, the proposed circuit achieves an excellent false-triggering immunity against fast power-up pulses. All investigated circuits are fabricated in a 65-nm CMOS process. Performance superiorities of the proposed circuit are fully verified by both simulation and test results. Moreover, the proposed circuit offers an efficient on-chip ESD protection scheme considering the worst discharge case in the utilized process.
