The ability to predict the anti-interference communications performance of unmanned aerial vehicle(UAV)data links is critical for intelligent route planning of UAVs in real combat scenarios.Previous research in this a...The ability to predict the anti-interference communications performance of unmanned aerial vehicle(UAV)data links is critical for intelligent route planning of UAVs in real combat scenarios.Previous research in this area has encountered several limitations:Classifiers exhibit low training efficiency,their precision is notably reduced when dealing with imbalanced samples,and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary.This paper proposes the sequential Latin hypercube sampling(SLHS)-support vector machine(SVM)-AdaBoost algorithm,which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization.Additionally,it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm.Through comparison,the modeling efficiency,prediction accuracy on the test set,and macro-averaged values of precision,recall,and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%,5.7%,36.0%,25.0%,and 34.2%,respectively,compared with Grid-SVM.Additionally,these values are improved by 22.2%,2.1%,11.3%,2.8%,and 7.4%,respectively,compared with particle swarm optimization(PSO)-SVM-AdaBoost.Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm,the classification prediction model of anti-interference performance of UAV data links,which took factors like three-dimensional position of UAV and antenna bearing into consideration,is established and used to assess the safety of the classical flying path and optimize the flying route.It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path,whereas intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered meanwhile reducing the route length by at least 2.3%,thus benefiting both safety and operation efficiency.展开更多
With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power i...With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power interference can severely degrade SAR imaging and signal processing,often rendering target detection impossible.This highlights the urgent need for robust anti-interference solutions in both the signal processing and image processing domains.While current methods address interference across various domains,techniques such as waveform modification and spatial filtering typically increase the system costs and complexity.To overcome these limitations,we propose a novel approach that leverages the multi-domain characteristics of interference to efficiently suppress narrowband interference and repeater modulation interference.Specifically,narrowband interference is mitigated using notch filtering,a signal processing technique that effectively filters out unwanted frequencies,while repeater modulation interference is addressed through strong signal amplitude normalization,which enhances both the signal and image processing quality.These methods were validated through tests on real SAR data,demonstrating significant improvements in the imaging performance and system robustness.Our approach offers valuable insights for advancing anti-interference technologies in SAR systems and provides a cost-effective solution to enhance their resilience in complex electronic warfare environments.展开更多
The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimat...The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.展开更多
This paper presents the study and application of the electronic device anti-interference techniques underhigh voltage and/or heavy current electro-magnetic circumstance in power system.[
When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be op...When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be optimized. The existing Dolph-Chebyshev weighting method can get the lowest side lobe level under given main lobe width, but for the other non-uniform circular array and nonlinear array, the low side lobe pattern needs to be designed specially. The second order cone programming optimization （SOCP） algorithm proposed in the paper transforms the optimization of the beam pattern into a standard convex optimization problem. Thus there is a paradigm to follow for any array formation, which not only achieves the purpose of Dolph-Chebyshev weighting, but also solves the problem of the increased side lobe when the signal is at end fire direction The simulation proves that the SOCP algorithm can detect the weak target better than the conventional beam forming.展开更多
An RF transceiver composed of a zero-IF receiver and a direct up-conversion transmitter for cognitive radio applications is presented. The adjustable channel filter array in the receiver is used to suppress adjacent c...An RF transceiver composed of a zero-IF receiver and a direct up-conversion transmitter for cognitive radio applications is presented. The adjustable channel filter array in the receiver is used to suppress adjacent channel interference in televisions signal coexistence environments. The low noise amplifier （LNA） with wide dynamic range and high linearity is employed to enhance the anti-interference competence of the zero-IF receiver. Meanwhile, the high linearity power amplifier （PA） .is used to promote the adjacent channel power ratio （ACPR） characteristic of the direct up-conversion transmitter. The measured error vector magnitude （EVM） results show that the anti-interference competence of the zero-IF receiver is dramatically enhanced by employing a channel filter array. The measured ACPR of the direct up-conversion transmitter is -47. 98 dBc on the channel centered at 714 MHz when the output power is 27 dBm.展开更多
Shallow conductive heterogeneity can lead to static shifts ain the apparent resistivity sounding curve of controlled-source audio-frequency magnetotellurics(CSAMT).The static effect will shift the apparent resistivity...Shallow conductive heterogeneity can lead to static shifts ain the apparent resistivity sounding curve of controlled-source audio-frequency magnetotellurics(CSAMT).The static effect will shift the apparent resistivity curves along with axial log-log coordinates.Such an effect,if not properly processed,can distort the resistivity of rock formation and the depth of interfaces,and even make the geological structures unrecognizable.In this paper,we discuss the reasons and characteristics of the static shift and summarize the previous studies regarding static shift correction.Then,we propose the Guided Image Filtering algorithm to suppress static shifts in CSAMT.In detail,we use the multi-window superposition method to superimpose 1D signals into a 2D matrix image,which is subsequently processed with Guided Image Filtering.In the synthetic model study and field examples,the Guided Image Filtering algorithm has effectively corrected and suppressed static shifts,and finally improved the precision of data interpretation.展开更多
To suppress the interference in the ultra-wideband （AI-UWB） system is a challenging problem. An anti-interference multiband orthogonal frequency-division multiplexing ultra-wideband （AI-UWB） system, based on sprea...To suppress the interference in the ultra-wideband （AI-UWB） system is a challenging problem. An anti-interference multiband orthogonal frequency-division multiplexing ultra-wideband （AI-UWB） system, based on spreading and interleaving is addressed. It will exploit the frequency diversity across the subcarriers and provide the robustness to narrow-band interference, by spreading the coded bit streams within each sub-band and interleaving across all sub-bands. Simulating results show that the spreading and interleaving provide about 5 dB to 10 dB advantages over the conventional multiband orthogonal frequency-division multiplexing ultra-wideband system in signal-to-interference ratio. Spreading and interleaving is an effective cure for enhancing the robustness to narrowband interference.展开更多
Accurate detection of important biomarkers with ultra-low levels in complex biological matrix is one of the frontier scientific issues because of possible signal interference of potential reductive agents and protein ...Accurate detection of important biomarkers with ultra-low levels in complex biological matrix is one of the frontier scientific issues because of possible signal interference of potential reductive agents and protein molecules.Herein,a self-powered anti-interference photoelectrochemical(PEC)immunosensor was explored for sensitive and specific detection of model target of cardiac troponinⅠ(cTnI).Specifically,a novel ternary heterojunction served as the photocathode to offer a remarkable current output and a zwitterionic peptide was introduced to build a robust antifouling biointerface.CuInS(CIS)film with porous network nanostructure was first prepared and then modified in order with ZnInS(ZIS)nanocrystals and Au nanoparticles to fabricate the Au/ZIS/CIS heterojunction photocathode.After capture cTnI antibody(Ab)was immobilized,the zwitterionic peptide KAEAKAEAPPPPC was then anchored to compete the immunosensor.The elaborated PEC immunosensor exhibited high sensitivity for target cTnI antigen(Ag)detection,with good anti-interference against reductive agents and nonspecific proteins.This integration strategy of heterojunction photocathode with zwitterionic peptide provides a new sight to develop advanced PEC immunosensors applying in practical biosamples.展开更多
With the rapid development of the city, it is necessar</span><span style="font-family:Verdana;">y to obtain geological information within 500 meters. Electrical prospecting is not only low cost a...With the rapid development of the city, it is necessar</span><span style="font-family:Verdana;">y to obtain geological information within 500 meters. Electrical prospecting is not only low cost and simple operation, but also solves the problem of insufficient drilling density in </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">survey</span><span style="font-family:Verdana;">. However, due to the dense urban buildings and strong electromagnetic interference, it is difficult for traditional electrical instruments to obtain effective data</span><span style="font-family:Verdana;">.</span><span style="font-family:""> </span><span style="font-family:Verdana;">An </span><span style="font-family:Verdana;">anti-interference electrical method instrument is designed.</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">In the application test of Tongzhou</span><span style="color:black;font-family:Verdana;"> core area in Beijing, the resistivity sounding data collected by </span></span><span style="font-family:"color:black;"><span style="font-family:Verdana;">anti-interference</span><span style="font-family:Verdana;"> electrical method </span><span style="font-family:Verdana;">instrument</span><span style="font-family:Verdana;"> is stable and reliable;inversion results of sounding are basically consistent with borehole data;</span><span style="font-family:Verdana;">the known Zhangjiawan fault and Yaoxinzhuang fault are obvious;basement karst collapse area inferred is basically coincident with the historical collapse area. It is proved that the anti-interference electrical </span><span style="font-family:Verdana;">method</span><span style="font-family:Verdana;"> instrument is effective and can be applied to the geological survey of underground space in other cities.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th...Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.展开更多
With the rapid development of internet, users demand for the performance of wireless network is getting higher and higher. Under this background, with the rise of small base stations such as micro-cellular base statio...With the rapid development of internet, users demand for the performance of wireless network is getting higher and higher. Under this background, with the rise of small base stations such as micro-cellular base stations and the appearance of low-power base stations such as micro-cellular base stations and femto-cellular base stations, wireless cellular networks are gradually transformed into emerging heterogeneous wireless networks, and the number of base stations is also greatly increased. Due to the limitation of bandwidth resources, anti-interference and handoff have become common concerns at home and abroad.展开更多
Automatic gauge control(AGC in the article)is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill.Most traditional AGC control algorithms need stable external system condi...Automatic gauge control(AGC in the article)is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill.Most traditional AGC control algorithms need stable external system conditions and hard to stabilize under complex interference that meets coverage requirements.This paper presents a new anti-interference strategy for AGC control of 20-Hi cold reversing mill.The proposed algorithm introduces a united dynamic weights algorithm of feed forward-mass flow to avoid the complex interference problem in AGC control,the relevant control strategy is provided to eliminate the adverse effects.At the same time,the D-value between feed forward-mass flow pre-computation and thickness measurement deviation is dynamic compared,the final gap position regulation is calculated by developing a set of united dynamic weights between feed forward control and mass flow control.Finally,the output of controllers is sent to actuator though a constant rate smoothing.The proposed strategy is compared with conventional AGC control on Experimental platform and project application,the results show that the proposed strategy is more stable than comparison method and majority of system uncertainty produced by mentioned interference is significantly eliminated.展开更多
Integrated circuit technology based on analog electronic and digital electronic technology is more and more widely used in secondary equipment such as microcomputer protection devices. With the development of technolo...Integrated circuit technology based on analog electronic and digital electronic technology is more and more widely used in secondary equipment such as microcomputer protection devices. With the development of technology, the complexity of anti-interference problem in the secondary loop is increasing due to the diversity of technical routes and the complexity of types of devices in the power system. In the station of new energy power plant, there are many kinds of secondary circuits, which make the anti-interference problem of the circuits especially prominent. This paper attempts to analyze and discuss the common interference sources and anti-interference measures from the common interference sources and interference ways in the secondary circuits of the new energy power plant station.展开更多
In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms...In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.展开更多
In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and t...In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.展开更多
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered so...Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.展开更多
文摘The ability to predict the anti-interference communications performance of unmanned aerial vehicle(UAV)data links is critical for intelligent route planning of UAVs in real combat scenarios.Previous research in this area has encountered several limitations:Classifiers exhibit low training efficiency,their precision is notably reduced when dealing with imbalanced samples,and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary.This paper proposes the sequential Latin hypercube sampling(SLHS)-support vector machine(SVM)-AdaBoost algorithm,which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization.Additionally,it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm.Through comparison,the modeling efficiency,prediction accuracy on the test set,and macro-averaged values of precision,recall,and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%,5.7%,36.0%,25.0%,and 34.2%,respectively,compared with Grid-SVM.Additionally,these values are improved by 22.2%,2.1%,11.3%,2.8%,and 7.4%,respectively,compared with particle swarm optimization(PSO)-SVM-AdaBoost.Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm,the classification prediction model of anti-interference performance of UAV data links,which took factors like three-dimensional position of UAV and antenna bearing into consideration,is established and used to assess the safety of the classical flying path and optimize the flying route.It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path,whereas intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered meanwhile reducing the route length by at least 2.3%,thus benefiting both safety and operation efficiency.
文摘With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power interference can severely degrade SAR imaging and signal processing,often rendering target detection impossible.This highlights the urgent need for robust anti-interference solutions in both the signal processing and image processing domains.While current methods address interference across various domains,techniques such as waveform modification and spatial filtering typically increase the system costs and complexity.To overcome these limitations,we propose a novel approach that leverages the multi-domain characteristics of interference to efficiently suppress narrowband interference and repeater modulation interference.Specifically,narrowband interference is mitigated using notch filtering,a signal processing technique that effectively filters out unwanted frequencies,while repeater modulation interference is addressed through strong signal amplitude normalization,which enhances both the signal and image processing quality.These methods were validated through tests on real SAR data,demonstrating significant improvements in the imaging performance and system robustness.Our approach offers valuable insights for advancing anti-interference technologies in SAR systems and provides a cost-effective solution to enhance their resilience in complex electronic warfare environments.
基金supported by the National Natural Science Foundation of China(41174162).
文摘The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.
文摘This paper presents the study and application of the electronic device anti-interference techniques underhigh voltage and/or heavy current electro-magnetic circumstance in power system.[
基金Special Item of National Major Scientific Apparatus Development(No.2013YQ140431)
文摘When signal-to-interference ratio is low, the energy of strong interference leaked from the side lobe of beam pattern will infect the detection of weak target. Therefore, the beam pattern needs to be optimized. The existing Dolph-Chebyshev weighting method can get the lowest side lobe level under given main lobe width, but for the other non-uniform circular array and nonlinear array, the low side lobe pattern needs to be designed specially. The second order cone programming optimization （SOCP） algorithm proposed in the paper transforms the optimization of the beam pattern into a standard convex optimization problem. Thus there is a paradigm to follow for any array formation, which not only achieves the purpose of Dolph-Chebyshev weighting, but also solves the problem of the increased side lobe when the signal is at end fire direction The simulation proves that the SOCP algorithm can detect the weak target better than the conventional beam forming.
基金The National Natural Science Foundation of China(No.60621002)the National High Technology Research and Development Program of China(863 Program)(No.2009AA011801)
文摘An RF transceiver composed of a zero-IF receiver and a direct up-conversion transmitter for cognitive radio applications is presented. The adjustable channel filter array in the receiver is used to suppress adjacent channel interference in televisions signal coexistence environments. The low noise amplifier （LNA） with wide dynamic range and high linearity is employed to enhance the anti-interference competence of the zero-IF receiver. Meanwhile, the high linearity power amplifier （PA） .is used to promote the adjacent channel power ratio （ACPR） characteristic of the direct up-conversion transmitter. The measured error vector magnitude （EVM） results show that the anti-interference competence of the zero-IF receiver is dramatically enhanced by employing a channel filter array. The measured ACPR of the direct up-conversion transmitter is -47. 98 dBc on the channel centered at 714 MHz when the output power is 27 dBm.
基金sponsored by the Basic Science Center Project of National Natural Science Foundation of China(72088101)。
文摘Shallow conductive heterogeneity can lead to static shifts ain the apparent resistivity sounding curve of controlled-source audio-frequency magnetotellurics(CSAMT).The static effect will shift the apparent resistivity curves along with axial log-log coordinates.Such an effect,if not properly processed,can distort the resistivity of rock formation and the depth of interfaces,and even make the geological structures unrecognizable.In this paper,we discuss the reasons and characteristics of the static shift and summarize the previous studies regarding static shift correction.Then,we propose the Guided Image Filtering algorithm to suppress static shifts in CSAMT.In detail,we use the multi-window superposition method to superimpose 1D signals into a 2D matrix image,which is subsequently processed with Guided Image Filtering.In the synthetic model study and field examples,the Guided Image Filtering algorithm has effectively corrected and suppressed static shifts,and finally improved the precision of data interpretation.
基金the National "863" High Technology Research Program of China (2005AA123320)Universities Natural Science Research Project of Jiangsu Province (05KJB510101).
文摘To suppress the interference in the ultra-wideband （AI-UWB） system is a challenging problem. An anti-interference multiband orthogonal frequency-division multiplexing ultra-wideband （AI-UWB） system, based on spreading and interleaving is addressed. It will exploit the frequency diversity across the subcarriers and provide the robustness to narrow-band interference, by spreading the coded bit streams within each sub-band and interleaving across all sub-bands. Simulating results show that the spreading and interleaving provide about 5 dB to 10 dB advantages over the conventional multiband orthogonal frequency-division multiplexing ultra-wideband system in signal-to-interference ratio. Spreading and interleaving is an effective cure for enhancing the robustness to narrowband interference.
基金supported by the National Natural Science Foundation of China(Nos.22074073,21275087)the Natural Science Foundation of Shandong Province of China(No.ZR2021YQ11)the Taishan Scholar Program of Shandong Province of China(No.ts20110829)。
文摘Accurate detection of important biomarkers with ultra-low levels in complex biological matrix is one of the frontier scientific issues because of possible signal interference of potential reductive agents and protein molecules.Herein,a self-powered anti-interference photoelectrochemical(PEC)immunosensor was explored for sensitive and specific detection of model target of cardiac troponinⅠ(cTnI).Specifically,a novel ternary heterojunction served as the photocathode to offer a remarkable current output and a zwitterionic peptide was introduced to build a robust antifouling biointerface.CuInS(CIS)film with porous network nanostructure was first prepared and then modified in order with ZnInS(ZIS)nanocrystals and Au nanoparticles to fabricate the Au/ZIS/CIS heterojunction photocathode.After capture cTnI antibody(Ab)was immobilized,the zwitterionic peptide KAEAKAEAPPPPC was then anchored to compete the immunosensor.The elaborated PEC immunosensor exhibited high sensitivity for target cTnI antigen(Ag)detection,with good anti-interference against reductive agents and nonspecific proteins.This integration strategy of heterojunction photocathode with zwitterionic peptide provides a new sight to develop advanced PEC immunosensors applying in practical biosamples.
文摘With the rapid development of the city, it is necessar</span><span style="font-family:Verdana;">y to obtain geological information within 500 meters. Electrical prospecting is not only low cost and simple operation, but also solves the problem of insufficient drilling density in </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">survey</span><span style="font-family:Verdana;">. However, due to the dense urban buildings and strong electromagnetic interference, it is difficult for traditional electrical instruments to obtain effective data</span><span style="font-family:Verdana;">.</span><span style="font-family:""> </span><span style="font-family:Verdana;">An </span><span style="font-family:Verdana;">anti-interference electrical method instrument is designed.</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">In the application test of Tongzhou</span><span style="color:black;font-family:Verdana;"> core area in Beijing, the resistivity sounding data collected by </span></span><span style="font-family:"color:black;"><span style="font-family:Verdana;">anti-interference</span><span style="font-family:Verdana;"> electrical method </span><span style="font-family:Verdana;">instrument</span><span style="font-family:Verdana;"> is stable and reliable;inversion results of sounding are basically consistent with borehole data;</span><span style="font-family:Verdana;">the known Zhangjiawan fault and Yaoxinzhuang fault are obvious;basement karst collapse area inferred is basically coincident with the historical collapse area. It is proved that the anti-interference electrical </span><span style="font-family:Verdana;">method</span><span style="font-family:Verdana;"> instrument is effective and can be applied to the geological survey of underground space in other cities.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金supported by Yunnan Provincial Basic Research Project(202401AT070344,202301AT070443)National Natural Science Foundation of China(62263014,52207105)+1 种基金Yunnan Lancang-Mekong International Electric Power Technology Joint Laboratory(202203AP140001)Major Science and Technology Projects in Yunnan Province(202402AG050006).
文摘Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy.
文摘With the rapid development of internet, users demand for the performance of wireless network is getting higher and higher. Under this background, with the rise of small base stations such as micro-cellular base stations and the appearance of low-power base stations such as micro-cellular base stations and femto-cellular base stations, wireless cellular networks are gradually transformed into emerging heterogeneous wireless networks, and the number of base stations is also greatly increased. Due to the limitation of bandwidth resources, anti-interference and handoff have become common concerns at home and abroad.
文摘Automatic gauge control(AGC in the article)is the key technology of product thickness accuracy and flatness quality in modern cold rolling mill.Most traditional AGC control algorithms need stable external system conditions and hard to stabilize under complex interference that meets coverage requirements.This paper presents a new anti-interference strategy for AGC control of 20-Hi cold reversing mill.The proposed algorithm introduces a united dynamic weights algorithm of feed forward-mass flow to avoid the complex interference problem in AGC control,the relevant control strategy is provided to eliminate the adverse effects.At the same time,the D-value between feed forward-mass flow pre-computation and thickness measurement deviation is dynamic compared,the final gap position regulation is calculated by developing a set of united dynamic weights between feed forward control and mass flow control.Finally,the output of controllers is sent to actuator though a constant rate smoothing.The proposed strategy is compared with conventional AGC control on Experimental platform and project application,the results show that the proposed strategy is more stable than comparison method and majority of system uncertainty produced by mentioned interference is significantly eliminated.
文摘Integrated circuit technology based on analog electronic and digital electronic technology is more and more widely used in secondary equipment such as microcomputer protection devices. With the development of technology, the complexity of anti-interference problem in the secondary loop is increasing due to the diversity of technical routes and the complexity of types of devices in the power system. In the station of new energy power plant, there are many kinds of secondary circuits, which make the anti-interference problem of the circuits especially prominent. This paper attempts to analyze and discuss the common interference sources and anti-interference measures from the common interference sources and interference ways in the secondary circuits of the new energy power plant station.
基金supported by the National Natural Science Foundation of China(No.62373027).
文摘In disaster relief operations,multiple UAVs can be used to search for trapped people.In recent years,many researchers have proposed machine le arning-based algorithms,sampling-based algorithms,and heuristic algorithms to solve the problem of multi-UAV path planning.The Dung Beetle Optimization(DBO)algorithm has been widely applied due to its diverse search patterns in the above algorithms.However,the update strategies for the rolling and thieving dung beetles of the DBO algorithm are overly simplistic,potentially leading to an inability to fully explore the search space and a tendency to converge to local optima,thereby not guaranteeing the discovery of the optimal path.To address these issues,we propose an improved DBO algorithm guided by the Landmark Operator(LODBO).Specifically,we first use tent mapping to update the population strategy,which enables the algorithm to generate initial solutions with enhanced diversity within the search space.Second,we expand the search range of the rolling ball dung beetle by using the landmark factor.Finally,by using the adaptive factor that changes with the number of iterations.,we improve the global search ability of the stealing dung beetle,making it more likely to escape from local optima.To verify the effectiveness of the proposed method,extensive simulation experiments are conducted,and the result shows that the LODBO algorithm can obtain the optimal path using the shortest time compared with the Genetic Algorithm(GA),the Gray Wolf Optimizer(GWO),the Whale Optimization Algorithm(WOA)and the original DBO algorithm in the disaster search and rescue task set.
基金Supported by the Natural Science Foundation of Chongqing(General Program,NO.CSTB2022NSCQ-MSX0884)Discipline Teaching Special Project of Yangtze Normal University(csxkjx14)。
文摘In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
文摘Open caissons are widely used in foundation engineering because of their load-bearing efficiency and adaptability in diverse soil conditions.However,accurately predicting their undrained bearing capacity in layered soils remains a complex challenge.This study presents a novel application of five ensemble machine(ML)algorithms-random forest(RF),gradient boosting machine(GBM),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),and categorical boosting(CatBoost)-to predict the undrained bearing capacity factor(Nc)of circular open caissons embedded in two-layered clay on the basis of results from finite element limit analysis(FELA).The input dataset consists of 1188 numerical simulations using the Tresca failure criterion,varying in geometrical and soil parameters.The FELA was performed via OptumG2 software with adaptive meshing techniques and verified against existing benchmark studies.The ML models were trained on 70% of the dataset and tested on the remaining 30%.Their performance was evaluated using six statistical metrics:coefficient of determination(R²),mean absolute error(MAE),root mean squared error(RMSE),index of scatter(IOS),RMSE-to-standard deviation ratio(RSR),and variance explained factor(VAF).The results indicate that all the models achieved high accuracy,with R²values exceeding 97.6%and RMSE values below 0.02.Among them,AdaBoost and CatBoost consistently outperformed the other methods across both the training and testing datasets,demonstrating superior generalizability and robustness.The proposed ML framework offers an efficient,accurate,and data-driven alternative to traditional methods for estimating caisson capacity in stratified soils.This approach can aid in reducing computational costs while improving reliability in the early stages of foundation design.
