Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion...Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
Cotton producers have substantially reduced their inputs(labor,nutrients,and management)mainly by adopting a shortseason cropping management that is characterized by late sowing,high density,and reduced fertilization ...Cotton producers have substantially reduced their inputs(labor,nutrients,and management)mainly by adopting a shortseason cropping management that is characterized by late sowing,high density,and reduced fertilization with one-time application at the first bloom stage without lint yield reduction.However,it has been hypothesized that one-time fertilization at an earlier growth stage could be a more effective and economic management practice.A two-year field experiment was conducted by applying five fertilizer one-time fertilization at 0(FT1),5(FT2),10(FT3),15(FT4),and 20(FT5)days after the first flower appeared in the field and one three-split fertilizer application taken as the conventional control(FT6),making six treatments altogether.Cotton growth period,biomass accumulation,yield,and its formation were quantified.The results showed that the one-time fertilization did not affect the cotton growth progress as compared to FT6,however,the total crop cycles for FT3–FT5 were 3 days shorter.FT1 produced the highest cotton lint yield(1396 kg ha–1),which was similar to the FT6 but higher than the other treatments,and could be attributed to more bolls per unit area and higher lint percentage.Cotton yield was positively correlated with cotton plant biomass accumulated.FT1 had both the highest average(VT)(193.7 kg ha–1 d–1)and the highest maximum(VM)(220.9 kg ha–1 d–1)rates during the fast biomass accumulation period.These results suggest that one-time fertilizer application at the first flower stage might be an adjustment that is more effective than at first bloom,and allowed for easier decision making for application date due to non counting of plants with flowers is needed.展开更多
The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-...The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-out-of-2 rational secret sharing based on Bayesian game and considering different classes of protocol parties, we propose a 2-out-of-2 secret sharing scheme to solve cooperative problem of a rational secret sharing scheme being played only once. Moreover, we prove that the strategy is a perfect Bayesian equilibrium, adopted only by the parties in their decision-making according to their belief system （denoted by the probability distribution） and Bayes rule, without requiring simultaneous channels.展开更多
To solve the critical problems of lithium rich cathode materials, e.g., structure instability and short cycle life, we have successfully prepared a ZrO2-coated and Zr-doping xLi2MnO3·(1–x)LiMO2 hollow architectu...To solve the critical problems of lithium rich cathode materials, e.g., structure instability and short cycle life, we have successfully prepared a ZrO2-coated and Zr-doping xLi2MnO3·(1–x)LiMO2 hollow architecture via one-time sintering process. The modified structural materials as lithium-ion cathodes present good structural stability and superior cycle performance in LIBs. The discharge capacity of the ZrO2-coated and Zr-doped hollow pristine is 220 mAh g-1 at the 20th cycle at 0.2 C(discharge capacity loss, 2.7%)and 150 m Ah g-1 at the 100 th cycle at 1 C(discharge capacity loss, 17.7%), respectively. However, hollow pristine electrode only delivers 203 m Ah g-1 at the 20 th cycle at 0.2 C and 124 mAh g-1 at the 100 th cycle at 1 C, respectively, and the corresponding to capacity retention is 92.2% and 72.8%, respectively.Diffusion coefficients of modified hollow pristine electrode are much higher than that of hollow pristine electrode after 100 cycles(approach to 1.4 times). In addition, we simulate the adsorption reaction of HF on the surface of ZrO2-coated layer by the first-principles theory. The calculations prove that the adsorption energy of HF on the surface of ZrO2-coated layer is about-1.699 e V, and the ZrO2-coated layer could protect the hollow spherical xLi2MnO3·(1–x)LiMO2 from erosion by HF. Our results would be applicable for systematic amelioration of high-performance lithium rich material for anode with the respect of practical application.展开更多
We ayptanalyze Kim et. al's one-time proxy signature scheme used in mobileagents, and then a successful forgery is introduced It is showed that a dishonest customer cansuccessfully forge a valid one-time proxy sig...We ayptanalyze Kim et. al's one-time proxy signature scheme used in mobileagents, and then a successful forgery is introduced It is showed that a dishonest customer cansuccessfully forge a valid one-time proxy signature by impersonating the stiver Furthermore, he canrequest the server with responsibility for the forged bidding information.展开更多
A 32 kbit OTP(one-time programmable)memory for MCUs(micro-controller units)used in remote controllers was designed.This OTP memory is used for program and data storage.It is required to apply 5.5V to BL(bit-line)and 1...A 32 kbit OTP(one-time programmable)memory for MCUs(micro-controller units)used in remote controllers was designed.This OTP memory is used for program and data storage.It is required to apply 5.5V to BL(bit-line)and 11V to WL(word-line)for a OTP cell of 0.35μm ETOX(EEPROM tunnel oxide)type by MagnaChip.We use 5V transistors on column data paths to reduce the area of column data paths since they require small areas.In addition,we secure device reliability by using HV(high-voltage)transistors in the WL driver.Furthermore,we change from a static logic to a dynamic logic used for the WL driver in the core circuit.Also,we optimize the WD(write data)switch circuit.Thus,we can implement them with a small-area design.In addition,we implement the address predecoder with a small-area logic circuit.The area of the designed 32 kbit OTP with 5V and HV devices is 674.725μm×258.75μm(=0.1745mm2)and is 56.3% smaller than that using 3.3V devices.展开更多
The one-time pad(OTP)is an applicationlayer encryption technique to achieve the informationtheoretic security,and the physical-layer secret key generation(SKG)technique is a promising candidate to provide the random k...The one-time pad(OTP)is an applicationlayer encryption technique to achieve the informationtheoretic security,and the physical-layer secret key generation(SKG)technique is a promising candidate to provide the random keys for OTP.In this paper,we propose a joint SKG and OTP encryption scheme with the aid of a reconfigurable intelligent surface(RIS)to boost secret key rate.To maximize the efficiency of secure communication,we divide the process of secure transmission into two stages:SKG and then encrypted packet transmission.Meanwhile,we design an optimal algorithm for allocating time slots for SKG to maximize SKG efficiency without security risk.Furthermore,we design a key updating protocol based on our SKG scheme for OTP encryption.Simulation results verify that our scheme can generate keys securely and efficiently,and significantly improve the secure communication performance in an intelligent IoT system.展开更多
Lightweight roof greening is an important way for improving urban ecological environment and has good ecological and social benefits, but the investment is- too-high for the investors. Therefore, it is necessary to im...Lightweight roof greening is an important way for improving urban ecological environment and has good ecological and social benefits, but the investment is- too-high for the investors. Therefore, it is necessary to improve the system of lightweight roof greening. This study introduced a lightweight roof greening mode with low cost, simple construction, rapid formation, good economic benefit and convenient curing.展开更多
Glass ceramics was made by the one-time sintering method using the main raw material of iron tailings. On the basis of quaternary system of CaO-MgO-Al2O3-SiO2, using DTA, XRD and SEM, the effects of different nucleati...Glass ceramics was made by the one-time sintering method using the main raw material of iron tailings. On the basis of quaternary system of CaO-MgO-Al2O3-SiO2, using DTA, XRD and SEM, the effects of different nucleating agents and mixing amounts as well as heat treatment on the crystallization of railings glass ceramics were studied. The experimental results show that, nucleating agent and heat treatment are two necessary conditions for one-time sintering preparation of tailings glass ceramics namely, only adding nucleating agent or experiencing heat treatment, the quaternary system can not crystallize. The composite nucleating agent consisting of Cr2O3 and TiO2 can further lead to the crystallization of the CaO-MgO-A1203-SiO2 quaternary system at the lower temperature, with the major phase of diopside. In the range of mass content, 0%-4%, crystal intensity and crystal content grow. But when mass content is more than 4%, the crystal size will become coarser and the crystal distribution will be less regular. Different heat treatment regimes do not change the composition of the crystalline major phase in the glass ceramics crystallization of CaO-MgO-Al2O3-SiO2 system. In the range of 30-60 minutes, with the extension of nucleation and crystallization, crystallization degree enhanced, but if the holding time surpasses 60 minutes, the crystallization is worse.展开更多
In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state throu...In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown,and the state is permanently preserved.The memory unit features a current ratio of more than 10^(3),a read voltage window of 6 V,a programming time of less than 10^(−4)s,a stability of more than 108 read cycles,and a lifetime of far more than 10 years.Besides,the fabrication of the device is fully compatible with commercial Si-based GaN process platforms,which is of great significance for the realization of low-cost read-only memory in all-GaN integration.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
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
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
Objective:To observe the effectiveness and safety of one-time endodontics in the treatment of chronic apical periodontitis with sinus tract in pediatric deciduous teeth.Methods:109 cases of children with chronic apica...Objective:To observe the effectiveness and safety of one-time endodontics in the treatment of chronic apical periodontitis with sinus tract in pediatric deciduous teeth.Methods:109 cases of children with chronic apical periodontitis with sinus tract in the deciduous teeth treated in our hospital from January 2022 to December 2023 were selected and grouped by the randomized numerical table method,with 54 cases in the experimental group and 55 cases in the control group.The experimental group was treated with one-time endodontics and the control group was treated with conventional endodontics.Results:After the treatment,the total effective rate of treatment was higher in the experimental group than in the control group(P<0.05);the incidence of adverse events was lower in the experimental group than in the control group(P<0.05);the satisfaction of the children's family members was higher in the experimental group than in the control group(P<0.05);the pain duration was lower in the experimental group than in the control group(P<0.05).Conclusion:In the experimental group,children with chronic apical periodontitis with sinus tract of the deciduous teeth were given one-time endodontic treatment,and the results of its implementation were relatively good.展开更多
文摘Aiming to solve the steering instability and hysteresis of agricultural robots in the process of movement,a fusion PID control method of particle swarm optimization(PSO)and genetic algorithm(GA)was proposed.The fusion algorithm took advantage of the fast optimization ability of PSO to optimize the population screening link of GA.The Simulink simulation results showed that the convergence of the fitness function of the fusion algorithm was accelerated,the system response adjustment time was reduced,and the overshoot was almost zero.Then the algorithm was applied to the steering test of agricultural robot in various scenes.After modeling the steering system of agricultural robot,the steering test results in the unloaded suspended state showed that the PID control based on fusion algorithm reduced the rise time,response adjustment time and overshoot of the system,and improved the response speed and stability of the system,compared with the artificial trial and error PID control and the PID control based on GA.The actual road steering test results showed that the PID control response rise time based on the fusion algorithm was the shortest,about 4.43 s.When the target pulse number was set to 100,the actual mean value in the steady-state regulation stage was about 102.9,which was the closest to the target value among the three control methods,and the overshoot was reduced at the same time.The steering test results under various scene states showed that the PID control based on the proposed fusion algorithm had good anti-interference ability,it can adapt to the changes of environment and load and improve the performance of the control system.It was effective in the steering control of agricultural robot.This method can provide a reference for the precise steering control of other robots.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金the National Key Research and Development Program of China(Grant No.2022YFF0711400)which provided valuable financial support and resources for my research and made it possible for me to deeply explore the unknown mysteries in the field of lunar geologythe National Space Science Data Center Youth Open Project(Grant No.NSSDC2302001),which has not only facilitated the smooth progress of my research,but has also built a platform for me to communicate and cooperate with experts in the field.
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
基金supported by the National Natural Science Foundation of China (31271665)the Pairing Program of Key Laboratory of Oasis Ecology Agriculture, Xinjiang Production and Construction Group with Eminent Scholars in Elite Universities, China (201601)
文摘Cotton producers have substantially reduced their inputs(labor,nutrients,and management)mainly by adopting a shortseason cropping management that is characterized by late sowing,high density,and reduced fertilization with one-time application at the first bloom stage without lint yield reduction.However,it has been hypothesized that one-time fertilization at an earlier growth stage could be a more effective and economic management practice.A two-year field experiment was conducted by applying five fertilizer one-time fertilization at 0(FT1),5(FT2),10(FT3),15(FT4),and 20(FT5)days after the first flower appeared in the field and one three-split fertilizer application taken as the conventional control(FT6),making six treatments altogether.Cotton growth period,biomass accumulation,yield,and its formation were quantified.The results showed that the one-time fertilization did not affect the cotton growth progress as compared to FT6,however,the total crop cycles for FT3–FT5 were 3 days shorter.FT1 produced the highest cotton lint yield(1396 kg ha–1),which was similar to the FT6 but higher than the other treatments,and could be attributed to more bolls per unit area and higher lint percentage.Cotton yield was positively correlated with cotton plant biomass accumulated.FT1 had both the highest average(VT)(193.7 kg ha–1 d–1)and the highest maximum(VM)(220.9 kg ha–1 d–1)rates during the fast biomass accumulation period.These results suggest that one-time fertilizer application at the first flower stage might be an adjustment that is more effective than at first bloom,and allowed for easier decision making for application date due to non counting of plants with flowers is needed.
基金Supported by the Major National Science and Technology program (2011ZX03005-002)the National Natural Science Foundation of China (60872041, 61072066, 60963023, 60970143)the Fundamental Research Funds for the Central Universities (JY10000903001, JY10000901034)
文摘The rational secret sharing cannot be realized in the case of being played only once, and some punishments in the one-time rational secret sharing schemes turn out to be empty threats. In this paper, after modeling 2-out-of-2 rational secret sharing based on Bayesian game and considering different classes of protocol parties, we propose a 2-out-of-2 secret sharing scheme to solve cooperative problem of a rational secret sharing scheme being played only once. Moreover, we prove that the strategy is a perfect Bayesian equilibrium, adopted only by the parties in their decision-making according to their belief system （denoted by the probability distribution） and Bayes rule, without requiring simultaneous channels.
基金the financial support by the Natural Science Foundation of Guangdong Province(2019A1515012111)the National Natural Science Foundation of China(51804199 and 51604081)+2 种基金the Science and Technology Innovation Commission of Shenzhen(JCYJ20190808173815205 and 20180123)the Shenzhen Science and Technology Program(KQTD20180412181422399)“Chenguang Program”supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission(16CG40)。
文摘To solve the critical problems of lithium rich cathode materials, e.g., structure instability and short cycle life, we have successfully prepared a ZrO2-coated and Zr-doping xLi2MnO3·(1–x)LiMO2 hollow architecture via one-time sintering process. The modified structural materials as lithium-ion cathodes present good structural stability and superior cycle performance in LIBs. The discharge capacity of the ZrO2-coated and Zr-doped hollow pristine is 220 mAh g-1 at the 20th cycle at 0.2 C(discharge capacity loss, 2.7%)and 150 m Ah g-1 at the 100 th cycle at 1 C(discharge capacity loss, 17.7%), respectively. However, hollow pristine electrode only delivers 203 m Ah g-1 at the 20 th cycle at 0.2 C and 124 mAh g-1 at the 100 th cycle at 1 C, respectively, and the corresponding to capacity retention is 92.2% and 72.8%, respectively.Diffusion coefficients of modified hollow pristine electrode are much higher than that of hollow pristine electrode after 100 cycles(approach to 1.4 times). In addition, we simulate the adsorption reaction of HF on the surface of ZrO2-coated layer by the first-principles theory. The calculations prove that the adsorption energy of HF on the surface of ZrO2-coated layer is about-1.699 e V, and the ZrO2-coated layer could protect the hollow spherical xLi2MnO3·(1–x)LiMO2 from erosion by HF. Our results would be applicable for systematic amelioration of high-performance lithium rich material for anode with the respect of practical application.
文摘We ayptanalyze Kim et. al's one-time proxy signature scheme used in mobileagents, and then a successful forgery is introduced It is showed that a dishonest customer cansuccessfully forge a valid one-time proxy signature by impersonating the stiver Furthermore, he canrequest the server with responsibility for the forged bidding information.
基金Project supported by the Second Stage of Brain Korea 21 Projects,Korea
文摘A 32 kbit OTP(one-time programmable)memory for MCUs(micro-controller units)used in remote controllers was designed.This OTP memory is used for program and data storage.It is required to apply 5.5V to BL(bit-line)and 11V to WL(word-line)for a OTP cell of 0.35μm ETOX(EEPROM tunnel oxide)type by MagnaChip.We use 5V transistors on column data paths to reduce the area of column data paths since they require small areas.In addition,we secure device reliability by using HV(high-voltage)transistors in the WL driver.Furthermore,we change from a static logic to a dynamic logic used for the WL driver in the core circuit.Also,we optimize the WD(write data)switch circuit.Thus,we can implement them with a small-area design.In addition,we implement the address predecoder with a small-area logic circuit.The area of the designed 32 kbit OTP with 5V and HV devices is 674.725μm×258.75μm(=0.1745mm2)and is 56.3% smaller than that using 3.3V devices.
基金supported by National key research and development program of China, Joint research of IoT security system and key technologies based on quantum key (2020YFE0200600)
文摘The one-time pad(OTP)is an applicationlayer encryption technique to achieve the informationtheoretic security,and the physical-layer secret key generation(SKG)technique is a promising candidate to provide the random keys for OTP.In this paper,we propose a joint SKG and OTP encryption scheme with the aid of a reconfigurable intelligent surface(RIS)to boost secret key rate.To maximize the efficiency of secure communication,we divide the process of secure transmission into two stages:SKG and then encrypted packet transmission.Meanwhile,we design an optimal algorithm for allocating time slots for SKG to maximize SKG efficiency without security risk.Furthermore,we design a key updating protocol based on our SKG scheme for OTP encryption.Simulation results verify that our scheme can generate keys securely and efficiently,and significantly improve the secure communication performance in an intelligent IoT system.
基金Supported by Science and Technology Planning Project of Guangdong Province,China(No.2015B090904008)Soft Science Planning Project of Guangdong Province(2014B090903015)Ecological Environment Construction and Protection(Techand)Engineering and Technological Research Center(YKHZZ[2013]1589)~~
文摘Lightweight roof greening is an important way for improving urban ecological environment and has good ecological and social benefits, but the investment is- too-high for the investors. Therefore, it is necessary to improve the system of lightweight roof greening. This study introduced a lightweight roof greening mode with low cost, simple construction, rapid formation, good economic benefit and convenient curing.
基金Funded by The National Key Technology R & D Program of China for the 11th Five-Year Plan(2006BAJ04A04)
文摘Glass ceramics was made by the one-time sintering method using the main raw material of iron tailings. On the basis of quaternary system of CaO-MgO-Al2O3-SiO2, using DTA, XRD and SEM, the effects of different nucleating agents and mixing amounts as well as heat treatment on the crystallization of railings glass ceramics were studied. The experimental results show that, nucleating agent and heat treatment are two necessary conditions for one-time sintering preparation of tailings glass ceramics namely, only adding nucleating agent or experiencing heat treatment, the quaternary system can not crystallize. The composite nucleating agent consisting of Cr2O3 and TiO2 can further lead to the crystallization of the CaO-MgO-A1203-SiO2 quaternary system at the lower temperature, with the major phase of diopside. In the range of mass content, 0%-4%, crystal intensity and crystal content grow. But when mass content is more than 4%, the crystal size will become coarser and the crystal distribution will be less regular. Different heat treatment regimes do not change the composition of the crystalline major phase in the glass ceramics crystallization of CaO-MgO-Al2O3-SiO2 system. In the range of 30-60 minutes, with the extension of nucleation and crystallization, crystallization degree enhanced, but if the holding time surpasses 60 minutes, the crystallization is worse.
基金supported in part by the National Key Research and Development Program of China under Grant 2022YFB3604400in part by the Youth Innovation Promotion Association of Chinese Academy Sciences (CAS)+4 种基金in part by the CAS-Croucher Funding Scheme under Grant CAS22801in part by National Natural Science Foundation of China under Grant 62334012, Grant 62074161, Grant 62004213, Grant U20A20208, and Grant 62304252in part by the Beijing Municipal Science and Technology Commission project under Grant Z201100008420009 and Grant Z211100007921018in part by the University of CASin part by the IMECAS-HKUST-Joint Laboratory of Microelectronics
文摘In this work,a novel one-time-programmable memory unit based on a Schottky-type p-GaN diode is proposed.During the programming process,the junction switches from a high-resistance state to a low-resistance state through Schottky junction breakdown,and the state is permanently preserved.The memory unit features a current ratio of more than 10^(3),a read voltage window of 6 V,a programming time of less than 10^(−4)s,a stability of more than 108 read cycles,and a lifetime of far more than 10 years.Besides,the fabrication of the device is fully compatible with commercial Si-based GaN process platforms,which is of great significance for the realization of low-cost read-only memory in all-GaN integration.
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.
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.
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.
文摘Objective:To observe the effectiveness and safety of one-time endodontics in the treatment of chronic apical periodontitis with sinus tract in pediatric deciduous teeth.Methods:109 cases of children with chronic apical periodontitis with sinus tract in the deciduous teeth treated in our hospital from January 2022 to December 2023 were selected and grouped by the randomized numerical table method,with 54 cases in the experimental group and 55 cases in the control group.The experimental group was treated with one-time endodontics and the control group was treated with conventional endodontics.Results:After the treatment,the total effective rate of treatment was higher in the experimental group than in the control group(P<0.05);the incidence of adverse events was lower in the experimental group than in the control group(P<0.05);the satisfaction of the children's family members was higher in the experimental group than in the control group(P<0.05);the pain duration was lower in the experimental group than in the control group(P<0.05).Conclusion:In the experimental group,children with chronic apical periodontitis with sinus tract of the deciduous teeth were given one-time endodontic treatment,and the results of its implementation were relatively good.
