A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP...A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.展开更多
Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection syste...Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China.It was difficult to adjust the output power,and the anti-interference ability was weak in these systems.In order to resolve these problems,the target detection method based on analog sine-wave modulation was studied.The spectral detecting system was set up in the aspects of working principle,electric circuit,and optical path.Lab testing was performed.The results showed that the reflected signal from the target varied inversely with detection distances.It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology.Furthermore,quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system.The research laid the foundation for the future development of the corresponding instrument.展开更多
Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,an...Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
As the performance of the box-type multiple launch rocket system(BMLRS)improves,its mechanical structures,particularly the plane clearance design between the slider on the rocket and the guide inside the launch canist...As the performance of the box-type multiple launch rocket system(BMLRS)improves,its mechanical structures,particularly the plane clearance design between the slider on the rocket and the guide inside the launch canister,have grown increasingly complex.However,deficiencies still exist in the current launch modeling theory for BMLRS.In this study,a multi-rigid-flexible-body launch dynamics model coupling the launch platform and rocket was established using the multibody system transfer matrix method and the Newton-Euler formulation.Furthermore,considering the bending of the launch canister,a detection algorithm for slider-guide plane clearance contact was proposed.To quantify the contact force and friction effect between the slider and guide,the contact force model and modified Coulomb model were introduced.Both the modal and launch tests were conducted.Additionally,the modal convergence was verified.By comparing the modal experiments and simulation results,the maximum relative error of the eigenfrequency is 3.29%.thereby verifying the accuracy of the developed BMLRS dynamics model.Furthermore,the launch test validated the proposed plane clearance contact model.Moreover,the study investigated the influence of various model parameters on the dynamic characteristics of BMLRS,including launch canister bending stiffness,slider and guide material,slider-guide clearance,slider length and layout.This analysis of influencing factors provides a foundation for future optimization in BMLRS design.展开更多
To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Differen...To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.展开更多
Military object detection and identification is a key capability in surveillance and reconnaissance.It is a major factor in warfare effectiveness and warfighter survivability.Inexpensive,portable,and rapidly deployabl...Military object detection and identification is a key capability in surveillance and reconnaissance.It is a major factor in warfare effectiveness and warfighter survivability.Inexpensive,portable,and rapidly deployable small unmanned aerial systems(s UAS)in conjunction with powerful deep learning(DL)based object detection models are expected to play an important role for this application.To prove overall feasibility of this approach,this paper discusses some aspects of designing and testing of an automated detection system to locate and identify small firearms left at the training range or at the battlefield.Such a system is envisioned to involve an s UAS equipped with a modern electro-optical(EO)sensor and relying on a trained convolutional neural network(CNN).Previous study by the authors devoted to finding projectiles on the ground revealed certain challenges such as small object size,changes in aspect ratio and image scale,motion blur,occlusion,and camouflage.This study attempts to deal with these challenges in a realistic operational scenario and go further by not only detecting different types of firearms but also classifying them into different categories.This study used a YOLOv2CNN(Res Net-50 backbone network)to train the model with ground truth data and demonstrated a high mean average precision(m AP)of 0.97 to detect and identify not only small pistols but also partially occluded rifles.展开更多
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which ent...Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.展开更多
To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode ...To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.How...The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.However,conventional polarization detection systems are often bulky and complex,limiting their poten⁃tial for broader applications.To address the challenges of miniaturization,integrated polarization detectors have been extensively explored in recent years,achieving significant advancements in performance and functionality.In this review,we focus mainly on integrated polarization detectors with innovative features,including infinitely high polarization discrimination,ultrahigh sensitivity to polarization state change,full Stokes parameters measure⁃ment,and simultaneous perception of polarization and other key properties of light.Lastly,we discuss the oppor⁃tunities and challenges for the future development of integrated polarization photodetectors.展开更多
Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the envir...Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.展开更多
Bovine serum albumin(BSA)and glycine(Gly)dual-ligand-modified copper nanoclusters(BSA-Gly CuNCs)with high fluorescence intensity were synthesized by a one-pot strategy.Based on the competitive fluorescence quenching a...Bovine serum albumin(BSA)and glycine(Gly)dual-ligand-modified copper nanoclusters(BSA-Gly CuNCs)with high fluorescence intensity were synthesized by a one-pot strategy.Based on the competitive fluorescence quenching and dynamic quenching effects of ornidazole(ONZ)on BSA-Gly CuNCs,a simple and sensitive detection method for ONZ was successfully developed.The experimental results demonstrate that the addition of the small molecule Gly can more effectively protect CuNCs,and thus enhance its fluorescence intensity and stability.The proposed assay allowed for the detection of ONZ in a linear range of 0.28 to 52.60μmol·L^(-1)and a detection limit of 0.069μmol·L^(-1).Compared with the single-ligand-modified CuNCs,dual-ligand-modified BSA-Gly CuNCs had higher fluorescence intensity,stability,and sensing ability and were successfully applied to evaluate ONZ in actual ONZ tablets.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalo...Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.展开更多
Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to tre...Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma,bronchitis,and emphysema,has a narrow therapeutic window with a safe plasma concentration ranging from 55.5-111.0μmol·L^(-1)in adults.Accurate monitoring of TP levels is essential because too low or too high can cause se-rious side effects.In this regard,non-enzymatic electrochemical sensors offer a practical solution with rapidity,portability,and high sensitivity.This article aims to provide a comprehensive review of the recent developments of non-enzymatic electrochemical sensors for TP detection,highlighting the basic principles,electro-oxidation mechanisms,catalytic effects,and the role of modifying materials on electrode performance.Carbon-based electrodes such as glassy carbon electrodes(GCEs),carbon paste electrodes(CPEs),and carbon screen-printed electrodes(SPCEs)have become the primary choices for non-enzymatic sensors due to their chemical stability,low cost,and flexibility in modification.This article identifies the sig-nificant contribution of various modifying materials,including nanomaterials such as carbon nanotubes(CNTs),graphene,metal oxides,and multi-element nanocomposites.These modifications enhance sensors’electron transfer,sensitivity,and selectivity in detecting TP at low concentrations in complex media such as blood plasma and pharmaceutical samples.The electro-oxidation mechanism of TP is also discussed in depth,emphasizing the hydroxyl and carbonyl reaction pathways strongly influenced by pH and electrode materials.These mechanisms guide the selection of the appropriate electrode ma-terial for a particular application.The main contribution of this article is to identify superior modifying materials that can improve the performance of non-enzymatic electrochemical sensors.In a recent study,the combination of multi-element nanocomposites based on titanium dioxide(TiO_(2)),CNTs,and gold nanoparticles(AuNPs)resulted in the lowest detection limit of 3×10^(-5)μmol·L^(-1),reflecting the great potential of these materials for developing high-performance electrochemical sensors.The main conclusion of this article is the importance of a multidisciplinary approach in electrode material design to support the sensitivity and selectivity of TP detection.In addition,there is still a research gap in understanding TP’s more detailed oxidation mechanism,especially under pH variations and complex environments.Therefore,further research on electrode modification and analysis of the TP oxidation mechanism are urgently needed to improve the accuracy and sta-bility of the sensor while expanding its applications in pharmaceutical monitoring and medical diagnostics.By integrating various innovative materials and technical approaches,this review is expected to be an essential reference for developing efficient and affordable non-enzymatic electrochemical sensors.展开更多
The abnormal metabolic activity of the tumor can increase the oxygen consumption in tumor cells,and the poor blood perfusion often happens in tumor regions as well,which are the main reasons that result in a hypoxic s...The abnormal metabolic activity of the tumor can increase the oxygen consumption in tumor cells,and the poor blood perfusion often happens in tumor regions as well,which are the main reasons that result in a hypoxic situation in the tumor.A fluorescence probe,AQD,with selective response toward hypoxia was designed for the detection of hypoxic tumor cells,which was obtained by the covalent connection of a large planar conjugated fluorophore with good fluorescence stability and a N,N-dimethylaniline moiety via the azo bond.The introduction of the azo bond in AQD caused significant fluorescence emission quenching,and the probe was reduced under hypoxic conditions to release the fluorophore via breaking the azo bond,resulting in the gradual recovery of fluorescence emission.Probe AQD exhibited a remarkable fluorescence response in hypoxic conditions,high selectivity,and good biocompatibility,which was successfully used for the imaging of hypoxic tumor cells and realized the detection of hypoxic A549 cells.展开更多
The effect of external vibration on the velocity uniformity of the moving mechanism of the angular mir⁃ror translational Fourier transform interferometer(hereinafter referred to as interferometer)can be quantitatively...The effect of external vibration on the velocity uniformity of the moving mechanism of the angular mir⁃ror translational Fourier transform interferometer(hereinafter referred to as interferometer)can be quantitatively analysed by the interferometer optical range difference velocity stability.The article proposes a more comprehen⁃sive method of analysing the optical range difference velocity uniformity for the reliability of the interferometer ki⁃nematic mechanism under the influence of on-orbit microvibration in the process of space spectroscopy detection.The method incorporates the structural response of the interferometer caused by external excitation into the stabili⁃ty analysis as one of the influencing factors,so as to reflect the reliability of the interferometer in orbit more realis⁃tically,and judge the microvibration criticality that the interferometer can withstand more accurately.At the same time,an optical surface model of the interferometer is established to further theoretically characterise the effect of microvibration on the homogeneity of the interferometric mechanism.The method discussed in the article pro⁃vides a way of thinking for the judgement of the reliability of the mechanism movement under the external excita⁃tion perturbation as well as the research on the optimisation of the mechanism control.展开更多
Herein,copper nanoclusters(Cu NCs)were synthesized in aqueous solution through a chemical reduction method using polyethyleneimine as reducing agent and protective ligand,with Cu(NO_(3))_(2)as copper source.Subse-quen...Herein,copper nanoclusters(Cu NCs)were synthesized in aqueous solution through a chemical reduction method using polyethyleneimine as reducing agent and protective ligand,with Cu(NO_(3))_(2)as copper source.Subse-quently,composite fluorescent nanoparticles,chitosan-functionalized silica nanoparticles(CSNPs)-coated Cu NCs(Cu NCs/CSNPs),were synthesized via a reverse microemulsion method.Compared with Cu NCs,the composite Cu NCs/CSNPs exhibited an increased quantum yield and enhanced fluorescence sensing performance.Based on the composite Cu NCs/CSNPs,a fluorescence method for the detection of cefixime fluorescence quenching was estab-lished.The technique was simple,sensitive,and selective for detecting cefixime.The fluorescence quenching effi-ciency of Cu NCs/CSNPs was linearly related to the concentration of cefixime in the range of 3.98-38.5µmol·L^(-1)(1.81-17.46 mg·L^(-1)),with a limit of detection of 0.0455µmol·L^(-1)(20.6µg·L^(-1)).展开更多
Two new complexes,[Zn_(2)(L1)(HL1)(NO_(3))]·CH_(3)OH(1)and[Zn_(3)(L2)(L3)_(3)Cl]·CH_(3)OH(2),were successfully synthesized by‘one-pot’method based on cinnoline-3-ylhydrazine ligand and zinc with 2-hydroxy-...Two new complexes,[Zn_(2)(L1)(HL1)(NO_(3))]·CH_(3)OH(1)and[Zn_(3)(L2)(L3)_(3)Cl]·CH_(3)OH(2),were successfully synthesized by‘one-pot’method based on cinnoline-3-ylhydrazine ligand and zinc with 2-hydroxy-4-methoxybenzaldehyde and 2-hydroxy-3-methoxybenzaldehyde ligands,respectively,where H_(2)L1=5-methoxy-2-(phthalazin-1-ylhydrazonomethyl)-phenol,H_(2)L2=2-methoxy-6-(phthalazin-1-yl-hydrazonomethyl)-phenol,HL3=2-(1,8-dihydro-[1,2,4]triazolo[3,4-α]phthalazin-3-yl)-6-methoxy-phenol.Complexes 1 and 2 were characterized by infrared spectroscopy,elemental analysis,single-crystal X-ray diffraction,powder X-ray diffraction,etc.It is worth noting that the cinnolin-3-yl-hydrazine ligand and 2-hydroxy-3-methoxybenzaldehyde form two types of Schiff bases(H_(2)L2 and HL3)when in situ reacting and coordinating with Zn(Ⅱ),and HL3 also has two coordination modes.In addition,the fluorescence performance showed that complex 1 can achieve selective and sensitive sensing of Al^(3+)in water with a detection limit of 6.37μmol·L^(-1).CCDC:2413978,1;2413979,2.展开更多
基金Foundation item: Project(2009AA04Z143) supported by the National High Technology Research and Development Program of ChinaProject (E2011203004) supported by Natural Science Foundation of Hebei Province, ChinaProjects(2011BAF15B03, 2011BAF15B02) supported by the National Science Plan of China
文摘A high-precision shape detecting system of cold rolling strip is developed to meet industrial application, which mainly consists of the shape detecting roller, the collecting ring, the digital signal processing （DSP） shape signal processing board and the shape control model. Based on the shape detecting principle, the shape detecting roller is designed with a new integral structure for improving the precision of shape detecting and avoiding scratching strip surface. Based on the DSP technology, the DSP shape signal processing circuit board is designed and embedded in the shape detecting system for the reliability and stability of shape signal processing. The shape detecting system was successfully used in Angang 1 250 mm HC 6-high reversible cold rolling mill. The precision of shape detecting is 0.2 I and the shape deviation is controlled within 6 1 after the close loop shape control is input.
基金Supported by the National“863”Project of China(2010AA10A301)National Technology Support Project for the 12th Five-year Plan(2011BAD20B07)
文摘Target detection is one of the key technology of precision chemical application.Previously the digital coding modulation technique was commonly used to emit and receive the optical signal in the target detection systems previously in China.It was difficult to adjust the output power,and the anti-interference ability was weak in these systems.In order to resolve these problems,the target detection method based on analog sine-wave modulation was studied.The spectral detecting system was set up in the aspects of working principle,electric circuit,and optical path.Lab testing was performed.The results showed that the reflected signal from the target varied inversely with detection distances.It indicated that it was feasible to establish the target detection system using analog sine-wave modulation technology.Furthermore,quantitative measurement of the reflected optical signal for near-infrared and visible light could be achieved by using this system.The research laid the foundation for the future development of the corresponding instrument.
基金supported by the Natural Science Foundation of China,Grant No.62103052.
文摘Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
基金supported by National Natural Science Foundation of China(Grant No.92266201).
文摘As the performance of the box-type multiple launch rocket system(BMLRS)improves,its mechanical structures,particularly the plane clearance design between the slider on the rocket and the guide inside the launch canister,have grown increasingly complex.However,deficiencies still exist in the current launch modeling theory for BMLRS.In this study,a multi-rigid-flexible-body launch dynamics model coupling the launch platform and rocket was established using the multibody system transfer matrix method and the Newton-Euler formulation.Furthermore,considering the bending of the launch canister,a detection algorithm for slider-guide plane clearance contact was proposed.To quantify the contact force and friction effect between the slider and guide,the contact force model and modified Coulomb model were introduced.Both the modal and launch tests were conducted.Additionally,the modal convergence was verified.By comparing the modal experiments and simulation results,the maximum relative error of the eigenfrequency is 3.29%.thereby verifying the accuracy of the developed BMLRS dynamics model.Furthermore,the launch test validated the proposed plane clearance contact model.Moreover,the study investigated the influence of various model parameters on the dynamic characteristics of BMLRS,including launch canister bending stiffness,slider and guide material,slider-guide clearance,slider length and layout.This analysis of influencing factors provides a foundation for future optimization in BMLRS design.
基金This work was supported by the National Key Research and Development Program Topics(2020YFC2200902)the National Natural Science Foundation of China(11872110).
文摘To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.
文摘Military object detection and identification is a key capability in surveillance and reconnaissance.It is a major factor in warfare effectiveness and warfighter survivability.Inexpensive,portable,and rapidly deployable small unmanned aerial systems(s UAS)in conjunction with powerful deep learning(DL)based object detection models are expected to play an important role for this application.To prove overall feasibility of this approach,this paper discusses some aspects of designing and testing of an automated detection system to locate and identify small firearms left at the training range or at the battlefield.Such a system is envisioned to involve an s UAS equipped with a modern electro-optical(EO)sensor and relying on a trained convolutional neural network(CNN).Previous study by the authors devoted to finding projectiles on the ground revealed certain challenges such as small object size,changes in aspect ratio and image scale,motion blur,occlusion,and camouflage.This study attempts to deal with these challenges in a realistic operational scenario and go further by not only detecting different types of firearms but also classifying them into different categories.This study used a YOLOv2CNN(Res Net-50 backbone network)to train the model with ground truth data and demonstrated a high mean average precision(m AP)of 0.97 to detect and identify not only small pistols but also partially occluded rifles.
基金supported by National Natural Science Foundation of China(62371225,62371227)。
文摘Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed.By combining the advantages of both the explicit and the implicit matrix inversion,this paper introduces a new low-complexity signal detection algorithm.Firstly,the relationship between implicit and explicit techniques is analyzed.Then,an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration.However,its complexity is still high for higher iterations.Thus,it is applied only for first two iterations.For subsequent iterations,we propose a novel trace iterative method(TIM)based low-complexity algorithm,which has significantly lower complexity than higher Newton iterations.Convergence guarantees of the proposed detector are also provided.Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.
文摘To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
基金Supported by the National Key Research and Development Program of China(2022YFA1404602)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0580000)+3 种基金the National Natural Science Foundation of China(U23B2045,62305362)the Program of Shanghai Academic/Technology Research Leader(22XD1424400)the Fund of SITP Innovation Foundation(CX-461 and CX-522)Special Project to Seize the Commanding Heights of Science and Technology of Chinese Academy of Sciences,subtopic(GJ0090406-6).
文摘The polarization properties of light are widely applied in imaging,communications,materials analy⁃sis,and life sciences.Various methods have been developed that can measure the polarization information of a target.However,conventional polarization detection systems are often bulky and complex,limiting their poten⁃tial for broader applications.To address the challenges of miniaturization,integrated polarization detectors have been extensively explored in recent years,achieving significant advancements in performance and functionality.In this review,we focus mainly on integrated polarization detectors with innovative features,including infinitely high polarization discrimination,ultrahigh sensitivity to polarization state change,full Stokes parameters measure⁃ment,and simultaneous perception of polarization and other key properties of light.Lastly,we discuss the oppor⁃tunities and challenges for the future development of integrated polarization photodetectors.
文摘Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
文摘Bovine serum albumin(BSA)and glycine(Gly)dual-ligand-modified copper nanoclusters(BSA-Gly CuNCs)with high fluorescence intensity were synthesized by a one-pot strategy.Based on the competitive fluorescence quenching and dynamic quenching effects of ornidazole(ONZ)on BSA-Gly CuNCs,a simple and sensitive detection method for ONZ was successfully developed.The experimental results demonstrate that the addition of the small molecule Gly can more effectively protect CuNCs,and thus enhance its fluorescence intensity and stability.The proposed assay allowed for the detection of ONZ in a linear range of 0.28 to 52.60μmol·L^(-1)and a detection limit of 0.069μmol·L^(-1).Compared with the single-ligand-modified CuNCs,dual-ligand-modified BSA-Gly CuNCs had higher fluorescence intensity,stability,and sensing ability and were successfully applied to evaluate ONZ in actual ONZ tablets.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
基金National Natural Science Foundation of China(12103020,12363009)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Open Project Program of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology)(Macao FDCT grant No.002/2024/SKL)Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191,2023CYZ26970)。
文摘Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.
基金the funding from Lembaga Penelitian dan Pengabdian Masyarakat(LPPM)Universitas Indonesia,by Riset Kolaborasi Indonesia(RKI)-World Class University(WCU)Program with grant number NKB-1067/UN2-RST/HKP.05.00/2023 and NKB-781/UN2.RST/HKP.05.00/2024.
文摘Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma,bronchitis,and emphysema,has a narrow therapeutic window with a safe plasma concentration ranging from 55.5-111.0μmol·L^(-1)in adults.Accurate monitoring of TP levels is essential because too low or too high can cause se-rious side effects.In this regard,non-enzymatic electrochemical sensors offer a practical solution with rapidity,portability,and high sensitivity.This article aims to provide a comprehensive review of the recent developments of non-enzymatic electrochemical sensors for TP detection,highlighting the basic principles,electro-oxidation mechanisms,catalytic effects,and the role of modifying materials on electrode performance.Carbon-based electrodes such as glassy carbon electrodes(GCEs),carbon paste electrodes(CPEs),and carbon screen-printed electrodes(SPCEs)have become the primary choices for non-enzymatic sensors due to their chemical stability,low cost,and flexibility in modification.This article identifies the sig-nificant contribution of various modifying materials,including nanomaterials such as carbon nanotubes(CNTs),graphene,metal oxides,and multi-element nanocomposites.These modifications enhance sensors’electron transfer,sensitivity,and selectivity in detecting TP at low concentrations in complex media such as blood plasma and pharmaceutical samples.The electro-oxidation mechanism of TP is also discussed in depth,emphasizing the hydroxyl and carbonyl reaction pathways strongly influenced by pH and electrode materials.These mechanisms guide the selection of the appropriate electrode ma-terial for a particular application.The main contribution of this article is to identify superior modifying materials that can improve the performance of non-enzymatic electrochemical sensors.In a recent study,the combination of multi-element nanocomposites based on titanium dioxide(TiO_(2)),CNTs,and gold nanoparticles(AuNPs)resulted in the lowest detection limit of 3×10^(-5)μmol·L^(-1),reflecting the great potential of these materials for developing high-performance electrochemical sensors.The main conclusion of this article is the importance of a multidisciplinary approach in electrode material design to support the sensitivity and selectivity of TP detection.In addition,there is still a research gap in understanding TP’s more detailed oxidation mechanism,especially under pH variations and complex environments.Therefore,further research on electrode modification and analysis of the TP oxidation mechanism are urgently needed to improve the accuracy and sta-bility of the sensor while expanding its applications in pharmaceutical monitoring and medical diagnostics.By integrating various innovative materials and technical approaches,this review is expected to be an essential reference for developing efficient and affordable non-enzymatic electrochemical sensors.
文摘The abnormal metabolic activity of the tumor can increase the oxygen consumption in tumor cells,and the poor blood perfusion often happens in tumor regions as well,which are the main reasons that result in a hypoxic situation in the tumor.A fluorescence probe,AQD,with selective response toward hypoxia was designed for the detection of hypoxic tumor cells,which was obtained by the covalent connection of a large planar conjugated fluorophore with good fluorescence stability and a N,N-dimethylaniline moiety via the azo bond.The introduction of the azo bond in AQD caused significant fluorescence emission quenching,and the probe was reduced under hypoxic conditions to release the fluorophore via breaking the azo bond,resulting in the gradual recovery of fluorescence emission.Probe AQD exhibited a remarkable fluorescence response in hypoxic conditions,high selectivity,and good biocompatibility,which was successfully used for the imaging of hypoxic tumor cells and realized the detection of hypoxic A549 cells.
文摘The effect of external vibration on the velocity uniformity of the moving mechanism of the angular mir⁃ror translational Fourier transform interferometer(hereinafter referred to as interferometer)can be quantitatively analysed by the interferometer optical range difference velocity stability.The article proposes a more comprehen⁃sive method of analysing the optical range difference velocity uniformity for the reliability of the interferometer ki⁃nematic mechanism under the influence of on-orbit microvibration in the process of space spectroscopy detection.The method incorporates the structural response of the interferometer caused by external excitation into the stabili⁃ty analysis as one of the influencing factors,so as to reflect the reliability of the interferometer in orbit more realis⁃tically,and judge the microvibration criticality that the interferometer can withstand more accurately.At the same time,an optical surface model of the interferometer is established to further theoretically characterise the effect of microvibration on the homogeneity of the interferometric mechanism.The method discussed in the article pro⁃vides a way of thinking for the judgement of the reliability of the mechanism movement under the external excita⁃tion perturbation as well as the research on the optimisation of the mechanism control.
文摘Herein,copper nanoclusters(Cu NCs)were synthesized in aqueous solution through a chemical reduction method using polyethyleneimine as reducing agent and protective ligand,with Cu(NO_(3))_(2)as copper source.Subse-quently,composite fluorescent nanoparticles,chitosan-functionalized silica nanoparticles(CSNPs)-coated Cu NCs(Cu NCs/CSNPs),were synthesized via a reverse microemulsion method.Compared with Cu NCs,the composite Cu NCs/CSNPs exhibited an increased quantum yield and enhanced fluorescence sensing performance.Based on the composite Cu NCs/CSNPs,a fluorescence method for the detection of cefixime fluorescence quenching was estab-lished.The technique was simple,sensitive,and selective for detecting cefixime.The fluorescence quenching effi-ciency of Cu NCs/CSNPs was linearly related to the concentration of cefixime in the range of 3.98-38.5µmol·L^(-1)(1.81-17.46 mg·L^(-1)),with a limit of detection of 0.0455µmol·L^(-1)(20.6µg·L^(-1)).
文摘Two new complexes,[Zn_(2)(L1)(HL1)(NO_(3))]·CH_(3)OH(1)and[Zn_(3)(L2)(L3)_(3)Cl]·CH_(3)OH(2),were successfully synthesized by‘one-pot’method based on cinnoline-3-ylhydrazine ligand and zinc with 2-hydroxy-4-methoxybenzaldehyde and 2-hydroxy-3-methoxybenzaldehyde ligands,respectively,where H_(2)L1=5-methoxy-2-(phthalazin-1-ylhydrazonomethyl)-phenol,H_(2)L2=2-methoxy-6-(phthalazin-1-yl-hydrazonomethyl)-phenol,HL3=2-(1,8-dihydro-[1,2,4]triazolo[3,4-α]phthalazin-3-yl)-6-methoxy-phenol.Complexes 1 and 2 were characterized by infrared spectroscopy,elemental analysis,single-crystal X-ray diffraction,powder X-ray diffraction,etc.It is worth noting that the cinnolin-3-yl-hydrazine ligand and 2-hydroxy-3-methoxybenzaldehyde form two types of Schiff bases(H_(2)L2 and HL3)when in situ reacting and coordinating with Zn(Ⅱ),and HL3 also has two coordination modes.In addition,the fluorescence performance showed that complex 1 can achieve selective and sensitive sensing of Al^(3+)in water with a detection limit of 6.37μmol·L^(-1).CCDC:2413978,1;2413979,2.
