冶金尘泥的转底炉处理工艺是目前钢铁行业采用的主要处置工艺,但在实际生产过程中经常出现还原焙烧不均匀的问题。利用微观扫描电子显微镜(scanning electron microscopy,SEM)分析结合宏观Maps统计分析,对冶金尘泥还原焙烧的不均匀性进...冶金尘泥的转底炉处理工艺是目前钢铁行业采用的主要处置工艺,但在实际生产过程中经常出现还原焙烧不均匀的问题。利用微观扫描电子显微镜(scanning electron microscopy,SEM)分析结合宏观Maps统计分析,对冶金尘泥还原焙烧的不均匀性进行详细的可视化、数据化分析。研究结果表明,冶金尘泥在焙烧温度为1250℃、焙烧时间为15 min的条件下,熟球金属化率达到89.04%、脱锌率达到81.66%、抗压强度达到3.03 kN,熟球金属化率和脱锌率会随着焙烧温度提高和焙烧时间延长而进一步提高,但熟球抗压强度在焙烧时间过长时反而逐渐降低;熟球Maps统计分析表明,提高焙烧温度更有利于提高熟球外圈和下部的还原程度,而延长焙烧时间也更有利于提高熟球下部还原程度,但对熟球内部和外圈还原程度的提升作用比较相似;同时,提高焙烧温度也更有利于提升熟球下部的致密化程度,降低熟球上、下孔隙结构的不均匀性,进而显著提高熟球整体抗压强度;但焙烧时间过长会导致熟球中小孔隙融合为大孔隙,反而降低熟球抗压强度。此外,熟球中硅酸盐(渣相)和浮氏体(FexO)更容易破裂,而金属铁(Fe)可延缓裂纹蔓延,因而,适当提高熟球金属化率、降低硅酸盐(渣相)含量也有利于提高其抗压强度。基于Maps统计分析探究了冶金尘泥还原焙烧过程中物相及孔隙的变化规律,分析结果可以为转底炉工艺处理冶金尘泥的生产实践提供指导和建议。展开更多
The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from...The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).展开更多
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-syn...Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-synchrosqueezing transform(TMssT)was proposed to capture these transient impulses for fault diagnosis.However,the TMSST,which is based on the short-time Fourier transform(STFT),suffers from unclear high-frequency re-presentations owing to the fixed sliding window used in the STFT.To address this limitation,the current study combined TMSST with the S-transform and a local maximum method to enhance the time-frequency representation for improved signal analysis.Furthermore,an extractive reconstruction algorithm that binds the maximum value of the spectral envelope is proposed for spectral decomposition.To validate the proposed technique,a simulated noise-added signal and four experimental bearing defect datasets were used.The results demonstrate that the proposed technique can effectively and accurately extract fault features from bearing signals regardless of whether the bearings operate under constant or varying speed conditions.This study offers a novel and efficient approach for fault diagnosis in mechanical systems with complex dynamic behaviors.展开更多
The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,sate...The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.展开更多
Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps i...Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Hot compression tests for GH4706 alloy were performed at a true strain of 1.2 within the temperature range of 950-1150℃ and the strain rate range of 0.001-1 s^(-1).The optimal hot deformation temperature and strain r...Hot compression tests for GH4706 alloy were performed at a true strain of 1.2 within the temperature range of 950-1150℃ and the strain rate range of 0.001-1 s^(-1).The optimal hot deformation temperature and strain rate range were determined using nephogram maps of dynamic recrystallization fraction,average grain size,and grain distribution standard deviation.Processing maps at true strains from 0.4 to 0.9 were generated based on flow stress curves to identify the strain corresponding to optimal microstructure homogenization efficiency at various temperatures and strain rates.Results show that within the optimal parameter range,under the conditions of 1150℃ and 0.01 s^(-1),the true strain of about 0.6 results in the optimal microstructure homogenization efficiency.The grain orientation spread maps obtained from the experiment also confirms this conclusion.This study provides an effective method for microstructure homogenization control of GH4706 alloy and an effective reference for the minimum strain threshold of the local part of the forging in engineering.展开更多
Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sin...Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range.The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance randomness.Extensive numerical experiments validate the effectiveness of SQQLSR.The proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in unpredictability.The bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher randomness.The system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force attacks.Additionally,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution techniques.This approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption algorithm.Theencryption method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor modifications.The robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential attacks.Additionally,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s reliability.These results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.展开更多
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat...With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.展开更多
Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mec...Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.展开更多
文摘冶金尘泥的转底炉处理工艺是目前钢铁行业采用的主要处置工艺,但在实际生产过程中经常出现还原焙烧不均匀的问题。利用微观扫描电子显微镜(scanning electron microscopy,SEM)分析结合宏观Maps统计分析,对冶金尘泥还原焙烧的不均匀性进行详细的可视化、数据化分析。研究结果表明,冶金尘泥在焙烧温度为1250℃、焙烧时间为15 min的条件下,熟球金属化率达到89.04%、脱锌率达到81.66%、抗压强度达到3.03 kN,熟球金属化率和脱锌率会随着焙烧温度提高和焙烧时间延长而进一步提高,但熟球抗压强度在焙烧时间过长时反而逐渐降低;熟球Maps统计分析表明,提高焙烧温度更有利于提高熟球外圈和下部的还原程度,而延长焙烧时间也更有利于提高熟球下部还原程度,但对熟球内部和外圈还原程度的提升作用比较相似;同时,提高焙烧温度也更有利于提升熟球下部的致密化程度,降低熟球上、下孔隙结构的不均匀性,进而显著提高熟球整体抗压强度;但焙烧时间过长会导致熟球中小孔隙融合为大孔隙,反而降低熟球抗压强度。此外,熟球中硅酸盐(渣相)和浮氏体(FexO)更容易破裂,而金属铁(Fe)可延缓裂纹蔓延,因而,适当提高熟球金属化率、降低硅酸盐(渣相)含量也有利于提高其抗压强度。基于Maps统计分析探究了冶金尘泥还原焙烧过程中物相及孔隙的变化规律,分析结果可以为转底炉工艺处理冶金尘泥的生产实践提供指导和建议。
基金Sichuan Science and Technology Program(2025ZNSFSC1341)Fundamental Research Funds for the Central Universities(J2022-090,25CAFUC04087)。
文摘The hot compression deformation behavior of Mg-6Zn-1Mn-0.5Ca(ZM61-0.5Ca)and Mg-6Zn-1Mn-2Sn-0.5Ca(ZMT612-0.5Ca)alloys was investigated at deformation temperatures ranging from 250℃to 400℃and strain rates varying from 0.001 s^(-1) to 1 s^(-1).The results show that the addition of Sn promotes dynamic recrystallization(DRX),and CaMgSn phases can act as nucleation sites during the compression deformation.Flow stress increases with increasing the strain rate and decreasing the temperature.Both the ZM61-0.5Ca and ZMT612-0.5Ca alloys exhibit obvious DRX characteristics.CaMgSn phases can effectively inhibit dislocation motion with the addition of Sn,thus increasing the peak fl ow stress of the alloy.The addition of Sn increases the hot deformation activation energy of the ZM61-0.5Ca alloy from 199.654 kJ/mol to 276.649 kJ/mol,thus improving the thermal stability of the alloy.For the ZMT612-0.5Ca alloy,the optimal hot deformation parameters are determined to be a deformation temperature range of 350–400℃and a strain rate range of 0.001–0.01 s^(-1).
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金Supported by National Natural Science Foundation of China(Grant No.62271230)Shandong Provincial Central Guidance on Local Science and Technology Development Fund(Grant No.YDZX2022178).
文摘Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-synchrosqueezing transform(TMssT)was proposed to capture these transient impulses for fault diagnosis.However,the TMSST,which is based on the short-time Fourier transform(STFT),suffers from unclear high-frequency re-presentations owing to the fixed sliding window used in the STFT.To address this limitation,the current study combined TMSST with the S-transform and a local maximum method to enhance the time-frequency representation for improved signal analysis.Furthermore,an extractive reconstruction algorithm that binds the maximum value of the spectral envelope is proposed for spectral decomposition.To validate the proposed technique,a simulated noise-added signal and four experimental bearing defect datasets were used.The results demonstrate that the proposed technique can effectively and accurately extract fault features from bearing signals regardless of whether the bearings operate under constant or varying speed conditions.This study offers a novel and efficient approach for fault diagnosis in mechanical systems with complex dynamic behaviors.
基金National Natural Science Foundation of China(Grant Nos.42388102,42030105,42192535)the Open Fund of State Key Laboratory of Precision Geodesy,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences(Grant No.SKLPG2025-1-5)。
文摘The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.
基金National Natural Science Foundation of China(No.42301518)Hubei Key Laboratory of Regional Development and Environmental Response(No.2023(A)002)Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources(Ministry of Education)(No.TDSYS202304).
文摘Image-maps,a hybrid design with satellite images as background and map symbols uploaded,aim to combine the advantages of maps’high interpretation efficiency and satellite images’realism.The usability of image-maps is influenced by the representations of background images and map symbols.Many researchers explored the optimizations for background images and symbolization techniques for symbols to reduce the complexity of image-maps and improve the usability.However,little literature was found for the optimum amount of symbol loading.This study focuses on the effects of background image complexity and map symbol load on the usability(i.e.,effectiveness and efficiency)of image-maps.Experiments were conducted by user studies via eye-tracking equipment and an online questionnaire survey.Experimental data sets included image-maps with ten levels of map symbol load in ten areas.Forty volunteers took part in the target searching experiments.It has been found that the usability,i.e.,average time viewed(efficiency)and average revisits(effectiveness)of targets recorded,is influenced by the complexity of background images,a peak exists for optimum symbol load for an image-map.The optimum levels for symbol load for different image-maps also have a peak when the complexity of the background image/image map increases.The complexity of background images serves as a guideline for optimum map symbol load in image-map design.This study enhanced user experience by optimizing visual clarity and managing cognitive load.Understanding how these factors interact can help create adaptive maps that maintain clarity and usability,guiding AI algorithms to adjust symbol density based on user context.This research establishes the practices for map design,making cartographic tools more innovative and more user-centric.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金National Key R&D Program Project(2022YFB3705103)。
文摘Hot compression tests for GH4706 alloy were performed at a true strain of 1.2 within the temperature range of 950-1150℃ and the strain rate range of 0.001-1 s^(-1).The optimal hot deformation temperature and strain rate range were determined using nephogram maps of dynamic recrystallization fraction,average grain size,and grain distribution standard deviation.Processing maps at true strains from 0.4 to 0.9 were generated based on flow stress curves to identify the strain corresponding to optimal microstructure homogenization efficiency at various temperatures and strain rates.Results show that within the optimal parameter range,under the conditions of 1150℃ and 0.01 s^(-1),the true strain of about 0.6 results in the optimal microstructure homogenization efficiency.The grain orientation spread maps obtained from the experiment also confirms this conclusion.This study provides an effective method for microstructure homogenization control of GH4706 alloy and an effective reference for the minimum strain threshold of the local part of the forging in engineering.
基金funded by Kementerian Pendidikan Tinggi,Sains,dan Teknologi(Kemdiktisaintek),Indonesia,grant numbers 108/E5/PG.02.00.PL/2024,027/LL6/PB/AL.04/2024,061/A.38-04/UDN-09/VI/2024.
文摘Data security has become a growing priority due to the increasing frequency of cyber-attacks,necessitating the development of more advanced encryption algorithms.This paper introduces Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps(SQQLSR),a quantum-based chaos map designed to generate one-dimensional chaotic sequences with an ultra-wide parameter range.The proposed model leverages quantum superposition using Hadamard gates and quantum rotations along the X,Y,and Z axes to enhance randomness.Extensive numerical experiments validate the effectiveness of SQQLSR.The proposed method achieves a maximum Lyapunov exponent(LE)of≈55.265,surpassing traditional chaotic maps in unpredictability.The bifurcation analysis confirms a uniform chaotic distribution,eliminating periodic windows and ensuring higher randomness.The system also generates an expanded key space exceeding 10^(40),enhancing security against brute-force attacks.Additionally,SQQLSR is applied to image encryption using a simple three-layer encryption scheme combining permutation and substitution techniques.This approach is intentionally designed to highlight the impact of SQQLSR-generated chaotic sequences rather than relying on a complex encryption algorithm.Theencryption method achieves an average entropy of 7.9994,NPCR above 99.6%,and UACI within 32.8%–33.8%,confirming its strong randomness and sensitivity to minor modifications.The robustness tests against noise,cropping,and JPEG compression demonstrate its resistance to statistical and differential attacks.Additionally,the decryption process ensures perfect image reconstruction with an infinite PSNR value,proving the algorithm’s reliability.These results highlight SQQLSR’s potential as a lightweight yet highly secure encryption mechanism suitable for quantum cryptography and secure communications.
基金supported by Natural Science Foundation of China(No.62371231)Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu under Grant BK20222001Jiangsu Provincial Key Research and Development Program(No.BE2023027).
文摘With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions.
基金funded by the Norwegian Research Council(NFR project 302701 Climate Smart Forestry Norway).
文摘Assessing forest vulnerability to disturbances at a high spatial resolution and for regional and national scales has become attainable with the combination of remote sensing-derived high-resolution forest maps and mechanistic risk models. This study demonstrated large-scale and high-resolution modelling of wind damage vulnerability in Norway. The hybrid mechanistic wind damage model, ForestGALES, was adapted to map the critical wind speeds(CWS) of damage across Norway using a national forest attribute map at a 16 m × 16 m spatial resolution. P arametrization of the model for the Norwegian context was done using the literature and the National Forest Inventory data. This new parametrization of the model for Norwegian forests yielded estimates of CWS significantly different from the default parametrization. Both parametrizations fell short of providing acceptable discrimination of the damaged area following the storm of November 19, 2021 in the central southern region of Norway when using unadjusted CWS. After adjusting the CWS and the storm wind speeds by a constant factor, the Norwegian parametrization provided acceptable discrimination and was thus defined as suitable to use in future studies, despite the lack of field-and laboratory experiments to directly derive parameters for Norwegian forests. The windstorm event used for model validation in this study highlighted the challenges of predicting wind damage to forests in landscapes with complex topography. Future studies should focus on further developing ForestGALES and new datasets describing extreme wind climates to better represent the wind and tree interactions in complex topography, and predict the level of risk in order to develop local climate-smart forest management strategies.
