Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-div...Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-division multiplexing-based visible light communication(OFDM-VLC)system is presented.In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC,a quantized mathematical model of NHS OFDM-VLC is established.Based on the proposed quantized model,a closed-form bit error rate(BER)expression is derived.The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC.In addition,channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.展开更多
The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand bo...The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fine details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fine sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identification,eff ectively capturing their contours and shapes.This method,which integrates seismic facies,single-well sedimentary microfacies,and machine learning techniques,enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.展开更多
The issue of privacy leakage in distributed consensus has garnered significant attention over the years,but existing studies often overlook the challenges posed by limited communication in algorithm design.This paper ...The issue of privacy leakage in distributed consensus has garnered significant attention over the years,but existing studies often overlook the challenges posed by limited communication in algorithm design.This paper addresses the issue of privacy preservation in distributed weighted average consensus under limited communication scenarios.Specifically targeting directed and unbalanced topologies,we propose a privacy-preserving implementation protocol that incorporates the Paillier homomorphic encryption scheme.The protocol encrypts only the 1-bit quantized messages exchanged between agents,thus ensuring both the correctness of the consensus result and the confidentiality of each agent's initial state.To demonstrate the practicality of the proposed method,we carry out numerical simulations that illustrate its ability to reach consensus effectively while ensuring the protection of private information.展开更多
The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classificati...The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.展开更多
A new phenomenological model(axionic QCD string)is constructed to study the topological issues of the QCD vacuum and hadron structure.It provides an alternative way of tackling the Strong CP problem,which is different...A new phenomenological model(axionic QCD string)is constructed to study the topological issues of the QCD vacuum and hadron structure.It provides an alternative way of tackling the Strong CP problem,which is different from the traditional Peccei–Quinn approach.Neither new particle nor extra symmetry is introduced,and the role of the Peccei–Quinn axion is played by a quasiparticle arising from the phase of the quark condensate,dubbed as axionic excitation.The derivative of this excitation field is decomposed into a regular part and a singular part,and the latter contains vorticity from the string configuration.A hidden gauge symmetry is revealed in this decomposition and vorticity is represented by an emergent gauge field associated with anomalies.These components,together with the anomaly-inflow mechanism,complete the effective Lagrangian description for the axionic QCD string.展开更多
Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnos...Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.展开更多
We consider a relativistic two-fluid model of superfluidity,in which the superfluid is described by an order parameter that is a complex scalar field satisfying the nonlinear Klein-Gordon equation(NLKG).The coupling t...We consider a relativistic two-fluid model of superfluidity,in which the superfluid is described by an order parameter that is a complex scalar field satisfying the nonlinear Klein-Gordon equation(NLKG).The coupling to the normal fluid is introduced via a covariant current-current interaction,which results in the addition of an effective potential,whose imaginary part describes particle transfer between superfluid and normal fluid.Quantized vorticity arises in a class of singular solutions and the related vortex dynamics is incorporated in the modified NLKG,facilitating numerical analysis which is usually very complicated in the phenomenology of vortex filaments.The dual transformation to a string theory description(Kalb-Ramond)of quantum vorticity,the Magnus force,and the mutual friction between quantized vortices and normal fluid are also studied.展开更多
This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 bac...This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.展开更多
In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of un...In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of unknown external disturbances.Firstly,VTs are constructed for each QUAV,and the QUAV is restricted into the corresponding VT by the artificial potential field,which is distributed around the boundary of the VT.Thus,the collisions between QUAVs are avoided.Besides,the boundaries of the VTs are flexible by the modification signals,which are generated by the self-regulating auxiliary systems,to make the repulsive force smaller and give more buffer space for QUAVs without collision.Then,a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism.Furthermore,a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance.On this basis,a distributed ET collision avoidance coordinated controller is proposed.Then,the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants.The boundedness of the closed-loop signals is verified by the Lyapunov method.Finally,simulation and experimental results are performed to demonstrate the superiority of the proposed control method.展开更多
In order to overcome data-quantization, networked-induced delay, network packet dropouts and wrong sequences in the nonlinear networked control system, a novel nonlinear networked control system model is built by the ...In order to overcome data-quantization, networked-induced delay, network packet dropouts and wrong sequences in the nonlinear networked control system, a novel nonlinear networked control system model is built by the T-S fuzzy method. Two time-varying quantizers are added in the model. The key analysis steps in the method are to construct an improved interval-delay-dependent Lyapunov functional and to introduce the free-weighting matrix. By making use of the parallel distributed compensation technology and the convexity of the matrix function, the improved criteria of the stabilization and stability are obtained. Simulation experiments show that the parameters of the controllers and quantizers satisfying a certain performance can be obtained by solving a set of LMIs. The application of the nonlinear mass-spring system is provided to show that the proposed method is effective.展开更多
A VQ based efficient speech recognition method is introduced, and the key parameters of this method are comparatively studied. This method is especially designed for mandarin speaker dependent small size word set r...A VQ based efficient speech recognition method is introduced, and the key parameters of this method are comparatively studied. This method is especially designed for mandarin speaker dependent small size word set recognition. It has less complexity, less resource consumption but higher ARR (accurate recognition rate) compared with traditional HMM or NN approach. A large scale test on the task of 11 mandarin digits recognition shows that the WER(word error rate) can reach 3 86%. This method is suitable for being embedded in PDA (personal digital assistant), mobile phone and so on to perform voice controlling like digits dialing, name dialing, calculating, voice commanding, etc.展开更多
Quantized control systems design is motivated by the convergence of controls and communications to address modern engineering applications involving the use of information technology. This paper presents an overview o...Quantized control systems design is motivated by the convergence of controls and communications to address modern engineering applications involving the use of information technology. This paper presents an overview of recent developments on the control of linear and nonlinear systems when the control input is subject to quantization or the quantized states or outputs are used as feedback measurements. The co-existence of high-dimeasionality, quantization, nonlinearity and uncertainty poses great challenges to quantized control of nonlinear systems and thus calls for new ideas and techniques. The field of quantized nonlinear control is still at its infancy. Preliminary results in our recent work based on input-to-state stability and cyclic-small-gain theorems are reviewed. The open problems in quantized nonlinear control are also outlined.展开更多
Networked control systems are spatially distributed systems in which the communication between sensors, actuators,and controllers occurs through a shared band-limited digital communication network. Several advantages ...Networked control systems are spatially distributed systems in which the communication between sensors, actuators,and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices,increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems,process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘control over networks’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘control over networks’, providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.展开更多
This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and ac...This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.展开更多
To solve the problems of ladle slag detection system （SDS）, such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model （HMM） was put forward. The...To solve the problems of ladle slag detection system （SDS）, such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model （HMM） was put forward. The physical process of continuous casting was analyzed, and vibration signal was considered as the main detecting signal according to the difference in shock vibration generated by molten steel and slag because of their difference in density. Automatic control experiment platform oriented to SDS was established, and vibration sensor was installed far away from molten steel, which could solve the problem of easy power consumption by the sensor. The combina- tion of vector quantization technology with learning process parameters of HMM was optimized, and its revaluation formula was revised to enhance its recognition effectiveness. Industrial field experiments proved that this system requires low cost and little rebuilding for current devices, and its slag detection rate can exceed 95%.展开更多
For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing...For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing research trends,it is significant to consolidate the latest knowledge and information to keep up with the research needs.In this paper,the results of different aspects of NCSs,such as quantization,estimation,fault detection and networked predictive control,are summarized.In addition,with the development of cloud technique,cloud control systems are proposed for the further development of NCSs.展开更多
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
基金supported by the National Natural Science Foundation of China(No.62201508)the Zhejiang Provincial Natural Science Foundation of China(Nos.LZ21F010001 and LQ23F010004)the State Key Laboratory of Millimeter Waves of Southeast University,China(No.K202212).
文摘Quantization noise caused by analog-to-digital converter(ADC)gives rise to the reliability performance degradation of communication systems.In this paper,a quantized non-Hermitian symmetry(NHS)orthogonal frequency-division multiplexing-based visible light communication(OFDM-VLC)system is presented.In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC,a quantized mathematical model of NHS OFDM-VLC is established.Based on the proposed quantized model,a closed-form bit error rate(BER)expression is derived.The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC.In addition,channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.
基金Collaborative Project Grant from the Exploration and Development Research Institute of SINOPEC Northwest Oilfi eld Company(Grant No.KY2021-S-104).
文摘The Tarim Basin has revealed numerous tight sandstone oil and gas reservoirs.The tidal fl at zone in the Shunbei area is currently in the detailed exploration stage,requiring a comprehensive description of the sand body distribution characteristics for rational exploration well deployment.However,using a single method for sand body prediction has yielded poor results.Seismic facies analysis can eff ectively predict the macro-development characteristics of sedimentary sand bodies but lacks the resolution to capture fine details.In contrast,single-well sedimentary facies analysis can describe detailed sand body development but struggles to reveal broader trends.Therefore,this study proposes a method that combines seismic facies analysis with single-well sedimentary microfacies analysis,using the lower section of the Kepingtage Formation in the Shunbei area as a case study.First,seismic facies were obtained through unsupervised vector quantization to control the macro-distribution characteristics of sand bodies,while principal component analysis(PCA)was applied to improve the depiction of fine sand body details from seismic attributes.Based on 3D seismic data,well-logging data,and geological interpretation results,a detailed structural interpretation was performed to establish a high-precision stratigraphic framework,thereby enhancing the accuracy of sand body prediction.Seismic facies analysis was then conducted to obtain the macro-distribution characteristics of the sand bodies.Subsequently,core data and logging curves from individual wells were used to clarify the vertical development characteristics of tidal channels and sandbars.Next,PCA was employed to select the seismic attributes most sensitive to sand bodies in diff erent sedimentary facies.Results indicate that RMS amplitude in the subtidal zone and instantaneous phase in the intertidal zone are the most sensitive to sand bodies.A comparative analysis of individual seismic attributes for sand body characterization revealed that facies-based delineation improved the accuracy of sand body identification,eff ectively capturing their contours and shapes.This method,which integrates seismic facies,single-well sedimentary microfacies,and machine learning techniques,enhances the precision of sand body characterization and off ers a novel approach to sand body prediction.
基金supported by National Natural Science Foundation of China under Grants 62203045,62433020 and T2293770。
文摘The issue of privacy leakage in distributed consensus has garnered significant attention over the years,but existing studies often overlook the challenges posed by limited communication in algorithm design.This paper addresses the issue of privacy preservation in distributed weighted average consensus under limited communication scenarios.Specifically targeting directed and unbalanced topologies,we propose a privacy-preserving implementation protocol that incorporates the Paillier homomorphic encryption scheme.The protocol encrypts only the 1-bit quantized messages exchanged between agents,thus ensuring both the correctness of the consensus result and the confidentiality of each agent's initial state.To demonstrate the practicality of the proposed method,we carry out numerical simulations that illustrate its ability to reach consensus effectively while ensuring the protection of private information.
文摘The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text classification.However,BERT’s size and computational demands limit its practicality,especially in resource-constrained settings.This research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization techniques.Despite Bengali being the sixth most spoken language globally,NLP research in this area is limited.Our approach addresses this gap by creating an efficient BERT-based model for Bengali text.We have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory size.Our best results demonstrate significant improvements in both speed and efficiency.For instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 MB.These results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.
基金supported by the Natural Science Foundation of Fujian Province(Grant No.2022J011130)the Research Starting Grant from Minjiang University(Grant No.30804317)。
文摘A new phenomenological model(axionic QCD string)is constructed to study the topological issues of the QCD vacuum and hadron structure.It provides an alternative way of tackling the Strong CP problem,which is different from the traditional Peccei–Quinn approach.Neither new particle nor extra symmetry is introduced,and the role of the Peccei–Quinn axion is played by a quasiparticle arising from the phase of the quark condensate,dubbed as axionic excitation.The derivative of this excitation field is decomposed into a regular part and a singular part,and the latter contains vorticity from the string configuration.A hidden gauge symmetry is revealed in this decomposition and vorticity is represented by an emergent gauge field associated with anomalies.These components,together with the anomaly-inflow mechanism,complete the effective Lagrangian description for the axionic QCD string.
基金funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant numbers AP14969403 and AP23485820.
文摘Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.
文摘We consider a relativistic two-fluid model of superfluidity,in which the superfluid is described by an order parameter that is a complex scalar field satisfying the nonlinear Klein-Gordon equation(NLKG).The coupling to the normal fluid is introduced via a covariant current-current interaction,which results in the addition of an effective potential,whose imaginary part describes particle transfer between superfluid and normal fluid.Quantized vorticity arises in a class of singular solutions and the related vortex dynamics is incorporated in the modified NLKG,facilitating numerical analysis which is usually very complicated in the phenomenology of vortex filaments.The dual transformation to a string theory description(Kalb-Ramond)of quantum vorticity,the Magnus force,and the mutual friction between quantized vortices and normal fluid are also studied.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 62376215)the Key Research and Development Project of Shaanxi Province(No.2025CY-YBXM-044)+3 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(Nos.2022RC069 and 2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)the Ningbo Top Medical and Health Research Program(No.2023030716).
文摘This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.
基金supported in part by the National Key R&D Program of China(No.2023YFB4704400)in part by the National Natural Science Foundation of China(Nos.U23B2036,U2013201).
文摘In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of unknown external disturbances.Firstly,VTs are constructed for each QUAV,and the QUAV is restricted into the corresponding VT by the artificial potential field,which is distributed around the boundary of the VT.Thus,the collisions between QUAVs are avoided.Besides,the boundaries of the VTs are flexible by the modification signals,which are generated by the self-regulating auxiliary systems,to make the repulsive force smaller and give more buffer space for QUAVs without collision.Then,a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism.Furthermore,a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance.On this basis,a distributed ET collision avoidance coordinated controller is proposed.Then,the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants.The boundedness of the closed-loop signals is verified by the Lyapunov method.Finally,simulation and experimental results are performed to demonstrate the superiority of the proposed control method.
基金The National Natural Science Foundation of China(No.60474049,60835001)Specialized Research Fund for Doctoral Program of Higher Education(No.20090092120027)
文摘In order to overcome data-quantization, networked-induced delay, network packet dropouts and wrong sequences in the nonlinear networked control system, a novel nonlinear networked control system model is built by the T-S fuzzy method. Two time-varying quantizers are added in the model. The key analysis steps in the method are to construct an improved interval-delay-dependent Lyapunov functional and to introduce the free-weighting matrix. By making use of the parallel distributed compensation technology and the convexity of the matrix function, the improved criteria of the stabilization and stability are obtained. Simulation experiments show that the parameters of the controllers and quantizers satisfying a certain performance can be obtained by solving a set of LMIs. The application of the nonlinear mass-spring system is provided to show that the proposed method is effective.
文摘A VQ based efficient speech recognition method is introduced, and the key parameters of this method are comparatively studied. This method is especially designed for mandarin speaker dependent small size word set recognition. It has less complexity, less resource consumption but higher ARR (accurate recognition rate) compared with traditional HMM or NN approach. A large scale test on the task of 11 mandarin digits recognition shows that the WER(word error rate) can reach 3 86%. This method is suitable for being embedded in PDA (personal digital assistant), mobile phone and so on to perform voice controlling like digits dialing, name dialing, calculating, voice commanding, etc.
基金Supported by National Science Foundation of USA （DMS-0906659. ECCS-1230040）
文摘Quantized control systems design is motivated by the convergence of controls and communications to address modern engineering applications involving the use of information technology. This paper presents an overview of recent developments on the control of linear and nonlinear systems when the control input is subject to quantization or the quantized states or outputs are used as feedback measurements. The co-existence of high-dimeasionality, quantization, nonlinearity and uncertainty poses great challenges to quantized control of nonlinear systems and thus calls for new ideas and techniques. The field of quantized nonlinear control is still at its infancy. Preliminary results in our recent work based on input-to-state stability and cyclic-small-gain theorems are reviewed. The open problems in quantized nonlinear control are also outlined.
基金supported in part by the Australian Research Council Discovery Project(DP160103567)
文摘Networked control systems are spatially distributed systems in which the communication between sensors, actuators,and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices,increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems,process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘control over networks’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘control over networks’, providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.
基金supported by the National Natural Science Foundation of China(61673045)Beijing Natural Science Foundation(4152040)
文摘This paper conducts a survey on iterative learn-ing control(ILC)with incomplete information and associated control system design,which is a frontier of the ILC field.The incomplete information,including passive and active types,can cause data loss or fragment due to various factors.Passive incomplete information refers to incomplete data and information caused by practical system limitations during data collection,storage,transmission,and processing,such as data dropouts,delays,disordering,and limited transmission bandwidth.Active incomplete information refers to incomplete data and information caused by man-made reduction of data quantity and quality on the premise that the given objective is satisfied,such as sampling and quantization.This survey emphasizes two aspects:the first one is how to guarantee good learning performance and tracking performance with passive incomplete data,and the second is how to balance the control performance index and data demand by active means.The promising research directions along this topic are also addressed,where data robustness is highly emphasized.This survey is expected to improve understanding of the restrictive relationship and trade-off between incomplete data and tracking performance,quantitatively,and promote further developments of ILC theory.
基金Item Sponsored by National Natural Science Foundation of China (50374061)863 National High Technology Program of China(2004AA-1Z2060)
文摘To solve the problems of ladle slag detection system （SDS）, such as high cost, short service life, and inconvenient maintenance, a new SDS realization method based on hidden Markov model （HMM） was put forward. The physical process of continuous casting was analyzed, and vibration signal was considered as the main detecting signal according to the difference in shock vibration generated by molten steel and slag because of their difference in density. Automatic control experiment platform oriented to SDS was established, and vibration sensor was installed far away from molten steel, which could solve the problem of easy power consumption by the sensor. The combina- tion of vector quantization technology with learning process parameters of HMM was optimized, and its revaluation formula was revised to enhance its recognition effectiveness. Industrial field experiments proved that this system requires low cost and little rebuilding for current devices, and its slag detection rate can exceed 95%.
基金supported by National Basic Research Program of China(973 Program)(No.2012CB720000)National Natural Science Foundation of China(Nos.61225015 and 60974011)+3 种基金Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.61321002)Beijing Municipal Natural Science Foundation(Nos.4102053 and 4101001)Beijing Natural Science Foundation(Nos.4132042)Beijing Higher Education Young Elite Teacher Project(No.YETP1212)
文摘For the past decades,networked control systems(NCSs),as an interdisciplinary subject,have been one of the main research highlights and many fruitful results from different aspects have been achieved.With these growing research trends,it is significant to consolidate the latest knowledge and information to keep up with the research needs.In this paper,the results of different aspects of NCSs,such as quantization,estimation,fault detection and networked predictive control,are summarized.In addition,with the development of cloud technique,cloud control systems are proposed for the further development of NCSs.
