Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas.This study took Gansu Province,China,a typical area with comp...Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas.This study took Gansu Province,China,a typical area with complex terrain and variable climate,as the research subject.Based on Google Earth Engine,we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022,and quantitatively analyzed the main causes of regional differences in surface water area.The findings revealed that surface water area in Gansu Province expanded by 406.88 km2 from 1985 to 2022.Seasonal surface water area exhibited significant fluctuations,while permanent surface water area showed a steady increase.Notably,terrestrial water storage exhibited a trend of first decreasing and then increasing,correlated with the dynamics of surface water area.Climate change and human activities jointly affected surface hydrological processes,with the impact of climate change being slightly higher than that of human activities.Spatially,climate change affected the'source'of surface water to a greater extent,while human activities tended to affect the'destination'of surface water.Challenges of surface water resources faced by inland arid and semi-arid areas like Gansu Province are multifaceted.Therefore,we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water'supply-demand'balance strategies.The study not only sheds light on the dynamics of surface water area in Gansu Province,but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.展开更多
Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however...Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.展开更多
The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,th...The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.展开更多
The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an import...The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.展开更多
The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is ...The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.展开更多
Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are...Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are limited in detecting new inconstant patterns and identifying malicious traffic traces in real time.Therefore,there is an urgent need to implement more effective intrusion detection technologies to protect computer security.Methods In this study,we designed a hybrid IDS by combining our incremental learning model(KANSOINN)and active learning to learn new log patterns and detect various network anomalies in real time.Conclusions Experimental results on the NSLKDD dataset showed that KAN-SOINN can be continuously improved and effectively detect malicious logs.Meanwhile,comparative experiments proved that using a hybrid query strategy in active learning can improve the model learning efficiency.展开更多
This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and m...This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.展开更多
Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an al...Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.展开更多
A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spect...A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.展开更多
Inpatient falls from beds in hospitals are a common problem.Such falls may result in severe injuries.This problem can be addressed by continuous monitoring of patients using cameras.Recent advancements in deep learnin...Inpatient falls from beds in hospitals are a common problem.Such falls may result in severe injuries.This problem can be addressed by continuous monitoring of patients using cameras.Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient.Along with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their health.High computation-intensive models are required to monitor every action of the patient precisely.This requirement limits the applicability of such networks.Hence,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall detection.Motivated by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their poses.The whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition network.The compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human poses.Finally,the activity recognition network classifies the patients’activities based on their poses.The proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.展开更多
Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are d...Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.展开更多
In this work, a novel voice activity detection (VAD) algorithm that uses speech absence probability (SAP) based on Teager energy (TE) was proposed for speech enhancement. The proposed method employs local SAP (LSAP) b...In this work, a novel voice activity detection (VAD) algorithm that uses speech absence probability (SAP) based on Teager energy (TE) was proposed for speech enhancement. The proposed method employs local SAP (LSAP) based on the TE of noisy speech as a feature parameter for voice activity detection (VAD) in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the ability to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP (GSAP) derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.展开更多
In this study, integrated multi-wall carbon nanotube (MWCNT) electrodes were prepared in the holes of glass directly by microwave plasma chemical vapour deposition (MWPCVD). The electrochemical behaviour of catech...In this study, integrated multi-wall carbon nanotube (MWCNT) electrodes were prepared in the holes of glass directly by microwave plasma chemical vapour deposition (MWPCVD). The electrochemical behaviour of catechol at the integrated MWCNT electrodes was investigated. The oxygen plasma treated CNT electrodes had better electrochemical performance for the analysis of catechol than that of as-synthesized CNT electrodes. Both the as-synthesized CNTs and plasma treated CNTs were characterized by TEM(transmission electron microscopy, XPS(X-ray photoelectron spectroscopy) and Raman spectroscopy. The results revealed that the oxygen plasma activation is an effective method to enhance the electrochemical properties of CNT electrodes.展开更多
Considering mass and stiffness of piezoelectric layers and damage effects of composite layers, nonlinear dynamic equations of damaged piezoelectric smart laminated plates are derived. The derivation is based on the Ha...Considering mass and stiffness of piezoelectric layers and damage effects of composite layers, nonlinear dynamic equations of damaged piezoelectric smart laminated plates are derived. The derivation is based on the Hamilton's principle, the higher- order shear deformation plate theory, von Karman type geometrically nonlinear straindisplacement relations, and the strain energy equivalence theory. A negative velocity feedback control algorithm coupling the direct and converse piezoelectric effects is used to realize the active control and damage detection with a closed control loop. Simply supported rectangular laminated plates with immovable edges are used in numerical computation. Influence of the piezoelectric layers' location on the vibration control is in- vestigated. In addition, effects of the degree and location of damage on the sensor output voltage are discussed. A method for damage detection is introduced.展开更多
[ Objective] This study was to develop a detection method for assaying calf intestine alkaline phasphatase activity in scientific research. [ Method ] By simulating the conditions and buffers for assaying calf intesti...[ Objective] This study was to develop a detection method for assaying calf intestine alkaline phasphatase activity in scientific research. [ Method ] By simulating the conditions and buffers for assaying calf intestine alkaline phosphatase used in the scientific research, the parameters influencing substmte concentration, reaction duration, eoloration time and buffer pH were optimized. [ Result] The activity of alkaline phosphatase detected varied hugely among different buffers, and potassium ferricyanide solution added with boracic acid achieved the stable coloration. The optimal water bath time was determined as 10 rain, and the substrate concentration was optimized as 0.04 moL/L. The increasing temperature did not have a large influence on low temperature enzymatic activity while did on high coneentration enzymatic activity. When the buffer pH was 7.0 - 8.0, the detection stability of alkaline phosphatase could be maintained well The ion intensity of buff- er had little effects on alkaline phasphatase activity. [ Conclusion] A detection method for assaying calf intestine alkaline phosphatase activity in scientific research was successfully developed in this study.展开更多
We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture o...We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture of Laplacian and Gaussian distributions. We analyse and numerically evaluate the proposed test for various noisy environments. In addition, we address the problem of effectively recognizing the possible presence of cyber exploits in the voice transmission channel. We then introduce another sequential test, designed to detect rapidly and accurately the presence of such exploits, named Cyber Attacks Sequential Detection of Change Test (CA-SDCT). We analyse and numerically evaluate the latter test. Experimental results and comparisons with other proposed methods are also presented.展开更多
We try to extend the current configuredgrant(CG)uplink scheme in 5G New Radio(NR)to support massive potential users and study activity detection under this scenario.Characteristics of the continuously varying channel ...We try to extend the current configuredgrant(CG)uplink scheme in 5G New Radio(NR)to support massive potential users and study activity detection under this scenario.Characteristics of the continuously varying channel and the multiple repetition scheme are utilized to improve the detection accuracy,which can be an enhancement to existing activity detection algorithms.Numerical results under 3 GPP TDL(Tapped Delay Line)fading channel show the superiority of our algorithm.And system-level simulation reveals that enhancements on activity detection can improve reliability and reduce latency.展开更多
Natural Killer (NK) cells are specific immune cells in human immune system. They have a quick effect and can exert a cytotoxic function without prior sensitization, and they show great application potential in cell-ba...Natural Killer (NK) cells are specific immune cells in human immune system. They have a quick effect and can exert a cytotoxic function without prior sensitization, and they show great application potential in cell-based immunotherapy, anti-infection<em> in vivo</em>. NK cell activity in peripheral blood can be used as one of the biomarkers of immune function response. It has a great positive guiding significance for the clinical prognosis of tumor patients, the prevention of cancer and anti-aging. The clinical detection strategies of NK cell activity in circulation mainly grouped into five types: methyl thiazolyl tetrazolium colorimetric, lactate dehydrogenase release, radionuclide labeling, flow cytometry and NK Vue cytokine release method. It has played an important role in different stages of clinical application development. This paper will make a comparative review of the above-mentioned detection strategies for the NK cell activity.展开更多
Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detect...Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.展开更多
This paper presents an improved Voice Activity Detection (VAD) algorithm which uses the Signal-to-Noise Ratio (SNR) measure. We assume that noise Power Spectral Density (PSD) in each spectral bin follows a Rayleigh di...This paper presents an improved Voice Activity Detection (VAD) algorithm which uses the Signal-to-Noise Ratio (SNR) measure. We assume that noise Power Spectral Density (PSD) in each spectral bin follows a Rayleigh distribution. Rayleigh distributions with its asymmetric tail charac- teristics give a better description of the noise PSD distribution than Gaussian distribution. Under this assumption, a new threshold updating expression is derived. Since the analytical integral of the false alarm probability, the threshold updating expression can be represented without the inverse comple- mentary error function and low computational complexity is achieved in our system. Experimental results show that the proposed VAD outperforms or at least is comparable with the VAD scheme presented by Davis under several noise environments and has a lower computational complexity.展开更多
基金This research was supported by the Third Xinjiang Scientific Expedition Program(2021xjkk010102)the National Natural Science Foundation of China(41261047,41761043)+1 种基金the Science and Technology Plan of Gansu Province,China(20YF3FA042)the Youth Teacher Scientific Capability Promoting Project of Northwest Normal University,Gansu Province,China(NWNU-LKQN-17-7).
文摘Understanding the dynamics of surface water area and their drivers is crucial for human survival and ecosystem stability in inland arid and semi-arid areas.This study took Gansu Province,China,a typical area with complex terrain and variable climate,as the research subject.Based on Google Earth Engine,we used Landsat data and the Open-surface Water Detection Method with Enhanced Impurity Control method to monitor the spatiotemporal dynamics of surface water area in Gansu Province from 1985 to 2022,and quantitatively analyzed the main causes of regional differences in surface water area.The findings revealed that surface water area in Gansu Province expanded by 406.88 km2 from 1985 to 2022.Seasonal surface water area exhibited significant fluctuations,while permanent surface water area showed a steady increase.Notably,terrestrial water storage exhibited a trend of first decreasing and then increasing,correlated with the dynamics of surface water area.Climate change and human activities jointly affected surface hydrological processes,with the impact of climate change being slightly higher than that of human activities.Spatially,climate change affected the'source'of surface water to a greater extent,while human activities tended to affect the'destination'of surface water.Challenges of surface water resources faced by inland arid and semi-arid areas like Gansu Province are multifaceted.Therefore,we summarized the surface hydrology patterns typical in inland arid and semi-arid areas and tailored surface water'supply-demand'balance strategies.The study not only sheds light on the dynamics of surface water area in Gansu Province,but also offers valuable insights for ecological protection and surface water resource management in inland arid and semi-arid areas facing water scarcity.
基金supported by a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 22CTAP-C163951-02).
文摘Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.
基金supported by National Key Research and Development Program of China under Grants 2021YFB1600500,2021YFB3201502,and 2022YFB3207704Natural Science Foundation of China(NSFC)under Grants U2233216,62071044,61827901,62088101 and 62201056+1 种基金supported by Shandong Province Natural Science Foundation under Grant ZR2022YQ62supported by Beijing Nova Program,Beijing Institute of Technology Research Fund Program for Young Scholars under grant XSQD-202121009.
文摘The extra-large scale multiple-input multiple-output(XL-MIMO)for the beyond fifth/sixth generation mobile communications is a promising technology to provide Tbps data transmission and stable access service.However,the extremely large antenna array aperture arouses the channel near-field effect,resulting in the deteriorated data rate and other challenges in the practice communication systems.Meanwhile,multi-panel MIMO technology has attracted extensive attention due to its flexible configuration,low hardware cost,and wider coverage.By combining the XL-MIMO and multi-panel array structure,we construct multi-panel XL-MIMO and apply it to massive Internet of Things(IoT)access.First,we model the multi-panel XL-MIMO-based near-field channels for massive IoT access scenarios,where the electromagnetic waves corresponding to different panels have different angles of arrival/departure(AoAs/AoDs).Then,by exploiting the sparsity of the near-field massive IoT access channels,we formulate a compressed sensing based joint active user detection(AUD)and channel estimation(CE)problem which is solved by AMP-EM-MMV algorithm.The simulation results exhibit the superiority of the AMP-EM-MMV based joint AUD and CE scheme over the baseline algorithms.
文摘The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.
基金This research has been funded by the Research General Direction at Universidad Santiago de Cali under call No.01-2021.
文摘The most salient argument that needs to be addressed universally is Early Breast Cancer Detection(EBCD),which helps people live longer lives.The Computer-Aided Detection(CADs)/Computer-Aided Diagnosis(CADx)sys-tem is indeed a software automation tool developed to assist the health profes-sions in Breast Cancer Detection and Diagnosis(BCDD)and minimise mortality by the use of medical histopathological image classification in much less time.This paper purposes of examining the accuracy of the Convolutional Neural Network(CNN),which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early iden-tification of breast cell malignancies formation of masses and Breast microcalci-fications on the mammogram.When we have insufficient data for a new domain that is desired to be handled by a pre-trained Convolutional Neural Network of Residual Network(ResNet50)for Breast Cancer Detection and Diagnosis,to obtain the Discriminative Localization,Convolutional Neural Network with Class Activation Map(CAM)has also been used to perform breast microcalcifications detection tofind a specific class in the Histopathological image.The test results indicate that this method performed almost 225.15%better at determining the exact location of disease(Discriminative Localization)through breast microcalci-fications images.ResNet50 seems to have the highest level of accuracy for images of Benign Tumour(BT)/Malignant Tumour(MT)cases at 97.11%.ResNet50’s average accuracy for pre-trained Convolutional Neural Network is 94.17%.
基金Supported by SJTU-HUAWEI TECH Cybersecurity Innovation Lab。
文摘Background With the development of information technology,there is a significant increase in the number of network traffic logs mixed with various types of cyberattacks.Traditional intrusion detection systems(IDSs)are limited in detecting new inconstant patterns and identifying malicious traffic traces in real time.Therefore,there is an urgent need to implement more effective intrusion detection technologies to protect computer security.Methods In this study,we designed a hybrid IDS by combining our incremental learning model(KANSOINN)and active learning to learn new log patterns and detect various network anomalies in real time.Conclusions Experimental results on the NSLKDD dataset showed that KAN-SOINN can be continuously improved and effectively detect malicious logs.Meanwhile,comparative experiments proved that using a hybrid query strategy in active learning can improve the model learning efficiency.
基金supported by National Natural Science Foundation of China(NSFC)under Grant No.62001190The work of J.Wen was supported by NSFC(Nos.11871248,61932010,61932011)+3 种基金the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019),Guangdong Major Project of Basic and Applied Basic Research(2019B030302008)the Fundamental Research Funds for the Central Universities(No.21618329)The work of P.Fan was supported by National Key R&D Project(No.2018YFB1801104)NSFC Project(No.6202010600).
文摘This paper proposes some low complexity algorithms for active user detection(AUD),channel estimation(CE)and multi-user detection(MUD)in uplink non-orthogonal multiple access(NOMA)systems,including single-carrier and multi-carrier cases.In particular,we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers(ADMM),where fast decaying feature of non-zero components in sparse signal is considered.More importantly,the reliable estimated information is used for AUD,and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users.Then,the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure.Besides,we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users’data,which avoids the Hessian matrix inverse.The convergence and the complexity of proposed algorithms are analyzed and discussed finally.Simulation results show that the proposed algorithms have better performance in terms of AUD,CE and MUD.Moreover,we can detect active users perfectly for multi-carrier NOMA system.
文摘Ever since the COVID-19 pandemic started in Wuhan,China,much research work has been focusing on the clinical aspect of SARS-CoV-2.Researchers have been leveraging on various Artificial Intelligence techniques as an alternative to medical approach in understanding the virus.Limited studies have,however,reported on COVID-19 transmission pattern analysis,and using geography features for prediction of potential outbreak sites.Predicting the next most probable outbreak site is crucial,particularly for optimizing the planning of medical personnel and supply resources.To tackle the challenge,this work proposed distance-based similarity measures to predict the next most probable outbreak site together with its magnitude,when would the outbreak likely to happen and the duration of the outbreak.The work began with preprocessing of 1365 patient records from six districts in the most populated state named Selangor in Malaysia.The dataset was then aggregated with population density information and human elicited geography features that might promote the transmission of COVID-19.Empirical findings indicated that the proposed unified decision-making approach outperformed individual distance metric in predicting the total cases,next outbreak location,and the time interval between start dates of two similar sites.Such findings provided valuable insights for policymakers to perform Active Case Detection.
基金supported by the KERI Primary Research Program through the Korea Research Council for Industrial Science & Technology funded by the Ministry of Science,ICT and Future Planning (No.15-12-N0101-46)
文摘A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.
基金the Deanship of Scientific Research at Majmaah University for funding this work under Project No.R-2023-667.
文摘Inpatient falls from beds in hospitals are a common problem.Such falls may result in severe injuries.This problem can be addressed by continuous monitoring of patients using cameras.Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient.Along with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their health.High computation-intensive models are required to monitor every action of the patient precisely.This requirement limits the applicability of such networks.Hence,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall detection.Motivated by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their poses.The whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition network.The compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human poses.Finally,the activity recognition network classifies the patients’activities based on their poses.The proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.
基金supported by the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province of China(Project No.2023NSFSC0008)+1 种基金Uranium Geology Program of China Nuclear Geology(No.202205-6)the Sichuan Science and Technology Program(No.2021JDTD0018)。
文摘Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.
基金Project supported by Inha University Research GrantProject(10031764) supported by the Strategic Technology Development Program of Ministry of Knowledge Economy, Korea
文摘In this work, a novel voice activity detection (VAD) algorithm that uses speech absence probability (SAP) based on Teager energy (TE) was proposed for speech enhancement. The proposed method employs local SAP (LSAP) based on the TE of noisy speech as a feature parameter for voice activity detection (VAD) in each frequency subband, rather than conventional LSAP. Results show that the TE operator can enhance the ability to discriminate speech and noise and further suppress noise components. Therefore, TE-based LSAP provides a better representation of LSAP, resulting in improved VAD for estimating noise power in a speech enhancement algorithm. In addition, the presented method utilizes TE-based global SAP (GSAP) derived in each frame as the weighting parameter for modifying the adopted TE operator and improving its performance. The proposed algorithm was evaluated by objective and subjective quality tests under various environments, and was shown to produce better results than the conventional method.
基金the Natural Science Foundation of China(No.50572075,50302007)the Key Project of Chinese Ministry of Education(206098)the Youth Sunshine Project of Wuhan City China(No.20045006071-39)
文摘In this study, integrated multi-wall carbon nanotube (MWCNT) electrodes were prepared in the holes of glass directly by microwave plasma chemical vapour deposition (MWPCVD). The electrochemical behaviour of catechol at the integrated MWCNT electrodes was investigated. The oxygen plasma treated CNT electrodes had better electrochemical performance for the analysis of catechol than that of as-synthesized CNT electrodes. Both the as-synthesized CNTs and plasma treated CNTs were characterized by TEM(transmission electron microscopy, XPS(X-ray photoelectron spectroscopy) and Raman spectroscopy. The results revealed that the oxygen plasma activation is an effective method to enhance the electrochemical properties of CNT electrodes.
基金Project supported by the National Natural Science Foundation of China(No.10572049)
文摘Considering mass and stiffness of piezoelectric layers and damage effects of composite layers, nonlinear dynamic equations of damaged piezoelectric smart laminated plates are derived. The derivation is based on the Hamilton's principle, the higher- order shear deformation plate theory, von Karman type geometrically nonlinear straindisplacement relations, and the strain energy equivalence theory. A negative velocity feedback control algorithm coupling the direct and converse piezoelectric effects is used to realize the active control and damage detection with a closed control loop. Simply supported rectangular laminated plates with immovable edges are used in numerical computation. Influence of the piezoelectric layers' location on the vibration control is in- vestigated. In addition, effects of the degree and location of damage on the sensor output voltage are discussed. A method for damage detection is introduced.
基金Supported by Key Project in the National Science&Technology Pillar Program during the 11thFive-year Plan Period(2009BAK61B04)
文摘[ Objective] This study was to develop a detection method for assaying calf intestine alkaline phasphatase activity in scientific research. [ Method ] By simulating the conditions and buffers for assaying calf intestine alkaline phosphatase used in the scientific research, the parameters influencing substmte concentration, reaction duration, eoloration time and buffer pH were optimized. [ Result] The activity of alkaline phosphatase detected varied hugely among different buffers, and potassium ferricyanide solution added with boracic acid achieved the stable coloration. The optimal water bath time was determined as 10 rain, and the substrate concentration was optimized as 0.04 moL/L. The increasing temperature did not have a large influence on low temperature enzymatic activity while did on high coneentration enzymatic activity. When the buffer pH was 7.0 - 8.0, the detection stability of alkaline phosphatase could be maintained well The ion intensity of buff- er had little effects on alkaline phasphatase activity. [ Conclusion] A detection method for assaying calf intestine alkaline phosphatase activity in scientific research was successfully developed in this study.
文摘We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture of Laplacian and Gaussian distributions. We analyse and numerically evaluate the proposed test for various noisy environments. In addition, we address the problem of effectively recognizing the possible presence of cyber exploits in the voice transmission channel. We then introduce another sequential test, designed to detect rapidly and accurately the presence of such exploits, named Cyber Attacks Sequential Detection of Change Test (CA-SDCT). We analyse and numerically evaluate the latter test. Experimental results and comparisons with other proposed methods are also presented.
基金supported in part by the Key Research and Development Program of China under Grant 2018YFB1801102in part by the National Natural Science Foundation of China under Grant 61631013+3 种基金in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621091in part by the Civil Aerospace Technology Project under Grant D040202in part by the Tsinghua-Qualcomm Joint Projectin part by the Tsinghua University Initiative Scientific Research Program under Grant 20193080005。
文摘We try to extend the current configuredgrant(CG)uplink scheme in 5G New Radio(NR)to support massive potential users and study activity detection under this scenario.Characteristics of the continuously varying channel and the multiple repetition scheme are utilized to improve the detection accuracy,which can be an enhancement to existing activity detection algorithms.Numerical results under 3 GPP TDL(Tapped Delay Line)fading channel show the superiority of our algorithm.And system-level simulation reveals that enhancements on activity detection can improve reliability and reduce latency.
文摘Natural Killer (NK) cells are specific immune cells in human immune system. They have a quick effect and can exert a cytotoxic function without prior sensitization, and they show great application potential in cell-based immunotherapy, anti-infection<em> in vivo</em>. NK cell activity in peripheral blood can be used as one of the biomarkers of immune function response. It has a great positive guiding significance for the clinical prognosis of tumor patients, the prevention of cancer and anti-aging. The clinical detection strategies of NK cell activity in circulation mainly grouped into five types: methyl thiazolyl tetrazolium colorimetric, lactate dehydrogenase release, radionuclide labeling, flow cytometry and NK Vue cytokine release method. It has played an important role in different stages of clinical application development. This paper will make a comparative review of the above-mentioned detection strategies for the NK cell activity.
基金supported by the National Natural Science Foundation of China under Grant No.61671109.
文摘Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.
基金Supported by the National Natural Science Foundation of China (No. 60874060)
文摘This paper presents an improved Voice Activity Detection (VAD) algorithm which uses the Signal-to-Noise Ratio (SNR) measure. We assume that noise Power Spectral Density (PSD) in each spectral bin follows a Rayleigh distribution. Rayleigh distributions with its asymmetric tail charac- teristics give a better description of the noise PSD distribution than Gaussian distribution. Under this assumption, a new threshold updating expression is derived. Since the analytical integral of the false alarm probability, the threshold updating expression can be represented without the inverse comple- mentary error function and low computational complexity is achieved in our system. Experimental results show that the proposed VAD outperforms or at least is comparable with the VAD scheme presented by Davis under several noise environments and has a lower computational complexity.