The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large...The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large-slope faulty satellite and a high False Alarm Risk(FAR)for a small-slope faulty satellite.From the theoretical analysis of the high MDR and FAR cause,the optimal slope is determined,and thereby the optimal test statistic for fault detection is conceived,which can minimize the FAR with the MDR not exceeding its allowable value.To construct a test statistic approximate to the optimal one,the CorrelationWeighted LSR(CW-LSR)algorithm is proposed.The CW-LSR test statistic remains the sum of pseudorange residual squares,but the square for the most potentially faulty satellite,judged by correlation analysis between the pseudorange residual and observation error,is weighted with an optimal-slope-based factor.It does not obey the same distribution but has the same noncentral parameter with the optimal test statistic.The superior performance of the CW-LSR algorithm is verified via simulation,both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition.展开更多
The Least Squares Residual(LSR)algorithm is commonly used in the Receiver Autonomous Integrity Monitoring(RAIM).However,LSR algorithm presents high Missed Detection Risk(MDR)caused by a large-slope faulty satellite an...The Least Squares Residual(LSR)algorithm is commonly used in the Receiver Autonomous Integrity Monitoring(RAIM).However,LSR algorithm presents high Missed Detection Risk(MDR)caused by a large-slope faulty satellite and high False Alert Risk(FAR)caused by a small-slope faulty satellite.In this paper,the LSR algorithm is improved to reduce the MDR for a large-slope faulty satellite and the FAR for a small-slope faulty satellite.Based on the analysis of the vertical critical slope,the optimal decentralized factor is defined and the optimal test statistic is conceived,which can minimize the FAR with the premise that the MDR does not exceed its allowable value of all three directions.To construct a new test statistic approximating to the optimal test statistic,the Optimal Decentralized Factor weighted LSR(ODF-LSR)algorithm is proposed.The new test statistic maintains the sum of pseudo-range residual squares,but the specific pseudo-range residual is weighted with a parameter related to the optimal decentralized factor.The new test statistic has the same decentralized parameter with the optimal test statistic when single faulty satellite exists,and the difference between the expectation of the new test statistic and the optimal test statistic is the minimum when no faulty satellite exists.The performance of the ODFLSR algorithm is demonstrated by simulation experiments.展开更多
A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although the mechanism of...A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although the mechanism of selecting the fixed number of indexes improves the reconstruction efficiency, it also brings the problem of low index selection accuracy. Based on the full study of the theory of compressed sensing, we propose a dynamic indexes selection strategy based on residual update to improve the performance of the compressed sampling matching pursuit algorithm (CoSaMP). As an extension of CoSaMP algorithm, the proposed algorithm adopts a residual comparison strategy to improve the accuracy of backtracking selected indexes. This backtracking strategy can efficiently select backtracking indexes. And without increasing the computational complexity, the proposed improvement algorithm has a higher exact reconstruction rate and peak signal to noise ratio (PSNR). Simulation results demonstrate the proposed algorithm significantly outperforms the CoSaMP for image recovery and one-dimensional signal.展开更多
Message passing algorithms,whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages,provide a powerf...Message passing algorithms,whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages,provide a powerful toolkit in tackling hard computational tasks in optimization,inference,and learning problems.In the context of constraint satisfaction problems(CSPs),when a control parameter(such as constraint density)is tuned,multiple threshold phenomena emerge,signaling fundamental structural transitions in their solution space.Finding solutions around these transition points is exceedingly challenging for algorithm design,where message passing algorithms suffer from a large message fiuctuation far from convergence.Here we introduce a residual-based updating step into message passing algorithms,in which messages with large variation between consecutive steps are given high priority in the updating process.For the specific example of model RB(revised B),a typical prototype of random CSPs with growing domains,we show that our algorithm improves the convergence of message updating and increases the success probability in finding solutions around the satisfiability threshold with a low computational cost.Our approach to message passing algorithms should be of value for exploring their power in developing algorithms to find ground-state solutions and understand the detailed structure of solution space of hard optimization problems.展开更多
The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagati...The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.展开更多
Wireless sensor networks (WSNs) are mostly deployed in a remote working environment, since sensor nodes are small in size, cost-efficient, low-power devices, and have limited battery power supply. Because of limited p...Wireless sensor networks (WSNs) are mostly deployed in a remote working environment, since sensor nodes are small in size, cost-efficient, low-power devices, and have limited battery power supply. Because of limited power source, energy consumption has been considered as the most critical factor when designing sensor network protocols. The network lifetime mainly depends on the battery lifetime of the node. The main concern is to increase the lifetime with respect to energy constraints. One way of doing this is by turning off redun-dant nodes to sleep mode to conserve energy while active nodes can provide essential k-coverage, which improves fault-tolerance. Hence, we use scheduling algorithms that turn off redundant nodes after providing the required coverage level k. The scheduling algorithms can be implemented in centralized or localized schemes, which have their own advantages and disadvantages. To exploit the advantages of both schemes, we employ both schemes on the network according to a threshold value. This threshold value is estimated on the performance of WSN based on network lifetime comparison using centralized and localized algorithms. To extend the network lifetime and to extract the useful energy from the network further, we go for compromise in the area covered by nodes.展开更多
X oilfield has successfully adopted horizontal wells to develop strong bottom water reservoirs, as a typical representative of development styles in the Bohai offshore oilfield. At present, many contributions to metho...X oilfield has successfully adopted horizontal wells to develop strong bottom water reservoirs, as a typical representative of development styles in the Bohai offshore oilfield. At present, many contributions to methods of inverting relative permeability curve and forecasting residual recoverable reserves had been made by investigators, but rarely involved in horizontal wells’ in bottom water reservoir. As the pore volume injected was less (usually under 30 PV), the relative permeability curve endpoint had become a serious distortion. That caused a certain deviation in forecasting residual recoverable reserves in the practical value of field directly. For the performance of water cresting, the common method existed some problems, such as no pertinence, ineffectiveness and less affecting factors considered. This paper adopts the streamlines theory with two phases flowing to solve that. Meanwhile, based on the research coupling genetic algorithm, optimized relative permeability curve was calculated by bottom-water drive model. The residual oil saturation calculated was lower than the initial’s, and the hydrophilic property was more reinforced, due to improving the pore volume injected vastly. Also, the study finally helped us enhance residual recoverable reserves degree at high water cut stage, more than 20%, taking Guantao sandstone as an example. As oil field being gradually entering high water cut stage, this method had a great significance to evaluate the development effect and guide the potential of the reservoir.展开更多
Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presen...Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.展开更多
Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in tur...Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in turn increases the autonomy of the system.Even though the focus on protecting against sensor attacks increases,there is still uncertainty about the optimal timing for attack detection.Existing systems often struggle to manage the trade-off between latency and false alarm rate,leading to inefficiencies in real-time anomaly detection.This paper presents a framework designed to monitor,predict,and control dynamic systems with a particular emphasis on detecting and adapting to changes,including anomalies such as“drift”and“attack”.The proposed algorithm integrates a Transformer-based Attention Generative Adversarial Residual model,which combines the strengths of generative adversarial networks,residual networks,and attention algorithms.The system operates in two phases:offline and online.During the offline phase,the proposed model is trained to learn complex patterns,enabling robust anomaly detection.The online phase applies a trained model,where the drift adapter adjusts the model to handle data changes,and the attack detector identifies deviations by comparing predicted and actual values.Based on the output of the attack detector,the controller makes decisions then the actuator executes suitable actions.Finally,the experimental findings show that the proposed model balances detection accuracy of 99.25%,precision of 98.84%,sensitivity of 99.10%,specificity of 98.81%,and an F1-score of 98.96%,thus provides an effective solution for dynamic and safety-critical environments.展开更多
番茄在生长过程中易受以病毒病为主的病害侵袭,对其产量和质量造成巨大影响。针对现有番茄植株病害检测方法存在精度低、泛化能力差的问题,构建了一种基于改进YOLOv8-AM算法的检测模型。通过对Plant Village Dataset公共数据集和实地采...番茄在生长过程中易受以病毒病为主的病害侵袭,对其产量和质量造成巨大影响。针对现有番茄植株病害检测方法存在精度低、泛化能力差的问题,构建了一种基于改进YOLOv8-AM算法的检测模型。通过对Plant Village Dataset公共数据集和实地采集的番茄病毒病害检测数据集进行数据增强,构成番茄病毒病害最终的训练数据集和验证数据集。同时,基于YOLOv8模型框架,借鉴残差网络和人眼视觉注意力机制,引入ResBlock+CBAM结构,设计ResCBAM模块,提升模型对关键特征的提取能力。在Plant Village Dataset公共数据集上进行训练与测试,并在番茄病毒病自建数据集上进行试验预测与验证,最终基于Java平台的Spring Boot框架,开发出一种基于YOLOv8-AM的番茄病毒病害检测系统。试验结果表明:在公共数据集上,YOLOv8-AM算法的精确率、召回率分别为92.47%和93.91%,均值平均精度为97.82%,模型的检测速度为31.89 FPS、尺寸为23.83 MB,改进算法在保持检测速度的同时精度均高于现有模型;在自建数据集上,YOLOv8-AM算法的均值平均精度为89.76%,模型泛化能力较强。利用改进的YOLOv8-AM算法能够实现对番茄病毒病害的快速检测,为作物植株的病害识别与防治提供技术支撑。展开更多
基金co-supported by the National Natural Science Foundation of China (Nos. 41804024, 41804026)the Open Fund of Shaanxi Key Laboratory of Integrated and Intelligent Navigation of China (No. SKLIIN-20190205)
文摘The Least Squares Residual(LSR)algorithm,one of the classical Receiver Autonomous Integrity Monitoring(RAIM)algorithms for Global Navigation Satellite System(GNSS),presents a high Missed Detection Risk(MDR)for a large-slope faulty satellite and a high False Alarm Risk(FAR)for a small-slope faulty satellite.From the theoretical analysis of the high MDR and FAR cause,the optimal slope is determined,and thereby the optimal test statistic for fault detection is conceived,which can minimize the FAR with the MDR not exceeding its allowable value.To construct a test statistic approximate to the optimal one,the CorrelationWeighted LSR(CW-LSR)algorithm is proposed.The CW-LSR test statistic remains the sum of pseudorange residual squares,but the square for the most potentially faulty satellite,judged by correlation analysis between the pseudorange residual and observation error,is weighted with an optimal-slope-based factor.It does not obey the same distribution but has the same noncentral parameter with the optimal test statistic.The superior performance of the CW-LSR algorithm is verified via simulation,both reducing the FAR for a small-slope faulty satellite with the MDR not exceeding its allowable value and reducing the MDR for a large-slope faulty satellite at the expense of FAR addition.
文摘The Least Squares Residual(LSR)algorithm is commonly used in the Receiver Autonomous Integrity Monitoring(RAIM).However,LSR algorithm presents high Missed Detection Risk(MDR)caused by a large-slope faulty satellite and high False Alert Risk(FAR)caused by a small-slope faulty satellite.In this paper,the LSR algorithm is improved to reduce the MDR for a large-slope faulty satellite and the FAR for a small-slope faulty satellite.Based on the analysis of the vertical critical slope,the optimal decentralized factor is defined and the optimal test statistic is conceived,which can minimize the FAR with the premise that the MDR does not exceed its allowable value of all three directions.To construct a new test statistic approximating to the optimal test statistic,the Optimal Decentralized Factor weighted LSR(ODF-LSR)algorithm is proposed.The new test statistic maintains the sum of pseudo-range residual squares,but the specific pseudo-range residual is weighted with a parameter related to the optimal decentralized factor.The new test statistic has the same decentralized parameter with the optimal test statistic when single faulty satellite exists,and the difference between the expectation of the new test statistic and the optimal test statistic is the minimum when no faulty satellite exists.The performance of the ODFLSR algorithm is demonstrated by simulation experiments.
文摘A large number of sparse signal reconstruction algorithms have been continuously proposed, but almost all greedy algorithms add a fixed number of indices to the support set in each iteration. Although the mechanism of selecting the fixed number of indexes improves the reconstruction efficiency, it also brings the problem of low index selection accuracy. Based on the full study of the theory of compressed sensing, we propose a dynamic indexes selection strategy based on residual update to improve the performance of the compressed sampling matching pursuit algorithm (CoSaMP). As an extension of CoSaMP algorithm, the proposed algorithm adopts a residual comparison strategy to improve the accuracy of backtracking selected indexes. This backtracking strategy can efficiently select backtracking indexes. And without increasing the computational complexity, the proposed improvement algorithm has a higher exact reconstruction rate and peak signal to noise ratio (PSNR). Simulation results demonstrate the proposed algorithm significantly outperforms the CoSaMP for image recovery and one-dimensional signal.
基金supported by Guangdong Major Project of Basic and Applied Basic Research No.2020B0301030008Science and Technology Program of Guangzhou No.2019050001+2 种基金the Chinese Academy of Sciences Grant QYZDJ-SSWSYS018the National Natural Science Foundation of China(Grant No.12171479)supported by the National Natural Science Foundation of China(Grant Nos.11301339 and 11491240108)。
文摘Message passing algorithms,whose iterative nature captures complicated interactions among interconnected variables in complex systems and extracts information from the fixed point of iterated messages,provide a powerful toolkit in tackling hard computational tasks in optimization,inference,and learning problems.In the context of constraint satisfaction problems(CSPs),when a control parameter(such as constraint density)is tuned,multiple threshold phenomena emerge,signaling fundamental structural transitions in their solution space.Finding solutions around these transition points is exceedingly challenging for algorithm design,where message passing algorithms suffer from a large message fiuctuation far from convergence.Here we introduce a residual-based updating step into message passing algorithms,in which messages with large variation between consecutive steps are given high priority in the updating process.For the specific example of model RB(revised B),a typical prototype of random CSPs with growing domains,we show that our algorithm improves the convergence of message updating and increases the success probability in finding solutions around the satisfiability threshold with a low computational cost.Our approach to message passing algorithms should be of value for exploring their power in developing algorithms to find ground-state solutions and understand the detailed structure of solution space of hard optimization problems.
基金supported by the State Key Program of National Natural Science of China (Grant No.60532030)the New Century Excellent Talents in University (Grant No.NCET-08-0333)the Natural Science Foundation of Shandong Province (Grant No.Y2007G10)
文摘The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.
文摘Wireless sensor networks (WSNs) are mostly deployed in a remote working environment, since sensor nodes are small in size, cost-efficient, low-power devices, and have limited battery power supply. Because of limited power source, energy consumption has been considered as the most critical factor when designing sensor network protocols. The network lifetime mainly depends on the battery lifetime of the node. The main concern is to increase the lifetime with respect to energy constraints. One way of doing this is by turning off redun-dant nodes to sleep mode to conserve energy while active nodes can provide essential k-coverage, which improves fault-tolerance. Hence, we use scheduling algorithms that turn off redundant nodes after providing the required coverage level k. The scheduling algorithms can be implemented in centralized or localized schemes, which have their own advantages and disadvantages. To exploit the advantages of both schemes, we employ both schemes on the network according to a threshold value. This threshold value is estimated on the performance of WSN based on network lifetime comparison using centralized and localized algorithms. To extend the network lifetime and to extract the useful energy from the network further, we go for compromise in the area covered by nodes.
文摘X oilfield has successfully adopted horizontal wells to develop strong bottom water reservoirs, as a typical representative of development styles in the Bohai offshore oilfield. At present, many contributions to methods of inverting relative permeability curve and forecasting residual recoverable reserves had been made by investigators, but rarely involved in horizontal wells’ in bottom water reservoir. As the pore volume injected was less (usually under 30 PV), the relative permeability curve endpoint had become a serious distortion. That caused a certain deviation in forecasting residual recoverable reserves in the practical value of field directly. For the performance of water cresting, the common method existed some problems, such as no pertinence, ineffectiveness and less affecting factors considered. This paper adopts the streamlines theory with two phases flowing to solve that. Meanwhile, based on the research coupling genetic algorithm, optimized relative permeability curve was calculated by bottom-water drive model. The residual oil saturation calculated was lower than the initial’s, and the hydrophilic property was more reinforced, due to improving the pore volume injected vastly. Also, the study finally helped us enhance residual recoverable reserves degree at high water cut stage, more than 20%, taking Guantao sandstone as an example. As oil field being gradually entering high water cut stage, this method had a great significance to evaluate the development effect and guide the potential of the reservoir.
文摘Transmission of data over the internet has become a critical issue as a result of the advancement in technology, since it is possible for pirates to steal the intellectual property of content owners. This paper presents a new digital watermarking scheme that combines some operators of the Genetic Algorithm (GA) and the Residue Number (RN) System (RNS) to perform encryption on an image, which is embedded into a cover image for the purposes of watermarking. Thus, an image watermarking scheme uses an encrypted image. The secret image is embedded in decomposed frames of the cover image achieved by applying a three-level Discrete Wavelet Transform (DWT). This is to ensure that the secret information is not exposed even when there is a successful attack on the cover information. Content creators can prove ownership of the multimedia content by unveiling the secret information in a court of law. The proposed scheme was tested with sample data using MATLAB2022 and the results of the simulation show a great deal of imperceptibility and robustness as compared to similar existing schemes.
文摘Cyber-Physical Systems integrated with information technologies introduce vulnerabilities that extend beyond traditional cyber threats.Attackers can non-invasively manipulate sensors and spoof controllers,which in turn increases the autonomy of the system.Even though the focus on protecting against sensor attacks increases,there is still uncertainty about the optimal timing for attack detection.Existing systems often struggle to manage the trade-off between latency and false alarm rate,leading to inefficiencies in real-time anomaly detection.This paper presents a framework designed to monitor,predict,and control dynamic systems with a particular emphasis on detecting and adapting to changes,including anomalies such as“drift”and“attack”.The proposed algorithm integrates a Transformer-based Attention Generative Adversarial Residual model,which combines the strengths of generative adversarial networks,residual networks,and attention algorithms.The system operates in two phases:offline and online.During the offline phase,the proposed model is trained to learn complex patterns,enabling robust anomaly detection.The online phase applies a trained model,where the drift adapter adjusts the model to handle data changes,and the attack detector identifies deviations by comparing predicted and actual values.Based on the output of the attack detector,the controller makes decisions then the actuator executes suitable actions.Finally,the experimental findings show that the proposed model balances detection accuracy of 99.25%,precision of 98.84%,sensitivity of 99.10%,specificity of 98.81%,and an F1-score of 98.96%,thus provides an effective solution for dynamic and safety-critical environments.
文摘番茄在生长过程中易受以病毒病为主的病害侵袭,对其产量和质量造成巨大影响。针对现有番茄植株病害检测方法存在精度低、泛化能力差的问题,构建了一种基于改进YOLOv8-AM算法的检测模型。通过对Plant Village Dataset公共数据集和实地采集的番茄病毒病害检测数据集进行数据增强,构成番茄病毒病害最终的训练数据集和验证数据集。同时,基于YOLOv8模型框架,借鉴残差网络和人眼视觉注意力机制,引入ResBlock+CBAM结构,设计ResCBAM模块,提升模型对关键特征的提取能力。在Plant Village Dataset公共数据集上进行训练与测试,并在番茄病毒病自建数据集上进行试验预测与验证,最终基于Java平台的Spring Boot框架,开发出一种基于YOLOv8-AM的番茄病毒病害检测系统。试验结果表明:在公共数据集上,YOLOv8-AM算法的精确率、召回率分别为92.47%和93.91%,均值平均精度为97.82%,模型的检测速度为31.89 FPS、尺寸为23.83 MB,改进算法在保持检测速度的同时精度均高于现有模型;在自建数据集上,YOLOv8-AM算法的均值平均精度为89.76%,模型泛化能力较强。利用改进的YOLOv8-AM算法能够实现对番茄病毒病害的快速检测,为作物植株的病害识别与防治提供技术支撑。
