Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of dis...Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).展开更多
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical chall...With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.展开更多
Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optim...Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradientbased optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.展开更多
The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisup...The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.展开更多
In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm i...In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.展开更多
Template matching methods have been widely utilized to detect fabric defects in textile quality control. In this paper, a novel approach is proposed to design a flexible classifier for distinguishing flaws from twill ...Template matching methods have been widely utilized to detect fabric defects in textile quality control. In this paper, a novel approach is proposed to design a flexible classifier for distinguishing flaws from twill fabrics by statistically learning from the normal fabric texture. Statistical information of natural and normal texture of the fabric can be extracted via collecting and analyzing the gray image. On the basis of this, both judging threshold and template are acquired and updated adaptively in real-time according to the real textures of fabric, which promises more flexibility and universality. The algorithms are experimented with images of fault free and faulty textile samples.展开更多
An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audio...An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audiometric transducer such as an earphone. The calibrated sound intensities for an audio-logical examination can be obtained in terms of the sound pressure levels of pure-tonal sinusoidal signals in eight-banded frequency ranges (250, 500, 1 000, 2 000, 3 000, 4 000, 6 000 and 8 000 Hz), and with mapping of the input sound pressure levels by the weight coefficients that are tuned by the delta learning rule. With this scheme, the sound intensities, which evoke eight-banded sound pressure levels by 5 dB steps from a minimum of 25 dB to a maximum of 80 dB, can be generated without volume displacement. Consequently, these sound intensities can be utilized to accurately determine the hearing threshold of a subject in the ambulatory audiometric testing environment.展开更多
In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to ...In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.展开更多
Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial ...Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.展开更多
基金Project supported by the Joint Fund of the National Natural Science Foundation of China–“Ye Qisun”Science Fund(Grant No.U2341251)。
文摘Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).
基金supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
基金supported by the National Natural Science Foundation of China(61971007&61571013).
文摘With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.
基金Project supported by the Natural Science Foundation of Shandong Province,China(Grant No.ZR2021MF051)。
文摘Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradientbased optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.
基金Our research is funded by National Key R&D Program of China(2021YFC3320302)Fundamental Research(JCKY2020210B019)+1 种基金Natural Science Foundation of Heilongjiang Province(No.F2018006)Network threat depth analysis software(KY10800210013).
文摘The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.
基金supported by the National Natural Science Foundation of China(61100103)
文摘In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.
基金supported by National Natural Science Foundation of China (No.60672039)
文摘Template matching methods have been widely utilized to detect fabric defects in textile quality control. In this paper, a novel approach is proposed to design a flexible classifier for distinguishing flaws from twill fabrics by statistically learning from the normal fabric texture. Statistical information of natural and normal texture of the fabric can be extracted via collecting and analyzing the gray image. On the basis of this, both judging threshold and template are acquired and updated adaptively in real-time according to the real textures of fabric, which promises more flexibility and universality. The algorithms are experimented with images of fault free and faulty textile samples.
基金supported by the grant of the Korean Ministry of Education, Science and Technology (The Regional Core Research Program/Chungbuk BIT Research-Oriented University Consortium)
文摘An efficient calibration algorithm for an ambulatory audiometric test system is proposed. This system utilizes a personal digital assistant (PDA) device to generate the correct sound pressure level (SPL) from an audiometric transducer such as an earphone. The calibrated sound intensities for an audio-logical examination can be obtained in terms of the sound pressure levels of pure-tonal sinusoidal signals in eight-banded frequency ranges (250, 500, 1 000, 2 000, 3 000, 4 000, 6 000 and 8 000 Hz), and with mapping of the input sound pressure levels by the weight coefficients that are tuned by the delta learning rule. With this scheme, the sound intensities, which evoke eight-banded sound pressure levels by 5 dB steps from a minimum of 25 dB to a maximum of 80 dB, can be generated without volume displacement. Consequently, these sound intensities can be utilized to accurately determine the hearing threshold of a subject in the ambulatory audiometric testing environment.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.
基金The completion of this research was made possible thanks to The Natural Sciences and Engineering Research Council of Canada(NSERC)and a start-up grant from Concordia University.
文摘Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.