The continuous investment into manpower resource, is the radical power toassure the sustainable development of enterprises. The enterprises both at home and abroad attachhigh importance to the continuous education of ...The continuous investment into manpower resource, is the radical power toassure the sustainable development of enterprises. The enterprises both at home and abroad attachhigh importance to the continuous education of their employees and consolidate training to inspiretheir employees. In order to face increasingly drastic global competition, the telecom enterprisesin our country should consolidate continuous education, make training plans to adapt to thelong-term development of the enterprises and establish the effective mechanism of encouragement ofcontinuous education.展开更多
Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,kno...Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.展开更多
Threshold public key encryption allows a set of servers to decrypt a ciphertext if a given threshold of authorized servers cooperate. In the setting of threshold public key encryption, we consider the question of how ...Threshold public key encryption allows a set of servers to decrypt a ciphertext if a given threshold of authorized servers cooperate. In the setting of threshold public key encryption, we consider the question of how to correctly decrypt a ciphertext where all servers continually leak information about their secret keys to an external attacker. Dodis et al. and Akavia et al. show two concrete schemes on how to store secrets on continually leaky servers. However, their construc- tions are only interactive between two servers. To achieve continual leakage security among more than two servers, we give the first threshold public key encryption scheme against adaptively chosen ciphertext attack in the continual leak- age model under three static assumptions. In our model, the servers update their keys individually and asynchronously, without any communication between two servers. Moreover, the update procedure is re-randomized and the randomness can leak as well.展开更多
Seismic microzonation for Almaty city for the first time use probabilistic approach and hazard is expressed in terms of not only macroseismic intensity,but also Peak Ground Acceleration(PGA).To account for the effects...Seismic microzonation for Almaty city for the first time use probabilistic approach and hazard is expressed in terms of not only macroseismic intensity,but also Peak Ground Acceleration(PGA).To account for the effects of local soil conditions,the continual approach proposed by A.S.Aleshin[1,2]was used,in which soil coefficients are a function of the continuously changing seismic rigidity.Soil coefficients were calculated using the new data of geological and geophysical surveys and findings of previous geotechnical studies.The used approach made it possible to avoid using soil categories and a jump change in characteristics of soil conditions and seismic impact.The developed seismic microzonation maps are prepared for further introduction into the normative documents of the Republic of Kazakhstan.展开更多
Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep lear...Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.展开更多
As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include ...As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.展开更多
The evolution of temperature field of the continual motion induction cladding and the depth of heat affected zone are studied in this study.A three-dimensional finite element model for the point type continual inducti...The evolution of temperature field of the continual motion induction cladding and the depth of heat affected zone are studied in this study.A three-dimensional finite element model for the point type continual induction cladding is established to investigate temperature distributions of fixed and motion induction cladding modes.The novel inductor is designed for cladding of curved surfaces.The modeling reliability is verified by the temperature measurements.The influence of process parameters on the maximum temperature and the generation and transfer of heat are studied.Quantitative calculation is performed to its melting rate to verify the temperature distribution and microstructures.The results show that a good metallurgical bond can be formed between the cladding layer and substrate.The melting rate gradually falls from the top of the cladding layer to the substrate,and the grain size in the substrate gradually rises.The heat affected zone is relatively small compared to integral heating.展开更多
In this paper, the theorem of structure continual variation of truss structure in the analysis of structure reliability is derived, and it is used to generate limit state function automatically. We can avoid repeated ...In this paper, the theorem of structure continual variation of truss structure in the analysis of structure reliability is derived, and it is used to generate limit state function automatically. We can avoid repeated assembly of global stiffness matrix and repeated inverse operations of the matrix caused by constant changes of structure topology. A new criterion of degenerate of the structure into mechanism is introduced. The calculation examples are satisfactory.展开更多
The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine t...The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.展开更多
In this paper, various forms of functional on blending energy principles of composite laminated plates are gir en, which guarantee satisfied continual conditions of displacements and stress between layers, and then th...In this paper, various forms of functional on blending energy principles of composite laminated plates are gir en, which guarantee satisfied continual conditions of displacements and stress between layers, and then the reliability of the functional are proved by the computing example.展开更多
We examined the adaptation of the postural response to repeated predictable platform oscillations. Our main goals were to determine whether the short-term changes that occurred during a minute long continuous postural...We examined the adaptation of the postural response to repeated predictable platform oscillations. Our main goals were to determine whether the short-term changes that occurred during a minute long continuous postural perturbation trial were maintained in subsequent trials and to determine how many trials were required before participants fully adapted to the postural task. Ten participants performed ten minute-long postural trials on a platform that oscillated at 0.25 Hz before increasing to 0.50 Hz half way through each trial. Postural muscle onset latencies, burst amplitudes, and anterior posterior displacements of the center of pressure (COP) and center of mass (COM) were calculated for the last five cycles performed in each trial at 0.50 Hz. The postural strategy evolved in two phases: 1) immediate decrease in COP displacement;2) earlier activation of the postural muscles with smaller muscle burst amplitudes. After seven trials the postural response remained consistent.展开更多
BACKGROUND Sedation during endoscopic ultrasonography(EUS)poses many challenges and moderate-to-deep sedation are often required.The conventional method to preform moderate-to-deep sedation is generally intravenous be...BACKGROUND Sedation during endoscopic ultrasonography(EUS)poses many challenges and moderate-to-deep sedation are often required.The conventional method to preform moderate-to-deep sedation is generally intravenous benzodiazepine alone or in combination with opioids.However,this combination has some limitations.Intranasal medication delivery may be an alternative to this sedation regimen.AIM To determine,by continual reassessment method(CRM),the minimal effective dose of intranasal sufentanil(SUF)when combined with intranasal dexmedetomidine(DEX)for moderate sedation of EUS in at least 95%of patients(ED95).METHODS Thirty patients aged 18-65 and scheduled for EUS were recruited in this study.Subjects received intranasal DEX and SUF for sedation.The dose of DEX(1μg/kg)was fixed,while the dose of SUF was assigned sequentially to the subjects using CRM to determine ED95.The sedation status was assessed by modified observer’s assessment of alertness/sedation(MOAA/S)score.The adverse events and the satisfaction scores of patients and endoscopists were recorded.RESULTS The ED95 was intranasal 0.3μg/kg SUF when combined with intranasal 1μg/kg DEX,with an estimated probability of successful moderate sedation for EUS of 94.9%(95%confidence interval:88.1%-98.9%).When combined with intranasal 1μg/kg DEX,probabilities of successful moderate sedation at each dose level of intranasal SUF were as follows:0μg/kg SUF,52.8%;0.1μg/kg SUF,75.4%;0.2μg/kg SUF,89.9%;0.3μg/kg SUF,94.9%;0.4μg/kg SUF,98.0%;0.5μg/kg SUF,99.0%.CONCLUSION The ED95 needed for moderate sedation for EUS is intranasal 0.3μg/kg SUF when combined with intranasal 1μg/kg DEX,based on CRM.展开更多
文摘The continuous investment into manpower resource, is the radical power toassure the sustainable development of enterprises. The enterprises both at home and abroad attachhigh importance to the continuous education of their employees and consolidate training to inspiretheir employees. In order to face increasingly drastic global competition, the telecom enterprisesin our country should consolidate continuous education, make training plans to adapt to thelong-term development of the enterprises and establish the effective mechanism of encouragement ofcontinuous education.
基金supported in part by the National Natura Science Foundation of China(U2013602,61876181,51521003)the Nationa Key R&D Program of China(2020YFB13134)+2 种基金Shenzhen Science and Technology Research and Development Foundation(JCYJ20190813171009236)Beijing Nova Program of Science and Technology(Z191100001119043)the Youth Innovation Promotion Association,Chinese Academy of Sciences。
文摘Continual learning(CL)studies the problem of learning to accumulate knowledge over time from a stream of data.A crucial challenge is that neural networks suffer from performance degradation on previously seen data,known as catastrophic forgetting,due to allowing parameter sharing.In this work,we consider a more practical online class-incremental CL setting,where the model learns new samples in an online manner and may continuously experience new classes.Moreover,prior knowledge is unavailable during training and evaluation.Existing works usually explore sample usages from a single dimension,which ignores a lot of valuable supervisory information.To better tackle the setting,we propose a novel replay-based CL method,which leverages multi-level representations produced by the intermediate process of training samples for replay and strengthens supervision to consolidate previous knowledge.Specifically,besides the previous raw samples,we store the corresponding logits and features in the memory.Furthermore,to imitate the prediction of the past model,we construct extra constraints by leveraging multi-level information stored in the memory.With the same number of samples for replay,our method can use more past knowledge to prevent interference.We conduct extensive evaluations on several popular CL datasets,and experiments show that our method consistently outperforms state-of-the-art methods with various sizes of episodic memory.We further provide a detailed analysis of these results and demonstrate that our method is more viable in practical scenarios.
基金This work was supported by the Science and Technology on Communication Security Laboratory Foundation （9140C110301110C1103）, the Weaponry Equipment Pre-Research Foundation, the PLA General Armament Department （9140A04020311DZ02）, and the National Natural Science Foundation of China （61370203）.
文摘Threshold public key encryption allows a set of servers to decrypt a ciphertext if a given threshold of authorized servers cooperate. In the setting of threshold public key encryption, we consider the question of how to correctly decrypt a ciphertext where all servers continually leak information about their secret keys to an external attacker. Dodis et al. and Akavia et al. show two concrete schemes on how to store secrets on continually leaky servers. However, their construc- tions are only interactive between two servers. To achieve continual leakage security among more than two servers, we give the first threshold public key encryption scheme against adaptively chosen ciphertext attack in the continual leak- age model under three static assumptions. In our model, the servers update their keys individually and asynchronously, without any communication between two servers. Moreover, the update procedure is re-randomized and the randomness can leak as well.
基金provided through the Ministry of Education and Sciencecarried out as a part of the project“Development of the Seismic Microzonation Map for the Territory of Almaty City on a New Methodical Base”(state registration No 0115RK02701)funded within the state funding.
文摘Seismic microzonation for Almaty city for the first time use probabilistic approach and hazard is expressed in terms of not only macroseismic intensity,but also Peak Ground Acceleration(PGA).To account for the effects of local soil conditions,the continual approach proposed by A.S.Aleshin[1,2]was used,in which soil coefficients are a function of the continuously changing seismic rigidity.Soil coefficients were calculated using the new data of geological and geophysical surveys and findings of previous geotechnical studies.The used approach made it possible to avoid using soil categories and a jump change in characteristics of soil conditions and seismic impact.The developed seismic microzonation maps are prepared for further introduction into the normative documents of the Republic of Kazakhstan.
基金Supported by the National Key Research and Development Program of China(No.2022ZD0115503).
文摘Modelling,predicting and analysing driver behaviours are essential to advanced driver assistance systems(ADAS)and the comprehensive understanding of complex driving scenarios.Recently,with the development of deep learning(DL),numerous driver behaviour learning(DBL)methods have been proposed and applied in connected vehicles(CV)and intelligent transportation systems(ITS).This study provides a review of DBL,which mainly focuses on typical applications in CV and ITS.First,a comprehensive review of the state-of-the-art DBL is presented.Next,Given the constantly changing nature of real driving scenarios,most existing learning-based models may suffer from the so-called“catastrophic forgetting,”which refers to their inability to perform well in previously learned scenarios after acquiring new ones.As a solution to the aforementioned issue,this paper presents a framework for continual driver behaviour learning(CDBL)by leveraging continual learning technology.The proposed CDBL framework is demonstrated to outperform existing methods in behaviour prediction through a case study.Finally,future works,potential challenges and emerging trends in this area are highlighted.
基金supported by the National Natural Science Foundation of China(Nos.52272440,51875375)the China Postdoctoral Science Foundation Funded Project(No.2021M701503).
文摘As a data-driven approach, Deep Learning(DL)-based fault diagnosis methods need to collect the relatively comprehensive data on machine fault types to achieve satisfactory performance. A mechanical system may include multiple submachines in the real-world. During condition monitoring of a mechanical system, fault data are distributed in a continuous flow of constantly generated information and new faults will inevitably occur in unconsidered submachines, which are also called machine increments. Therefore, adequately collecting fault data in advance is difficult. Limited by the characteristics of DL, training existing models directly with new fault data of new submachines leads to catastrophic forgetting of old tasks, while the cost of collecting all known data to retrain the models is excessively high. DL-based fault diagnosis methods cannot learn continually and adaptively in dynamic environments. A new Continual Learning Fault Diagnosis method(CLFD) is proposed in this paper to solve a series of fault diagnosis tasks with machine increments. The stability–plasticity dilemma is an intrinsic issue in continual learning. The core of CLFD is the proposed Dual-branch Adaptive Aggregation Residual Network(DAARN).Two types of residual blocks are created in each block layer of DAARN: steady and dynamic blocks. The stability–plasticity dilemma is solved by assigning them with adaptive aggregation weights to balance stability and plasticity, and a bi-level optimization program is used to optimize adaptive aggregation weights and model parameters. In addition, a feature-level knowledge distillation loss function is proposed to further overcome catastrophic forgetting. CLFD is then applied to the fault diagnosis case with machine increments. Results demonstrate that CLFD outperforms other continual learning methods and has satisfactory robustness.
基金Project(51575415)supported by the National Natural Science Foundation of ChinaProject(2016CFA077)supported by the Natural Science Foundation of Hubei Province of ChinaProject(2018-YS-026)supported by the Excellent Dissertation Cultivation Funds of Wuhan University of Technology,China。
文摘The evolution of temperature field of the continual motion induction cladding and the depth of heat affected zone are studied in this study.A three-dimensional finite element model for the point type continual induction cladding is established to investigate temperature distributions of fixed and motion induction cladding modes.The novel inductor is designed for cladding of curved surfaces.The modeling reliability is verified by the temperature measurements.The influence of process parameters on the maximum temperature and the generation and transfer of heat are studied.Quantitative calculation is performed to its melting rate to verify the temperature distribution and microstructures.The results show that a good metallurgical bond can be formed between the cladding layer and substrate.The melting rate gradually falls from the top of the cladding layer to the substrate,and the grain size in the substrate gradually rises.The heat affected zone is relatively small compared to integral heating.
文摘In this paper, the theorem of structure continual variation of truss structure in the analysis of structure reliability is derived, and it is used to generate limit state function automatically. We can avoid repeated assembly of global stiffness matrix and repeated inverse operations of the matrix caused by constant changes of structure topology. A new criterion of degenerate of the structure into mechanism is introduced. The calculation examples are satisfactory.
文摘The primary goal of a phase I clinical trial is to find the maximum tolerable dose of a treatment. In this paper, we propose a new stepwise method based on confidence bound and information incorporation to determine the maximum tolerable dose among given dose levels. On the one hand, in order to avoid severe even fatal toxicity to occur and reduce the experimental subjects, the new method is executed from the lowest dose level, and then goes on in a stepwise fashion. On the other hand, in order to improve the accuracy of the recommendation, the final recommendation of the maximum tolerable dose is accomplished through the information incorporation of an additional experimental cohort at the same dose level. Furthermore, empirical simulation results show that the new method has some real advantages in comparison with the modified continual reassessment method.
文摘In this paper, various forms of functional on blending energy principles of composite laminated plates are gir en, which guarantee satisfied continual conditions of displacements and stress between layers, and then the reliability of the functional are proved by the computing example.
文摘We examined the adaptation of the postural response to repeated predictable platform oscillations. Our main goals were to determine whether the short-term changes that occurred during a minute long continuous postural perturbation trial were maintained in subsequent trials and to determine how many trials were required before participants fully adapted to the postural task. Ten participants performed ten minute-long postural trials on a platform that oscillated at 0.25 Hz before increasing to 0.50 Hz half way through each trial. Postural muscle onset latencies, burst amplitudes, and anterior posterior displacements of the center of pressure (COP) and center of mass (COM) were calculated for the last five cycles performed in each trial at 0.50 Hz. The postural strategy evolved in two phases: 1) immediate decrease in COP displacement;2) earlier activation of the postural muscles with smaller muscle burst amplitudes. After seven trials the postural response remained consistent.
基金Supported by the Research Foundation of Beijing Friendship Hospital,Capital Medical University,No. yyqdkt2018-16the Beijing Municipal Administration of Hospitals’ Youth Program,No. QML20190101the Scientific Research Common Program of Beijing Municipal Commission of Education,No. KM202010025021
文摘BACKGROUND Sedation during endoscopic ultrasonography(EUS)poses many challenges and moderate-to-deep sedation are often required.The conventional method to preform moderate-to-deep sedation is generally intravenous benzodiazepine alone or in combination with opioids.However,this combination has some limitations.Intranasal medication delivery may be an alternative to this sedation regimen.AIM To determine,by continual reassessment method(CRM),the minimal effective dose of intranasal sufentanil(SUF)when combined with intranasal dexmedetomidine(DEX)for moderate sedation of EUS in at least 95%of patients(ED95).METHODS Thirty patients aged 18-65 and scheduled for EUS were recruited in this study.Subjects received intranasal DEX and SUF for sedation.The dose of DEX(1μg/kg)was fixed,while the dose of SUF was assigned sequentially to the subjects using CRM to determine ED95.The sedation status was assessed by modified observer’s assessment of alertness/sedation(MOAA/S)score.The adverse events and the satisfaction scores of patients and endoscopists were recorded.RESULTS The ED95 was intranasal 0.3μg/kg SUF when combined with intranasal 1μg/kg DEX,with an estimated probability of successful moderate sedation for EUS of 94.9%(95%confidence interval:88.1%-98.9%).When combined with intranasal 1μg/kg DEX,probabilities of successful moderate sedation at each dose level of intranasal SUF were as follows:0μg/kg SUF,52.8%;0.1μg/kg SUF,75.4%;0.2μg/kg SUF,89.9%;0.3μg/kg SUF,94.9%;0.4μg/kg SUF,98.0%;0.5μg/kg SUF,99.0%.CONCLUSION The ED95 needed for moderate sedation for EUS is intranasal 0.3μg/kg SUF when combined with intranasal 1μg/kg DEX,based on CRM.
