Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
Algorithms are the primary component of Artificial Intelligence(AI).The algorithm is the process in AI that imitates the human mind to solve problems.Currently evaluating the performance of AI is achieved by evaluatin...Algorithms are the primary component of Artificial Intelligence(AI).The algorithm is the process in AI that imitates the human mind to solve problems.Currently evaluating the performance of AI is achieved by evaluating AI algorithms by metric scores on data sets.However the evaluation of algorithms in AI is challenging because the evaluation of the same type of algorithm has many data sets and evaluation metrics.Different algorithms may have individual strengths and weaknesses in evaluation metric scores on separate data sets,lacking the credibility and validity of the evaluation.Moreover,evaluation of algorithms requires repeated experiments on different data sets,reducing the attention of researchers to the research of the algorithms itself.Crucially,this approach to evaluating comparative metric scores does not take into account the algorithm’s ability to solve problems.And the classical algorithm evaluation of time and space complexity is not suitable for evaluating AI algorithms.Because classical algorithms input is infinite numbers,whereas AI algorithms input is a data set,which is limited and multifarious.According to the AI algorithm evaluation without response to the problem solving capability,this paper summarizes the features of AI algorithm evaluation and proposes an AI evaluation method that incorporates the problem-solving capabilities of algorithms.展开更多
The objective of this work is to calculate and compare the energy eigenvalue of Hulthen Potential using the NU method and AIM method. Using these two methods the energy eigenvalue calculated from the NU method is less...The objective of this work is to calculate and compare the energy eigenvalue of Hulthen Potential using the NU method and AIM method. Using these two methods the energy eigenvalue calculated from the NU method is less than AIM method. Moreover, the energy eigenvalue calculated from both methods is charge independent and only depends upon the quantum numbers and screening parameters, while the third term of energy eigenvalue calculated using the NU method is only dependent on screening parameters.展开更多
Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue feat...Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue features pioneering research that integrates AI-driven methods with AM,enabling the design and fabrication of complex,optimized structures with enhanced properties.展开更多
A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate...A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.展开更多
Background GD-11,a novel brain cytoprotective drug,was designed to be actively taken up and transported across the blood-brain barrier via the glucose transporter.This study aimed to evaluate the safety and efficacy o...Background GD-11,a novel brain cytoprotective drug,was designed to be actively taken up and transported across the blood-brain barrier via the glucose transporter.This study aimed to evaluate the safety and efficacy of GD-11 for improving the recovery of patients with acute ischaemic stroke(AIS).Methods A double-blind,randomised,placebo-controlled,phase 2 trial was conducted at 15 clinical sites in China.Patients aged 18-80 years with AIS within 48 hours were randomly assigned(1:1:1)to receive 160 mg GD-11,80 mg GD-11 and placebo,two times a day for 10 days.The primary endpoint was a modified Rankin Scale(mRS)score of 0-1 at 90 days after treatment.The safety outcome was any adverse events within 90 days.Results From 17 November 2022 to 22 March 2023,a total of 80 patients in the 160 mg GD-11 group,79 patients in the 80 mg GD-11 group and 80 patients in the placebo group were included.The proportion of an mRS score of 0-1 at day 90 was 77.5%in the 160 mg GD-11 group,72.2%in the 80 mg GD-11 group and 67.5%in the placebo group.Though no significant difference was found(p=0.3671),a numerically higher proportion was observed in the GD-11 group,especially in the 160 mg GD-11 group.The incidence of adverse events was similar across the three groups(p=0.1992).Conclusion GD-11 was safe and well-tolerated.A dosage of GD-11160 mg two times a day was recommended for a large trial to investigate the efficacy.展开更多
The requirements for ensuring functional safety have always been very high.Modern safety-related systems are becoming increasingly complex, making also the safety integrity assessment more complex and time-consuming. ...The requirements for ensuring functional safety have always been very high.Modern safety-related systems are becoming increasingly complex, making also the safety integrity assessment more complex and time-consuming. This trend is further intensified by the fact that AI-based algorithms are finding their way into safety-related systems or will do so in the future. However, existing and expected standards and regulations for the use of AI methods pose significant challenges for the development of embedded AI software in functional safety-related systems. The consideration of essential requirements from various perspectives necessitates an intensive examination of the subject matter, especially as diferent standards have to be taken into account depending on the final application. There are also diferent targets for the “safe behavior” of a system depending on the target application. While stopping all movements of a machine in industrial production plants is likely to be considered a “safe state”, the same condition might not be considered as safe in flying aircraft, driving cars or medicine equipment like heart pacemaker. This overall complexity is operationalized in our approach in such a way that it is straightforward to monitor conformity with the requirements. To support safety integrity assessments and reduce the required efort, a Self-Enforcing Network(SEN) model is presented in which developers or safety experts can indicate the degree of fulfillment of certain requirements with possible impact on the safety integrity of a safety-related system. The result evaluated by the SEN model indicates the achievable safety integrity level of the assessed system, which is additionally provided by an explanatory component.展开更多
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
基金funded by the General Program of the National Natural Science Foundation of China grant number[62277022].
文摘Algorithms are the primary component of Artificial Intelligence(AI).The algorithm is the process in AI that imitates the human mind to solve problems.Currently evaluating the performance of AI is achieved by evaluating AI algorithms by metric scores on data sets.However the evaluation of algorithms in AI is challenging because the evaluation of the same type of algorithm has many data sets and evaluation metrics.Different algorithms may have individual strengths and weaknesses in evaluation metric scores on separate data sets,lacking the credibility and validity of the evaluation.Moreover,evaluation of algorithms requires repeated experiments on different data sets,reducing the attention of researchers to the research of the algorithms itself.Crucially,this approach to evaluating comparative metric scores does not take into account the algorithm’s ability to solve problems.And the classical algorithm evaluation of time and space complexity is not suitable for evaluating AI algorithms.Because classical algorithms input is infinite numbers,whereas AI algorithms input is a data set,which is limited and multifarious.According to the AI algorithm evaluation without response to the problem solving capability,this paper summarizes the features of AI algorithm evaluation and proposes an AI evaluation method that incorporates the problem-solving capabilities of algorithms.
文摘The objective of this work is to calculate and compare the energy eigenvalue of Hulthen Potential using the NU method and AIM method. Using these two methods the energy eigenvalue calculated from the NU method is less than AIM method. Moreover, the energy eigenvalue calculated from both methods is charge independent and only depends upon the quantum numbers and screening parameters, while the third term of energy eigenvalue calculated using the NU method is only dependent on screening parameters.
文摘Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue features pioneering research that integrates AI-driven methods with AM,enabling the design and fabrication of complex,optimized structures with enhanced properties.
文摘A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.
基金supported by Beijing Municipal Science&Technology Commission(Z221100007422050)Capital's Funds for Health Improvement and Research(2020-1-2041,2022-2G 2049).
文摘Background GD-11,a novel brain cytoprotective drug,was designed to be actively taken up and transported across the blood-brain barrier via the glucose transporter.This study aimed to evaluate the safety and efficacy of GD-11 for improving the recovery of patients with acute ischaemic stroke(AIS).Methods A double-blind,randomised,placebo-controlled,phase 2 trial was conducted at 15 clinical sites in China.Patients aged 18-80 years with AIS within 48 hours were randomly assigned(1:1:1)to receive 160 mg GD-11,80 mg GD-11 and placebo,two times a day for 10 days.The primary endpoint was a modified Rankin Scale(mRS)score of 0-1 at 90 days after treatment.The safety outcome was any adverse events within 90 days.Results From 17 November 2022 to 22 March 2023,a total of 80 patients in the 160 mg GD-11 group,79 patients in the 80 mg GD-11 group and 80 patients in the placebo group were included.The proportion of an mRS score of 0-1 at day 90 was 77.5%in the 160 mg GD-11 group,72.2%in the 80 mg GD-11 group and 67.5%in the placebo group.Though no significant difference was found(p=0.3671),a numerically higher proportion was observed in the GD-11 group,especially in the 160 mg GD-11 group.The incidence of adverse events was similar across the three groups(p=0.1992).Conclusion GD-11 was safe and well-tolerated.A dosage of GD-11160 mg two times a day was recommended for a large trial to investigate the efficacy.
文摘The requirements for ensuring functional safety have always been very high.Modern safety-related systems are becoming increasingly complex, making also the safety integrity assessment more complex and time-consuming. This trend is further intensified by the fact that AI-based algorithms are finding their way into safety-related systems or will do so in the future. However, existing and expected standards and regulations for the use of AI methods pose significant challenges for the development of embedded AI software in functional safety-related systems. The consideration of essential requirements from various perspectives necessitates an intensive examination of the subject matter, especially as diferent standards have to be taken into account depending on the final application. There are also diferent targets for the “safe behavior” of a system depending on the target application. While stopping all movements of a machine in industrial production plants is likely to be considered a “safe state”, the same condition might not be considered as safe in flying aircraft, driving cars or medicine equipment like heart pacemaker. This overall complexity is operationalized in our approach in such a way that it is straightforward to monitor conformity with the requirements. To support safety integrity assessments and reduce the required efort, a Self-Enforcing Network(SEN) model is presented in which developers or safety experts can indicate the degree of fulfillment of certain requirements with possible impact on the safety integrity of a safety-related system. The result evaluated by the SEN model indicates the achievable safety integrity level of the assessed system, which is additionally provided by an explanatory component.
