The high-precision requirements will always be constrained due to the complicated operating conditions of the ground-based telescope. Owing to various internal and external disturbances, it is necessary to study a con...The high-precision requirements will always be constrained due to the complicated operating conditions of the ground-based telescope. Owing to various internal and external disturbances, it is necessary to study a control method, which should have a good ability on disturbance rejection and a good adaptability on system parameter variation. The traditional proportional-integral(PI) controller has the advantage of simple and easy adjustment, but it cannot deal with the disturbances well in different situations. This paper proposes a simplified active disturbance rejection control law, whose debugging is as simple as the PI controller, and with better disturbance rejection ability and parameter adaptability. It adopts a simplified second-order extended state observer(ESO) with an adjustable parameter to accommodate the significant variation of the inertia during the different design stages of the telescope. The gain parameter of the ESO can be adjusted online with a recursive least square estimating method once the system parameter has changed significantly. Thus, the ESO can estimate the total disturbances timely and the controller will compensate them accordingly. With the adjustable parameter of the ESO, the controller can always achieve better performance in different applications of the telescope. The simulation and experimental verification of the control law was conducted on a 1.2-meter ground based telescope. The results verify the necessity of adjusting the parameter of the ESO, and demonstrate better disturbance rejection ability in a large range of speed variations during the design stages of the telescope.展开更多
We find extremely large low-magnetic-field magnetoresistance （~350% at 0.2 T and ~180% at 0.1 T） in germa- nium at room temperature and the magnetoresistanee is highly sensitive to the surface roughness. This unique...We find extremely large low-magnetic-field magnetoresistance （~350% at 0.2 T and ~180% at 0.1 T） in germa- nium at room temperature and the magnetoresistanee is highly sensitive to the surface roughness. This unique magnetoelectric property is applied to fabricate logic architecture which could perform basic Boolean logic in- cluding AND, OR, NOR and NAND. Our logic device may pave the way for a high performance microprocessor and may make the germanium family more advanced.展开更多
A survey involving 6103 participants from five Chinese provinces was conducted to evaluate the threshold value of urinary cadmium （UCd） for renal dysfunction as benchmark dose low （BMDL）. The urinary N-acetyl-13-D...A survey involving 6103 participants from five Chinese provinces was conducted to evaluate the threshold value of urinary cadmium （UCd） for renal dysfunction as benchmark dose low （BMDL）. The urinary N-acetyl-13-D-glucosaminidase （UNAG） was chosen as an effect biomarker. The UCd BMDLs for UNAG ranged from 2.18μg/g creatinine （cr） to 4.26μg/g cr in the populations of different provinces. The selection of the sample population and area affect the evaluation of the BMDL. The reference level of UCd for renal effects was further evaluated based on the data of all 6103 subjects. With benchmark responses （BMR） of 10%/5%, the overall UCd BMDLs for males in the total population were 3.73/2.08 μg/g cr. The BMD was slightly lower in females, thereby indicating that females may be relatively more sensitive to Cd exposure than are males.展开更多
Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inheren...Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inherently challenging due to the unstructured,context-sensitive,and heterogeneous nature of online communication.To address these challenges,we propose a novel intelligent computational framework,Multi-domain Opinion Leader Agents Emotion Prediction(MOAEP).Our framework comprises three key components:(1)An Automatic Question Generation(AQG)module employing“Who,What,Where,When,Why,and How”(5W1H)questioning to systematically explore topic dimensions;(2)A Multi-domain Opinion Leader Agents(MOA)module that integrates enhanced Large Language Models(LLMs)with Retrieval-Augmented Generation(RAG)to produce domain-specific responses;and(3)An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses,enabling proactive social computing analysis that surpasses conventional post-event methods.Experimental results demonstrate the framework’s efficacy:the AQG module generates high-fidelity outputs,while the influencer agents maintain consistent performance,achieving an average“Generative Pre-trained Transformer 4”(GPT-4)evaluation score of 6.85(on a 0-10 scale)across multiple dimensions.In a social media conflict case study,“Russia-Ukraine War”,our framework successfully predicts key influencers’perspectives and aligns emotional forecasts with observed real-world sentiment trends.These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos. 12122304 and 11973041)in part by the Youth Innovation Promotion Association CAS (No. 2019218)。
文摘The high-precision requirements will always be constrained due to the complicated operating conditions of the ground-based telescope. Owing to various internal and external disturbances, it is necessary to study a control method, which should have a good ability on disturbance rejection and a good adaptability on system parameter variation. The traditional proportional-integral(PI) controller has the advantage of simple and easy adjustment, but it cannot deal with the disturbances well in different situations. This paper proposes a simplified active disturbance rejection control law, whose debugging is as simple as the PI controller, and with better disturbance rejection ability and parameter adaptability. It adopts a simplified second-order extended state observer(ESO) with an adjustable parameter to accommodate the significant variation of the inertia during the different design stages of the telescope. The gain parameter of the ESO can be adjusted online with a recursive least square estimating method once the system parameter has changed significantly. Thus, the ESO can estimate the total disturbances timely and the controller will compensate them accordingly. With the adjustable parameter of the ESO, the controller can always achieve better performance in different applications of the telescope. The simulation and experimental verification of the control law was conducted on a 1.2-meter ground based telescope. The results verify the necessity of adjusting the parameter of the ESO, and demonstrate better disturbance rejection ability in a large range of speed variations during the design stages of the telescope.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11174169,11234007 and 51471093
文摘We find extremely large low-magnetic-field magnetoresistance （~350% at 0.2 T and ~180% at 0.1 T） in germa- nium at room temperature and the magnetoresistanee is highly sensitive to the surface roughness. This unique magnetoelectric property is applied to fabricate logic architecture which could perform basic Boolean logic in- cluding AND, OR, NOR and NAND. Our logic device may pave the way for a high performance microprocessor and may make the germanium family more advanced.
基金financially supported by Special Funds of the State Environmental Protection Public Welfare Industry(201009049201309049)+1 种基金National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2013BAI12B03)the Fundamental Research Funds for the Central Universities(2015JBM108)
文摘A survey involving 6103 participants from five Chinese provinces was conducted to evaluate the threshold value of urinary cadmium （UCd） for renal dysfunction as benchmark dose low （BMDL）. The urinary N-acetyl-13-D-glucosaminidase （UNAG） was chosen as an effect biomarker. The UCd BMDLs for UNAG ranged from 2.18μg/g creatinine （cr） to 4.26μg/g cr in the populations of different provinces. The selection of the sample population and area affect the evaluation of the BMDL. The reference level of UCd for renal effects was further evaluated based on the data of all 6103 subjects. With benchmark responses （BMR） of 10%/5%, the overall UCd BMDLs for males in the total population were 3.73/2.08 μg/g cr. The BMD was slightly lower in females, thereby indicating that females may be relatively more sensitive to Cd exposure than are males.
基金supported in part by the National Natural Science Foundation of China(Nos.62301510,62271455,and 72474198)the Public Computing Cloud,CUC.
文摘Understanding influencers’perspectives and predicting public sentiment are crucial for event assessment and guidance in computational social systems,enabling more informed decision-making.However,this task is inherently challenging due to the unstructured,context-sensitive,and heterogeneous nature of online communication.To address these challenges,we propose a novel intelligent computational framework,Multi-domain Opinion Leader Agents Emotion Prediction(MOAEP).Our framework comprises three key components:(1)An Automatic Question Generation(AQG)module employing“Who,What,Where,When,Why,and How”(5W1H)questioning to systematically explore topic dimensions;(2)A Multi-domain Opinion Leader Agents(MOA)module that integrates enhanced Large Language Models(LLMs)with Retrieval-Augmented Generation(RAG)to produce domain-specific responses;and(3)An emotion prediction engine that synthesizes agent interactions to forecast collective emotional responses,enabling proactive social computing analysis that surpasses conventional post-event methods.Experimental results demonstrate the framework’s efficacy:the AQG module generates high-fidelity outputs,while the influencer agents maintain consistent performance,achieving an average“Generative Pre-trained Transformer 4”(GPT-4)evaluation score of 6.85(on a 0-10 scale)across multiple dimensions.In a social media conflict case study,“Russia-Ukraine War”,our framework successfully predicts key influencers’perspectives and aligns emotional forecasts with observed real-world sentiment trends.These findings underscore the potential of MOAEP to provide actionable insights for decision-making in computational social science.
