Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects s...Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.展开更多
Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high com...Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.展开更多
A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the mai...A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.展开更多
As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components o...As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera,infrared thermal imager and multispectral temperature measurement system were used for pressure,temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.展开更多
Based on the definition of component ontology, an effective component classification mechanism and a facet named component relationship are proposed. Then an application domain oriented, hierarchical component organiz...Based on the definition of component ontology, an effective component classification mechanism and a facet named component relationship are proposed. Then an application domain oriented, hierarchical component organization model is established. At last a hierarchical component semantic network (HCSN) described by ontology interchange language(OIL) is presented and then its function is described. Using HCSN and cooperating with other components retrieving algorithms based on component description, other components information and their assembly or composite modes related to the key component can be found. Based on HCSN, component directory library is catalogued and a prototype system is constructed. The prototype system proves that component library organization based on this model gives guarantee to the reliability of component assembly during program mining.展开更多
在直流输电系统中,换流阀阀基电子VBE(valve base electronics)设备的稳定运作对维护直流系统安全至关重要。传统的阀基电子设备电路板(VBE板)元件失效检测方法依赖于耗时的人工检查或基于规则的自动化系统,这些方法通常检测效率低下且...在直流输电系统中,换流阀阀基电子VBE(valve base electronics)设备的稳定运作对维护直流系统安全至关重要。传统的阀基电子设备电路板(VBE板)元件失效检测方法依赖于耗时的人工检查或基于规则的自动化系统,这些方法通常检测效率低下且准确性有限。针对该问题,提出一种基于改进的SqueezeNet深度学习模型的VBE板元件失效区域识别方法。通过引入深度可分离卷积和残差连接,所提改进SqueezeNet模型旨在提高元件失效检测的准确性,同时降低计算资源的需求。在VBE板元件失效数据集上的实验结果表明,所提方法在元件失效检测准确率和运算效率方面均优于传统方法和标准SqueezeNet模型,准确率达到了95.27%,比原模型高出4.45%。不仅提升了VBE板元件失效检测的效率和准确性,而且为电力系统中类似设备的元件失效诊断提供了新的技术参考。展开更多
Humic acids can promote the germination of many vegetable seeds,but the key active components remain unclear.This study utilized nutrient content,cross polarization magic angle spin ^(13)C solid magnetic resonance(CPM...Humic acids can promote the germination of many vegetable seeds,but the key active components remain unclear.This study utilized nutrient content,cross polarization magic angle spin ^(13)C solid magnetic resonance(CPMAS-^(13)C-NMR)and ultra-high performance liquid chromatography-mass spectrometry(UHPLC-MS)to characterize the chemical components of humic acids.Tomato seed germination index(GI)was determined with the goal of screening the key active components of humic acids.Humic acids had a significantly higher nutrient content,except for the total nitrogen(TN)and the total phosphorus(TP)contents.Humic acids had a higher content of O-CH_(3)/NCH,aromatic C-O and carbonyl C compared to weathered coal,with significantly lower anomeric C,aromatic C and O-alkyl C/alkyl C.There were 611 different compounds identified among the test materials using UHPLC-MS.Humic acids also had a significantly higher GI(158.0%and 153.1%)than weathered coal(85.5%).The organic matter(OM),TP and available potassium(AK)contents in humic acids were significantly positively correlated with GI,and available phosphorus(AP)was significantly negatively correlated.Among the carbon components,O-CH3/NCH,aromatic C-O and O-alkyl C/alkyl C were significantly positively correlated with GI,while anomeric C was significantly negatively correlated.Furthermore,among the top 10 positive and five negative correlation compounds,lipids and lipid-like molecules[armexifolin,boviquinone 4,3-methyladipic acid,lxocarpalactone A,monic acid,DG(20:1(11Z)/18:4(6Z,9Z,12Z,15Z)/0:0),and brassinolide]and organic acids and derivatives(N-acetylglutamic acid,8-hydroxy-5,6-octadienoic acid,acetyl-L-tyrosine,and hydroxyprolyl-methionine)in humic acids might be crucial active components for improving tomato seed germination.The results provided direct evidence for the identification of bioactive molecules of humic acids,and a scientific basis for the precise utilization of bioactive molecular components of humic acids in sustainable agricultural development.展开更多
基金funded by the National Natural Science Foundation of China Youth Fund(Grant No.62304022)Science and Technology on Electromechanical Dynamic Control Laboratory(China,Grant No.6142601012304)the 2022e2024 China Association for Science and Technology Innovation Integration Association Youth Talent Support Project(Grant No.2022QNRC001).
文摘Metal Additive Manufacturing(MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micromechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting(SLM) additive manufacturing(AM) process of specialized Micro Electromechanical System(MEMS) components, multiparameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through highprecision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components.
文摘Restoration of phase aberrations is crucial for addressing atmospheric turbulence in light propagation.Traditional restoration algorithms based on Zernike polynomials(ZPs)often encounter challenges related to high computational complexity and insufficient capture of high-frequency phase aberration components,so we proposed a Principal-Component-Analysis-based method for representing phase aberrations.This paper discusses the factors influencing the accuracy of restoration,mainly including the sample space size and the sampling interval of D/r_(0),on the basis of characterizing phase aberrations by Principal Components(PCs).The experimental results show that a larger D/r_(0)sampling interval can ensure the generalization ability and robustness of the principal components in the case of a limited amount of original data,which can help to achieve high-precision deployment of the model in practical applications quickly.In the environment with relatively strong turbulence in the test set of D/r_(0)=24,the use of 34 terms of PCs can improve the corrected Strehl ratio(SR)from 0.007 to 0.1585,while the Strehl ratio of the light spot after restoration using 34 terms of ZPs is only 0.0215,demonstrating almost no correction effect.The results indicate that PCs can serve as a better alternative in representing and restoring the characteristics of atmospheric turbulence induced phase aberrations.These findings pave the way to use PCs of phase aberrations with fewer terms than traditional ZPs to achieve data dimensionality reduction,and offer a reference to accelerate and stabilize the model and deep learning based adaptive optics correction.
基金Project(70671039) supported by the National Natural Science Foundation of China
文摘A combined model based on principal components analysis (PCA) and generalized regression neural network (GRNN) was adopted to forecast electricity price in day-ahead electricity market. PCA was applied to mine the main influence on day-ahead price, avoiding the strong correlation between the input factors that might influence electricity price, such as the load of the forecasting hour, other history loads and prices, weather and temperature; then GRNN was employed to forecast electricity price according to the main information extracted by PCA. To prove the efficiency of the combined model, a case from PJM (Pennsylvania-New Jersey-Maryland) day-ahead electricity market was evaluated. Compared to back-propagation (BP) neural network and standard GRNN, the combined method reduces the mean absolute percentage error about 3%.
基金the support of the National Natural Science Foundation of China(Grant No.12302440)China Postdoctoral Science Foundation(Grant No.2023M741713)。
文摘As a kind of high-efficiency explosive with compound destructive capability, the energy output law of thermobaric explosives has been receiving great attention. In order to investigate the effects of main components on the explosive characteristics of thermobaric explosives, various high explosives and oxidants were selected to formulate five different types of thermobaric explosive. Then they were tested in both open space and closed space respectively. Pressure measurement system, high-speed camera,infrared thermal imager and multispectral temperature measurement system were used for pressure,temperature and fireball recording. The effects of different components on the explosive characteristics of thermobaric explosive were analyzed. The results showed that in open space, the overpressure is dominated by the high explosives content in the formulation. The addition of the oxidants will decrease the explosion overpressure but will increase the duration and overall brightness of the fireball. While in closed space, the quasi-static pressure formed after the explosion is positively correlated with the temperature and gas production. In addition, it was found that the differences in shell constraints can also alter the afterburning reaction of thermobaric explosives, thus affecting their energy output characteristics. PVC shell constraint obviously increases the overpressure and makes the fireball burn more violently.
文摘Based on the definition of component ontology, an effective component classification mechanism and a facet named component relationship are proposed. Then an application domain oriented, hierarchical component organization model is established. At last a hierarchical component semantic network (HCSN) described by ontology interchange language(OIL) is presented and then its function is described. Using HCSN and cooperating with other components retrieving algorithms based on component description, other components information and their assembly or composite modes related to the key component can be found. Based on HCSN, component directory library is catalogued and a prototype system is constructed. The prototype system proves that component library organization based on this model gives guarantee to the reliability of component assembly during program mining.
文摘在直流输电系统中,换流阀阀基电子VBE(valve base electronics)设备的稳定运作对维护直流系统安全至关重要。传统的阀基电子设备电路板(VBE板)元件失效检测方法依赖于耗时的人工检查或基于规则的自动化系统,这些方法通常检测效率低下且准确性有限。针对该问题,提出一种基于改进的SqueezeNet深度学习模型的VBE板元件失效区域识别方法。通过引入深度可分离卷积和残差连接,所提改进SqueezeNet模型旨在提高元件失效检测的准确性,同时降低计算资源的需求。在VBE板元件失效数据集上的实验结果表明,所提方法在元件失效检测准确率和运算效率方面均优于传统方法和标准SqueezeNet模型,准确率达到了95.27%,比原模型高出4.45%。不仅提升了VBE板元件失效检测的效率和准确性,而且为电力系统中类似设备的元件失效诊断提供了新的技术参考。
基金Supported by the National Natural Science Foundation of China(42207371)the Technological Project of Jiangsu Vocational College of Agriculture and Forestry(2021kj17)+1 种基金Yafu Technology Innovation and Service Major Project of Jiangsu Vocational College of Agriculture and Forestry(2024kj01)Key Research Projects of Jiangsu Vocational College of Agriculture and Forestry(2023kj14)。
文摘Humic acids can promote the germination of many vegetable seeds,but the key active components remain unclear.This study utilized nutrient content,cross polarization magic angle spin ^(13)C solid magnetic resonance(CPMAS-^(13)C-NMR)and ultra-high performance liquid chromatography-mass spectrometry(UHPLC-MS)to characterize the chemical components of humic acids.Tomato seed germination index(GI)was determined with the goal of screening the key active components of humic acids.Humic acids had a significantly higher nutrient content,except for the total nitrogen(TN)and the total phosphorus(TP)contents.Humic acids had a higher content of O-CH_(3)/NCH,aromatic C-O and carbonyl C compared to weathered coal,with significantly lower anomeric C,aromatic C and O-alkyl C/alkyl C.There were 611 different compounds identified among the test materials using UHPLC-MS.Humic acids also had a significantly higher GI(158.0%and 153.1%)than weathered coal(85.5%).The organic matter(OM),TP and available potassium(AK)contents in humic acids were significantly positively correlated with GI,and available phosphorus(AP)was significantly negatively correlated.Among the carbon components,O-CH3/NCH,aromatic C-O and O-alkyl C/alkyl C were significantly positively correlated with GI,while anomeric C was significantly negatively correlated.Furthermore,among the top 10 positive and five negative correlation compounds,lipids and lipid-like molecules[armexifolin,boviquinone 4,3-methyladipic acid,lxocarpalactone A,monic acid,DG(20:1(11Z)/18:4(6Z,9Z,12Z,15Z)/0:0),and brassinolide]and organic acids and derivatives(N-acetylglutamic acid,8-hydroxy-5,6-octadienoic acid,acetyl-L-tyrosine,and hydroxyprolyl-methionine)in humic acids might be crucial active components for improving tomato seed germination.The results provided direct evidence for the identification of bioactive molecules of humic acids,and a scientific basis for the precise utilization of bioactive molecular components of humic acids in sustainable agricultural development.
