In this paper, we conduct research on the development of mechanical and electrical integration of system function principle and related technologies. Along with the rapid and continuous development of modem science an...In this paper, we conduct research on the development of mechanical and electrical integration of system function principle and related technologies. Along with the rapid and continuous development of modem science and technology, it ＇ s for the penetration and cross of different subjects great push, the more important is caused by technological revolution in the field of engineering and mechanical engineering field under the rapid development of computer technology and microelectronic technology and penetration to the mechanical and electrical integration, which is formed by the mechanical industry lead to trigger a particularly large changes in the mechanical industry management system and mode of production, product and technical structure, composition and function, thus result in industrial production from the previous mechanical electrification progressively electromechanical integration which lead the trend of the current technology.展开更多
In order to improve the quality of graduation project (thesis) of mechanical and electrical undergraduate, cultivate students’ comprehensive innovation ability and practical application ability, and explore the integ...In order to improve the quality of graduation project (thesis) of mechanical and electrical undergraduate, cultivate students’ comprehensive innovation ability and practical application ability, and explore the integration of discipline competition into graduation project (thesis). The integration of subject competition with graduation project (thesis) of mechanical and electrical undergraduate course can improve the innovation and practicability of the selected topic, make up for the problem of short writing cycle, and strengthen students’ ability of analysis, research, writing and experimental design. This paper analyzes the implementation cases of the deep integration of subject competition and graduation design (thesis), and emphatically expounds the methods of the deep integration of subject competition and graduation design (thesis), so as to finally achieve the purpose of improving the graduation design (thesis) of electromechanical undergraduate. The research results of this paper have far-reaching practical significance.展开更多
Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of co...Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.展开更多
Photovoltaic (PV) modules have emerged as an ideal technology of choice for <span>harvesting vastly available renewable energy resources. However, the effi</span>ciency <span>of PV modules remains si...Photovoltaic (PV) modules have emerged as an ideal technology of choice for <span>harvesting vastly available renewable energy resources. However, the effi</span>ciency <span>of PV modules remains significantly lower than that of other renewable</span> energy sources such as wind and hydro. One of the critical elements affecting a photovoltaic module’s efficiency is the variety of external climatic conditions under which it is installed. In this work, the effect of simulated snow loads was evaluated on the performance of PV modules with different <span>types of cells and numbers of busbars. According to ASTM-1830 and IEC-1215</span> standards, a load of 5400 Pa was applied to the surface of PV modules for 3 hours. An indigenously developed pneumatic airbag test setup was used for the uniform application of this load throughout the test, which was validated by load cell and pressure gauge. Electroluminescence (EL) imaging and solar flash tests were performed before and after the application of load to characterize the performance and effect of load on PV modules. Based on these tests, the maxi<span>mum power output, efficiency, fill factor and series resistance were deter</span>mined. The results show that polycrystalline modules are the most likely to withstand the snow loads as compared to monocrystalline PV modules. A maximum drop of 32.13% in the power output and a 17.6% increase in series resistance were observed in the modules having more cracks. These findings demonstrated the efficacy of the newly established test setup and the potential of snow loads for reducing the overall performance of PV module.展开更多
Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularl...Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.展开更多
The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric an...The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric and Magnetic fields. Also, every moving particle has a De Broglie wavelength determined by its mass and velocity. This paper shows that all of these properties of a particle can be derived from a single wave function equation for that particle. Wave functions for the Electron and the Positron are presented and principles are provided that can be used to calculate the wave functions of all the fundamental particles in Physics. Fundamental particles such as electrons and positrons are considered to be point particles in the Standard Model of Physics and are not considered to have a structure. This paper demonstrates that they do indeed have structure and that this structure extends into the space around the particle’s center (in fact, they have infinite extent), but with rapidly diminishing energy density with the distance from that center. The particles are formed from Electromagnetic standing waves, which are stable solutions to the Schrödinger and Classical wave equations. This stable structure therefore accounts for both the wave and particle nature of these particles. In fact, all of their properties such as mass, spin and electric charge, can be accounted for from this structure. These particle properties appear to originate from a single point at the center of the wave function structure, in the same sort of way that the Shell theorem of gravity causes the gravity of a body to appear to all originate from a central point. This paper represents the first two fully characterized fundamental particles, with a complete description of their structure and properties, built up from the underlying Electromagnetic waves that comprise these and all fundamental particles.展开更多
文摘In this paper, we conduct research on the development of mechanical and electrical integration of system function principle and related technologies. Along with the rapid and continuous development of modem science and technology, it ＇ s for the penetration and cross of different subjects great push, the more important is caused by technological revolution in the field of engineering and mechanical engineering field under the rapid development of computer technology and microelectronic technology and penetration to the mechanical and electrical integration, which is formed by the mechanical industry lead to trigger a particularly large changes in the mechanical industry management system and mode of production, product and technical structure, composition and function, thus result in industrial production from the previous mechanical electrification progressively electromechanical integration which lead the trend of the current technology.
文摘In order to improve the quality of graduation project (thesis) of mechanical and electrical undergraduate, cultivate students’ comprehensive innovation ability and practical application ability, and explore the integration of discipline competition into graduation project (thesis). The integration of subject competition with graduation project (thesis) of mechanical and electrical undergraduate course can improve the innovation and practicability of the selected topic, make up for the problem of short writing cycle, and strengthen students’ ability of analysis, research, writing and experimental design. This paper analyzes the implementation cases of the deep integration of subject competition and graduation design (thesis), and emphatically expounds the methods of the deep integration of subject competition and graduation design (thesis), so as to finally achieve the purpose of improving the graduation design (thesis) of electromechanical undergraduate. The research results of this paper have far-reaching practical significance.
基金Supported by the National Natural Science Foundation of China(No.61305042,61202098)Projects of Center for Remote Sensing Mission Study of China National Space Administration(No.2012A03A0939)Science and Technological Research of Key Projects of Education Department of Henan Province of China(No.13A520071)
文摘Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.
文摘Photovoltaic (PV) modules have emerged as an ideal technology of choice for <span>harvesting vastly available renewable energy resources. However, the effi</span>ciency <span>of PV modules remains significantly lower than that of other renewable</span> energy sources such as wind and hydro. One of the critical elements affecting a photovoltaic module’s efficiency is the variety of external climatic conditions under which it is installed. In this work, the effect of simulated snow loads was evaluated on the performance of PV modules with different <span>types of cells and numbers of busbars. According to ASTM-1830 and IEC-1215</span> standards, a load of 5400 Pa was applied to the surface of PV modules for 3 hours. An indigenously developed pneumatic airbag test setup was used for the uniform application of this load throughout the test, which was validated by load cell and pressure gauge. Electroluminescence (EL) imaging and solar flash tests were performed before and after the application of load to characterize the performance and effect of load on PV modules. Based on these tests, the maxi<span>mum power output, efficiency, fill factor and series resistance were deter</span>mined. The results show that polycrystalline modules are the most likely to withstand the snow loads as compared to monocrystalline PV modules. A maximum drop of 32.13% in the power output and a 17.6% increase in series resistance were observed in the modules having more cracks. These findings demonstrated the efficacy of the newly established test setup and the potential of snow loads for reducing the overall performance of PV module.
文摘Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.
文摘The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric and Magnetic fields. Also, every moving particle has a De Broglie wavelength determined by its mass and velocity. This paper shows that all of these properties of a particle can be derived from a single wave function equation for that particle. Wave functions for the Electron and the Positron are presented and principles are provided that can be used to calculate the wave functions of all the fundamental particles in Physics. Fundamental particles such as electrons and positrons are considered to be point particles in the Standard Model of Physics and are not considered to have a structure. This paper demonstrates that they do indeed have structure and that this structure extends into the space around the particle’s center (in fact, they have infinite extent), but with rapidly diminishing energy density with the distance from that center. The particles are formed from Electromagnetic standing waves, which are stable solutions to the Schrödinger and Classical wave equations. This stable structure therefore accounts for both the wave and particle nature of these particles. In fact, all of their properties such as mass, spin and electric charge, can be accounted for from this structure. These particle properties appear to originate from a single point at the center of the wave function structure, in the same sort of way that the Shell theorem of gravity causes the gravity of a body to appear to all originate from a central point. This paper represents the first two fully characterized fundamental particles, with a complete description of their structure and properties, built up from the underlying Electromagnetic waves that comprise these and all fundamental particles.
