A number of experiments in different areas of mechanical engineering and engineering science at the University of North Carolina at Charlotte, USA, have been revisited to improve the experiments' scope and their e...
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A number of experiments in different areas of mechanical engineering and engineering science at the University of North Carolina at Charlotte, USA, have been revisited to improve the experiments' scope and their educational value for the student. Various experiments are being updated to assist in the ongoing curriculum revision, and to bring in data acquisition methods and protocols into the curriculum. Two cases are presented in this paper to illustrate the benefits and efficiency gained when simple data acquisition programs are included in the curriculum.
Huazhong University of science and Technology (HUST) is a key research-based comprehensive university in Wuhan, China,under the direct supervision of the Ministry of Education of China. Renowned as the epitome of the ...
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Huazhong University of science and Technology (HUST) is a key research-based comprehensive university in Wuhan, China,under the direct supervision of the Ministry of Education of China. Renowned as the epitome of the development of higher education in new China, it is a “211” Project,“985” Project,“Double First-Class” university in China.
This article describes the approach to promote project-based learning and interdisciplinarity within established engineering undergraduate programs at the University of Brasilia. The implementation process and some re...
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Dear Editor,This letter proposes a deep synchronization control(DSC) method to synchronize grid-forming converters with power grids. The method involves constructing a novel controller for grid-forming converters base...
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Dear Editor,This letter proposes a deep synchronization control(DSC) method to synchronize grid-forming converters with power grids. The method involves constructing a novel controller for grid-forming converters based on the stable deep dynamics model. To enhance the performance of the controller, the dynamics model is optimized within the deep reinforcement learning(DRL) framework. Simulation results verify that the proposed method can reduce frequency deviation and improve active power responses.
The muzzle blast overpressure induces disturbances in the flow field inside the crew compartment(FFICC)of a truck-mounted howitzer during the artillery *** overpressure is the primary factor preventing personnel from ...
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The muzzle blast overpressure induces disturbances in the flow field inside the crew compartment(FFICC)of a truck-mounted howitzer during the artillery *** overpressure is the primary factor preventing personnel from firing artillery within the *** investigate the overpressure characteristics of the FFICC,a foreign trade equipment model was used as the research object,and a numerical model was established to analyze the propagation of muzzle blast from the muzzle to the interior of the crew compartment under extreme firing *** comparative verification,the muzzle blast experiment included overpressure data from both the flow field outside the crew compartment(FFOCC)and the FFICC,as well as the acceleration data of the crew compartment structure(Str-CC).The research findings demonstrate that the overpressure-time curves of the FFICC exhibit multi-peak characteristics,while the pressure wave shows no significant *** enclosed nature of the cab hinders the dissipation of pressure wave energy within the FFICC,leading to sustained high-amplitude *** frameskin structure helps attenuate the impact of muzzle blast on the ***,local high overpressure caused by the convex or concave features of the cab's exterior significantly amplifies the overpressure amplitude within the FFICC.
This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-sta...
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This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is ***, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.
A simplified calculation of the specimen’s stress-strain curve is generally conducted using the two-wave method by the split Hopkinson pressure bar(SHPB),which aligns the onset of the transmitted and reflected ***,th...
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A simplified calculation of the specimen’s stress-strain curve is generally conducted using the two-wave method by the split Hopkinson pressure bar(SHPB),which aligns the onset of the transmitted and reflected ***,this approach neglects the travel time of elastic waves within the *** the travel time of elastic waves,this study quantitatively investigates the error characteristics and patterns of stress,strain,and strain rate in the specimen under different conditions using the theoretical two-wave method,and compares the results with those obtained using the onset-aligned twowave *** study reveals that the stress-time curves derived from the theoretical two-wave method are lower than the actual stress curves,whereas those obtained from the onset-aligned two-wave method are consistently higher than the actual stress curves,with the stress deviation approximating a constant value when the dimensionless time exceeds *** starting point of the stress-strain curves obtained by the theoretical two-wave method is not zero but a point on the strain axis,whereas the onset-aligned two-wave method always starts at ***,the slopes of the stress-strain curves obtained by both methods differ from the actual Young’s modulus of the material,and functional relationships between the slopes and the actual Young’s modulus are *** research offers theoretical guidance for the refined design of SHPB experiments and the accurate processing of data.
Jacket platforms constitute the foundational infrastructure of offshore oil and gas field *** to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solve...
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Jacket platforms constitute the foundational infrastructure of offshore oil and gas field *** to efficiently and accurately monitor the mechanical properties of jacket structures is one of the key problems to be solved to ensure the safe operation of the *** address the practical engineering problem that it is difficult to monitor the stress response of the tubular joints of jacket platforms online,a digital twin reduced-order method for real-time prediction of the stress response of tubular joints is *** the offline construction phase,multi-scale modeling and multi-parameter experimental design methods are used to obtain the stress response data set of the jacket *** orthogonal decomposition is employed to extract the main feature information from the snapshot matrix,resulting in a reduced-order *** leave-one-out cross-validation method is used to select the optimal modal order for constructing the reduced-order model(ROM).In the online prediction phase,a digital twin model of the tubular joint is established,and the prediction performance of the ROM is analyzed and verified through using random environmental load and field environmental monitoring *** results indicate that,compared with traditional numerical simulations of tubular joints,the ROM based on the proposed reduced-order method is more efficient in predicting the stress response of tubular joints while ensuring accuracy and robustness.
This article presents an improved Elman neural network for reducing building vibrations during *** adjustment coefficient is proposed to be added to the Elman network’s output layer to improve the controller’s perfo...
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This article presents an improved Elman neural network for reducing building vibrations during *** adjustment coefficient is proposed to be added to the Elman network’s output layer to improve the controller’s performance when used to minimize vibrations in *** parameters of the proposed Elman neural network model are optimized using the Balancing Composite Motion Optimization *** effectiveness of the proposed method is assessed using a three-story structure with an active dampening mechanism on the first *** study also takes into account two kinds of Elman neural network input variables:displacement and velocity data on the first floor,as well as displacement and velocity readings across all three *** research uses two measures of fitness functions in the optimal process,the structure’s peak displacement and acceleration,to determine the best parameters for the proposed *** effectiveness of the proposed method is demonstrated in restraining the vibration of the structure under a variety of ***,the findings indicate that the proposed model maintains sustainability even when the maximum value of the actuator device is dropped.
The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear *** a fault occurs,the distribution of fault characteri...
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The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear *** a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different *** address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault ***,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two *** improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault *** addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test *** proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
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