This work addresses optimality aspects related to composite laminates having layers with different orientations.RegressionNeuralNetworks can model the mechanical behavior of these laminates,specifically the stressstra...This work addresses optimality aspects related to composite laminates having layers with different orientations.RegressionNeuralNetworks can model the mechanical behavior of these laminates,specifically the stressstrain relationship.If this model has strong generalization ability,it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem(OP)related to the orientations of composite laminates.To solve OPs,this paper proposes an optimization framework(OFW)that connects the two components,the optimal solution search mechanism and the RNN model.The OFW has two modules:the search mechanism(Adaptive Hybrid Topology PSO)and the Prediction and Computation Module(PCM).The PCM undertakes all the activities concerning the OP at hand:the stress-strain model,constraints checking,and computation of the objective function.Two case studies about the layers’orientations of laminated specimens are conducted to validate the proposed framework.The specimens belong to“Off-axis oriented specimens”and are subjects of two OPs.The algorithms for AHTPSO and for the two PCMs(one for each problem)are proposed and implemented by MATLAB scripts and functions.Simulations are carried out for different initial conditions.The solutions demonstrated that the OFW is effective and has a highly acceptable computational complexity.The limitation of using the OFWis the generalization ability of the RNN model or any other regression models.To harness the RNN model efficiently,it must have a very good generalization power.If this condition ismet,the OFWcan be integrated into any design process to make optimal choices of the layers’orientations.展开更多
The ways which are used today in order to light houses, offices, and most of </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">indoor a...The ways which are used today in order to light houses, offices, and most of </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">indoor areas are inefficient as a lot of energy is consumed unnecessarily during the day time. Mainly this problem</span><span style="font-size:10pt;font-family:""> </span><span style="font-family:Verdana;">because the interior lighting design consider the worst case when the light service is at night, </span><span style="font-family:Verdana;">which</span><span style="font-family:Verdana;"> is not always valid. Also in most cases the lighting system design rel</span><span style="font-family:Verdana;">ies</span><span style="font-family:Verdana;"> on people to control the lights switching on and off. This problem is also one of the design concern</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> in Green Building. In this paper, a solution to this problem and a method for people’s comfort who use the indoor facilities in industrial building</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> is presented. In the proposed smart lighting system, lights switch on automatically when there is somebody in the room or in the occupied space and switch off when there is no occupancy. In addition to this known technique, adjustment of the brightness level of the lights will be possible via the personal computer or any other smart device. In this method, for the illumination level in the area, where is needed to be controlled for better energy saving, </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">light automatically is measured by </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">sensor and considering the amount of background lights coming from outside, automatically the brightness of lights is controlled to reach the preset level that determined for that room. By the means of this method, it is possible to provide better user comfort, avoid human forcedness to switch the light on and off, and hence effective energy sav</span><span style="font-family:Verdana;">ing</span><span style="font-family:Verdana;">. Arduino controller is used to build the controller and to demonstrate the results. Economic analysis was done to calculate the percentage of the energy saving that can be obtained by implementing the proposed smart lighting controller. As an outcome </span><span style="font-family:Verdana;">of </span><span style="font-family:Verdana;">the economic analysis, energy saving norm for an office with </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">standard size was calculated.展开更多
Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the po...Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the power system.COVID-19 has brought unpredictable uncertainties to the power grid.These changes and uncertainties pose a challenge to conventional electric load forecasting.Therefore,aiming to load forecasting under the background of the pandemic,this paper proposes a power load segmented forecasting method based on the pandemic stage division method,attention mechanism,and bi-directional long and short-term memory artificial neural network quantile regression model(ESD-ABiLSTMQR).According to the development degree of the pandemic,considering characteristics of different development stages of the pandemic,the pandemic is divided into four stages by using the analytic hierarchy process method(AHP):initial stage,outbreak stage,control stage,and recovery stage.A segmented load forecasting model based on LSTM and attention mechanism is established to forecast load in different time series.Cases used data from the pandemic in Wuhan,China,for verification.Results show the segmented forecasting method can analyze load characteristics of each stage and can effectively improve the accuracy of load forecasting.展开更多
The exact feedback linearization method implies an accurate knowledge of the model and its parameters.This assumption is an inherent limitation of the method,suffering from robustness issues.In general,the model struc...The exact feedback linearization method implies an accurate knowledge of the model and its parameters.This assumption is an inherent limitation of the method,suffering from robustness issues.In general,the model structure is only partially known and its parameters present uncertainties.The current paper extends the classical exact feedback linearization to the robust feedback linearization by adding an appropriatelydesigned robust control layer.This is then able to ensure robust stability and robust performance for the given uncertain system in a desired region of attraction.We consider the case of full relative degree input-affine nonlinear systems,which are of great practical importance in the literature.The inner loop contains the feedback linearization input for the nominal system and the resulting residual nonlinearities can always be characterized as inverse additive uncertainties.The constructive proofs provide exact representations of the uncertainty models in three considered scenarios:unmatched,fully-matched,and partially-matched uncertainties.The uncertainty model will be a descriptor system,which also represents one of the novelties of the paper.Our approach leads to a simplified control structure and a less conservative coverage of the uncertainty set compared to current alternatives.The end-to-end procedure is emphasized on an illustrative example,in two different hypotheses.展开更多
In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220...In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220 kV and a length of 250 km. These overhead power lines (L-213, L-214) connect the 220/110/35 kV “Songino” substation with the “Mandal” substation and form system networks. This paper presents the challenges encountered when implementing single-phase automatic reclosing (SPAR) devices and compares the changes in power system parameters before and after SPAR deployment for a long 220 kV line. Simulations and analyses were carried out using DIgSILENT PowerFactory software, focusing on rotor angle stability, and the overall impact on the power system during short-circuit faults. The evaluation also utilized measurement data from the Wide Area Monitoring System (WAMS) to compare system behavior pre- and post-implementation of SPAR. The findings reveal that SPAR significantly enhances system reliability and stability, effectively mitigating the risk of oscillations and stability loss triggered by short circuits. This improvement contributes to a more resilient power system, reducing the potential for disturbances caused by faults.展开更多
The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant a...The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output parameters.This paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient,Nusselt number,and friction factor of water-based Fe_(3)O_(4)-TiO_(2) magnetic hybrid nanofluids in a mini heat sink under magnetic fields.The data considered for the ANN network is at different Reynolds numbers(239 to 1874),different volume concentrations(0%to 2.0%),and different magnetic fields(250 to 1000 G),respectively.Three types of ANN back-propagation algorithms Levenberg-Marquardt(LM),Broyden-Fletcher-Goldfarb-Shanno Quasi Newton(BFGS),and Variable Learning Rate Gradient Descent(VLGD)were used to train the heat transfer coefficient,Nusselt number,and friction factor data,respectively.The ANOVA t-test analysis was also performed to determine the relative accuracy of the three ANN algorithms.The Nusselt number of 2.0%vol.of Fe_(3)O_(4)-TiO_(2) hybrid nanofluid is enhanced by 38.16%without a magnetic field,and it is further enhanced by 88.93%with the magnetic field of 1000 Gauss at a Reynolds number of 1874,with respect to the base fluid.A total of 126 datasets of heat transfer coefficient,Nusselt number,and friction factor were used as input and output data.The three ANN algorithms of LM,BFGS,and VLGD,have shown good acceptance with the experimental data with root-mean-square errors of 0.34883,0.25341,and 1.0202 with correlation coefficients(R2)of 0.99954,0.9967,and 0.94501,respectively,for the Nusselt number data.Moreover,the three ANN algorithms predict root-mean-square errors of 0.001488,0.005041,and 0.006924 with correlation coefficients(R2)of 0.99982,0.99976,and 0.99486,respectively,for the friction factor data.Compared to BFGS and VLGD algorithms,the LM algorithm predicts high accuracy for Nusselt number,and friction factor data.The proposed Nusselt number and friction factor correlations are also discussed.展开更多
Y_(2.80)Pr_(0.20)Fe_(5)O_(12)/Ba_(0.6)Sr_(0.4)TiO_(3)thin film heterostructure was claimed by Jiao et al.in Journal of Materials Science&Technology 202(2024)174 as a tunable dielectric material with superior perfo...Y_(2.80)Pr_(0.20)Fe_(5)O_(12)/Ba_(0.6)Sr_(0.4)TiO_(3)thin film heterostructure was claimed by Jiao et al.in Journal of Materials Science&Technology 202(2024)174 as a tunable dielectric material with superior performance achieved under simultaneous application of the electric and magnetic fields at a particular frequency of 1.44 MHz.However,here we express concerns regarding the real presence of the phenomenon at 1.44 MHz in the studied structures based on the literature and dielectric spectroscopy principles.We also note that the presented dielectric results and their interpretations are contradictory.展开更多
The widespread use of personal care products poses significant risks to both ecosystems and human health.The construction of photocatalysts with the advantages of enhanced visible light absorption,efficient charge sep...The widespread use of personal care products poses significant risks to both ecosystems and human health.The construction of photocatalysts with the advantages of enhanced visible light absorption,efficient charge separation and high stability is important for the degradation of persistent environmental pollutants.Herein,we have prepared two D-A heterojunction organic semiconductor photocatalysts:PV-BDTC1:IDTCN/Biochar and PVBDTC2:IDTCN/Biochar by a simple design method that can be widely applied.Under visible light irradiation,PV-BDTC2:IDTCN/Biochar showed better removal ability for indomethacin,with reaction rate constants 14.7 and 5.1 times higher than those of PV-BDTC2:IDTCN and Biochar,respectively.This is attributed to the construction of a heterojunction between the donor and the acceptor,which expands the light absorption range and promotes the separation of photogenerated carriers,thereby giving the material the ability to produce a large number of reactive oxygen species during the photocatalytic reaction.In addition,Biochar well ensures the stability of D-A heterojunction films under long-term operating conditions.This work highlight the potential of natural biomaterials in constructing effective D-A heterojunction organic photocatalysts/adsorbents for combatting water pollution.展开更多
This paper discusses an experimental investigation into the fluidity of AZ91D-1 wt.%Ca O magnesium melt via induction for thin-section investment casting.Plaster molds with thin spiral cavities(0.5 to 1.5 mm square se...This paper discusses an experimental investigation into the fluidity of AZ91D-1 wt.%Ca O magnesium melt via induction for thin-section investment casting.Plaster molds with thin spiral cavities(0.5 to 1.5 mm square sections)were designed and manufactured to assess the impact of casting conditions on filling length,as magnesium alloys cause severe melting and melt-mold exothermic reactions,making investment casting challenging.Combinations of traditional Mg-mold reaction mitigation techniques,such as applying a protective mold coating(Yttria)and vacuum,were examined to determine their role in the filling process.The results suggest that when induction is employed to melt reactive alloys,these methods are not always beneficial,as initially thought.Particularly at higher melt temperatures,the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity:vacuum induced melt levitation which promotes oxidation with the residual atmosphere;and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction.This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings,providing a viable route for the precision manufacturing of stent biomedical devices.展开更多
Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the resul...Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.展开更多
Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for ...Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline（NURBS） for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system（DCS） and measurement coordinate system（MCS） for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization（QPSO） is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.展开更多
An approach to identify interpretable fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. The number of fuzzy rules is determined by fu...An approach to identify interpretable fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. The number of fuzzy rules is determined by fuzzy cluster validity indices. A modified fuzzy clustering algorithm,combined with the least square method, is used to identify the initial fuzzy model. An orthogonal least square algorithm and a method of merging similar fuzzy sets are then used to remove the redundancy of the fuzzy model and improve its interpretability. Next, in order to attain high accuracy, while preserving interpretability, a constrained Levenberg-Marquardt method is utilized to optimize the precision of the fuzzy model. Finally, the proposed approach is applied to a PH neutralization process, and the results show its validity.展开更多
The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performanc...The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performance. According to the new generation geometrical product specification（GPS）, the error and its measurement uncertainty should be evaluated together. The mathematical model of the minimum zone conicity error is established and an improved immune evolutionary algorithm（IlEA） is proposed to search for the conicity error. In the IIEA, initial antibodies are firstly generated by using quasi-random sequences and two kinds of affinities are calculated. Then, each antibody clone is generated and they are self-adaptively mutated so as to maintain diversity. Similar antibody is suppressed and new random antibody is generated. Because the mathematical model of conicity error is strongly nonlinear and the input quantities are not independent, it is difficult to use Guide to the expression of uncertainty in the measurement（GUM） method to evaluate measurement uncertainty. Adaptive Monte Carlo method（AMCM） is proposed to estimate measurement uncertainty in which the number of Monte Carlo trials is selected adaptively and the quality of the numerical results is directly controlled. The cone parts was machined on lathe CK6140 and measured on Miracle NC 454 Coordinate Measuring Machine（CMM）. The experiment results confirm that the proposed method not only can search for the approximate solution of the minimum zone conicity error（MZCE） rapidly and precisely, but also can evaluate measurement uncertainty and give control variables with an expected numerical tolerance. The conicity errors computed by the proposed method are 20%-40% less than those computed by NC454 CMM software and the evaluation accuracy improves significantly.展开更多
Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controlle...Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controller design, it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties. In this paper, an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation. The improved DOB is based on the optimal achievable H 2 performance and can deal with disturbance observation for the nonminimum-phase delay systems. In this DOB-MPC advanced feedback control, the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level. The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system. The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties. Several simulations are performed to demonstrate the proposed control method for grinding process operation.展开更多
Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou...Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.展开更多
The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi...The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi-model and double layered optimization is introduced for coordinated control of USC unit. The linear programming(LP) combined with quadratic programming(QP) is used in steady optimization for computation of the ideal value of dynamic optimization. Three inputs(i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs(i.e. load, main steam temperature and main steam pressure). The step response models for the dynamic matrix control(DMC) are constructed using the three inputs and the three outputs. Piecewise models are built at selected operation points. Double-layered multi-model predictive controller is implemented in simulation with satisfactory performance.展开更多
Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study ...Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study has been done to obtain the faulted current data. In order to improve the detection speed and accuracy, two fast arc fault detection methods have been proposed in this paper with the analysis of only half cycle data. Both Fast Fourier Transform（FFT） and Wavelet Packets Decomposition（WPD） have been adopted to distinguish arc fault currents from normal operation currents. Analysis results show that Alternating Current（AC） arcs can be effectively and accurately detected with the proposed half cycle data based methods. Moreover,experimental verification results have also been provided.展开更多
The nonlinear dynamic and static deflection of a micro/nano gyroscope under DC voltages and base rotation are investigated. The gyroscope undertakes two cou- pled bending motions along the drive and sense directions a...The nonlinear dynamic and static deflection of a micro/nano gyroscope under DC voltages and base rotation are investigated. The gyroscope undertakes two cou- pled bending motions along the drive and sense directions and subjected to electrostatic actuations and intermolecular forces. The nonlinear governing equations of motion for the system with the effect of electrostatic force, intermolecular tractions and base rotation are derived using extended Hamilton principle. Under constant voltage, the gyroscope finds the preformed shape. First, the deflection of the rnicro/nano gyroscope under electrostatic forces is obtained by static and dynamic analyses. Furthermore, the static and dynamic in- stability of the system are investigated. Afterward the oscillatory behavior of the pre-deformed micro/nano gyroscope around equilibrium is studied. The effects of intermolecular and nonlinear parameters on the static and dynamic de- flection, natural frequencies and instability of the micro/nano gyroscope are studied. The presented model can be used to exactly determine static and the dynamic behavior of vibratory micro/nano gyroscopes.展开更多
The need to develop armour systems to protect against attacks from various sources is increasingly a matter of personal,social and national security.To develop innovative armour systems it is necessary to monitor deve...The need to develop armour systems to protect against attacks from various sources is increasingly a matter of personal,social and national security.To develop innovative armour systems it is necessary to monitor developments being made on the type,technology and performance of the threats(weapons,projectiles,explosives,etc.) Specifically,the use of high protection level helmets on the battlefield is essential.The development of evaluation methods that can predict injuries and trauma is therefore of major importance.However,the risk of injuries or trauma that can arise from induced accelerations is an additional consideration.To develop new materials and layouts for helmets it is necessary to study the effects caused by ballistic impacts in the human head on various scenarios.The use of numerical simulation is a fundamental tool in this process.The work here presented focuses on the use of numerical simulation(finite elements analysis) to predict the consequences of bullet impacts on military helmets on human injuries.The main objectives are to assess the level and probability of head trauma using the Head Injury Criterion,caused by the impact of a 9 mm NATO projectile on a PASGT helmet and to quantify the relevance of projectile plasticity on the whole modelling process.The accelerations derived from the impact phenomenon and the deformations caused on the helmet are evaluated using fully three-dimensional models of the helmet,head,neck and projectile.Impact studies are done at impact angles ranging from 0 to 75°.Results are presented and discussed in terms of HIC and probability of acceleration induced trauma levels.Thorough comparison analyses are done using a rigid and a deformable projectile and it is observed that plastic deformation of the projectile is a significant energy dissipation mechanism in the whole impact process.展开更多
Augmented UD identification (AUDI) technique is derived from the traditional recursive least-squares (RLS) algorithm and has been developed rapidly during the last decade. AUDI is a cluster of identification algorithm...Augmented UD identification (AUDI) technique is derived from the traditional recursive least-squares (RLS) algorithm and has been developed rapidly during the last decade. AUDI is a cluster of identification algorithms based on matrix factorization methods (such as QR and LDL) and thus shows its stable performance in system identification applications. An AUDI algorithm with resetting strategy (RAUDI) has much ability in rapid time-varying SISO system identification. In this paper, an endeavor to expand the RAUDI in MIMO system identification is made and a comparative experiement is done to exhibit its good ability in rapidly changing parameter estimate in MIMO system.展开更多
基金supported by the Ministry of Research,Innovation and Digitization,CNCS/CCCDI–UEFISCDI(Romania),Nr.11/2024,within PNCDI IV.The APC received no external funding.
文摘This work addresses optimality aspects related to composite laminates having layers with different orientations.RegressionNeuralNetworks can model the mechanical behavior of these laminates,specifically the stressstrain relationship.If this model has strong generalization ability,it can be coupled with a metaheuristic algorithm–the PSO algorithm used in this article–to address an optimization problem(OP)related to the orientations of composite laminates.To solve OPs,this paper proposes an optimization framework(OFW)that connects the two components,the optimal solution search mechanism and the RNN model.The OFW has two modules:the search mechanism(Adaptive Hybrid Topology PSO)and the Prediction and Computation Module(PCM).The PCM undertakes all the activities concerning the OP at hand:the stress-strain model,constraints checking,and computation of the objective function.Two case studies about the layers’orientations of laminated specimens are conducted to validate the proposed framework.The specimens belong to“Off-axis oriented specimens”and are subjects of two OPs.The algorithms for AHTPSO and for the two PCMs(one for each problem)are proposed and implemented by MATLAB scripts and functions.Simulations are carried out for different initial conditions.The solutions demonstrated that the OFW is effective and has a highly acceptable computational complexity.The limitation of using the OFWis the generalization ability of the RNN model or any other regression models.To harness the RNN model efficiently,it must have a very good generalization power.If this condition ismet,the OFWcan be integrated into any design process to make optimal choices of the layers’orientations.
文摘The ways which are used today in order to light houses, offices, and most of </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">indoor areas are inefficient as a lot of energy is consumed unnecessarily during the day time. Mainly this problem</span><span style="font-size:10pt;font-family:""> </span><span style="font-family:Verdana;">because the interior lighting design consider the worst case when the light service is at night, </span><span style="font-family:Verdana;">which</span><span style="font-family:Verdana;"> is not always valid. Also in most cases the lighting system design rel</span><span style="font-family:Verdana;">ies</span><span style="font-family:Verdana;"> on people to control the lights switching on and off. This problem is also one of the design concern</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> in Green Building. In this paper, a solution to this problem and a method for people’s comfort who use the indoor facilities in industrial building</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> is presented. In the proposed smart lighting system, lights switch on automatically when there is somebody in the room or in the occupied space and switch off when there is no occupancy. In addition to this known technique, adjustment of the brightness level of the lights will be possible via the personal computer or any other smart device. In this method, for the illumination level in the area, where is needed to be controlled for better energy saving, </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">light automatically is measured by </span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">sensor and considering the amount of background lights coming from outside, automatically the brightness of lights is controlled to reach the preset level that determined for that room. By the means of this method, it is possible to provide better user comfort, avoid human forcedness to switch the light on and off, and hence effective energy sav</span><span style="font-family:Verdana;">ing</span><span style="font-family:Verdana;">. Arduino controller is used to build the controller and to demonstrate the results. Economic analysis was done to calculate the percentage of the energy saving that can be obtained by implementing the proposed smart lighting controller. As an outcome </span><span style="font-family:Verdana;">of </span><span style="font-family:Verdana;">the economic analysis, energy saving norm for an office with </span><span style="font-family:Verdana;">a </span><span style="font-family:Verdana;">standard size was calculated.
基金supported by National Natural Science Foundation of China(No.52407074)Anhui Provincial Natural Science Foundation Youth Project,China(2308085QE177)Key Research Projects in Natural Sciences of Universities funded by the Department of Education in Anhui Province,China(2023AH050092).
文摘Affected by the pandemic coronavirus-19(COVID-19),significant changes have taken place in all aspects of social production and residents’lives,as well as in the energy supply and consumption characteristics of the power system.COVID-19 has brought unpredictable uncertainties to the power grid.These changes and uncertainties pose a challenge to conventional electric load forecasting.Therefore,aiming to load forecasting under the background of the pandemic,this paper proposes a power load segmented forecasting method based on the pandemic stage division method,attention mechanism,and bi-directional long and short-term memory artificial neural network quantile regression model(ESD-ABiLSTMQR).According to the development degree of the pandemic,considering characteristics of different development stages of the pandemic,the pandemic is divided into four stages by using the analytic hierarchy process method(AHP):initial stage,outbreak stage,control stage,and recovery stage.A segmented load forecasting model based on LSTM and attention mechanism is established to forecast load in different time series.Cases used data from the pandemic in Wuhan,China,for verification.Results show the segmented forecasting method can analyze load characteristics of each stage and can effectively improve the accuracy of load forecasting.
基金funded by the project new smart and adaptive robotics solutions for personalized minimally invasive surgery in cancer treatment−ATHENA,European Union-NextGenerationEU and Romanian Government,under National Recovery and Resilience Plan for Romania(CF116/15.11.2022)through the Romanian Ministry of Research,Innovation and Digitalization(within Component 9,investment I8)。
文摘The exact feedback linearization method implies an accurate knowledge of the model and its parameters.This assumption is an inherent limitation of the method,suffering from robustness issues.In general,the model structure is only partially known and its parameters present uncertainties.The current paper extends the classical exact feedback linearization to the robust feedback linearization by adding an appropriatelydesigned robust control layer.This is then able to ensure robust stability and robust performance for the given uncertain system in a desired region of attraction.We consider the case of full relative degree input-affine nonlinear systems,which are of great practical importance in the literature.The inner loop contains the feedback linearization input for the nominal system and the resulting residual nonlinearities can always be characterized as inverse additive uncertainties.The constructive proofs provide exact representations of the uncertainty models in three considered scenarios:unmatched,fully-matched,and partially-matched uncertainties.The uncertainty model will be a descriptor system,which also represents one of the novelties of the paper.Our approach leads to a simplified control structure and a less conservative coverage of the uncertainty set compared to current alternatives.The end-to-end procedure is emphasized on an illustrative example,in two different hypotheses.
文摘In order to increase the stability of the Mongolia power system, a single-phase automatic reclosing device (SPAR) was introduced on double-circuit power lines built with a size of 330 kV, operating on a voltage of 220 kV and a length of 250 km. These overhead power lines (L-213, L-214) connect the 220/110/35 kV “Songino” substation with the “Mandal” substation and form system networks. This paper presents the challenges encountered when implementing single-phase automatic reclosing (SPAR) devices and compares the changes in power system parameters before and after SPAR deployment for a long 220 kV line. Simulations and analyses were carried out using DIgSILENT PowerFactory software, focusing on rotor angle stability, and the overall impact on the power system during short-circuit faults. The evaluation also utilized measurement data from the Wide Area Monitoring System (WAMS) to compare system behavior pre- and post-implementation of SPAR. The findings reveal that SPAR significantly enhances system reliability and stability, effectively mitigating the risk of oscillations and stability loss triggered by short circuits. This improvement contributes to a more resilient power system, reducing the potential for disturbances caused by faults.
基金supported by the Recovery and Resilience Plan(PRR)and by European Funds Next Generation EU under the Project“AET—Alliance for Energy Transition,”no.C644914747-00000023,investment project no.56 of the Incentive System“Agendas for Business Innovation”.
文摘The experimental analysis takes too much time-consuming process and requires considerable effort,while,the Artificial Neural Network(ANN)algorithms are simple,affordable,and fast,and they allow us to make a relevant analysis in establishing an appropriate relationship between the input and output parameters.This paper deals with the use of back-propagation ANN algorithms for the experimental data of heat transfer coefficient,Nusselt number,and friction factor of water-based Fe_(3)O_(4)-TiO_(2) magnetic hybrid nanofluids in a mini heat sink under magnetic fields.The data considered for the ANN network is at different Reynolds numbers(239 to 1874),different volume concentrations(0%to 2.0%),and different magnetic fields(250 to 1000 G),respectively.Three types of ANN back-propagation algorithms Levenberg-Marquardt(LM),Broyden-Fletcher-Goldfarb-Shanno Quasi Newton(BFGS),and Variable Learning Rate Gradient Descent(VLGD)were used to train the heat transfer coefficient,Nusselt number,and friction factor data,respectively.The ANOVA t-test analysis was also performed to determine the relative accuracy of the three ANN algorithms.The Nusselt number of 2.0%vol.of Fe_(3)O_(4)-TiO_(2) hybrid nanofluid is enhanced by 38.16%without a magnetic field,and it is further enhanced by 88.93%with the magnetic field of 1000 Gauss at a Reynolds number of 1874,with respect to the base fluid.A total of 126 datasets of heat transfer coefficient,Nusselt number,and friction factor were used as input and output data.The three ANN algorithms of LM,BFGS,and VLGD,have shown good acceptance with the experimental data with root-mean-square errors of 0.34883,0.25341,and 1.0202 with correlation coefficients(R2)of 0.99954,0.9967,and 0.94501,respectively,for the Nusselt number data.Moreover,the three ANN algorithms predict root-mean-square errors of 0.001488,0.005041,and 0.006924 with correlation coefficients(R2)of 0.99982,0.99976,and 0.99486,respectively,for the friction factor data.Compared to BFGS and VLGD algorithms,the LM algorithm predicts high accuracy for Nusselt number,and friction factor data.The proposed Nusselt number and friction factor correlations are also discussed.
基金the FCT/MEC(PIDDAC)within FCT independent researcher grant 2021.02284.CEECIND/CP1659/CT0018(DOI 10.54499/2021.02284.CEECIND/CP1659/CT0018)+1 种基金supported by national funds,through FCT in the scope of the framework contract foreseen in numbers 4,5,and 6 of article 23 of the Decree Law 57/2016,of 29 August,UIDB/00481/2020 and UIDP/00481/2020,DOI 10.54499/UIDB/00481/2020 and DOI 10.54499/UIDP/00481/2020CENTRO-01-0145-FEDER-022083-Centro Portugal Regional Operational Programme(Centro2020),under the PORTUGAL 2020 Partnership Agreement,through the European Regional Develop-ment Fund.
文摘Y_(2.80)Pr_(0.20)Fe_(5)O_(12)/Ba_(0.6)Sr_(0.4)TiO_(3)thin film heterostructure was claimed by Jiao et al.in Journal of Materials Science&Technology 202(2024)174 as a tunable dielectric material with superior performance achieved under simultaneous application of the electric and magnetic fields at a particular frequency of 1.44 MHz.However,here we express concerns regarding the real presence of the phenomenon at 1.44 MHz in the studied structures based on the literature and dielectric spectroscopy principles.We also note that the presented dielectric results and their interpretations are contradictory.
基金supported by Guangxi Science and Technology Major Program(No.AA24206037)the Training Project of High-level Professional and Technical Talents of Guangxi University and Natural Science and Technology Innovation Development Multiplication Program of Guangxi University(No.2022BZRC006)+2 种基金the project of the Research on the electrochemical reaction mechanism of the anode of medium-lowtemperature direct ammonia SOFCs(No.20231063020006)the project of all solid-state high energy density energy storage system(No.20221063010031)the project of Shenzhen Overseas Talent upon Industrialization of 1 kw stack for direct ammonia SOFCs(No.20221061010002).
文摘The widespread use of personal care products poses significant risks to both ecosystems and human health.The construction of photocatalysts with the advantages of enhanced visible light absorption,efficient charge separation and high stability is important for the degradation of persistent environmental pollutants.Herein,we have prepared two D-A heterojunction organic semiconductor photocatalysts:PV-BDTC1:IDTCN/Biochar and PVBDTC2:IDTCN/Biochar by a simple design method that can be widely applied.Under visible light irradiation,PV-BDTC2:IDTCN/Biochar showed better removal ability for indomethacin,with reaction rate constants 14.7 and 5.1 times higher than those of PV-BDTC2:IDTCN and Biochar,respectively.This is attributed to the construction of a heterojunction between the donor and the acceptor,which expands the light absorption range and promotes the separation of photogenerated carriers,thereby giving the material the ability to produce a large number of reactive oxygen species during the photocatalytic reaction.In addition,Biochar well ensures the stability of D-A heterojunction films under long-term operating conditions.This work highlight the potential of natural biomaterials in constructing effective D-A heterojunction organic photocatalysts/adsorbents for combatting water pollution.
基金financed by National Funds through the Portuguese funding agency,FCT–Funda??o para a Ciência e a Tecnologia,within the strategic projects UIDB/04436/2020,UIDB/00481/2020 and LA/P/0063/2020(DOI 10.54499/LA/P/0063/2020)。
文摘This paper discusses an experimental investigation into the fluidity of AZ91D-1 wt.%Ca O magnesium melt via induction for thin-section investment casting.Plaster molds with thin spiral cavities(0.5 to 1.5 mm square sections)were designed and manufactured to assess the impact of casting conditions on filling length,as magnesium alloys cause severe melting and melt-mold exothermic reactions,making investment casting challenging.Combinations of traditional Mg-mold reaction mitigation techniques,such as applying a protective mold coating(Yttria)and vacuum,were examined to determine their role in the filling process.The results suggest that when induction is employed to melt reactive alloys,these methods are not always beneficial,as initially thought.Particularly at higher melt temperatures,the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity:vacuum induced melt levitation which promotes oxidation with the residual atmosphere;and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction.This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings,providing a viable route for the precision manufacturing of stent biomedical devices.
基金supported by National Natural Science Foundation of China (Grant No. 51075198)Jiangsu Provincial Natural Science Foundation of China (Grant No. BK2010479)+2 种基金Innovation Research of Nanjing Institute of Technology, China (Grant No. CKJ20100008)Jiangsu Provincial Foundation of 333 Talents Engineering of ChinaJiangsu Provincial Foundation of Six Talented Peak of China
文摘Straightness error is an important parameter in measuring high-precision shafts. New generation geometrical product speeifieation（GPS） requires the measurement uncertainty characterizing the reliability of the results should be given together when the measurement result is given. Nowadays most researches on straightness focus on error calculation and only several research projects evaluate the measurement uncertainty based on ＂The Guide to the Expression of Uncertainty in Measurement（GUM）＂. In order to compute spatial straightness error（SSE） accurately and rapidly and overcome the limitations of GUM, a quasi particle swarm optimization（QPSO） is proposed to solve the minimum zone SSE and Monte Carlo Method（MCM） is developed to estimate the measurement uncertainty. The mathematical model of minimum zone SSE is formulated. In QPSO quasi-random sequences are applied to the generation of the initial position and velocity of particles and their velocities are modified by the constriction factor approach. The flow of measurement uncertainty evaluation based on MCM is proposed, where the heart is repeatedly sampling from the probability density function（PDF） for every input quantity and evaluating the model in each case. The minimum zone SSE of a shaft measured on a Coordinate Measuring Machine（CMM） is calculated by QPSO and the measurement uncertainty is evaluated by MCM on the basis of analyzing the uncertainty contributors. The results show that the uncertainty directly influences the product judgment result. Therefore it is scientific and reasonable to consider the influence of the uncertainty in judging whether the parts are accepted or rejected, especially for those located in the uncertainty zone. The proposed method is especially suitable when the PDF of the measurand cannot adequately be approximated by a Gaussian distribution or a scaled and shifted t-distribution and the measurement model is non-linear.
基金supported by National Natural Science Foundation of China(Grant No. 51075198)Jiangsu Provincial Natural Science Foundation of China(Grant No. BK2010479)+1 种基金Jiangsu Provincial Project of 333 Talents Engineering of ChinaJiangsu Provincial Project of Six Talented Peak of China
文摘Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline（NURBS） for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system（DCS） and measurement coordinate system（MCS） for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization（QPSO） is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.
基金国家自然科学基金，Scientific Research Foundation of Nanjing University of Science and Technology
文摘An approach to identify interpretable fuzzy models from data is proposed. Interpretability, which is one of the most important features of fuzzy models, is analyzed first. The number of fuzzy rules is determined by fuzzy cluster validity indices. A modified fuzzy clustering algorithm,combined with the least square method, is used to identify the initial fuzzy model. An orthogonal least square algorithm and a method of merging similar fuzzy sets are then used to remove the redundancy of the fuzzy model and improve its interpretability. Next, in order to attain high accuracy, while preserving interpretability, a constrained Levenberg-Marquardt method is utilized to optimize the precision of the fuzzy model. Finally, the proposed approach is applied to a PH neutralization process, and the results show its validity.
基金Supported by National Natural Science Foundation of China(Grant No.51075198)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK2010479)+1 种基金Jiangsu Provincial Project of Six Talented Peaks of ChinaJiangsu Provincial Project of 333 Talents Engineering of China(Grant No.3-45)
文摘The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performance. According to the new generation geometrical product specification（GPS）, the error and its measurement uncertainty should be evaluated together. The mathematical model of the minimum zone conicity error is established and an improved immune evolutionary algorithm（IlEA） is proposed to search for the conicity error. In the IIEA, initial antibodies are firstly generated by using quasi-random sequences and two kinds of affinities are calculated. Then, each antibody clone is generated and they are self-adaptively mutated so as to maintain diversity. Similar antibody is suppressed and new random antibody is generated. Because the mathematical model of conicity error is strongly nonlinear and the input quantities are not independent, it is difficult to use Guide to the expression of uncertainty in the measurement（GUM） method to evaluate measurement uncertainty. Adaptive Monte Carlo method（AMCM） is proposed to estimate measurement uncertainty in which the number of Monte Carlo trials is selected adaptively and the quality of the numerical results is directly controlled. The cone parts was machined on lathe CK6140 and measured on Miracle NC 454 Coordinate Measuring Machine（CMM）. The experiment results confirm that the proposed method not only can search for the approximate solution of the minimum zone conicity error（MZCE） rapidly and precisely, but also can evaluate measurement uncertainty and give control variables with an expected numerical tolerance. The conicity errors computed by the proposed method are 20%-40% less than those computed by NC454 CMM software and the evaluation accuracy improves significantly.
基金Supported by the National Natural Science Foundation of China (61104084, 61290323)the Guangdong Education University-Industry Cooperation Projects (2010B090400410)
文摘Advanced feedback control for optimal operation of mineral grinding process is usually based on the model predictive control (MPC) dynamic optimization. Since the MPC does not handle disturbances directly by controller design, it cannot achieve satisfactory effects in controlling complex grinding processes in the presence of strong disturbances and large uncertainties. In this paper, an improved disturbance observer (DOB) based MPC advanced feedback control is proposed to control the multivariable grinding operation. The improved DOB is based on the optimal achievable H 2 performance and can deal with disturbance observation for the nonminimum-phase delay systems. In this DOB-MPC advanced feedback control, the higher-level optimizer computes the optimal operation points by maximize the profit function and passes them to the MPC level. The MPC acts as a presetting controller and is employed to generate proper pre-setpoint for the lower-level basic feedback control system. The DOB acts as a compensator and improves the operation performance by dynamically compensating the setpoints for the basic control system according to the observed various disturbances and plant uncertainties. Several simulations are performed to demonstrate the proposed control method for grinding process operation.
基金supported by the National Natural Science Foundation of China (61873079,51707050)
文摘Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.
基金Supported by the National Natural Science Foundation of China(60974119)
文摘The control of ultra-supercritical(USC) power unit is a difficult issue for its characteristic of the nonlinearity, large dead time and coupling of the unit. In this paper, model predictive control(MPC) based on multi-model and double layered optimization is introduced for coordinated control of USC unit. The linear programming(LP) combined with quadratic programming(QP) is used in steady optimization for computation of the ideal value of dynamic optimization. Three inputs(i.e. valve opening, coal flow and feedwater flow) are employed to control three outputs(i.e. load, main steam temperature and main steam pressure). The step response models for the dynamic matrix control(DMC) are constructed using the three inputs and the three outputs. Piecewise models are built at selected operation points. Double-layered multi-model predictive controller is implemented in simulation with satisfactory performance.
基金co-supported by the National Natural Science Foundation of China(Nos.51407144 and 51777169)the Aviation Research Funds(No.20164053029)+1 种基金the Fundamental Research Funds for the Central Universities(Nos.3102017ZY027 and 3102017GX08001)the Young Elite Scientist Sponsorship Program by CAST
文摘Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study has been done to obtain the faulted current data. In order to improve the detection speed and accuracy, two fast arc fault detection methods have been proposed in this paper with the analysis of only half cycle data. Both Fast Fourier Transform（FFT） and Wavelet Packets Decomposition（WPD） have been adopted to distinguish arc fault currents from normal operation currents. Analysis results show that Alternating Current（AC） arcs can be effectively and accurately detected with the proposed half cycle data based methods. Moreover,experimental verification results have also been provided.
文摘The nonlinear dynamic and static deflection of a micro/nano gyroscope under DC voltages and base rotation are investigated. The gyroscope undertakes two cou- pled bending motions along the drive and sense directions and subjected to electrostatic actuations and intermolecular forces. The nonlinear governing equations of motion for the system with the effect of electrostatic force, intermolecular tractions and base rotation are derived using extended Hamilton principle. Under constant voltage, the gyroscope finds the preformed shape. First, the deflection of the rnicro/nano gyroscope under electrostatic forces is obtained by static and dynamic analyses. Furthermore, the static and dynamic in- stability of the system are investigated. Afterward the oscillatory behavior of the pre-deformed micro/nano gyroscope around equilibrium is studied. The effects of intermolecular and nonlinear parameters on the static and dynamic de- flection, natural frequencies and instability of the micro/nano gyroscope are studied. The presented model can be used to exactly determine static and the dynamic behavior of vibratory micro/nano gyroscopes.
文摘The need to develop armour systems to protect against attacks from various sources is increasingly a matter of personal,social and national security.To develop innovative armour systems it is necessary to monitor developments being made on the type,technology and performance of the threats(weapons,projectiles,explosives,etc.) Specifically,the use of high protection level helmets on the battlefield is essential.The development of evaluation methods that can predict injuries and trauma is therefore of major importance.However,the risk of injuries or trauma that can arise from induced accelerations is an additional consideration.To develop new materials and layouts for helmets it is necessary to study the effects caused by ballistic impacts in the human head on various scenarios.The use of numerical simulation is a fundamental tool in this process.The work here presented focuses on the use of numerical simulation(finite elements analysis) to predict the consequences of bullet impacts on military helmets on human injuries.The main objectives are to assess the level and probability of head trauma using the Head Injury Criterion,caused by the impact of a 9 mm NATO projectile on a PASGT helmet and to quantify the relevance of projectile plasticity on the whole modelling process.The accelerations derived from the impact phenomenon and the deformations caused on the helmet are evaluated using fully three-dimensional models of the helmet,head,neck and projectile.Impact studies are done at impact angles ranging from 0 to 75°.Results are presented and discussed in terms of HIC and probability of acceleration induced trauma levels.Thorough comparison analyses are done using a rigid and a deformable projectile and it is observed that plastic deformation of the projectile is a significant energy dissipation mechanism in the whole impact process.
文摘Augmented UD identification (AUDI) technique is derived from the traditional recursive least-squares (RLS) algorithm and has been developed rapidly during the last decade. AUDI is a cluster of identification algorithms based on matrix factorization methods (such as QR and LDL) and thus shows its stable performance in system identification applications. An AUDI algorithm with resetting strategy (RAUDI) has much ability in rapid time-varying SISO system identification. In this paper, an endeavor to expand the RAUDI in MIMO system identification is made and a comparative experiement is done to exhibit its good ability in rapidly changing parameter estimate in MIMO system.
