Considering the susceptibility of planetary transmission to strong noise interference, a fault diagnosis method is proposed based on Morlet wavelet transform and 1D Convolutional Neural Network (1DCNN). Firstly, the acceleration captured from the transmission is dealt with angle resampling to reduce the dependence of speed on signal characteristics, and band-pass filtering is performed, which parameter is determined by using the fast spectral kurtosis algorithm. Subsequently, the filtered data is decomposed into several multiple intrinsic mode function (IMF) components with the EEMD algorithm and reconstructed according to the correlation coefficient parameter. Then reconstructed data is subjected to further noise reduction processing using wavelet transform and the amplitude in the scale parameter with the highest kurtosis as the input to 1DCNN for training and fault diagnosis classification. The experimental results show that the proposed method can effectively reduce noise interference, the fault diagnosis accuracy of the processed data is relatively high. The fault diagnosis accuracy of the processed data is significantly higher than that of the raw data.

To investigate the mechanism of coupled damage in human body caused by shock waves and fragment, a finite element model of the human thorax was established. The validity of the model was verified by comparing the thorax damage data under blast and fragment. LS-DYNA finite element software was used to numerically simulate the mechanical response of the thorax under combined shock waves and fragment loading, and to analyze the effects of loading modes, mass of TNT charge, and blast distances on damage to human thoracic organs. The results indicate that the coupling damage effect of organs near the impact area is appreciable under the combined shock waves and fragment loading, and the mechanical parameters of human organs exceed the sum caused by shock waves and fragment individually. As the mass of TNT charge increased, both the peak velocity of skeletons and the stress on organs at the non-impact area under combined loading increase, whereas the effect on the peak stress in organs at the impact area of fragment is smaller and much larger than the sum of the stresses under shock waves and fragment loading alone. Meanwhile, the gap between peak stress of the same organ under combined loading and shock waves loading alone widened for different masses of TNT charge. Furthermore, the combined loading sequence of shock waves and fragment affects the mechanical response of human organs. An approach for evaluating the probability of injury under combined loading was proposed. The calculation results show that in the near field, the injury probability is more sensitive to the impulse of the shock waves.

The need for assistive devices such as lower limb exoskeletons is steadily growing, making quick and precise gait recognition essential for optimal operation of these apparatuses. The knowledge distillation method was applied to gait recognition in this work. First, we took participant lower limb gait data, which we then resampled with step frequency taken into account. Next, multi-layer perceptron (MLP) with distinct layers were modeled by educators and students. The process of knowledge distillation was used to improve the accuracy of participant activity recognition by transferring knowledge from the sophisticated teacher model to the lightweight student model. On the training set, the teacher model's accuracy average was 96.4%. Following the process of knowledge distillation, the accuracy of the student model was 95.48%, representing a 1.12% improvement compared to the non-distilled model, with only 0.53M parameters and shorter training duration. The results demonstrate that knowledge distillation can improve the recognition speed and stability of gait recognition models, enabling more accurate and reliable results within the same patient population, thus providing more effective support for medical diagnosis and rehabilitation treatment.

Aiming at traffic sign problem, the traditional LeNet-5 network structure has low accuracy of traffic sign recognition, slow identification speed and ignores natural factors such as weather. A convolutional network structure model with two-channel and multi-scale based on LeNet-5 improvement is proposed by convolutional neural network technology. In the dual-channel structure, each channel contains two branching structure, and the number of convolution and image scale of each channel is different, making the feature extraction of different image scales richer. Secondly, the improved network structure greatly increases the number of convolutional kernels compared to the traditional LeNet-5 network structure. Finally, by changing the Sigmoid activation function to the ReLu activation function, changing the stochastic gradient descent algorithm to the Adam algorithm, and adding Dropout layers to prevent overfitting and setting the learning rate, thus increasing the traffic sign recognition rate. The recognition rate of the improved network is 98.6%, floating by 0.5%, relative to the traditional LeNet-5 network structure, the recognition rate increases by more than 15%, verifying that the improved network structure has a certain robustness.

The prediction of a stall precursor in an axial compressor is the basic guarantee to the stable operation of an aeroengine. How to predict and intelligently identify the instability of the system in advance is of great significance to the safety performance and active control of the aeroengine. In this paper, an aerodynamic system modeling method combination with the wavelet transform and gray wolf algorithm optimized support vector regression (WT-GWO-SVR) is proposed, which breaks through the fusion technology based on the feature correlation of chaotic data. Because of the chaotic characteristic represented by the sequence, the correlation-correlation (C-C) algorithm is adopted to reconstruct the phase space of the spatial modal. On the premise of finding out the local law of the dynamic system variety, the machine learning method is applied to model the reconstructed low-frequency components and high-frequency components, respectively. As the key part, the parameters of the SVR model are optimized by the gray wolf optimization algorithm (GWO) from the biological view inspired by the predatory behavior of gray wolves. In the definition of the hunting behaviors of gray wolves by mathematical equations, it is superior to algorithms such as differential evolution and particle swarm optimization. In order to further improve the prediction accuracy of the model, the multi-resolution and equivalent frequency distribution of the wavelet transform (WT) are used to train support vector regression. It is shown that the proposed WT-GWO-SVR hybrid model has a better prediction accuracy and reliability with the wavelet reconstruction coefficients as the inputs. In order to effectively identify the sign of the instability in the modeling system, a wavelet singular information entropy algorithm is proposed to detect the stall inception. By using the three sigma criteria as the identification strategy, the instability early warning can be given about 102r in advance, which is helpful for the active control.

Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is employed for flow field feature extraction and modeling object acquisition. The nonlinear characteristic presented in the system is addressed using fuzzy entropy as the identification strategy to provide a basis for instability determination. Using Sparrow Search Algorithm (SSA) optimization, a Radial Basis Function Neural Network (RBFNN) is achieved for the performance prediction of system status. A Logistic SSA solution is first established to seek the optimal parameters of the RBFNN to enhance prediction accuracy and stability. On the basis of the RBFNN-LSSA hybrid model, the stall inception is detected about 35.8 revolutions in advance using fuzzy entropy identification. To further improve the multi-step network model, a Tent SSA is introduced to promote the accuracy and robustness of the model. A wider range of potential solutions within the TSSA are explored by incorporating the Tent mapping function. The TSSA-based optimization method proves a suitable adaptation for complex nonlinear dynamic modeling. And this method demonstrates superior performance, achieving 42 revolutions of advance warning with multi-step prediction. This RBFNN-TSSA model represents a novel and promising approach to the application of system modeling. These findings contribute to enhancing the abnormal warning capability of dynamic systems in compressors.

大型工业锅炉内部燃烧环境恶劣复杂,对于炉膛内温度场的监测具有非常重要的意义.声学法测温作为一种新型的非接触测温方法,具有传播速度快、测量范围广、不受环境干扰等优点.为得到炉膛的温度场可视化结果,基于有限元法,利用COMSOL平台构建炉膛单峰偏斜温度场模型,模拟声波在炉膛内的传播情况,并根据互相关算法计算声波飞行时间,运用最小二乘法和插值算法对炉膛温度场进行还原.结果表明:运用互相关算法求得的飞行时间和理论飞行时间最大相对误差为 0.81%,误差原因在于声源信号的伪前移现象,还原出来的温度场和单峰偏斜温度场模型平均绝对误差为31 K,具有很好的重建效果.

Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10 -3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10 -2. The R 2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.

In order to improve the recognition rate for lower extremity motion patterns, this study designs a recognition method for such patterns, which integrates electromyography (EMG) and inertial measurement unit (IMU) signals in three posture modes, including walking on the ground, squatting, and extending seated legs, to address the difficulty with obtaining high signal-to-noise ratio EMG and IMU signals synchronously. Besides, this study proposes a synchronous analysis method for EMG and IMU dual-mode information to correct antipower frequency interference accelerometer signals. The collected signals are preprocessed to extract eigenvalues. And by using the kernel principal component analysis (KPCA), the information on these eigenvalues is fused. Finally, according to the characteristics of the data, a Bayesian-optimized XGBOOST algorithm is designed. Lower-limb movement patterns are classified with the feature vector put into the optimization algorithm. Multiperson experimental results show that the average recognition accuracy for different poses can reach 94.42%, the average F1 value 95.33%, and the average return value 95.68%, proving that the model proposed can be used to identify human motion intentions and its generalization ability can detect individual differences in human bodies.

A nonlinear electromagnetic energy harvester (EMEH) for the automobile suspension is designed. The magnetic flux density and the magnet repulsive force of the mechanical model are determined by finite element analysis. The dynamic model of a quarter car with the nonlinear EMEH under random road excitation is established. The influence of structure parameters on output characteristics of the nonlinear EMEH is studied. In order to further improve the output power, effects of the road class, the car speed, and the load resistance are investigated in detail. Studies have revealed that the higher the road class and the larger the car speed are, the better output characteristics of the nonlinear EMEH become. In order to prove that the nonlinear magnet repulsive force can significantly improve the output power, output characteristics of the nonlinear EMEH and the linear EMEH are compared. Finally, the power management circuit matching with the nonlinear EMEH is designed, which can convert the unstable alternating voltage into the stable direct voltage, and directly supply power to electronic devices. There are two innovations in this paper. One is the introduction of the nonlinear magnet repulsive force and the amplification bar in the structural design. Another is to design the power management circuit of the high energy conversion efficiency, which is up to 74.3% for the car speed 25 m/s on the road of class C.

Valence-inverted reactivity (VIR) is discovered here through high-level computations of excited states of Ni(II) complexes that are generated by triplet energy transfer. For example, the so-generated 3[(Ar)(bpy)NiII(Br)] species possesses a valence-inverted occupancy, dxy1dxz1dx2-y22, wherein the uppermost dx2-y2 orbital is metal-ligand antibonding. This state promotes C-H bond activation of THF and its cross-coupling to the aryl ligand. Thus, due to the metal-ligand antibonding character of dx2-y2, the dxy1dx2-y22 subshell opens a Ni-coordination site by shifting the bidentate bipyridine ligand to monodentate plus a dangling pyridine. The tricoordinate Ni(II) intermediate inserts into a C-H bond of THF, transfers a proton to the dangling pyridine moiety, and eventually generates an arylated THF by reductive-coupling. The calculated high kinetic isotope effect is in accord with experiment, both revealing C-H activation. The VIR pattern is novel, its cross-coupling reaction is highly useful, and it is generally expected to occur in other d8 complexes.

为实现快速步态状态判断,以更好地对下肢外骨骼进行高精度的步态识别和控制,进行了基于可穿戴惯性测量装置检测人体姿态变化的算法研究.通过对人体下肢的跌倒、转弯、蹲坐与起立等非周期性步态变化活动进行测算试验,获得了受试者实验过程中身体角度、下肢关节角速度和加速度变化等数据,随后应用随机森林等4种机器学习经典分类算法对受试者进行了活动识别对比分析,结果表明,决策树监督学习算法相对于其他算法,能够快速、准确地检测并判断出人体非周期性变化中的多种活动状态,历次识别精度均可达到99％以上,为可穿戴智能装备的开发与应用提供理论基础.

There are many deficiencies in contact temperature measurement, which can not provide a good guarantee for economy, efficiency and safety. Ultrasonic sensor temperature measurement technology has a good effect on the real-time monitoring of complex temperature field. This study takes the boiler furnace as the research object and the reconstruction of the temperature field in the furnace as the research purpose. Based on the finite element method, a single peak symmetrical temperature field model of the furnace is established on COMSOL platform to simulate the propagation of sound waves in the furnace, calculate the flight time according to the cross-correlation algorithm, and restore the temperature field of the furnace temperature field model by using the least square method and interpolation algorithm. The results show that the maximum relative error of flight time and theoretical flight time obtained by cross-correlation algorithm is 0.85%, and the average absolute error of reconstructed temperature field and single peak symmetric temperature field is 31K, which has a good reconstruction effect.

轮胎气压的盈亏影响了汽车的安全性、经济性和操纵稳定性.胎压监测系统(TPMS)越来越成为汽车的标准配备.胎压监测系统主要有直接式、间接式和混合式三种.间接式胎压监测系统由于其没有额外的硬件支出、不影响轮胎动平衡特性而具有一定优势.本文分析轮胎扭转高阶频点,得出轮胎扭转高阶频点与胎压变化的相关特征,建立了间接式胎压监测算法,实现了根据单一车辆轮速信号进行轮胎欠压预报,为胎压监测系统的建立提出一种新方法.

传统的离合器包箱测温技术存在响应速度慢、安装不便和测量误差较大等问题,提出一种采用超声波技术对离合器包箱内部温度进行测量的方法,这种方法能够很好地解决传统测温技术存在的问题,在工程应用中具有巨大的应用潜力.基于有限元分析软件COMSOL建立超声波在离合器包箱中传播的物理模型,并使用最小二乘法对离合器包箱的温度场进行还原,能够很好地解决离合器包箱内部物理场耦合复杂的问题.结果表明,使用超声波测量方法可以获得离合器包箱内部温度,超声波飞行时间的计算结果与理论结果的最大相对误差仅为0.26％;并且使用最小二乘法还原温度场的结果与理论结果一致.

We report herein that a palladium catalyst in combination with a dual phosphine ligand system catalyzes alkylation of silyl enol ether and enamide with a broad scope of tertiary, secondary, and primary alkyl bromides under mild irradiation conditions by blue light-emitting diodes. The reactions effectively deliver alpha-alkylated ketones and alpha-alkylated N-acyl ketimines, and it is difficult to prepare the latter by other methods in a stereoselective manner. The alpha-alkylated N-acyl ketimine products can be further subjected to chiral phosphoric acid-catalyzed asymmetric reduction with Hantzsch ester to deliver chiral N-acyl-protected alpha-arylated aliphatic amines in high enantioselectivity up to 99% ee, thus providing a method for facile synthesis of chiral alpha-arylated aliphatic amines, which are of importance in medicinal chemistry research. The N-acetyl ketimine product also reacted smoothly with various types of Grignard reagents to afford sterically bulky N-acetyl alpha-tertiary amines in high yields. Theoretical studies in combination with experimental investigation provide understanding of the reaction mechanism with respect to the dual ligand effect and the irradiation effect in the catalytic cycle. The reaction is suggested to proceed via a hybrid alkyl Pd(I)-radical species generated by inner-sphere electron transfer of phosphine-coordinated Pd(0) species with alkyl bromide. This intriguing hybrid alkyl Pd(I)-radical species is elucidated by theoretical calculation to be a triplet species coordinated by three phosphine atoms with a distorted tetrahedral geometry, and spin prohibition rather than metal-to-ligand charge transfer contributes to the kinetic stability of the hybrid alkyl Pd(I)-radical species to impede alkyl recombination to generate Pd(II) alkyl intermediate.

Reactivity of electronically excited base transition metals is an emerging frontier wherein mechanistic understanding is highly desired but mostly lacking. To reveal how C-O bond coupling reductive elimination (RE) is stimulated by excited Ni-II [Welin, E. R.; Le, C.; Arias-Rotondo, D. M.; McCusker, J. K.; MacMillan, D. W. C. Science 2017, 355, 380], we report here high-level theoretical modellings based on a combined ab initio protocol (CASSCF, CASPT2, DLPNO-CCSD(T)). In contrast to the experimental proposal of the d-d excited state, we find that the metal-to-ligand charge transfer (MLCT) excited state is most likely to stimulate the C-O coupling RE. This unprecedented assignment of the reactive excited state not only obviates the known thermodynamic prohibition of C-O coupling by ground state Ni-II, but also matches the experimental triplet energy requisite for energy transfer. In addition, the enhanced RE reactivity in excited Ni-II can be well rationalized by the Ni-III character of the MLCT state. The resolution of this intriguing mechanistic puzzle in the excited-state chemistry of a Ni-II complex underscores the potential of multireference methods in this field.

针对单级行星齿轮的裂纹故障问题,采用集中参数法建立了平移—扭转耦合动力学模型,分析了单个行星轮在不同裂纹程度下、不同行星轮故障数量下系统的响应特性.结果 表明,当单个行星轮发生裂纹故障时,随着裂纹程度的增大,系统振动剧烈程度变大,二倍频谱能量占比逐渐增大到97％,相轨图明显由内八字逐渐变为单圆周曲线,系统运动趋于平稳;当行星轮故障数量增大时,系统振动剧烈程度变小,频谱中二倍频能量占比逐渐减小到64％,相轨图内八字明显,系统运动趋于复杂.

The article uses LabView to develop a fault diagnosis system for wind power planetary transmissions. First, use LabView software to create a signal acquisition system. Then, the built wind turbine planetary transmission experiment platform was combined with the NI9171 data acquisition card and the upper computer. The hysteresis brake was used in the laboratory to simulate the external wind environment, and the planetary gear transmission planetary gear crack fault diagnosis and testing experiments were performed. The frequency domain signal is obtained from the initial time domain signal obtained by the experiment through the EMD and envelope demodulation method. The frequency of the corresponding planetary gear crack in the frequency domain signal is compared with the theoretical calculation of the planetary gear crack failure frequency. The results show that the frequency of the corresponding planetary gear cracks in the frequency domain spectra obtained by the experiment is basically consistent with the theoretical calculation of the frequency of cracks in planetary gears. The fault diagnosis system developed by LabView is reliable. The experiment provides a theoretical basis for fault diagnosis, optimisation design, and life prediction of wind turbine planetary transmissions.