Subjects: Medicine, Pharmacy >> Clinical Medicine Subjects: Biology >> Bioengineering Subjects: Physics >> Electromagnetism, Optics, Acoustics, Heat Transfer, Classical Mechanics, and Fluid Dynamics submitted time 2019-01-02
Abstract： Objective To investigate the effects of transcranial magnetoelectric stimulation (TMES) on temporal lobe epilepsy rats induced by kainic acid (KA). Methods 62 rats were divided into pretreatment (32 in total) and treatment (30 in total) groups according to the random number table method. The pretreatment group was further divided into 4 groups, and each group was stimulated by 0 %, 25%, 50%, 75% of the maximum current intensity (MCI) of the therapeutic apparatus respectively. According to the therapeutic efficacy, the optimal stimulation parameters under the experimental conditions was determined. The treatment group was further divided into 3 groups according to the random number table method, 10 in each group. Two groups (epilepsy-stimulating group, epilepsy-non-stimulating group) were epilepsy model rats that met the inclusion criteria. The stimulation parameters in the stimulating group were the best stimulation parameters explored in the pretreatment group; the rats in the non-stimulating group were treated the same before and after stimulation as the stimulating group. However, the therapeutic device has no effective energy output. The rats of third group difined as control were unmodeled control rats. All rats in the three groups were stimulated once a day for 40 minutes each for 14 days. The behavioral, histological and electrophysiological changes in the three groups of rats were recorded and compared to evaluate the efficacy of TMES therapy in epileptic rats. Results 50% MCI is the best stimulus intensity. The frequency of epileptic waves in epilepsy-stimulated rats was significantly lower than that in non-stimulated epileptic rats [(30.210 ± 4.580) beats/min vs. (31.380 ± 4.247) beats/min]. The difference was statistically significant (t = 3.235, P=0.001). The results of Timm staining showed that there was a statistically significant difference in the degree of staining between the three groups (F=17.429, P=0.000). The level of Timm staining in the inner molecular layer of dentate gyrus of the hippocampus in the epilepsy-stimulated group was significantly lower than that in the non-stimulated group, and the difference was statistically significant (P=0.027). Conclusion Transcranial magnetoelectric treatment can influence the formation of dentate gyrus neurons loop by improving epileptic rat dentate gyrus in epileptogenesis in changes of molecular layer organization degree, thereby reducing the frequency of epileptic EEG seizures.
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Awaiting Review
Subjects: Mechanical Engineering >> Other Disciplines of Mechanical Engineering Subjects: Computer Science >> Computer Application Technology submitted time 2017-12-26
Abstract： There are some shortcomings in the existing methods of fault diagnosis of planetary gearbox: First, the traditional methods are complex and can not effectively diagnose the planetary gearbox faults. Second, the methods based on convolution neural network mostly diagnose gearbox faults and rarely are used to diagnose planetary gearbox. In order to effectively diagnose complex faults and variable working conditions, fault tree structure, working condition parallel structure and multi-SoftMax convolution neural network are proposed for the first time. Fault tree structure can handle a variety of complex faults and see the diagnosis effect of each node. The parallel structure can handle variable conditions, and predict speed and load. A series of experiments are carried out using the vibration data of ours laboratory planetary gearbox, which indicated that the method can accurately diagnose the complex faults and variable working conditions of the planetary gearbox, and the accuracy is 97%. It is verified that the multi-SoftMax convolution neural network has strong generalization ability, and the advantages of the fault tree structure.
Peer Review Status:Awaiting Review
Subjects: Mechanical Engineering >> Other Disciplines of Mechanical Engineering Subjects: Computer Science >> Computer Application Technology submitted time 2017-12-26
Abstract： The existing methods of bearing diagnosis have some disadvantages: The conventional method has complex mathematical calculation and poor diagnosis effect. It generally only diagnoses the fault location and irrespective of the load and the fault size. The existing convolutional neural network method use the traditional convolution neural network. A network can only output a property and can not simultaneously diagnose multiple properties. In order to simultaneously diagnose the fault location, fault size and load, for the first time put forward a multi-attributes convolution neural network (MACNN) and applied to the bearing fault diagnosis. The multi-attribute convolution neural network is trained using one-dimensional vibration signal training . The advantages lies in overcoming the shortcomings of the traditional method: the diagnosis result of any combination of the fault attributes can be obtained, the network parameters are less, the method is simple, the generalization ability is strong and the accuracy rate is high. A series of tests have been carried out using the bearing data of Case Western Reserve University. The results show that the proposed method can accurately diagnose several properties of bearing faults with high accuracy and good generalization ability.
Peer Review Status:Awaiting Review
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