Software reliability allocation model of CNC system based on software architecture

Abstract In order to guarantee the implementation of reliability target, software reliability allocation model of CNC system was established based on software architecture. Software architecture of CNC system was set up, which decomposed CNC system into the functional units, reliability indexes of the system can be distributed into each component from top to bottom. The relative weight of software element in each level of the architecture was determined with analytic hierarchy process (AHP) method. The software reliability allocation model was built by taking the maximum practicability of CNC system as the target function, the reliability and cost function of component as the constraints. The reliability of each component was calculated through culture algorithm (CA). According to the result, the reliability allocation worked out is reasonable and feasible, and during the development of allocation model, the practicability of CNC system was guaranteed and development cost was also saved effectively

Combination of Walnut Peptide and Casein Peptide alleviates anxiety and improves memory in anxiety mices

IntroductionAnxiety disorders continue to prevail as the most prevalent cluster of mental disorders following the COVID-19 pandemic, exhibiting substantial detrimental effects on individuals’ overall well-being and functioning. Even after a search spanning over a decade for novel anxiolytic compounds, none have been approved, resulting in the current anxiolytic medications being effective only for a specific subset of patients. Consequently, researchers are investigating everyday nutrients as potential alternatives to conventional medicines. Our prior study analyzed the antianxiety and memory-enhancing properties of the combination of Walnut Peptide (WP) and Casein Peptide (CP) in zebrafish.Methods and ResultsBased on this work, our current research further validates their effects in mice models exhibiting elevated anxiety levels through a combination of gavage oral administration. Our results demonstrated that at 170 + 300 mg human dose, the WP + CP combination significantly improved performances in relevant behavioral assessments related to anxiety and memory. Furthermore, our analysis revealed that the combination restores neurotransmitter dysfunction observed while monitoring Serotonin, gamma-aminobutyric acid (GABA), dopamine (DA), and acetylcholine (ACh) levels. This supplementation also elevated the expression of brain-derived neurotrophic factor mRNA, indicating protective effects against the neurological stresses of anxiety. Additionally, there were strong correlations among behavioral indicators, BDNF (brain-derived neurotrophic factor), and numerous neurotransmitters.ConclusionHence, our findings propose that the WP + CP combination holds promise as a treatment for anxiety disorder. Besides, supplementary applications are feasible when produced as powdered dietary supplements or added to common foods like powder, yogurt, or milk

Milk fat globule membrane promotes brain development in piglets by enhancing the connection of white matter fiber trace

IntroductionBrain development during infancy is crucial for later health and development. Although Milk Fat Globule Membrane (MFGM) has been demonstrated to enhance brain development, further investigation is needed to determine the optimal dose.MethodsIn this study, 80 piglets aged 2 days were randomly assigned to four groups: Control group, MFGM-L (1.74 g MFGM per 100 g diet), MFGM-M (4.64 g MFGM per 100 g diet), and MFGM-H (6.09 g MFGM per 100 g diet). Daily body weight and milk intake of the piglets were recorded until 31 days postnatal. Learning and memory abilities were evaluated using the spatial T-maze test on day 15. MRI analysis was conducted to assess functional and structural changes in brain tissues. Additionally, mRNA and protein expression of brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NTF-3) in the hippocampus and prefrontal cortex were evaluated.ResultsThe results indicated that the MFGM supplemented diet significantly improved the accuracy of the piglets in the T-maze test, with the MFGM-L group exhibiting the best performance. MRI showed no volumetric differences in the gray and white matter between the groups. However, the fractional anisotropy in the left and right hippocampus of piglets in the MFGM-L group was significantly higher than in the other three groups. Furthermore, there was a strong correlation between the accuracy of the T-maze test and hippocampal fractional anisotropy.DiscussionThe MFGM supplemented diet also increased the expression of BDNF in the cerebral cortex. However, the changes in BDNF were not consistent with the results of the T-maze test. In conclusion, adding 1.74 g MFGM per 100 g diet can significantly improve neonatal piglets’ learning and memory abilities, potentially by enhancing the connection of white matter fiber bundles in the brain

Research on the Design Method of a Bionic Suspension Workpiece Based on the Wing Structure of an Albatross

An air suspension platform uses air pressure to realize the suspension function during the suspension process, and it has the disadvantage of large air pressure and a small suspension force. In this study, an air suspension platform was built using bionic design to reduce the required air pressure and increase the suspension force. A suspension structure mapping model was established according to the physiological structure characteristics of albatross wings. A bionic model was established by using the theoretical calculation formula and structural size parameters of the structural design. A 3D printer was used to manufacture the physical prototype of the suspended workpiece. Based on this, a suspension test rig was built. Six sets of contrast experiments were designed. The experimental results of the suspension test bench were compared with the theoretical calculation results. The results show that the buoyancy of the suspended workpiece with a V-shaped surface at a 15-degree attack angle was optimal for the same air pressure as the other workpieces. The surface structure of the suspended workpiece was applied to the air static pressure guide rail. By comparing the experimental data, the air pressure of the original air suspension guide rail was reduced by 37%, and the validity of the theory and design method was verified

A New Residual Life Prediction Method for Complex Systems Based on Wiener Process and Evidential Reasoning

A new residual life prediction method for complex systems based on Wiener process and evidential reasoning is proposed to predict the residual life of complex systems effectively. Moreover, the better maintenance strategies and decision supports are provided. For the residual life prediction of complex systems, the maximum likelihood method is adopted to estimate the drift coefficient, and the Bayesian method is adopted to update the parameters of Wiener process. The process of parameters estimation and the probability density function (PDF) of the residual life are deduced. To improve the accuracy of the residual life prediction results, the evidential reasoning (ER) is used to integrate the prediction results of Wiener process. Finally, a case study of gyroscope is examined to illustrate the feasibility and effectiveness of the proposed method, compared with fuzzy theory, which provides an important reference for the optimization of the reliability of complex systems and improvement

Cloud-Based Fault Tolerant Control for a DC Motor System

The fault tolerant control problem for a DC motor system is investigated in a cloud environment. Packet dropout phenomenon introduced by the limited-capacity communication channel is considered. Actuator faults are taken into consideration and fault diagnosis and fault tolerant control methods towards actuator faults are proposed to enhance the reliability of the whole cloud-based DC motor system. The fault diagnosis unit is then established with purpose of obtaining fault information. When the actuator fault is detected by comparing the residual signal with a predefined threshold, a residual matching approach is utilized to locate the fault. The fault can be further estimated by a least-squares filter. Based on the fault estimation, a fault tolerant controller is designed to guarantee the stability as well as the control performance of the DC motor system. Simulation result on a DC motor system shows the efficiency of the fault tolerant control method proposed in this paper

Cloud-Based Fault Tolerant Control for a DC Motor System

The fault tolerant control problem for a DC motor system is investigated in a cloud environment. Packet dropout phenomenon introduced by the limited-capacity communication channel is considered. Actuator faults are taken into consideration and fault diagnosis and fault tolerant control methods towards actuator faults are proposed to enhance the reliability of the whole cloud-based DC motor system. The fault diagnosis unit is then established with purpose of obtaining fault information. When the actuator fault is detected by comparing the residual signal with a predefined threshold, a residual matching approach is utilized to locate the fault. The fault can be further estimated by a least-squares filter. Based on the fault estimation, a fault tolerant controller is designed to guarantee the stability as well as the control performance of the DC motor system. Simulation result on a DC motor system shows the efficiency of the fault tolerant control method proposed in this paper

Sound Based Fault Diagnosis Method Based on Variational Mode Decomposition and Support Vector Machine

In industry, it is difficult to obtain data for monitoring equipment operation, as mechanical and electrical components tend to be complicated in nature. Considering the contactless and convenient acquisition of sound signals, a method based on variational mode decomposition and support vector machine via sound signals is proposed to accurately perform fault diagnoses. Firstly, variational mode decomposition is conducted to obtain intrinsic mode functions. The fisher criterion and canonical discriminant function are applied to overcome the fault diagnosis accuracy decline caused by intrinsic mode functions with multiple features. Then, the fault features obtained from these intrinsic mode functions are chosen as the final fault features. Experiments on a car folding rearview mirror based on sound signals were used to verify the superiority and feasibility of the proposed method. To further verify the superiority of the proposed model, these final fault features were taken as the input to the following classifiers to identify fault categories: support vector machine, k-nearest neighbors, and decision tree. The model support vector machine achieved an accuracy of 95.8%, i.e., better than the 95% and 94.2% of the other two models