The composite photocatalysts phosphomolybdic acid/titanium dioxide (PMo12/TiO2) applied for industrial wastewater degradation are prepared by sol-gel method and hydrothermal synthesis method . The composites are characterized by infrared spectrometer (IR), X-ray diffraction (XRD)，X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). The photocatalytic degradation of 2-buteny-4,6-dinitrophenol (DNBP) is investigated under ultraviolet light irradiation. The effects of the initial pH value, the amount and the reused times of catalyst on degradation of DNBP wastewater are also investigated. The results indicate that the photocatalytic activity of PMo12/TiO2 is significantly better than pure TiO2 photocatalyst when the illumination time is 5 hours, the pH value is 5.10 and the amount of catalyst is 1.00g/L. The degradation rate of the composite catalyst is 98.36%, the COD removal rate is 53.18%. After 3 times repeated use, the PMo12/TiO2 composite photocatalyst still has high activity.

The development of efficient and stable bifunctional non-noble metal catalysts for overall water splitting remains an important challenge. In this study, graphene-like carbon encapsulated Ni/Mo2C catalysts were prepared by hydrothermal and high temperature pyrolysis method. Due to the synergistic effect of Ni and Mo2C, the HER and OER performance of the catalysts was improved. The catalyst achieved an overpotential of 131 mV and a Tafel slope of 50.44 mV·dec-1 for the hydrogen evolution reaction (HER) in 0.5 M H2SO4, and an overpotential of 330 mV and a Tafel slope of 59.64 mV·dec-1 for the oxygen evolution reaction (OER) in 1.0 M KOH. The low overpotential and fast reaction kinetics make the water decomposition process more direct and efficient. This study provides a new approach for designing bifunctional catalysts for efficient water electrolysis applications.

Taking the mass fraction of n-propanol as the control index and the lowest heat load as the objective function, the main parameters of double-column pressure-swing distillation process were determined by single factor optimization with the UNIQUAC thermodynamic model using PRO/II process simulation software. On this basis, with the theoretical number of trays, feed position and reflux ratio as independent variables, and the mass fraction of n-propanol as the response value, a second-order regression model was established through Box-Benhnken central combination design and global optimization of process parameters was carried out. The correlation parameters show that the regression model can be used to analyze and predict the relationship between response values and independent variables. Under the conditions of optimum process parameters, the absolute deviation between the experimental value of n-propanol mass fraction and the predicted value by the model is 6.86%, which  indicate  that the second-order regression model is reliable and effective.

To study the characteristics and influencing factors of inter-turn short circuit fault of excitation winding of hydro-generator, the fault mechanism of inter-turn short circuit of excitation winding was analyzed, and the expressions of excitation magnetomotive force and air gap magnetic density under normal and fault conditions of hydro-generator were deduced. On this basis, a two-dimensional finite element simulation model of the hydro generator was established by using the field-circuit coupling method, and the harmonic characteristics of the rotor magnetic field during the excitation winding inter-turn short circuit fault of the hydro-generator were obtained through the excitation winding inter-turn short circuit fault simulation.

To effectively guarantee the quality of service (QoS) in heterogeneous Visible Light Communication (VLC) and Radio Frequency (RF) Communication networks, we design a network resource matching algorithm based on the guarantee of statistical delay, which aims to improve the utilization of network service resources. We construct a network queuing system with the switching service process and random arrival process, and the effective bandwidth and effective capacity theory is used for the analysis. The minimum service rate provided by RF is evaluated when the VLC network is interrupted by occlusion. The algorithm is simulated by Matlab, and the influence of the variable system parameter on this algorithm is analyzed. The algorithm could provide some guidance for the research of QoS guarantee and resource allocation in heterogeneous networks.

Cloud computing survivability is a new direction in network application security research. A system survivability emergency response strategy was constructed in the wide area network environment of cloud computing, which employed dynamic service drift technology and task scheduling method technology to achieve dynamic service drift in the case of a large number of service nodes. Dynamically distributing cloud services and data across multiple service nodes can eliminate the single failure point of traditional services. In order to enhance the triggering mechanism of service drift, an adaptive random autonomous scheduling algorithm was proposed, which compared with the traditional drift mechanism, added triggering conditions and introduced ant colony algorithm for scheduling. The conclusions show that the improved service drift model have significantly optimized anti-destruction ability, drift time, and drift efficiency, which can better ensure the continuity and reliability of the services provided, thus improving the survivability of big data service in the cloud computing environment.

In order to improve the convenience of simulation system operation, for the purpose of developing online simulation platform, this paper expounds the Web architecture design scheme based on Django, selected the single-capacitance water tank level control system as the research object, uses Python language to configure the difference model of the object in the Web background, The visual interface based on SVG is designed by Ajax technology. The results show that users can simulate the dynamic process of water tank liquid level in real time through the browser, which meets the requirements of online training.

This article presents a study on the liquid level control system of a double-capacity water tank. Through the mechanism analysis method, the transfer function and incremental state equation mathematical model are established, and a differential method is used to discretize the incremental state equation without linear processing to avoid system errors and equation time series. The dynamic matrix control algorithm is applied to control the incremental state equation, and a Matlab script program is used to implement the control process. The simulation results show that the DMC algorithm can effectively control the double-capacity water tank system to meet the control requirements, which can have important reference value for the research and application of double-capacity water tanks and predictive control algorithms.

The system adopts STM32 microcontroller as the main control core, based on IoT technology, driving MPU6050, MAX30102 and LM386 modules. Three sensors are designed with heart rate and blood oxygen circuit, motion circuit and sound detection circuit respectively, realizing real-time detection of human health data. Using ESP8266 to connect to the Internet, using MQTT protocol for publishing and subscribing topics, and uploading the collected data to EMQ Cloud. Using Visual Studio Code to design a WeChat mini program to visualize the data, and the data can also be viewed on the OLED screen of the device itself. The sensor data measurement and optimization are completed, and after testing, the relative error of the measurement data of this system is within 5%, and the accuracy of the data is reliable. It can measure human health data well and solve people's needs for their own health concerns.

Taking the box-type heating furnace as the research object, aiming at the lag and time-varying defects of PID control in temperature control, a new fuzzy PID control scheme with Smith compensation link is proposed, and the simulation is realized using MATLAB. It can be seen from the simulation results that, compared with Smith control and fuzzy control, the improved system has higher accuracy, enhanced anti-interference and robustness, more stable system transition process, and ideal overall control effect, which has good promotion value.

This paper uses WeChat WXML, WXSS, JavaScript and WeChat cloud to develop, construct and develop a fast and convenient WeChat applet, which aims to provide teachers and students with lost and found, notice of finding things, campus friends, barter, academic discussion, resources Sharing and other functions make it easy for teachers and students to obtain the information they need on this platform.

With the rapid development of the tourism industry, the problem of storing and processing increasingly large amounts of tourism data is receiving increasing attention. To solve the contradiction between users' personalised needs and information overload in tourism services, the improved FP-growth correlation analysis algorithm is used to achieve the recommendation of tourism services of interest to users. Based on the Hadoop cloud computing platform, a recommendation model for tourism services is constructed by fusing cloud computing technology with the improved FP-growth algorithm. The results show that as the number of users increases, the score of the research algorithm increases by about 40% compared to other recommendation algorithms, with a score of about 0.92. The research algorithm is the most effective in filtering out non-interesting travel service items and outdated data from users. When facing new users, the recall of the research algorithm is 0.832, the accuracy is 0.867, and the score of the research algorithm is 0.916. This research is innovative in implementing travel service recommendation on Hadoop cloud computing platform, which not only improves the accuracy of travel service recommendation, but also solves the cold start problem of the recommendation system.

In the current era of information explosion, how to realize the efficient connection between advanced technology and art, and how to decentralize visual design, has been widely concerned by many scholars. Research a method of human-machine collaboration using generation technology as an extension of the design process, combining convolutional neural networks with generating adversarial network models, and introducing conditional methods to connect conditional information at each layer of the generator structure. Optimize the above model by using spectral normalization and group normalization in conjunction, Finally, a conditional depth convolution to generate an adversarial network model (CDCGAN) is constructed. The research results show that the average accuracy of the CDCGAN model is 97.28%, and its extensibility and learning ability in the intelligent visual recognition design platform are excellent. In conclusion, CDCGAN model has good performance and accuracy, and can be well applied to intelligent visual recognition design platform.

Abstract: Ankle injury is a common form of human injury, and ankle rehabilitation has received more and more attention and research. The hybrid mechanism is used in the rehabilitation exercise after ankle injury, which can effectively improve the rehabilitation effect of the rehabilitation mechanical device. A 4- degree of freedom (2-UPU-PU)&R hybrid rehabilitation mechanism is proposed based on the analysis of the structural characteristics and motion form of human ankle joint. By using the modified K-G formula to analyze the degree of freedom of the ankle rehabilitation mechanism, the inverse solution of the position of the mechanism is solved based on the closed-loop vector method, and the working space of the mechanism is solved by using the search method. the limited output control system of position and speed is designed to verify that the designed mechanism can meet the needs of rehabilitation, ensure the safety of use, and provide theoretical guidance for the subsequent control system design.

Based on RecurDyn software, the simulation model of multi-body virtual prototype and wetland pavement model of flat wetland crawler bulldozer was established, and the dynamic simulation and comparative analysis of the steering process of flat wetland crawler bulldozer under different humidity pavement and different working conditions were carried out based on the multibody dynamics theory and method. By studying the influence of parameters such as steering angular velocity and turning radius on the steering performance of crawler bulldozer during steering, this paper provides a certain reference for the design and optimization of the traveling system of wetland crawler bulldozer.

In order to solve the problem of self-care of disabled or semi-disabled people, an autonomous positioning and navigation intelligent device is studied to assist people to realize the needs of sitting, lying, standing, squatting and walking in multiple position changes. For the problem that the generation value of the heuristic function in the traditional A* algorithm of autonomous navigation is smaller than the actual value, resulting in a larger search space and redundant paths, the research uses different node cost functions to select different models to improve the A* algorithm; by establishing the OPEN table and CLOSE table, in which the OPEN table stores the nodes to be investigated and the CLOSE table stores the non-investigable nodes, the path planning is carried out to enhance the directionality , improve the efficiency of the algorithm, the improved A* algorithm implements restricted query paths under the completion of regular expression constraints to achieve restricted, global and optimal autonomous path planning, which improves the practical application of autonomous navigation.

Aiming at the problem of blurred areas at the imaging edge caused by the fusion of infrared and visible light images, a fusion algorithm of infrared image (IFR) and visible light image (VBI) driven by the average gradient energy in NSCT domain is proposed. Firstly, NSCT transformation is performed on IFR and VBI respectively. The low frequency, intermediate frequency, and high frequency subband coefficients are obtained. The low frequency fusion adopts the method of taking the maximum average gradient energy (Average Gradient Energy, AVGE) for fusion, the intermediate frequency fusion adopts the maximum value of the spatial frequency (Spatial Frequency, STF) for fusion, and the high frequency A fusion method of Average Gradient Energy Pulse Coupled Neural Network (AVGE-PCNN) is proposed for fusion, and the final fusion is obtained by using Inverse Nonsubsampled Contourlet Transform (INSCT) In the experiment, three groups of IFR and VSI images of different scenes were used for fusion processing. The comparative experiments proved that the fusion method proposed in this paper has a good effect in improving image edge blur, and the subjective evaluation and objective evaluation are better than DWT, DTCWT, and NSCT algorithms.

In order to improve the efficiency of power grid fault diagnosis in the power maintenance department, the SSAE-CNN fusion network fault diagnosis model of AC and DC power grid based on stack sparse automatic encoder (SSAE) and convolutional neural network (CNN) is proposed. The experimental results show that the fault line diagnosis accuracy of the fusion network diagnosis model is 99.86%, which is 0.62% higher than the SSAE-BP diagnosis model and two percentage points higher than the general fault diagnosis model. The accuracy rate of fault type diagnosis is 99.93%, which is 0.66% higher than SSAE-BP fusion model. The proposed power grid fault diagnosis model is superior to the general model in both diagnosis accuracy and speed. It can accurately diagnose and classify all kinds of power grid faults and provide data reference for power grid fault diagnosis by the power maintenance department, which has important practical significance.