Deep learning has achieved great success with big data and powerful computing, but its performance is poor under sample constraint, mainly due to the construction of function space (clusters) and the design of algorithms under dataset constraint. Accordingly, a categorical review of deep learning under restricted samples is presented. In addition, according to the current research on the brain, the cognitive process of humankind is categorized in the brain with different regions, and the cognitive functions of each region are also different. Therefore, the training function of each region should also be different. At this point, an idea of deep learning method using functional evolution is proposed, trying to create a network structure composed of multiple functional modules, and the training procedure of the functional module used in this method is studied, aiming to explore the new area of "humanoid learning".

Adaptive Dynamic Programming (ADP) algorithm, as a research focus in the field of optimal control, has been widely applied in the field of Energy Management System (EMS). ADP is an effective tool for solving optimal control problems in complex nonlinear systems, which can adaptively adjust the control strategy through the input and output data of the system. The research progress of ADP algorithm and its application in EMS are introduced. Then the research status and algorithm implementation of ADP algorithm in discrete EMS and continuous EMS are analyzed. And at last the Real-time Adaptive Dynamic Programming (RT-ADP) algorithm and its feasibility are introduced.

With the continuous development of manipulator technology, the traditional rigid manipulator, such as space manipulator, surgical manipulator and man-machine interactive manipulator, etc. gradually has difficulties in meeting the needs of some new manipulator systems in terms of lightweight, motion flexibility, large space range, etc. More and more researchers pay attention to the research of flexible manipulator and its high-precision motion control. At present, some effective motion control methods have been proposed for flexible manipulator. However, when an actuator of the manipulator fails, it is difficult for conventional control methods to ensure the original control performances and may even lead to system instability. The research on the flexible manipulator motion control under the effect of actuator faults has important theoretical significance and application prospect. Firstly, the existing motion control methods of flexible manipulator are summarized. Then, according to the types of actuator faults, the effect of actuator performance fault, actuator completely damaged fault and sudden actuator fault on the system are analyzed, and the state of art methods that are used to overcome these actuator faults are summarized. Finally, the key problems to be further solved in the motion control of flexible manipulator under the effect of actuator faults are discussed, and three prospect directions summarized, which has reference value for the further research of flexible manipulator motion control.

Recently, unmanned autonomous vehicles (UAVs) have attracted a lot of attention in both military and civilian fields, where the trajectory tracking mission has been a popular research topic. In this paper, a robust Sliding Mode Control (SMC) is proposed for controlling a quadrotor UAV for 3D trajectory tracking in the presence of perturbations and parameter uncertainties. The nonlinear dynamics of a quadrotor with 6-DOF is first established. Then, a sliding mode controller with mass, inertia, and stiffness uncertainties is designed. The 3D tracking effectiveness of the controller is verified by modeling simulations in Matlab Simulink and Universal Mechanism software systems. Finally, further physical verification is done using a Pelican quadrotor platform with perturbations applied to the horizontal and vertical axes to verify its robustness. Both the simulation results and the practical implementation results show that the tracking effect and the robustness of the quadrotor UAV for a given trajectory are satisfactory, confirming the correctness and effectiveness of the proposed SMC control algorithm.

Identifying the sentiment polarity of movie customer group reviews may inspire the platform to optimize movie recommendation algorithms and improve services and provide suggestions for consumers' movie choices. A MABM sentiment polarity recognition model is proposed based on the multi-head self-attention bidirectional long-short term mechanism. Using the online review data of the well-known movie review website Douban Review as the corpus, text mining tools are used to pre-process the data. Then dividing the data into training and test sets according to 8:2, and using 10 machine learning models and 4 deep learning models as the control group, the effectiveness and robustness of the MABM model are evaluated by cross-validation and test set validation comparison. In the two sets of comparison experiments, it is found that the deep neural network model predicts overall better than the machine learning model, and the MABM model used as the control set has the best classification results. The MABM model can effectively identify the sentiment polarity of consumer reviews, providing management insights and algorithm improvement suggestions for movie recommendation platforms.

DNA-N6 methyladenine (6-mA) methylation modification is one of the most important epigenetic modification markers. The aberrant 6-mA modification can affect gene expression and lead to serious diseases. Therefore, the work of predicting the 6-mA site is of great significance for the understanding of the pathogenesis and treatment of diseases. In this paper, a long short-term memory (LSTM) neural network based on K-mer encoding method and one hot encoding method is proposed to predict methylation sites.Firstly, the information content of gene sequence is increased through K-mer coding method. Secondly, the information content after one hot encoding is converted into a composite encoding matrix. The LSTM model can extract more feature dimensions and types from the encoding matrix, to improve the prediction performance of the LSTM model for gene sequence. The cross validation experiment show that the proposed method can achieve accuracy of 93.7% on benchmark datasets. The sensitivity, specificity and matthews correlation coefficient of the trained model were 93.0%, 94.5% and 0.875, which outperformed existing 6-mA prediction methods. On the other six different species datasets, the proposed method can achieve the area under the curve (AUC) values from 0.9055 to 0.9262,which shows the applicability of the proposed method on animals, plants and microorganisms methylation tasks. The proposed method was applied on rice gene NC_ 029258.1, and the predictions were verified by the recently published online prediction tools. The results show that 86% to 96% of the prediction results are supported by these tools, indicating that the proposed method can be applied to large-scale site prediction and analysis of different species.

With excellent mechanical properties and heat resistance, polyimide has been one of the important dielectric materials in microelectronics industry. In recent years, with the rapid development of microelectronics industry and the rise of 5G communication technology, higher requirements are put forward for the dielectric constant and dielectric loss reduction of polyimide in terms of energy consumption requirements and signal reception. How to reduce as much as possible the dielectric constant and dielectric loss and retain the excellent properties of polyimide at the same time is an urgent problem to solve at present. The research and application progress of porous polyimides, polymers filler and inorganic nano filler composite modified polyimides and other composite low dielectric polyimides emerging in recent years are reviewed. How to reduce the dielectric constant of polyimides while maintaining other properties is discussed, and its development is prospected. It will provide a new idea for the design and preparation of new composite low dielectric polyimide materials.

Assume that the investment portfolio includes an derivative，we study the optimal investment and proportional reinsurance strategies for ambiguity averse insurers, who are concerned about the potential model ambiguity and aim to seek the robust optimal investment and reinsurance strategies. The ambiguity-averse insurers are allowed to purchase reinsurance treaty to mitigate individual claim risks; and can invest in a financial market consisting of one risk-free asset, one risky stock and one derivative. The ambiguity-averse insurers assumed to have exponential utility, and we formulate the optimization problem as an utility maximization problem. By applying the dynamic programming approach, we derive the HJB equation for the value function. Meanwhile, we obtain the closed-form solutions to the optimal investment and reinsurance strategies. Finally, we provide some numerical simulations together with sound economic implications. More important, we demonstrate the utility improvement when considering derivative trading and parameter ambiguity, and find that derivative trading can significantly improve utility when return volatility increases.

Unmanned aerial vehicles (UAVs) have found important applications in wireless communications and internet of things (IoT) due to their advantages of on-demand and swift deployment, high mobility, and high-quality line-of-sight communication links with ground users. In this paper, a UAV-enabled data collection system is studied, where a UAV collects data from multiple sensors and relays the collected data back to a fusion center. In order to maximize the end-to-end throughput of the data collection system, an optimization problem is formulated to jointly optimize the transmit power and bandwidth of the sensors, as well as the transmit power and flight trajectory of the UAV.The optimization problem is subject to the minimum data transmission constraint, the information-causality constraint, the average and peak transmit power constraint, the bandwidth allocation constraint, and the UAV mobility constraint, and this is a non-convex optimization problem which is difficult to solve. To tackle such a difficulty, this paper proposes an efficient alternative optimization algorithm based on the block coordinate descent method and the successive convex optimization method. The algorithm divides the original problem into a transmit power and bandwidth optimization sub-problem; and a UAV trajectory optimization sub-problem.These two sub-problems are both transformed into convex optimization problems by introducing slack variables and applying the first-order Taylor expansion method,then they can be solved iteratively and alternately. Computer simulation results show that the proposed algorithm can strike a balance between the data collection links and the data forwarding links, and can significantly improve the end-to-end throughput of the system, as compared to other three benchmark schemes, which demonstrates the necessity of optimizing power, bandwidth and flight trajectory jointly.

Research on the thermal adaptation behavior of the elderly in hot-humid rural areas of China is significant to the energy-saving optimization of village and town houses. This paper selects 7 residential houses in Chatang Village, Tanbu Town, Huadu District, Guangzhou City as the research object. Through the actual measurement of different environmental physical parameters and the subjective questionnaire survey method, the characteristics of thermal adaptation behavior of the elderly during the research are studied. The differences in the thermal adaptation behavior and personality of the elderly will be discussed. The results show that in summer, the elderly in hot-humid rural areas mainly use windows, curtains, electric fans, and change the activity space and other thermal adaptation behaviors for personalized thermal regulation. Among them, the analysis results of the frequency of opening/closing windows, curtains, and electric fans show that the thermal adaptation behaviors have nothing to do with gender, but there are significant age differences. The regulation behavior of the on/off electric fan has no significant effect on the indoor air temperature of the house, but it has a negative correlation with the indoor relative humidity. The rate of going out of the elderly in villages and towns is positively correlated with indoor air temperature and negatively correlated with relative humidity. The subjective thermal perception of the elderly in hot-humid rural areas has no major influence on their thermal adaptation behaviors such as opening windows and turning on electric fans, but has a greater influence on their outing rate.

To measure the development status, spatial and temporal divergence and development trend of the seven industries in North America, East Asia and Europe, Dagum Gini coefficient, Kernel density estimation and Markov chain method were used for empirical analysis. It is found that there are regional differences in the development levels of the seven industries in the three major regions, and the differences within different regions are ranked from high to low, and the main source of the overall gap is inter-regional differences; in addition, the probability of inter-stage transfer of the development levels of the seven industries is relatively small, with a certain degree of neighbor dependence. Based on both temporal and spatial dimensions, the current development status of the seven global industries is assessed, and the regional industrial development trends are predicted to provide evidence to support the current development status of key industries, and to provide a reference for the national decision on the global regional layout of key industries as well as the development direction.

Based on the perspective of government management and the development of institutions themselves, this paper constructs the evaluation index system of new R&D institutions in Guangdong Province, and evaluates the performance of new R&D institutions in Guangdong Province by Data Envelopment Analysis (DEA) method, It is found that there are significant differences in the input-output efficiency of the new-type R&D institutions in different regions, different attributes and different types of R&D. On the basis of the research results, the corresponding countermeasures and suggestions are put forward to provide theoretical support and practical guidance for the follow-up management and high-quality development.

The transaction volume of cosmetics is surging, as e-commerce advances rapidly in the context of the Internet and big data, resulting in a large amount of packaging waste that is difficult to recycle. To address the contradictory issues between the high cost of recycling and processing and the low recycling value in the plastic recycling and processing system for cosmetic bottles, reducing the cost of recycling and processing to within target cost range, and improving recycling efficiency to promote the recycling of cosmetics bottles, this paper applies the first creating method to the design idea generation of a plastic recycling and processing system for cosmetics bottles. We first analyzed the unmet needs of the recycling party, established an Extenics model of the contradictory problem, and determined the functional affair-element, the property characteristic-element and the substantial characteristic-element of the recycling system. We then performed extensible analysis and extension transformation on the conjugate part, obtained multiple schemes, judged whether the contradictory problem was solved or not, and used the dependent function of superiority evaluation method to derive the optimal creativity and deepen it. Using the superiority evaluation method, it was calculated that creativity S ₃ has the highest degree of superiority, the actual cost of recycling and processing was lower than the target cost after the enterprise assessment, and the contradiction problem was solvedand the creativity has been adopted by the enterprise. Finally, we verify the validity of the application of the first creating method in the design of plastic recycling and processing system for cosmetic bottles.

Organic Rankine cycle (ORC) power generation technology has a wide application potential in the recovery of medium-to-low temperature waste heat and medium-to-low temperature solar thermal power generation. The system optimization design is of great significance to improve system power generation efficiency and reduce the annual generation cost of the system. A synchronous optimization model of the equipment structure parameters and system operation parameters of the solar-driven ORC system is established. Considering the characteristics of solar radiation and ambient temperature changes with time, based on the time series aggregation method, five aggregation points were obtained as design conditions, and the optimal design schemes under the five aggregation design conditions are obtained. The results show that the solar radiation and the environmental design temperature are different, and the designed system structure parameters and operating schemes are different; When the solar radiation is low and the solar radiation value is high, the electricity production cost (EPC) is the lowest and the ORC efficiency is the highest.

In order to increase the diversity and novelty of structural design in the structural innovation design of automated warehouse, the extension innovation method is applied to the structural design of shuttle shelf, and the general process of its structural extension design given. According to the user's demand and original data, the extension model of shuttle shelf is established, and the mapping relationship among function event element, component matter element and connection relation element is analyzed, and the implication system of function event element set is established. The relevant analysis network of automated warehouse shelf structure design is established, the analysis and implement the corresponding active transformation expanded, and then the creative strategy set of automated warehouse structure design obtained. The optimal strategy has been applied to enterprise production, with good practicability.

A millimeter wave low noise amplifier (LNA) fabricated in 100nm gallium nitride (GaN) high electron mobility transistor (HEMT) process is presented. With a three-stage cascade topology and an optimum concurrent design, this LNA, covering a frequency range from 24 to 30 GHz for 5G millimeter wave Band n257 and n258 applications, achieves low noise figure of 2.4~2.5 dB, small signal gain of 21.1~24.1 dB, and output 1 dB power compression point of greater than 14.4 dBm.

Although the Chief Information Officers (CIOs) have the important responsibility for digital innovation in their organizations, it is difficult for them to meet the challenges of the organizational changes involved in relying on traditional structural power and expert roles. Drawing on political skill and issue selling theory, a study is conducted to explore how CIOs can use political skills to drive digital innovation. A theoretical model is presented and empirically tested using survey data collected from a sample of 218 matched pairs of CIOs and top business executives through multilevel regression analysis. The results suggest that: CIO political skill positively affects CIO issue selling effectiveness, which in turn positively affects enterprises digital innovation. CIO issue selling effectiveness fully mediates the positive effect of CIO political skills on enterprises digital innovation. Socio-behavioral integration between business executives positively moderates the relationship between CIO issue selling effectiveness and digital innovation. Accordingly, it is concluded that CIO political skills and issue selling effectiveness are enablers for digital innovation, and that CIO issue selling effectiveness and business executive socio-behavioral integration play a mediating and moderating effect, respectively. The research model proposed and empirically tested in this study bridges the research gap in CIOs effectively driving enterprises' digital innovation and can then provide some practical guidance.

Through mild heat treatment of soybeans at an elevated temperature, soybeans show an ordered porous structure, then the static adsorption method and FTIR analysis are used to study the adsorption of neutral red dyes and heavy metal ions. The research contents mainly include: the influence of initial concentration and the initial pH value on the adsorption, and the adsorption kinetic process. It is observed that in the range of acid to weak alkali, the removal rate to neutral red dye is better than other pH value range, and best when the pH is equal to 7. The initial concentration on the adsorption is also an important factor affecting the removal rate and adsorption capacity. At the same time, the removal rate of the adsorbent and the adsorbate in the first 20 minutes of contact is above 80% during the adsorption process. By fitting, the adsorption behavior is more consistent with the quasi-second-order kinetic model. Porous soybean is not only friendly to human beings, but also demonstrates the advantages of high adsorption capacity and low cost. Therefore, it will be a promising environmental cleaning material .

The problem of current algorithms is lack of timeliness and reliability in bridge crack detection. In this paper, a real-time algorithm on embedded platform is proposed. Firstly, moving average method is used to segment the image coarsely. Then, candidate crack fragments are selected by region growing method using the geometric features of their contours. Finally, a crack merging model with two criteria is built to merge the crack fragments recursively and suppress interference, based on the prior condition of bridge cracks. Experimental results show that the proposed method performs better than several existing methods by 115% at least, especially on extracting hairline cracks and complex cases with uneven illumination and dirty mark. Dealing with an image with 15 megapixel on embedded platform, it costs only 1.73 s.

Aiming at the problem of consensus enhancement in opinion dynamics of social network, a consensus opinion enhancement with intelligent perception (COEIP) model based on multi-agent reinforcement learning is proposed. In the Markov decision-making process in opinion dynamics, firstly, the decision-making model of agent is designed through bidirectional recurrent neural network to solve the problem of uncertain-length perception. Then, through the idea of difference reward, an effective reward function is designed for the three objectives of convergence efficiency, connectivity and communication cost. Finally, in order to optimize COEIP model, a multi-agent exploration and collaborative update algorithm based on policy gradient is designed, which can enable agents to adaptively learn the neighborhood selection strategy with multi-objective trade-off ability through the reward value in the process of interaction with each other. A large number of simulations verify that COEIP can effectively reconcile the contradictions between agents and reduce the number of clusters when the system is stable in the scenario of opinion dynamics of social network, thus enhancing the consensus opinion of the system. This model provides a new solution for improving the unity of people's opinions under large-scale social networks, which has important theoretical guiding significance.

To improve the tracking accuracy of the gait trajectory of the lower limb exoskeleton robot (LLER) , an experimental method of parameter identification is proposed for the LLER’s two-link dynamic model, including static and dynamic experiment. Combined with the wearer’s human parameters, the accurate dynamic model of the LLER human-machine collaborative system is deduced. The sliding mode control (SMC) with the upper bound of the model and a low-pass filter are adopted to track the gait trajectory precisely by MATLAB. The simulation shows that the waveforms of the hip and knee torques measured by experiment are basically consistent with the theoretical values by calculation, and the dynamic parameter identification results are correct. The human-machine collaborative system based on SMC can realize the accurate tracking of the reference gait trajectories of the hip and knee joints, and the low-pass filter can effectively reduce the high-frequency chattering caused by SMC. This research provides a solution for the identification of the dynamic parameters of LLER, a reference model for the model-based control method, and a reference method for precisely tracking the gait trajectory of LLER human-machine collaborative system

In order to clarify the effect of high temperature aging on light-stable modified asphalt materials, hindered amine light stabilizer was selected to prepare modified asphalt. The light-stablized modified asphalt is subjected to thermal aging treatment under different temperature and time conditions. The influence of thermal aging on the road performance of light-stabilized modified asphalt binder was studied by means of three index tests and Brookfield viscosity test. Atomic Force Microscope (Atomic Force Microscope, AFM) was used to characterize the surface roughness changes of light-stablized modified asphalt under different thermal aging conditions. The influence of thermal aging on the microscopic morphology of asphalt was analyzed by 3D morphology reconstruction and the influence of thermal aging on light-stable modified asphalt was clarified. The results showed that T770 modifier can alleviate the effect of thermal aging on asphalt binder. When the heat aging time exceeded 6 hours, honeycomb structures appeared in the base asphalt. As the aging time increased, the special structure formed on the surface of the base asphalt became smaller gradually. At 6h and 9h, honeycomb structure appeared in the T770 modified asphalt. According to the comparison of roughness data, the surface undulation of modified asphalt was greater than that of base asphalt, which indicated that under the action of heat aging the surface roughness of T770 modified asphalt was slightly higher than that of the base asphalt binder, demonstrating that the light-stable modified asphalt material had the higher sensitivity to heat aging.

Brain tumors are a major problem affecting the health status of the nation. To determine the extent of brain tumor in order to determine the next step in treatment, a puncture biopsy procedure of brain tumor tissue is often required. Magnetic resonance imaging (MRI) is commonly used to detect brain tumors due to its better soft-tissue resolution. Therefore, research related to MRI-compatible robots is necessary. Based on an MRI-compatible hydraulically driven puncture surgery robot, the kinematic model of the robot is derived based on the principle of hydraulic linker, and the dynamic model of the robot is obtained based on the relevant theory of fluid dynamics. In order to realize the accurate control of the designed robot system, the state feedback H _∞ control rate of this hydraulically driven system is designed according to the H _∞ control theory, which enables the robot to track the target signal quickly and stably. Finally, the average positioning accuracy of the designed robot system in x-axis, y-axis, z-axis, pitch-axis and roll-axis are obtained through experimental studies, and they are, respectively, 0.41 mm, 0.6 mm, 0.67 mm, 0.886° and 1.17°. Experimental results verify the performance of robot-assisted positioning puncture needles, and the dynamic model and control method of the robot have been given, which provide certain reference value for the research of the control algorithm of puncture robots.

To improve the performance of shunt hybrid active power filter, a specific sub-harmonic compensation control strategy based on linear active disturbance rejection controller is proposed. By establishing a mathematical model of the shunt hybrid active power filter, the third-order system control problem is simplified into a first-order active disturbance rejection control problem by combining the characteristics of active disturbance rejection control. At the same time, a first-order active disturbance rejection control for harmonic current tracking and a double-closed-loop active disturbance rejection control for the DC bus voltage are designed. Finally, by comparing the traditional PI control through simulation, the strategy makes the harmonic current tracking more stable, the DC-side voltage overshoot is 0, and the current and voltage shocks are effectively suppressed. The harmonic distortion of the grid current after compensation is 1.26%. The results show that the control strategy has good tracking and anti-disturbance capability, the algorithm is simple and has some engineering application value.

Low-dose single-photon emission computed tomography (SPECT) imaging can reduce the radiation damage to human bodies caused by radioactive tracers, and hence it is becoming more and more important in clinical practice. In a SPECT system, low-dose imaging can be achieved by acquiring projection data of sparse-view. The sparse-view projection data, if directly reconstructed by conventional iterative reconstruction methods, will inevitably lead to severe ray artifacts in the image domain. Existing clinical reconstruction methods usually introduce specific regularization to the optimization model to suppress ray artifacts. However, this type of methods may not adapt to projection data with various dosage, and the form of regularization heavily depends on prior knowledge. A novel neural network architecture is proposed to learn the mapping from the sparse-view projection data to the full-view projection data. The projection data of missing view angle is synthesized by the proposed neural network to improve the quality of reconstructed images. Numerical experiments show that, compared with the traditional iterative reconstruction method, the SSIM of the reconstructed image is increased by 59%, the NMSE is reduced by 67%, and the PSNR is increased by 2.48 dB. Therefore, the proposed method can better improve the image quality of sparse-view projection data.

Geological disasters caused by surface deformation are common, especially in urban areas, which seriously hinders the sustainable development of urbanization. In order to evaluate the risk of geological disasters caused by urban surface deformation, monitoring and analysis with high temporal and spatial resolution becomes particularly important. Based on Sentinel-1A satellite images from January 2019 to January 2021, SBAS InSAR and PS InSAR technology is used to obtain the time series of surface deformation in Nanchang, and wavelet periodic analysis and grey correlation analysis combined to evaluate the correlation between deformation area and climate environment. The research shows that the central urban area of Nanchang is affected by urban construction and clay layers, showing a large-scale subsidence signal; the agro-ecological areas in the southeast show uplift signals due to groundwater replenishment. Periodic analysis further shows that the surface deformation of Nanchang is affected by the change of precipitation. Combined with external data, this study examines the possibility of subsidence disasters of subway lines in areas with large deformation from multiple perspectives, thus providing reference for disaster prevention and reduction.

The carbon emission calculating method of light vehicle is studied, and the relationship between vehicle carbon emission and operating conditions analyzed. Based on the on-board test data of real drive emission (RDE) of vehicles, the carbon emission is represented by CO ₂ equivalent CO _2e. It is indicated that the carbon emission rate increases with the increase of vehicle speed and specific power. The Back Propagation (BP) neural network algorithm is used to establish the nonlinear relationship between vehicle carbon emission and speed, acceleration and vehicle specific power (VSP), and calculate the carbon emission factors under three bench test cycle conditions of World Light Vehicle Test Cycle (WLTC), New European Driving Cycle (NEDC) and China Light-duty Vehicle Test Cycle-commercial Car (CLTC-C). It is found that the carbon emission factors under the three bench test cycle conditions are higher than the actual road driving carbon emission factors, among which the carbon emission factor under WLTC is the highest, followed by NEDC and CLTC-C. The reason is that the test conditions with higher acceleration and speed lead to increased carbon emissions.

In term of the event-trigger impulsive mechanism, this paper designs a new event triggering function based on the Lyapunov function, and the leader-following consensus of multi-agent systems with randomly occurring uncertainties and randomly occurring nonlinearities is studied. Different from the control method of artificially setting the impulse time sequence, the generation of the impulsive moment depends on the designed triggering function, and when the trigger condition is met, the impulsive control is activated to reduce unnecessary control times and resource consumption. Based on impulsive differential equation theory, algebraic graph theory, and Lyapunov stability theory, the sufficiency conditions that controlled multi-agent systems can achieve the leader-following consensus are given. Meanwhile, the Zeno behavior can be excluded. Finally, the feasibility of the obtained results is verified by a numerical example.

The protective effect of Schisandra chinensis oil on H ₂O ₂-induced oxidative damage of HaCaT cells is evaluated by 3D skin model. Schisandra chinensis oil was extracted by steam distillation, and its chemical constituents analyzed by GC-MS. And the antioxidant activity of schisandra chinensis oil was evaluated by DPPH and ABTS free radical scavenging experiments, combined with 2D and 3D skin models induced by H ₂O ₂. The results showed that Schisandra chinensis oil mainly contained 37 compounds including ylenene, β-cimarene and L- α-pinene. Schisandra chinensis oil had good scavenging effect on DPPH and ABTS free radicals, with IC ₅₀ of 5.6 mg/mL and 9.4 mg/mL, respectively. What’s more, Schisandra chinensis oil can improve the survival rate of HaCaT cells after H ₂O ₂ treatment, increasing the epidermal thickness of 3D skin model, decreasing the level of Interleukin-6(IL-6), and up-regulating the activities of SOD and CAT enzymes. The results confirmed that Schisandra chinensis oil had protective effect on H ₂O ₂-induced oxidative stress injury model of HaCaT cells, which could lay a foundation for the development of anti-aging products of schisandra chinensis oil.

The traditional frost detection method based on image processing technology is difficult to flexibly and accurately detect the cold chain refrigeration unit in complex production environment. It is also easy to misjudge due to the influence of environmental factors such as illumination and fog. Therefore, a cold chain refrigerating machine frosting detection method based on spiking neural network is designed. Taking the refrigerator image as the input, the dynamic change of the frosting area of the refrigerator evaporator is automatically detected, the abnormalities caused by interference factors such as illumination and fog are corrected, and the double threshold divided by the cumulative value of pulse emission rate is used as the judgment basis of frosting degree. Experiments are carried out on several cold chain refrigerators put into the production environment. The results show that the designed spiking neural network can adaptively detect the dynamic region of the frosting area of the refrigerator evaporator under the actual production environment and the double threshold which is divided accurately can judge the frosting degree of the evaporator. The detection effect is good and the stability is strong, which can provide a reliable basis for the defrosting time of the defrosting strategy of the cold chain refrigeration unit.

The classic recommendation system focuses on the accuracy of recommendation. With the increase of users’ diversified needs, the diversity of recommendation results has attracted more and more attention. The accuracy and diversity of the recommendation always conflict with each other, and the traditional recommendation algorithms often ignore the difference of users’ activity in the system. A user stratification multi-objective recommendation algorithm is proposed based on the difference of users’ evaluation times on the items, which can provide better recommendation results for different users. Users are divided into three types with high, medium and low evaluation times, and three different initialization methods are proposed for the algorithm. By the comparative analysis of the initialization operations and parameter settings of the existing probability based multi-objective evolutionary algorithm, improved crossover and mutation operations are obtained. The experimental results verify that the enhanced multi-objective evolutionary algorithm can find better results with higher accuracy and diversity. Finally, a recommendation scheme based on user stratification is summarized, which helps to improve the recommendation effect for different users.

In order to study the changes of combustion characteristics of domestic gas stoves after mixing natural gas with hydrogen, numerical simulation and thermal experiment research are carried out on the application of natural gas hydrogen-mixing combustion technology in household swirl gas stoves. First, the detailed mechanism GRI-Mech 3.0 is simplified. Through comparison and verification, the error between the simplified mechanism and the original mechanism is less than 1%. Secondly, using simplified mechanism for numerical simulation of pure methane gas stove, the numerical simulation and thermal experimental calculation results are compared and verified. It is found that the maximum error of the average temperature of the combustion zone does not exceed 12%, which confirms the feasibility of the simulation. Finally, a numerical simulation study of methane hydrogen mixing conditions is carried out. The calculation results show that the primary air coefficient of domestic gas stoves gradually increases with the increase of the hydrogen mixing ratio, which has a great influence on the combustion characteristics. After adding 15% of the volume of hydrogen, CO emissions are reduced by about 9%; and the temperature remains basically stable, which confirms the effectiveness of methane hydrogenation technology. The research results have reference value for the popularization and application of natural gas mixed with hydrogen in the combustion of gas stoves.

Visual odometry is the cornerstone in the field of SLAM. Monocular visual odometry occupies an important position because of its low cost and less camera calibration, but it has some shortcomings such as scale uncertainty, scale drift, poor robustness, and so on. To solve these problems, based on ORB-SLAM3, we process a monocular visual-inertial navigation odometer with depth features, referred to as DF-VIO (Visual Inertial Odometry Based on Deep Features) , which uses depth features extracted by deep learning network to replace traditional artificial point features, and fuses artificial line features to enhance the robustness of the system in real complex scenes. Besides, the system provides a variety of pose tracking methods. In addition to the method based on the constant speed model and tracking reference keyframe, a pose tracking method based on the predicted repeatability map is also provided, which further improves the accuracy of system pose tracking. Comparative experiments are carried out on the open data set EuRoC, and the average trajectory error is reduced by 25.9% in pure visual mode and 8.6% in visual-inertial mode, which proves that the system proposed in this paper can be more robust in complex scenes.

The Sr ₂GdSbO ₆:Eu ³⁺ red phosphor with double perovskite structure was successfully prepared by high temperature solid phase method and its XRD, photoluminescence spectrum and fluorescence decay lifetime were characterized. The XRD of the Sr ₂Gd _0.8SbO ₆:0.2Eu ³⁺ phosphor is basically consistent with the peak position of the standard card, indicating that the doping of Eu ³⁺did not change the crystal structure of the matrix. The crystal parameters after Rietveld refinement also verify the result. The photoemission spectrum shows that the sample has a wide excitation area centered at 340 nm in the ultraviolet region; under the excitation of a 340 nm light source, the strongest emission of the sample is an orange-red emission band with a center wavelength of 593 nm. Using Dexter theory to analyze the concentration quenching mechanism of the sample is the dipole-dipole interaction. The fluorescence decay life of Sr ₂Gd _0.8SbO ₆:0.2Eu ³⁺ phosphors at room temperature is 4.202 ms, and the external quantum efficiency is 24.8%. When the temperature is raised to 150 ℃, the external quantum efficiency remains 68.8% of that at room temperature. The calculated color purity of Sr ₂GdSbO ₆:Eu ³⁺ phosphor can reach 99.5%. Sr ₂Gd _0.8SbO ₆:0.2Eu ³⁺ phosphor, blue phosphor, green phosphor and 365 nm UV chip package were made into LED devices which can emit bright white light. These results indicate that Sr ₂GdSbO ₆:Eu ³⁺ phosphor is a potential red phosphor for white LED.

To study the privacy protection of online car-hailing, an evolutionary game model including the government (regulatory, non-regulatory) and platform (self-discipline, non-self-discipline) is constructed, using the replication dynamic equation to find an evolutionary stable strategy, and using Matlab to simulate the situation under different circumstances. The influences of four parameters, including the degree of reputation improvement brought by government regulation, on the evolution path, are analyzed. The study finds : (1) When the benefits of government regulation are greater than those of non-regulation, and the benefits of platform self-discipline are greater than those of non-self-regulation, ideal result (regulation, self-regulation) will appear; (2) When the cost of platform self-regulation is low and the cost of government regulation is high, even if the government does not supervise, the platform will also be self-disciplined; (3) The increased public affirmation of government regulation and the reduction of government regulation costs leads the government to transform from non-regulation to regulation; (4) When the platform obtains positive benefits from society and reduces the cost of self-discipline, its strategy changes from non-self-discipline to self-discipline. These conclusions have guiding significance for the government to optimize the design of regulatory policy.

A preventive maintenance strategy for key components of ships is advanced based on the delay time theory. This strategy aims at the problem of high maintenance cost and low utilization rate caused by unreasonable maintenance of key parts. It bases on periodic inspection of each component, and divides the maintenance of each component into inspection maintenance and delay maintenance according to the different characteristics of defect and failure stage. And the idea of opportunity maintenance is introduced in the delay phase, to establish an opportunistic maintenance model for the ship’s key components. According to this strategy, the opportunistic maintenance strategy of the key parts under long-term use can be obtained in four steps: Base on component failure data and inspection maintenance records. Estimate model parameters by maximum likelihood estimation. Take detection cycle and delay time as optimization variables. Take the minimum cost ratio as the optimization objective. The result of this research indicates that this strategy can more effectively reduce the cost of ship maintenance, reduce downtime for maintenance, improve the efficiency of the use of the ship, and provide a decision basis and reference for ship maintenance, than the simple test maintenance strategy and traditional preventive maintenance method.

In order to improve the interaction between go playing and teaching, a drop guidance device for go robot is designed, including visual processing and motion control modules. For the visual processing module, a standard chessboard image extraction method based on multi-scale detection is proposed to improve the stability of chessboard image extraction. A separate chessboard detector is used to detect the pieces in the reflective area of the chessboard, and the detection effect of chess pieces in uneven illumination area is improved. For the motion control module, the high-precision digital actuators and laser guider are used to construct the motion actuator. The kinematics modeling is completed by using the telescopic joint to simulate the laser light path. An error compensation method based on perspective transformation is proposed to realize the mapping of joint variables. The motion end position compensation is implemented through simulation calculation. Finally, the accuracy of vision module and the effectiveness of error compensation method of motion control module are verified experimentally.

An asymmetric topology suitable for three-phase active power filter is proposed. The topology combines a passive filter with a low-power active filter to decrease the capacity and system losses of the voltage inverter (VSI) . It consists of a three-phase bridge, a two-phase dual-resonant LC passive filter and a phase coupled inductor. By reducing the number of devices, the topology design scheme explores the impact of the changes of APF device parameters, which will affect the compensation effect of APF. Finally, a control strategy based on virtual capacitor voltage compensation is proposed to control the system effectively. Simulation and experiments show that the topology and control strategy proposed are feasible.The scheme of this topology and its control strategy can reduce the number of devices and be used in combination with traditional symmetric control strategies, which is easy to be implemented in engineering.

The rapid development of wearable electronic devices has promoted the research of flexible supercapacitors. Conducting polymers have shown great application potential in flexible supercapacitors due to their high conductivity, fast and reversible redox reaction, and flexibility similar to traditional polymer materials. Pure conducting polymers electrodes exhibit limited electrochemical performance (short cycling life and low actual capacity), and the formation of composite materials with emerging two-dimensional materials can improve their electrochemical performance. The preparation and assembly strategies of two-dimensional materials (graphene, transition metal chalcogenides, and transition metal carbon/nitride) composites from one to three dimensions is discussed firstly, and then the latest progress in their application in flexible supercapacitors with different structures (fiber structure, sandwich structure and planar interdigital structure) is reviewed. Finally, the challenges and future development trend of conducting polymer matrix composites in flexible supercapacitors are presented.

CiteSpace is used to compare and analyze the knowledge atlas of Chinese and foreign periodicals, taking the BIM technology included in CNKI and Web of Science as the research sample. The results show that the application of Building Information Modeling (BIM) technology in engineering cost has developed rapidly in recent years, and the research is divided into four stages: development stage, extension stage, transition stage and innovation stage. “Construction Engineering” and “Management” are the common research hot spots at home and abroad. The future trends abroad are “Sustainable Development” and “Periodic Evaluation”, while “Fine Management” and “Personnel Training” are the future research trends at home and abroad. Based on this, the representative BIM technology (Lu Ban software and Innovaya) at home and abroad is described in detail. The research results can be used for reference to understand the application of BIM technology in engineering cost.