Aiming at the problems of low luminous efficiency and large working bias of AlGaN-based deep-UV (Ultraviolet) LEDs (Light Emitting Diodes), a nitrogen-polar AlGaN-based deep-UV LED device structure with n ⁺ -GaN/ Al _{0. 4}Ga_{0. 6}N/ p ⁺ -GaN tunnel junction is designed. The LED structure is consist of an electron supplying layer n-Al _{0. 65}Ga_{0. 35}N, a multiple quantum wells of Al _{0. 65} Ga_{0. 35} N/ Al _{0. 5} Ga_{0. 5} N, a compositionally graded p-Al _xGa1_-xN and n ⁺ -GaN/ Al _{0. 4}Ga_{0. 6}N/ p ⁺ -GaN tunnel junction. The simulation results show that the tunnel junction LED has higher internal quantum efficiency and light output power, and it has a lower turn-on voltage than the reference LED without tunnel junction. The improvement of the optoelectronic characteristics of the tunnel junction LED is attributed to the introduction of the tunnel junction improving the hole injection efficiency of the LED, and improving the current spreading capability of the LED device. The results of this work show that the simulation of carrier transport and optoelectronic characteristics of semiconductor devices through simulation software is helpful deepening the understanding of the physical characteristics of semiconductor devices. If the study of semiconductor device simulation software is added to the learning process of “ semiconductor device physics冶, it will help the cultivation of talents in the semiconductor field. 

To reduce the influence of complex and changeable electromagnetic environment on vehicles, the cable model is built in the engine compartment by using the three-dimensional electromagnetic field simulation software CST(Computer Simulation Technology) to study the influence of different factors on the electromagnetic coupling effect of vehicles. Through simulation, the peak relationship curves between induced voltage and induced current in the cable are drawn when the parameters such as cable length, cable height from the bottom of the car, cable relative distance, cable terminal resistance, conductor radius, and insulation layer thickness change. These conclusions can provide theoretical guidance for the design of vehicle wire harnesses, and provide a basis for the conductor radius selection, cable relative distance, height from the ground, and cable length in electromagnetic protection design. 

For the problem that LLE(Local Linear Embedding) fails to adequately preserve the structure between

neighborhoods in high-dimensional data, a new local linear embedding algorithm is proposed for fused

neighborhood distribution properties. The algorithm calculates the neighborhood distribution of each sample data,

then calculates the respective nearest neighborhood distribution difference of the KL ( Kullback-Leibler)

divergence measure between the different neighborhood point and its central sample, and finally optimizes the

reconstructed weight coefficient to obtain more accurate low-dimensional motor data. The effectiveness of the

algorithm is verified by three evaluations of visualization, Fisher measurement and identification accuracy.

In order to improve the torque ripple and flux ripple in the model predictive control system of PMSM (Permanent Magnet Synchronous Motor), a control system scheme is designed by learning the basic structure and control methods of PMSM. The scheme adjusts the duty cycle and voltage vector synchronously. The optimal expected voltage vector and action time at a certain sampling time are selected, and the optimal expected voltage vector and action time at the current sampling time are added to adjust the duty cycle coefficient of the sampling time. The feasibility and effectiveness of this method in improving the control performance of PMSM are verified by comparative analysis of the simulation model.

In order to improve the experimental teaching of relevant courses in automatic control, the design of a four-tank system is presented. The hardware is composed of general industrial components, while a Matlab programming environment is used for the software to directly control the system. A graphical user interface is also adopted to provide an easy way to run the system. The four-tank system model is built, and its parameters are identified based on a step response experiment. Furthermore, a distributed internal mode controller is designed for the liquid tracking control of the four-tank system. The results show that the four-tank system, using the internal mode controller, has little effect on the water level of the other tank when the water level of one tank changes, and the system has a satisfactory control effect. This experiment could play an important role in the teaching of relevant courses in automatic control.

Taget Detection of Photovoltatic Remote Sensing Based on Improved Yolov5 Model TONG Xifeng, DU Xin, WANG Zhibao (School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China) Abstract: Aiming at high-sensing photovoltaic image resolution, high environmental noise, and complex background, an improved Yolov5 model is proposed to achieve positioning of photovoltaic power plants. First of all, the CA(Coordinate Attention) mechanism is added to the compassionate layer of the main feature extraction network to improve the learning ability of the network characteristics; second, the Ghostconv network structure is added to Backbone, useing the Ghostconv network module to replace the Conv network module, designing a new GhostC3 network network instead of the original C3 network module to improve the learning efficiency of the model; finally, the GIoU_Loss function is changed to the SIoU_Loss function. Compared with the original Yolov5 method, the average accuracy of the improved algorithm mAP, accuracy, and recall rate reached 97. 5% , 98. 9% , and 94. 9% , respectively, which have increased by 1. 8% , 1. 7% , and 5. 8% , respectively. The algorithm has a good effect on photovoltaic detection.

The Artificial Bee Colony algorithm ( ABC: Artificial Bee Colony) suffers from the problems of difficult convergence and difficulty in maintaining the diversity of candidate solutions. In order to solve the Multi- Objective Optimization Problem (MOP: Multi-Objective Problem) the solution strategy of each part is improved. Based on the ABC algorithm framework, a multi-objective ABC algorithm based on an adaptive solution strategy is designed to compare the performance of the improved multi-objective ABC with other typical swarm intelligence algorithms in the practical application of engineering design problem of electromechanical actuator design. The experimental verification shows that the proposed MOABC / DD(Multi-Objective Artificial Bee Colony Based on Dominance and Decomposition) algorithm has better problem solving accuracy compared with typical algorithms in solving the benchmark test case of electromechanical actuator design problem. The experimental results of MOABC / DD are more stable, thus proving that MOABC / DD has high solution stability and robustness.

There are many kinds of raw materials required, and the market price of materials is affected by the price of raw materials. Under such conditions, the price of raw materials has changed, but the market price of materials has not changed. In order to improve the effect of prediction, it is necessary to obtain the time delay of price. This study uses binary density clustering method combined with DTW ( Dynamic Time Warping ) algorithm, to calculate the similarity between the market prices of commodities over different time intervals and the trends in raw material prices. It has been determined that the market prices of commodities lag behind the fluctuations in raw material prices by 11 weeks. As a result, the market price of cable materials can be predicted based on the trends in raw material prices from 11 weeks ago. This information can assist the procurement department of businesses in formulating rational procurement strategies.

 To monitor changes of indoor carbon dioxide concentrations in real time, a carbon dioxide alarm based on a gas sensor with a STC89C52 microcontroller as the core is designed. When the CO2 concentration in the air exceeds the preset value, the sound and light alarm function can be activated, and the indoor CO2 concentration value can be displayed in real-time. The hardware system includes a carbon dioxide sensor, signal conditioning circuit, analog-to-digital conversion circuit, STC89C52 microcontroller, and acousto-optic alarm unit. The software system includes data acquisition, data processing, alarm logic, and other functional units. The alarm can be activated in time when indoor CO2 concentration exceeds 1. 5% . 

Currently, SnO2(Tin Dioxide) has become the most commonly used material for the electron transport layer in high-performance PSCs(Perovskite Solar Cells). A strategy for optimizing SnO2 using a small-molecule chelator is proposed to address the problem of agglomeration-prone commercial SnO2 aqueous dispersions and the need to enhance the electrical and surface properties of SnO2 films. The PSCs with the device structure of ITO/ SnO2+SC / FA1-x MAx PbI3 / Spiro-OMeTAD/ Au are prepared by doping the SnO2 transport layer with a low-cost chelator, SC( Sodium Citrate). After the introduction of SC with an optimized concentration, the open-circuit voltage and fill factor of PSCs can reach up to 1. 135 V and 78. 23% , respectively, with a power conversion efficiency of 21. 53% . This represents a significant improvement compared to the devices without the introduction of SC. The characterization of the films and devices revealed that the doping of SC could enhance the electrical and surface properties of the SnO2 films, which in turn improves perovskite crystallization. As a result, defects in the device are reduced, recombination loss is lowered, and charge transport is promoted.

 In order to solve the problems such as large resistance and low efficiency of the traditional hydraulic power steering system during the driving process of commercial vehicles, improper maintenance after long-term use will lead to leakage, and may cause accidents, an electric auxiliary steering motor control is adopted. The electric auxiliary steering motor selects the permanent magnet synchronous motor. By using the fuzzy PID (Proportional Integral Derivative) algorithm, it can be modulated to the reference value accurately and quickly . The extended Kalman filter is used to estimate the rotor position of the permanent magnet synchronous motor, reduce the volume of the controller and reduce the cost. In the drive system, the diversity of large current coupling paths seriously affects the electromagnetic compatibility performance of the motor control system, so the drive system EMC ( Electro Magnetic Compatibility) design is adopted. From the experimental results, it is concluded that the permanent magnet synchronous motor control based on fuzzy PID algorithm and extended Kalman filter can be applied to the electric auxiliary steering motor control of commercial vehicles.

In order to reduce the inspection burden of electric power workers and realize intelligent inspection in substation, the algorithm of substation equipment defect detection is studied. Firstly, the data augmentation method is used to expand the initial dataset and various image processing method is used to generate the dataset with complex illumination environment. Then, the adaptive spatial feature fusion method is used to mitigate the inconsistency of different scale features in the feature pyramid, and the loss function of confidence is changed to Focal loss function to mitigate the imbalance between positive and negative samples. Based on the improved YOLOX-s(You Only Look Once X-s) network model, the algorithm of substation defect detection is designed. Finally, the detection effect of the improved YOLOX-s model is compared with that of other deep learning algorithms. Under the designed data set, the experiment shows that the comprehensive detection effect of the improved YOLOX-s network model is good, and the accuracy and real-time performance is satisfied. 

To meet the need of integrating scientific research into teaching of Emerging Engineering Education, a multi-dimensional and hierarchical integrated experimental platform based on Python is designed. Guided by the talent-training plan, hierarchical modules involving image recognition, machine learning and data analysis is designed from scientific research hotspots. Image recognition module starts from character recognition, then face and license plate recognition are realized by several algorithms. In the machine learning module, commonly used machine learning algorithms are studied and corn disease is identified by various methods based on Python. In the data processing and analysis module, Excel data processing experiment based on Python is designed to analyze the data of workload and bioinformatics data. The platform enables students to learn the application of Python in the experiments, and choose different experimental projects according to professional needs and research directions to realize the goal of teaching students in accordance with their aptitude. By applying the experimental platform to teaching practice, it is demonstrated that students have a deeper understanding of Python’s programming implementation in image recognition, machine learning, and data analysis and enhanced research interest. And the goal of integrating scientific research into teaching and improving the quality of undergraduate teaching could be achieved.

Aiming at the problem that the traditional named entity recognition method can not fully learn the complex sentence structure information of ancient Chinese and it is easy to cause information loss in the process of long sequence feature extraction, an ancient Chinese fusion of SikuBERT ( Siku Bidirectional Encoder Representation from Transformers) model and MHA (Multi-Head Attention) is proposed. First, the SikuBERT model is used to pre-train the ancient Chinese corpus, the information vector obtained from the training into the BiLSTM (Bidirectional Long Short-Term Memory) network is input to extract features, and then the output features of the BiLSTM layer are assigned different weights through MHA to reduce the information loss problem of long sequences. And finally the predicted sequence labels are obtained through CRF (Conditional Random Field) decoding. Experiments show that compared with commonly used BiLSTM-CRF, BERT-BiLSTM-CRF and other models, the F1 value of this method has been significantly improved, which verifies that this method can effectively improve the effect of ancient Chinese named entity recognition.

Automatic analysis of lumbar disc herniation requires precise segmentation of the multifidus muscle’s fatty infiltration site in spinal MRI ( Magnetic Resonance Imaging) images. An attention-based approach for segmenting the multifidus muscle in lumbar disc herniation patients is proposed to address issues including ambiguous boundaries between segmentation targets and adjacent components. The network utilizes an encoder- decoder structure, and the addition of an attention mechanism module to increase the network segmentation accuracy. After feature extraction, an atrous spatial pyramid pooling module is added to combine contextual data improving the performance of the network model. In comparison to the traditional U-Net algorithm, the experimental results demonstrate that this model improves the segmentation accuracy of the fatty infiltrated regions of multifidus muscle by improving the Dice coefficient by 7. 8% , Jaccard similarity coefficient by 10. 1% , and Hausdorff Distance by 69. 5% . 

The complex anatomical structure of the human body and significant individual differences, the limited two-dimensional anatomical images in textbooks often make it difficult for students to understand, and there are problems such as unclear learning objectives, unscientific learning methods, low learning efficiency, and poor learning outcomes during the learning process. To address a clinical anatomy teaching model based on image 3D visualization technology is proposed. The clinical teaching achievements based on 3D visualization technology in recent years are summarized, and the feasibility and superiority of 3D visualization technology in clinical teaching mode pointed out. Finally, the expansion content and development ideas of the visualization clinical teaching mode are discussed, and the possibility of applying the construction of clinical anatomy case library based on image visualization technology to the visualization teaching mode is prospected. 

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. It is difficult to carry out secure machine learning between heterogeneous data islands. A federated learning communication mode between heterogeneous data islands is proposed, which realizes the hybrid federated learning communication between horizontal and vertical, and breaks the communication barrier of the disunity of model structure between horizontal and vertical participants in traditional federated learning. Based on the special privacy requirements of the government, banks and other institutions, the third party aggregator is further removed on the basis of the hybrid federated learning model, and the calculation is carried out only among the participants, which greatly improves the privacy security of local data. In view of the computational speed bottleneck caused by vertical homomorphic encryption in the communication process in the above model, by increasing the local iteration round q, the encryption time of vertical federation learning is shortened by more than 10 times, and the computational bottleneck between horizontal and vertical participants is reduced, and the accuracy loss is less than 5% .

Most of the existing research on degree prediction in colleges and universities focuses on the construction of performance prediction models, ignoring the importance of degree early warning. Therefore, a degree early warning model based on support vector machine is proposed. A large number of experiments are carried out on the real data of 5 majors, including Broadcast and Television Directing Major, Chinese Language and Literature Major, Chemistry Major, Accounting Major and Mathematics and Applied Mathematics Major, in a university of 2018. The experimental results show that the constructed early warning model has good accuracy and practicality,which can become an important part of improving the teaching quality, and provide practical reference support for teachers to improve the teaching plan and for students to change their learning habits.

At present, due to environmental interference in the process of digital image acquisition and transmission, low-pixel images will appear, resulting in poor image reconstruction effect. For this reason, a digital image super-resolution reconstruction algorithm based on multi-scale residual is proposed. Use bilateral filtering algorithm to complete the dehazing processing of digital images. The brightness feature information and color information of digital images are classfied, and the distance threshold denoising method is used to denoise. To set convolution kernels of multiple sizes. In the process of image feature extraction, digital image features are obtained, and back-projection operations are performed on them. Based on the residual learning idea, the features extracted by the up-sampling and down-sampling processes are connected to realize digital image super-resolution reconstruction. The experimental results show that the proposed algorithm has high structural similarity, high PSNR (Peak Signal-to-Noise Ratio) and good reconstruction effect for image reconstruction. 

Due to the influence of complex terrain and canopy structure in forest, the accuracy of snow depth retrieval based on passive microwave remote sensing data is generally low. Based on the representative semi- empirical snow depth retrieval algorithm and combined with meteorological observation data, an optimization algorithm of semi-empirical snow depth retrieval in forest area in Northeast China was established in this paper. In this algorithm, the permittivity of vegetation varies with temperature and the accuracy of snow depth retrieval in forest is greatly improved. Compared with other representative semi-empirical algorithms, the RMSE(Root- Mean-Square Error) of the proposed algorithm is reduced by 2. 3 cm, Bias by 3. 7 cm on average and correlation (R) improved by 0. 11 on average. Compared with the commonly used snow depth retrieval algorithm based on machine learning, the RMSE of the proposed algorithm is reduced by 2. 17 cm, Bias by 1. 67 cm on average and R improved by 0. 22 on average.

 

 To address the issue of the lack of interoperability and data sharing among different information and application systems on campus, we aim to leverage data integration technology to merge diverse educational data sources. We intend to establish a multi-source, heterogeneous education big data mining and application platform. The platform system will utilize the output of artificial intelligence models and the input from a multi- source, heterogeneous education big data mining engine. It will be based on big data mining techniques to analyze and process multiple data sources, including student records, teaching resources, and social behavior information. This will enable functionalities such as educational sign diagnosis, intelligent learning state comparison, analysis of teaching impact factors, identification of potential issues, and prediction of teaching quality trends. Our goal is to scientifically enhance the quality of personalized campus teaching services, objectively assess the teaching proficiency of individuals and teaching teams, assist in analyzing the strengths and weaknesses of teaching individuals and teams, and provide robust support to decision-makers in managing the education system. 

For the problem that the K-means algorithm requires manual determination of the cluster numbers and random selection of initial clustering centers, which can fall into local optima, an improved parameter-free K-means algorithm is proposed by combining the density peak-based clustering algorithm CFSFDP(Clustering by Fast Search and Find of Density Peaks). First, the local density and dispersion of the sample points are calculated, then a decision diagram is established, and a vector of two parameters is composed. The distance from each point to the surrounding 5 points is calculated, and those with a distance greater than 2 times the mean square error and a density greater than the average density are filtered out. The filtered point is used as the initial clustering center of the algorithm. The number of statistical clustering centers k is used as the number of clusters, and the initial number of clusters k and the initial clustering centers are used as the initial parameters of the K-means algorithm to cluster data. The algorithm is tested on different types of data sets, including artificially created Gaussian data sets, UCI(University of California, Irvine) data sets, and real tool vibration data sets. The results show that the proposed algorithm maintains the global optimality of the traditional algorithm and validates its effectiveness. Since K-means is an unsupervised clustering method, it can reduce the workload and computational cost of manual data calibration, supervised training, etc. , while obtaining better tool state recognition results, which is of high practical significance for accurate real-time extraction of the operating state of the tool for computerized numerical control machine tools.

Affected by the stability of the hospital network, the data of paperless office network data is vulnerable to attack. Therefore, an adaptive encryption algorithm based on chaotic sequence is proposed. The key is automatically generated by random numbers, and the chaotic sequence is generated by three-dimensional chaotic system to generate the scrambled and grouped key sequence. Based on the service expectation, a node scheduling algorithm is designed to schedule the nodes of hospital paperless office network to ensure that the key encryption can be scheduled to the appropriate network nodes. Through cloud storage and improved knapsack algorithm, the adaptive encryption of hospital paperless office network is realized. In the paperless office network of a hospital, text data, image data and video data are selected for testing. The test result shows that the encrypted ciphertext presents a digital state, which is not easy to attract the attention of attackers. The mean square error between the ciphertext and plaintext of the three kinds of data is large, up to 258. 63. The data correlation of the three kinds of data after encryption is greatly weakened, which shows that the design algorithm can destroy the original correlation of the data and has good network data encryption ability.

Traditional cloud access security monitoring methods can not identify the pseudo characteristics of access data, and the server is vulnerable to storage space constraints, resulting in poor monitoring effect and data confidentiality. In order to detect network attacks and make timely response plans, a virtual machine access security monitoring algorithm based on active energy consumption and idle energy consumption is proposed. Using discrete wavelet transform method to process intrusion information of virtual machine access pages, the data autocorrelation function is obtained, and the amplification factor is the same, which is divided into different aggregation sequences. By calculating the energy consumption of nodes, the energy consumption of active and idle states are obtained, increasing the number of active slots of access path nodes, balancing the network energy load, and extending the network life cycle. A multi angle analysis model of virtual machine network is formed based on the characteristics of network access, its characteristic functions and processing forms are clarified. All virtual machine access effective domain data are obtained, the processor application rate is improved, the time average is calculated, and the security status of virtual machine access is perceived. Experimental results show that the proposed algorithm can monitor the attack situation with lower false positive rate and close to 100% detection accuracy, which is superior to the other two algorithms and proves the effectiveness of the proposed method.

In order to solve the problems of fuzzy boundary information and noise influence in identifying special geological bodies such as seismic faults and gas chimney in seismic images, the LBP / ENT edge detection algorithm is proposed, which is an improved combining LBP( Local Binary Patterns) algorithm and local ENT (Entropy). Rotation invariant unified local binary pattern is used to construct the traditional LBP. The transverse discontinuity of abnormal geological bodies is adapted in seismic images, local entropy is used to describe the local discrete characteristics of seismic images to improve the robustness of seismic noise. Compared with Canny operator, LBP Operator, local VAR(Variance) and LBP / VAR operator, the proposed LBP / ENT method is used to study the complex Marmousi theoretical model and the gas chimney on the actual seismic image. The results show that LBP / ENT can more clearly describe the edge information of seismic image and have better robustness to noise. It is concluded that the proposed LBP / ENT algorithm provides a feasible method and technology for detecting the edge information of special geological bodies on seismic images.