Research on the Use of Neural Network for the Prediction of College Students’ Mental Health

A healthy mental status of students plays an important role in getting quality of education. Hence, research on the prediction of college students’ mental health status is of great importance and considered as a hot area of research. In this specific research study, back propagation (BP) algorithm is adopted to learn verities of characteristics of different students from the historical data of the students including: psychological characteristics, basic personal characteristics, and socio-economic characteristics. In the initial stage of the modeling, data preprocessing steps are used to prepare the data to be used by the BP algorithm for building model. The rationales behind the use of BP algorithm are its capability of handling heterogeneity of data and exploring correlations among different characteristics. The proposed model enhances the capability of BP algorithm for risk prediction of psychological problem of the students and achieves higher precision of psychological problem prediction. The results obtained show that the error between the predicted and measured values is 0.88%.

LSALO-BP: A Hybrid Model for Children Adulthood Height Prediction

The prediction of children's adult height is a common procedure in childhood endocrinology. Through the prediction of children's adult height, it is possible to find abnormalities in children's growth and development. Many jobs in today's society have certain requirements for height, so the accuracy of children adulthood height prediction is important for children. Current methods for predicting adult height of children have some shortcomings such as inaccurate accuracy. To deal with these problems, this paper analyzes the data collected by the Chinese children and adolescents' physical and growth health projects in primary and secondary schools in Zhejiang Province, and proposes a method for predicting adult height based on back propagation neural network (BPNN) with the body composition of children and adolescents as input. Since the BP algorithm has the risk of falling into local optimization, and we propose LSALO-BP model that incorporates the ant lion optimizer (LSALO) into the BP algorithm as location strategy to avoid local optimization. The improvements achieved by the ant lion algorithm are mainly reflected in: improving the ant's walk mode, and enhancing the global search ability of the LSALO algorithm. The comparison experiment of 10 benchmark functions proves the feasibility and effectiveness of the location strategy. The LSALO-BP model is applied to the prediction of adult height of children and adolescents. The experimental results show that compared with other models, the LSALO-BP prediction model has increased the prediction accuracy by 6.67%~16.08% for boys and 4.67%~6.6% for girls, which can more accurately predict the adult height of children and adolescents.

Box Girder Optimization by Orthogonal Experiment Design and GA-BP Algorithm in the Gondola Car Body

Box girder is an important bearing and force transmitting component in the gondola car body; the rationality of its structure directly affects the life of the whole car body. In order to solve disadvantage of the traditional box girder optimization method, which mainly depends on design experience, the combined method of orthogonal experimental design and the genetic algorithm-back propagation (GA-BP) algorithm is used for the structural optimization of bolster beam in this paper. Nine groups of parameters were established by orthogonal experiment, which can give typical samples for GA-BP optimization. Then, the bolster beam is optimized by the GA-BP algorithm, and the new gondola car body model is established with the optimized parameters. The finite element analysis results show that the minimum stress is found by using the GA-BP algorithm, which is basically consistent with the simulation results. Finally, the results show that the combined method of orthogonal experimental design and GA-BP algorithm is feasible to the box girder optimization of the gondola car body. Meanwhile, the optimization results of bolster beam will provide a reference for the structural design of the heavy haul wagon body.

Novel Low Complexity BP Decoding Algorithms for Polar Codes: Simplifying on Non-Linear Operations

The parallel nature of the belief propagation (BP) decoding algorithm for polar codes opens up a real possibility of high throughput and low decoding latency during hardware implementation. To address the problem that the BP decoding algorithm introduces high-complexity non-linear operations in the iterative messages update process, this paper proposes to simplify these operations and develops two novel low complexity BP decoding algorithms, namely, exponential BP (Exp-BP) decoding algorithm and quantization function BP (QF-BP) decoding algorithm. The proposed algorithms simplify the compound hyperbolic tangent function by using probability distribution fitting techniques. Specifically, the Exp-BP algorithm simplifies two types of non-linear operations into single non-linear operation using the piece-wise exponential model function, which can approximate the hyperbolic tangent function in the updating formula. The QF-BP algorithm eliminates non-linear operations using the non-uniform quantization in the updating formula, which is effective in reducing computational complexity. According to the simulation results, the proposed algorithms can reduce the computational complexity up to 50% in each iteration with a loss of less than 0.1 dB compared with the BP decoding algorithm, which can facilitate the hardware implementation.

Optimal Electric Bus Scheduling under Travel Time Uncertainty: A Robust Model and Solution Method

With the increasing adoption of electric buses (e-buses), e-bus scheduling problem has become an essential part of transit operation planning. As e-buses have a limited battery capacity, e-bus scheduling problem aims to assign vehicles to timetabled service trips on the bus routes considering their charging demand. Affected by the dynamic operation environment, the travel time and energy consumption of the e-buses often display considerable randomness, resulting in unexpected trip start delays and battery energy shortages. In this paper, we addressed the e-bus scheduling problem under travel time uncertainty by robust optimization approaches. We consider the cardinality constrained uncertainty set to formulate a robust multidepot EVSP model considering trip time uncertainty and partial recharging. The model is developed based on the dynamic programming equations that we formulated for trip chain robustness checking. A branch-and-price (BP) algorithm is devised to generate provably high-quality solutions for large-scale instances. In the BP algorithm, an efficient label setting algorithm is developed to solve the robust resource-constrained shortest path subproblem. Comprehensive numerical experiments are conducted based on the bus routes in Shenzhen to demonstrate the effectiveness of the suggested methodology. The robustness of the schedules was evaluated through Monte Carlo simulation. The results show that the trip start delay and battery energy shortage caused by the travel time uncertainty can be effectively reduced at the expense of an increase in the operational cost. A trade-off should be made between the reduction in infeasibility rate and increase in operational cost to choose a proper uncertainty budget.

Research on the Evaluation Model of Educational Management Theory Based on Data Mining from the Perspective of Neural Network

The improvement of the theoretical quality of education management is an indispensable part of a country’s education modernization. However, the existing research on the evaluation of educational management theory is still relatively small, and there is a lack of scientific educational management theory evaluation model. Designing a comprehensive and accurate educational management theory evaluation model has important theoretical value and practical significance. It is possible to process a lot of information in parallel using the artificial neural network method. By optimizing the artificial neural network, data mining of characteristic information data can be realized. Therefore, this paper uses neural network to conduct data mining on education management theory and conduct a comprehensive system evaluation of education management theory. At the same time, the traditional BP algorithm is improved. To train a neural network with large amounts of data, the BP algorithm uses a lot of gradient calculation, which takes a long time and often results in training going to extremes in the local area. BP neural networks are trained using the particle swarm optimization algorithm, and the backward propagation process in the BP algorithm is replaced with particle swarm iteration. To improve algorithm execution efficiency and speed up neural network training, a large number of gradient operations can be avoided. This can help overcome the limitations of the BP algorithm when dealing with large amounts of data. The improved BP algorithm is applied to the evaluation system of education management theory, and the quality evaluation prediction of management education theory is realized.

Application of Multidirectional Mutation Genetic Algorithm and Its Optimization Neural Network in Intelligent Optimization of English Teaching Courses

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidirectional mutation genetic BPNN algorithm is applied to the intelligent optimization of English teaching courses. The simulation shows that the multidirectional mutation genetic BP neural network algorithm can solve the local optimization problem of traditional BP neural network. Finally, a control group and an experimental group are set up to verify the role of multidirectional mutation genetic algorithm and its optimization neural network in the intelligent optimization system of English teaching courses through the combination of summative and formative teaching evaluations. The data show that MMGA-BP algorithm can significantly improve the scores of academic students in English courses and has better teaching performance. The effect of vocabulary teaching under the guidance of MMGA-BP optimization theory is very significant, which plays a certain role in the intelligent curriculum optimization of the experimental class.

A 3-D SAR Imaging Method Based on Back Projection Algorithm

3D back projection (BP) algorithm is an imaging algorithm based on time domain echo data, which effectively solves the overlapping mask problem existing in 2D SAR. It can complete the imaging processing of echo signal under any geometry configuration, and has the advantages of high target focusing accuracy and high phase preservation. However, the high complexity and low efficiency of 3D BP imaging algorithm limit its application and development. In this paper, a 3d imaging method based on improved back projection algorithm is proposed. Aiming at the problem that existing imaging algorithms need 2D imaging first and then 3D imaging, an improved 3D BP algorithm is proposed to directly 3D imaging, which avoids 2d imaging processing. The proposed method simplifies the steps of the traditional 3D BP algorithm and improves the efficiency of the algorithm. The validity and effectiveness of the proposed method are verified by the 3d imaging results of simulated lattice targets.

BPLight-CNN: A Photonics-Based Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation (BP) algorithm. This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pretrained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high-performance deep learning training. We present the design for a convolutional neural network (CNN), BPLight-CNN , which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models, including LeNet and VGG-Net. The proposed design achieves (i) at least 34× speedup, 34× improvement in computational efficiency, and 38.5× energy savings during training; and (ii) 29× speedup, 31× improvement in computational efficiency, and 38.7× improvement in energy savings during inference compared with the state-of-the-art designs. All of these comparisons are done at a 16-bit resolution, and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared with the state-of-the-art.