Hongjun Zhang

Hongjun Zhang

Try to generate new bridge types using generative artificial intelligence technology. The grayscale images of the bridge facade with the change of component width was rendered by 3dsMax animation software, and then the OpenCV module performed an appropriate amount of geometric transformation (rotation, horizontal scale, vertical scale) to obtain the image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge. Based on Python programming language, TensorFlow and Keras deep learning platform framework, variational autoencoder was constructed and trained, and low-dimensional bridge-type latent space that is convenient for vector operations was obtained. Variational autoencoder can combine two bridge types on the basis of the original of human into one that is a new bridge type. Generative artificial intelligence technology can assist bridge designers in bridge-type innovation, and can be used as copilot.

Hongjun Zhang

Try to generate new bridge types using generative artificial intelligence technology. Symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge are used . Based on Python programming language, TensorFlow and Keras deep learning platform framework , as well as Wasserstein loss function and Lipschitz constraints, generative adversarial network is constructed and trained. From the obtained low dimensional bridge-type latent space sampling, new bridge types with asymmetric structures can be generated. Generative adversarial network can create new bridge types by organically combining different structural components on the basis of human original bridge types. It has a certain degree of human original ability. Generative artificial intelligence technology can open up imagination space and inspire humanity.

Hongjun Zhang

Try to generate new bridge types using generative artificial intelligence technology. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge , based on Python programming language, TensorFlow and Keras deep learning platform framework , PixelCNN is constructed and trained. The model can capture the statistical structure of the images and calculate the probability distribution of the next pixel when the previous pixels are given. From the obtained latent space sampling, new bridge types different from the training dataset can be generated. PixelCNN can organically combine different structural components on the basis of human original bridge types, creating new bridge types that have a certain degree of human original ability. Autoregressive models cannot understand the meaning of the sequence, while multimodal models combine regression and autoregressive models to understand the sequence. Multimodal models should be the way to achieve artificial general intelligence in the future.

Hongjun Zhang

Use denoising diffusion implicit model for bridge-type innovation. The process of adding noise and denoising to an image can be likened to the process of a corpse rotting and a detective restoring the scene of a victim being killed, to help beginners understand. Through an easy-to-understand algebraic method, derive the function formulas for adding noise and denoising, making it easier for beginners to master the mathematical principles of the model. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge , based on Python programming language, TensorFlow and Keras deep learning platform framework , denoising diffusion implicit model is constructed and trained. From the latent space sampling, new bridge types with asymmetric structures can be generated. Denoising diffusion implicit model can organically combine different structural components on the basis of human original bridge types, and create new bridge types.

Hongjun Zhang

Use energy-based model for bridge-type innovation. The loss function is explained by the game theory, the logic is clear and the formula is simple and clear. Thus avoid the use of maximum likelihood estimation to explain the loss function and eliminate the need for Monte Carlo methods to solve the normalized denominator. Assuming that the bridge-type population follows a Boltzmann distribution, a neural network is constructed to represent the energy function. Use Langevin dynamics technology to generate a new sample with low energy value, thus a generative model of bridge-type based on energy is established. Train energy function on symmetric structured image dataset of three span beam bridge, arch bridge, cable-stayed bridge, and suspension bridge to accurately calculate the energy values of real and fake samples. Sampling from latent space, using gradient descent algorithm, the energy function transforms the sampling points into low energy score samples, thereby generating new bridge types different from the dataset. Due to unstable and slow training in this attempt, the possibility of generating new bridge types is rare and the image definition of generated images is low.

Leye Zhang, Xiangxiang Tian, Chengli Zhang et al.

Stable Diffusion fine-tuning technique is tried to assist bridge-type innovation. The bridge real photo dataset is built, and Stable Diffusion is fine tuned by using four methods that are Textual Inversion, Dreambooth, Hypernetwork and Lora. All of them can capture the main characteristics of dataset images and realize the personalized customization of Stable Diffusion. Through fine-tuning, Stable Diffusion is not only a drawing tool, but also has the designer's innovative thinking ability. The fine tuned model can generate a large number of innovative new bridge types, which can provide rich inspiration for human designers. The result shows that this technology can be used as an engine of creativity and a power multiplier for human designers.

Leye Zhang, Xiangxiang Tian, Hongjun Zhang

This paper constructs question answering system for bridge design specification based on large language model. Three implementation schemes are tried: full fine-tuning of the Bert pretrained model, parameter-efficient fine-tuning of the Bert pretrained model, and self-built language model from scratch. Through the self-built question and answer task dataset, based on the tensorflow and keras deep learning platform framework, the model is constructed and trained to predict the start position and end position of the answer in the bridge design specification given by the user. The experimental results show that full fine-tuning of the Bert pretrained model achieves 100% accuracy in the training-dataset, validation-dataset and test-dataset, and the system can extract the answers from the bridge design specification given by the user to answer various questions of the user; While parameter-efficient fine-tuning of the Bert pretrained model and self-built language model from scratch perform well in the training-dataset, their generalization ability in the test-dataset needs to be improved. The research of this paper provides a useful reference for the development of question answering system in professional field.

Leye Zhang, Xiangxiang Tian, Chengli Zhang et al.

Deep Q-network algorithm is used to select economic span of bridge. Selection of bridge span has a significant impact on the total cost of bridge, and a reasonable selection of span can reduce engineering cost. Economic span of bridge is theoretically analyzed, and the theoretical solution formula of economic span is deduced. Construction process of bridge simulation environment is described in detail, including observation space, action space and reward function of the environment. Agent is constructed, convolutional neural network is used to approximate Q function,ε greedy policy is used for action selection, and experience replay is used for training. The test verifies that the agent can successfully learn optimal policy and realize economic span selection of bridge. This study provides a potential decision-making tool for bridge design.

Leye Zhang, Xiangxiang Tian, Hongjun Zhang

Attempt to use convolutional neural network to achieve kinematic analysis of plane bar structure. Through 3dsMax animation software and OpenCV module, self-build image dataset of geometrically stable system and geometrically unstable system. we construct and train convolutional neural network model based on the TensorFlow and Keras deep learning platform framework. The model achieves 100% accuracy on the training set, validation set, and test set. The accuracy on the additional test set is 93.7%, indicating that convolutional neural network can learn and master the relevant knowledge of kinematic analysis of structural mechanics. In the future, the generalization ability of the model can be improved through the diversity of dataset, which has the potential to surpass human experts for complex structures. Convolutional neural network has certain practical value in the field of kinematic analysis of structural mechanics. Using visualization technology, we reveal how convolutional neural network learns and recognizes structural features. Using pre-trained VGG16 model for feature extraction and fine-tuning, we found that the generalization ability is inferior to the self-built model.

Hongjun Zhang

Through examples of coordinate and probability transformation between different distributions, the basic principle of normalizing flow is introduced in a simple and concise manner. From the perspective of the distribution of random variable function, the essence of probability transformation is explained, and the scaling factor Jacobian determinant of probability transformation is introduced. Treating the dataset as a sample from the population, obtaining normalizing flow is essentially through sampling surveys to statistically infer the numerical features of the population, and then the loss function is established by using the maximum likelihood estimation method. This article introduces how normalizing flow cleverly solves the two major application challenges of high-dimensional matrix determinant calculation and neural network reversible transformation. Using symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge, constructing and training normalizing flow based on the Glow API in the TensorFlow Probability library. The model can smoothly transform the complex distribution of the bridge dataset into a standard normal distribution, and from the obtained latent space sampling, it can generate new bridge types that are different from the training dataset.

Hongjun Zhang, Jing Wang, Qinfeng Xiao et al.

The objective of this paper is to learn semantic representations for sleep stage classification from raw physiological time series. Although supervised methods have gained remarkable performance, they are limited in clinical situations due to the requirement of fully labeled data. Self-supervised learning (SSL) based on contrasting semantically similar (positive) and dissimilar (negative) pairs of samples have achieved promising success. However, existing SSL methods suffer the problem that many semantically similar positives are still uncovered and even treated as negatives. In this paper, we propose a novel SSL approach named SleepPriorCL to alleviate the above problem. Advances of our approach over existing SSL methods are two-fold: 1) by incorporating prior domain knowledge into the training regime of SSL, more semantically similar positives are discovered without accessing ground-truth labels; 2) via investigating the influence of the temperature in contrastive loss, an adaptive temperature mechanism for each sample according to prior domain knowledge is further proposed, leading to better performance. Extensive experiments demonstrate that our method achieves state-of-the-art performance and consistently outperforms baselines.