Artificial intelligence empowering museum space layout design: Insights from China.

The floor plan layout of museum exhibition spaces is the skeleton network of the museum, which determines the internal circulation and spatial form of the museum. This paper studies the method and practice of using artificial intelligence technology to assist in the space design of exhibition halls in urban cultural museums. First, it introduces the limitations of traditional space design methods for exhibition halls in urban cultural museums and the superiority and application prospects of the CGAN (conditional generative adversarial network) model in space design. Second, the principle and training process of the CGAN model are explained in detail, and the experimental results and analysis are given. By learning 100 floor plans of exhibition halls of urban culture museums, the CGAN model can generate a new floor plan design for an exhibition hall, which provides a new idea and innovative method for this design task. Finally, the limitations and future research directions of the CGAN model in the space design of urban cultural museum exhibition halls are discussed. The study shows that using the CGAN model to learn the floor plans of exhibition halls of urban cultural museums can effectively improve the innovation and practicability of space design and has the following advantages: (1) It can quickly generate a large number of exhibition hall floor plans, shorten the design cycle, and improve design efficiency. (2) The generated floor plan designs of the exhibition hall are diverse and personalized, meeting the design requirements of different scenarios and needs. (3) The method promotes the deep integration of space design and artificial intelligence technology and provides new possibilities and ideas for space design. These conclusions provide new ideas and methods for the space design of exhibition halls of urban cultural museums and provide a reference and inspiration for space design and intelligent applications in other fields, such as office space design, home decoration space design, landscape space design, and historical arcade and building renovation design.

Conditional generative adversarial network model for simulating intensity measures of aftershocks

Prediction for underground seismic intensity measures using conditional generative adversarial networks

This study uses Conditional Generative Adversarial Network (CGAN) to construct a method for generating floor plans for long-term care spaces in retirement home buildings to assist architects in improving interior space design. The results of this study show the following: (1) For the interior design of long-term care spaces in retirement home buildings, the CGAN model has strong understanding and calculation capabilities. The zoning layout of long-term care spaces in retirement home buildings has been completed, and the results show that the CGAN model has reference value. (2) Although there are several differences in the design of CGANs and authentic design, there are still many similarities. Some unreasonable results, such as space generation in corridors and elevator shafts, require further manual correction. (3) According to a later questionnaire survey on the satisfaction of architects and CGAN model design solutions, the difference between the two is not large, which also illustrates the great potential of CGANs for intervention in interior space design. This helps architects create more detailed plans based on the model, greatly increasing work efficiency. Moreover, additional interior space design possibilities can be explored, and to some extent, the architect’s subjective assumptions can also be corrected.

The floor plan designs of traditional museum exhibition halls are generally developed according to the position and streamlined accessibility of the exhibits. However, there are often many floors in the same building, and multi-story exhibition halls are similar, so architects often spend a large amount of time and energy designing floors individually. Thus, this paper proposes a conditional generative adversarial network (CGAN)-based method for designing the floor plans of museum exhibition halls, which can help architects to work more efficiently. In this study, the basic concepts and structures of CGAN are first introduced; then, the design and training process of the CGAN model used are described in detail, and the datasets and evaluation metrics adopted are briefly described. In the Results and Discussion sections, this paper presents an example of the generated floor plan design of a museum exhibition hall and evaluates and analyzes the floor plan design of a museum exhibition hall generated using the proposed method. Finally, this paper summarizes the advantages of the proposed method, but also notes its shortcomings. If the number of data sets is not sufficient, the scope of the application will be relatively small. For example, museums converted from certain historical buildings are not applicable. The research results show the following: (1) the method proposed in this paper takes advantage of the CGAN model and can generate a museum exhibition hall floor plan design with certain regularity according to the given conditions, rather than pure random generation. (2) This method can automatically generate a variety of plan designs for museum exhibition halls in different schemes, providing designers with more choices and flexibility. (3) This method can carry out design optimization through human–computer interaction, and iterative improvement can be carried out according to user needs and feedback, which improves the practicability of the design.

In this paper, a deep learning (DL) framework was proposed to predict the taxi-passenger demand while the spatial, the temporal, and external dependencies were considered simultaneously. The proposed DL framework combined a modified density-based spatial clustering algorithm with noise (DBSCAN) and a conditional generative adversarial network (CGAN) model. More specifically, the modified DBSCAN model was applied to produce a number of sub-networks considering the spatial correlation of taxi pick-up events in the road network. And the CGAN model, fed with the historical taxi passenger demand and other conditional information, was capable to predict the taxi-passenger demands. The proposed CGAN model was made up with two long short-term memory (LSTM) neural networks, which are termed as the generative network ${G}$ and the discriminative network ${D}$ , respectively. Adversarial training process was conducted to the two LSTMs. In the numerical experiment, different model layouts were compared. It was found that different network layouts provided reasonable accuracy. With limited training data, more LSTM layers in the generator network resulted in not only higher accuracy, but also more difficulties in training. Comparisons were also conducted between the proposed prediction model and four typical approaches, including the moving average method, the autoregressive integrated moving method, the neural network model, and the LSTM neural network model. The comparison results showed that the proposed model outperformed all the other methods. And the repeated experiment indicated that the proposed CGAN model provided significant better predictions than the LSTM model did. Future research was recommended to include more datasets for testing the model and more information for improving predictive performance.

Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by CFD data can be used for fast and accurate prediction of indoor airflow, but current methods have limitations, such as only predicting limited outputs rather than the entire flow field. Furthermore, conventional AI models are not always designed to predict different outputs based on a continuous input range, and instead make predictions for one or a few discrete inputs. This work addresses these gaps using a conditional generative adversarial network (CGAN) model approach, which is inspired by current state-of-the-art AI for synthetic image generation. We create a new Boundary Condition CGAN (BC-CGAN) model by extending the original CGAN model to generate 2D airflow distribution images based on a continuous input parameter, such as a boundary condition. Additionally, we design a novel feature-driven algorithm to strategically generate training data, with the goal of minimizing the amount of computationally expensive data while ensuring training quality of the AI model. The BC-CGAN model is evaluated for two benchmark airflow cases: an isothermal lid-driven cavity flow and a non-isothermal mixed convection flow with a heated box. We also investigate the performance of the BC-CGAN models when training is stopped based on different levels of validation error criteria. The results show that the trained BC-CGAN model can predict the 2D distribution of velocity and temperature with less than 5% relative error and up to about 75,000 times faster when compared to reference CFD simulations. The proposed feature-driven algorithm shows potential for reducing the amount of data and epochs required to train the AI models while maintaining prediction accuracy, particularly when the flow changes non-linearly with respect to an input.

This research takes the Suzhou Gardens, a World Cultural Heritage Site, as the object of study and investigates a rapid scheme generation approach for garden restoration and expansion projects, assisting designers in making scientific decisions. Considering the limitations of current garden design, which is inefficient and relies on human experience, this study proposes an intelligent generation framework based on a conditional generative adversarial network (CGAN). In constructing the CGAN model, we determine the spatial characteristics of the Suzhou Gardens and, combined with historical floor plan data, train the network. We then design an optimization strategy for the model training process and finally test and verify the generative space scheme. The research results indicate the following: (1) The CGAN model can effectively capture the key elements of the garden space and generate a planar scheme that conforms to the traditional space with an accuracy rate reaching 91.08%. (2) This model can be applied to projects ranging from 200 to 1000 square meters. The generated results can provide multiple scheme comparisons for update decisions, helping managers to efficiently select the optimal solution. (3) Decision-makers can conduct space utilization analyses and evaluations based on the generated results. In conclusion, this study can help decision-makers to efficiently generate and evaluate the feasibility of different design schemes, providing intelligent support for decision-making in urban renewal plans.

To efficiently use the finite wireless communication resource (radio spectrum), a Radio Environment Map (REM) is needed to monitor, analyse and provide rich awareness of spectrum activities in a radio propagating environment. REM shows radio coverage metrics in a geographical region. A REM construction model with few constraints and optimal performance is needed to better support cognitive radio for dynamic spectrum sharing (DSS) and other benefits of REM. This study aims to estimate fine-resolution REM from sparse radio signal strength measurement. In this study, we utilised conditional generative adversarial network (CGAN) to create a television spectrum radio environment map in order to improve cognitive television white space (TVWS) radio performance in real-time propagation environments. Measurement campaign was carried out to acquire a TV-band (470-862MHz) radio frequency and geographical dataset at Covenant University, Ota, Nigeria. A preprocessing procedure which was implemented with Python script was employed to group the dataset using Nigerian Communications Commission TV spectrum channel spacing and to create incomplete spectrograms for 49 channels. Xgboost, SVM, and kriging variogram models were explored to generate ground truth datasets for the CGAN model training, and the best algorithm was employed. A CGAN REM model was developed using U-Net as a generator and PatchGan as a discriminator. The U-Net generator is a 3-channel input, 16-layer architecture while the PatchGan discriminator is a 6-channel input, 7-layer architecture. The model performance was evaluated using mean square error (MSE) and mean absolute error (MAE). 12 different experiments were carried out varying the training parameters of the CGAN architecture to obtain an optimal model. The achieved root mean square error (RMSE) is 0.1145dBm and MAE is 0.0820dBm, which shows the deviation between the ground truth and the generated REM. This low deviation means that the proposed CGAN REM model possesses an improved accuracy in predicting the spectrum activities within the television spectrum which is considered appropriate for DSS technology. This study also revealed that 41 channels within TV-band in Covenant University are totally unoccupied.

Two-dimensional prediction of the superposition film cooling with trench based on conditional generative adversarial network

Biomedical data acquisition, and reaching sufficient samples of participants are difficult and time ans effort consuming processes. On the other hand, the success rates of computer aided diagnosis (CAD) algorithms are sample and feature space depended. In this paper, conditional generative adversarial network (CGAN) based enhanced feature generation is proposed to synthesize large sample datasets having higher class separability. Twenty five percent of five medical datasets are used to train CGAN, and the synthetic datasets with any sample size are evaluated and compared to originals. Thus, new datasets can be generated with the help of the CGAN model and lower sample collection. It helps physicians decreasing sample collection processes, and it increases accuracy rates of the CAD systems using generated enhanced data with enhanced feature vectors. The synthesized datasets are classified using nearest neighbor, radial basis function support vector machine and artificial neural network to analyze the effectiveness of the proposed CGAN model.

Short-term forecasting of wind power plays a critical role in day-ahead dispatching and economic operation of power systems. In this paper, a wind power prediction model based on Convolutional Neural Networks (CNN) and Conditional Generative Adversarial Network (CGAN) is proposed. With weather factor labels helping, the model is proposed to solve the problem of day-ahead prediction. In this model, K-means is applied to divide the historical wind farm data into several parts based on weather factors. At each time, the power of similar weather time can be found through grey relational analysis (GRA), which can be used as a label together with weather information. With the guidance of conditional labels, the generative model can generate samples more purposefully, and the discriminator can identify more accurately. By this way, the generator and the discriminator form a game so that CGAN model can improve the accuracy of wind power forecasting. Finally, three examples of Datang Hongxing Wind farm are given to test the validity of the proposed algorithm.

The dynamic monitoring of the temperature distribution of power equipment is crucial for safe operation. The monitoring method based on digital twin technology has received extensive attention due to its ability to provide more timely and more comprehensive analysis. However, the existing methods only use physics-driven or data-driven methods separately, which cannot simultaneously meet application requirements such as low cost, high efficiency, effective results and sufficient training data. Therefore, a new dynamic monitoring method of temperature distribution based on thermal knowledge and conditional generative adversarial network (CGAN) are proposed in this paper, with a high voltage cable joint taken as an example. First, the steady-state numerical model of the temperature field is established. The thermal image set under different operation conditions is obtained through simulation. The data-driven method based on the CGAN model is applied to learn the data laws between operation conditions and thermal images, and constructs a mapping relationship between them. Then, the physics-driven method based on thermal knowledge is used to reduce feature dimensions and extract input features from real-time measurement data. These input features drive the generator of the CGAN model to generate the thermal images dynamically. By comparing the generation results with numerical and experimental results, the proposed method can achieve the dynamic monitoring of temperature for cable joints, which has comprehensive advantages with regards to the time efficiency, computational cost, monitoring accuracy and generalization ability. It provides new insights into the digital twin technology of power equipment.

Aerodynamic/stealth design is becoming an important factor in the advanced airfoil design. In this work, a supervised machine learning method is proposed for aerodynamic and stealth integrated airfoil design. The conditional generative adversarial network (CGAN) is constructed for the multidisciplinary design of airfoil. Then, the generator and discriminator simply using deep neural network have good robustness and stability in training. The CGAN model also shows good generalization capability in the test set, with less than 1% error in fitting to the airfoil profile data, and the generated airfoils are within 10% error compared to the test airfoil aerodynamic stealth characteristics. In addition, the optimization results based on the CGAN model demonstrate that aerodynamic performance improvement would increase the airfoil camber and stealth performance improvement would sharpen the airfoil leading edge.

The objective of the paper is to detect whether a needle marker occurs in the pad area of a printed circuit board from images. Since there exist many irrelevant noises including other small circuit pads, it becomes difficult to find the pad area and detect the needle marker. In our proposed approach, we present a denoising method to find the pad area of a printed circuit board from images. The proposed method adopts a conditional generative adversarial network (CGAN) to denoise the images and has better results than traditional image processing techniques. The method proceeds in three steps: firstly, the input images are classified by “single pad” and “multiple pad” using a convolutional neural network (CNN). Secondly, the image with a single pad is denoised by a CGAN model. Thirdly, the image is cropped by the pad region and used to find the needle marker by pattern matching. The effectiveness of the proposed method is validated with the experimental results and shows that the needle marker is accurately detected.