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With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket.

Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation framework of spatial multi-target recognition is given. Second, by comparing and analyzing convolutional neural networks, a convolutional neural network model based on YOLOv3 is designed. Finally, seven satellite scale models are constructed based on systems tool kit (STK) and Solidworks. Multi targets, such as nozzle, star sensor, solar,etc., are selected as the recognition objects.

By labeling, training and testing the image data set, the accuracy of the proposed method for spatial multi-target recognition is 90.17%, which is improved compared with the recognition accuracy and rate based on the YOLOv1 model, thereby effectively verifying the correctness of the proposed method.

This paper only recognizes space multi-targets under ideal simulation conditions, but has not fully considered the space multi-target recognition under the more complex space lighting environment, nutation, precession, roll and other motion laws. In the later period, training and detection can be performed by simulating more realistic space lighting environment images or multi-target images taken by upper-level rocket to further verify the feasibility of multi-target recognition algorithms in complex space environments.

The research in this paper validates that the deep learning-based algorithm to recognize multiple targets in the space environment is feasible in terms of accuracy and rate.

The paper helps to set up an image data set containing six satellite models in STK and one digital satellite model that simulates spatial illumination changes and spins in Solidworks, and use the characteristics of spatial targets (such as rectangles, circles and lines) to provide prior values to the network convolutional layer.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

The study aims to devise a comprehensive evaluation model (CEM) for evaluating spatial equity in the layout of elderly service facilities (ESFs) to address the inequity in the layout of ESFs within city center communities characterized by limited land resources and a dense elderly population.

The CEM incorporates a suite of analytical tools, including accessibility assessment, Lorenz curve and Gini coefficient evaluations and spatial autocorrelation analysis. Utilizing this model, the study scrutinized the distributional equity of three distinct categories of ESFs in the city center of Xi’an and proposed targeted optimization strategies.

The findings reveal that (1) there are disparities in ESFs’ accessibility among different categories and communities, manifesting a distinct center (high) and periphery (low) distribution pattern; (2) there exists inequality in ESFs distribution, with nearly 50% of older adults accessing only 18% of elderly services, and these inequalities are more pronounced in urban areas with lower accessibility, and (3) approximately 14.7% of communities experience a supply-demand disequilibrium, with demand surpassing supply as a predominant issue in the ongoing development of ESFs.

The CEM formulated in this study offers policymakers, urban planners and service providers a scientific foundation and guidance for decision-making or policy amendment by promptly assessing and pinpointing areas of spatial inequity in ESFs and identifying deficiencies in their development.

Despite the enormous need to succeed in the urban model, scientists and policymakers should work consistently to create blueprints to regulate urbanization. The absence of coordination between the crucial requirements and the regional strategies of the local authorities leads to a lack of conformance in urban development. The purpose of this paper is to address this issue.

This study intends to manage future urban growth patterns using integrated methods and then employ the results in the genetic algorithm (GA) model to considerably improve growth behavior. Multi-temporal land-use datasets have been derived from remotely sensed images for the years 1990, 2000, 2010 and 2020. Urban growth patterns and processes were then analyzed with land-use-and-land-cover dynamics. Results were examined for simulation and utilization of the GA.

Model parameters were derived and evaluated, and a preliminary assessment of the effective coefficient in the formation of urbanization is analyzed, showing the city's urbanization pattern has followed along with the transportation infrastructure and outward growth, and the scattering rates are high, with an increase of 5.64% in building area associated with a decrease in agricultural lands and rangelands.

The research achieved a considerable improvement over the growth behavior. The conducted research design was the first of its type in that field to be executed to any specific growth pattern parameters in terms of regulating and policymaking. The method has integrated various artificial intelligence models to monitor, measure and optimize the projected growth by applying this design. Other research on the area was limited to projecting the future of Amman as it is an urbanized distressed city.

During the past several decades, the decision-making process and the decision-makers’ role in it have changed dramatically. Because of this, the use of analytical tools, such as Excel, have become an essential component of most organizations. The analytical tools in Excel can provide today’s decision-maker with a competitive advantage. We will illustrate several powerful Excel tools that facilitate the decision support process.

In order to optimize SCSWIRC, the simplification and further optimization method is proposed. SCSWIRC's optimization includes two levels. The first level refers to simplifying structural system from the perspective of components; the second level refers to optimizing components' sectional areas from the perspective of mechanics. The first level aims to remove redundant components, and the second level aims to reduce structural self-weight based on the first level. The purpose of the paper is to simplify SCSWIRC's structural system and optimize structural self-weight and reduce construction forming difficulty.

Grid-jumping layout and multi-objective optimization method is used to simplify and further optimize Spatial cable-truss structure without inner ring cables (SCSWIRC). Grid-jumping layout is used to simplify remove redundant components, and multi-objective optimization method is used to reduce structural self-weight. The detailed solving process is given based on grid-jumping layout and multi-objective optimization method.

Take SCSWIRC with a span of 100m as an example to verify the feasibility and correctness of the simplification and further optimization method. The optimization results show that 12 redundant components are removed and the self-weight reduces by 3.128t from original scheme to grid-jumping layout scheme 1. The self-weight reduces from 36.007t to 28.231t and feasible coefficient decreases from 1.0 to 0.627 from grid-jumping layout scheme 1 to multi-objective optimization scheme. The simplification and further optimization can not only remove the redundant components and simplify structural system to reduce construction forming difficulty, but also optimize structural self-weight under considering structural stiffness to reduce project costs.

The proposed method firstly simplifies SCSWIRC and then optimizes the simplified SCSWIRC, which can solve the optimization problem from the perspective of components and mechanics. Meanwhile, the optimal section solving method can be used to obtain circular steel tube size with the optimal stiffness of the same areas. The proposed method successfully solves the problem of construction forming and project cost, which promotes the application of SCSWIRC in practical engineering.

With the constant increasing scale of urban buildings, the contradiction between supply and demand of land use problems is more prominent. Therefore, the multi-objective space optimal allocation of urban land use based on spatial genetic algorithm was proposed in this paper. Firstly, the present situation of the urban land use resources was expounded; in view of the urban land use planning, a spatial genetic algorithm was proposed; then, the urban land was divided into different functional areas, and the land planning and design method was put forward; finally, taking a city's land space planning as an example, the optimal planning and design were carried out to the geological disasters, low hilly land and land overall utilization; by comparing the land use before and after the planning optimization, the advantages of land optimization design were confirmed.

This research presents a multi-objective optimization approach to integrate spatial planning measures in open-plan office environments in order to lower the risk of a workplace contagion. These measures were gathered, formalized, parameterized, and coded and integrated into a digital tool.

To demonstrate the research's approach, a simple design problem was designed, explored, and the results were evaluated. The researchers assumed an empty open office space, with the windows and doors (as exits and/or as access to amenities) already in place (Figure 1). The aim is to optimize the space planning, with the following objectives in mind: maximize the number of employees in a floor while maintaining physical distancing recommendations for avoiding infections; no face-to-face or back-to-back seating positions are allowed; maximize physical access to windows for natural ventilation; minimizing areas with potential “congestions” in the space, i.e. areas susceptible to overlapping foot traffic from numerous employees, which increases the potential for close encounters and minimizing the travel distance from the employee's desk to all neighbouring desks, hence reducing the foot traffic in the space. In the experiment, the following was assumed: the workspace layout is rectangular, the workstation desks are rectangular, the seating area, windows, and access to exits and amenities are well-defined.

It was found that configurations with desks parallel to the longer side of the space provided more employee capacity; however, they usually performed poorer in terms of the buzz score. On the other hand, configurations with desks perpendicular to the longer side of the space had, on average, better buzz scores, usually at the cost of the reduction of the number of potential employees. There was however one alternative in the latter set of configurations, which achieved above-average buzz and adjacency scores, and the potential to accommodate 56 employees, one of the highest capacities for employees in the solution space (the highest being 60). Designers could explore the design space further to make sure it complies with these basic spatial rules for mitigating the spread of infections, while experimenting with the workspace layout.

It is important to note that in order for a designer to handle any given design problem even with the aid of a computer system, it is important to provide a set of initial conditions and assumptions and a set of variables. In the universe of all possible variables, the designer can pick a number of variations of the initial conditions and run parallel experiments to compare their outcomes. In the experiment demonstrated here the following was assumed. The workspace layout is rectangular with predefined entrances/exits. Free flow of employees is allowed. No pre-set one-way paths. The workstation desks are rectangular. The seating area windows and access to amenities are well-defined.

This research presented a digital optimization approach to enhance the spatial planning process in open-plan office spaces, with the aim of mitigating the risks of infectious diseases' transmission. Spatial design considerations were gathered from literature and formalized as design objectives and constraints, then further parameterized and represented as numerical values and scores for objective evaluation. The design parameters, constraints and calculations to derive the scores for the designated design objectives were coded into a digital tool that can receive a building information model (BIM) model of an office space and provide preliminary furniture plans using a multi-objective optimization (MOO) approach. It is obvious that the furniture layouts that can be considered “acceptable”, based on this approach, are not considered “ready-to-implement” solutions, because designers need to integrate a multitude of other design factors in their design. This approach can still, however, be useful to help the designer integrate spatial considerations for slowing down a contagion.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement.

In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period.

In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed.

The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment.

An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.