Output Quantities Research Articles

Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature

Abstract Offshore wind turbines intend to take a rapidly growing share in the electric mix. The design, installation, and exploitation of these industrial assets are regulated by international standards, providing generic guidelines. Constantly, new projects reach unexploited wind resources, pushing back installation limits. Therefore, turbines are increasingly subject to uncertain environmental conditions, making long-term investment decisions riskier (at the design or end-of-life stage). Fortunately, numerical models of wind turbines enable to perform accurate multi-physics simulations of such systems when interacting with their environment. The challenge is then to propagate the input environmental uncertainties through these models and to analyze the distribution of output variables of interest. Since each call of such a numerical model can be costly, the estimation of statistical output quantities of interest (e.g., the mean value, the variance) has to be done with a restricted number of simulations. To do so, the present paper uses the kernel herding method as a sampling technique to perform Bayesian quadrature and estimate the fatigue damage. It is known from the literature that this method guarantees fast and accurate convergence together with providing relevant properties regarding subsampling and parallelization. Here, one numerically strengthens this fact by applying it to a real use case of an offshore wind turbine operating in Teesside, UK. Numerical comparison with crude and quasi-Monte Carlo sampling demonstrates the benefits one can expect from such a method. Finally, a new Python package has been developed and documented to provide quick open access to this uncertainty propagation method.

Breast morphology classification method based on generalized neural network

In order to establish a breast shape classification method with general indicators, this paper selects six measurement indicators that can characterize the three-dimensional shape of the breast, such as circumference, length, width, and height. The relevant data of the measurement indicators are used to establish a generalized neural network model, and the generalized neural network is applied to the evaluation of breast shape classification recognition. The MATLAB software toolbox is used, based on training sample data, We have established a generalized neural network (GRNN) model, which involves establishing corresponding functional relationships through training to approximate the training data. After obtaining the function relationship through training, the predicted values were obtained, and the prediction results reflected that the established generalized neural network GRNN model is quite reliable for predicting the category of breast shape. By establishing a mapping relationship between the input quantity of breast feature parameters and the output quantity of categories, training samples can be established. When encountering new test data, these data can also be used as test samples. Based on the established model, appropriate spreads can be adjusted to predict the output quantity, providing relevant personnel with basic and simple basis for judgment and evaluation. It can also provide convenience for customers in various consumption channels Accurate bra size recommendations can help consumers purchase bras that fit well and are comfortable.

Viewing Intentional Teaching Gestures: The Impact on Learners’ Japanese Output

Intentional Teaching Gestures are gestures that have been created as a pedagogical tool for second language teaching and learning and are used in many Japanese programs. Despite their widespread use, little is known about their impact on students’ oral language production. Therefore, the purpose of this study was to explore the effect of Intentional Teaching Gestures on students’ Japanese production. This paper reports on the quality of students’ oral language production from two Story Re-tell tasks, one with and one without viewing Intentional Teaching Gestures (ITG), after learning Japanese as a second language with ITG. Thematic analysis was undertaken of the Story Re-tell transcription data from 170 primary school participants. Findings identify that viewing ITG whilst doing Story Re-tell positively impacted learners’ oral production. The breadth of benefits included increases in the quantity of output that students produced, the expanded informational content included in their stories, and students’ increased use of a range of verbs and particles in their utterances. Viewing ITG also provided support for students’ structuring of utterances, supported self correction, and led to an improvement in their overall oral performance. These benefits have wide ranging pedagogical implications for Japanese teachers and learners.

Algorithmical determination of the equilibrium/stead position of a dynamic system by the finite-step method

The problem of algorithmic estimation of the equilibrium/stationary state of the dynamic system based on the minimum number of measurements of the output quantity at equidistant time points is considered. For this, a finite-step method was used, which consists in the formation, according to certain rules, of the sums and differences of the values of the output quantity, which are located symmetrically on the time axis relative to a certain moment that can be determined a priori. As a result, overdetermined systems of linear equations are formed with respect to the introduced fictitious unknowns, and based on the necessary condition for the existence of a solution to these systems, formulas are found to determine the equilibrium/stationary position of the dynamic system. Algorithms for determining the equilibrium /stationary state for the following mathematical models of the output value are given: in the form of a constant component and a damped exponent, in the form of a constant component and a damped sinusoid, in the form of a constant component and two undamped sinusoids, in the form of a constant component and a damped sinusoid. It is shown that in the absence of measurement errors of the output value, the error in estimating the equilibrium/stationary state of the transient process depends only on the error in solving the system of linear equations. Ways of using the redundant number of measurements are discussed.
 Keywords: equilibrium position, stationary state, dynamic system, mathematical model, finite-step method, numerical algorithm.

Influence of Tools and Cutting Strategy on Milling Conditions and Quality of Horizontal Thin-Wall Structures of Titanium Alloy Ti6Al4V.

Titanium and nickel alloys are used in the creation of components exposed to harsh and variable operating conditions. Such components include thin-walled structures with a variety of shapes created using milling. The driving factors behind the use of thin-walled components include the desire to reduce the weight of the structures and reduce the costs, which can sometimes be achieved by reducing the machining time. This situation necessitates, among other things, the use of new machining methods and/or better machining parameters. The available tools, geometrically designed for different strategies, allow working with similar and improved cutting parameters (increased cutting speeds or higher feed rates) without jeopardizing the necessary quality of finished products. This approach causes undesirable phenomena, such as the appearance of vibrations during machining, which adversely affect the surface quality including the surface roughness. A search is underway for cutting parameters that will minimize the vibration while meeting the quality requirements. Therefore, researching and evaluating the impact of cutting conditions are justified and common in scientific studies. In our work, we have focused on the quality characteristics of horizontal thin-walled structures from Ti6Al4V titanium alloys obtained in the milling process. Our experiments were conducted under controlled cutting conditions at a constant value of the material removal rate (2.03 cm3⁄min), while an increased value of the cut layer was used and tested for use in finishing machining. We used three different cutting tools, namely, one for general purpose machining, one for high-performance machining, and one for high-speed machining. Two strategies were adopted: adaptive face milling and adaptive cylindrical milling. The output quantities included the results of acceleration vibration amplitudes, and selected surface topography parameters of waviness (Wa and Wz) and roughness (Ra and Rz). The lowest values of the pertinent quantities were found for a sample machined with a high-performance tool using adaptive face milling. Surfaces typical of chatter vibrations were seen for all samples.

Time-dependent reliability of structures under consideration of fuzzy uncertainty

Time-dependent reliability (TDR) is the possibility that a structure or system under consideration can perform the intended function under specified service conditions over a given time instant under fuzzy uncertainty. This paper is mainly concerned to identify fuzzy variables, generate the membership function of the input variables and output quantities, analyze timedependent performance level, estimate the possibility safety index, and evaluate the TDR of the structures. To evaluate TDR by a possibilistic approach, the fuzzy uncertainty that is characterized by membership function is considered. The type of uncertainty defines the method of reliability analysis, i.e., aleatory uncertainty possesses a probabilistic approach, whereas epistemic uncertainty leads to a possibilistic approach. The TDR analysis of the structures subjected to sustained load and corrosion based on probability theory is well developed. On the contrary, the TDR of structures subjected to combined effects sustained load, corrosion, creep, and shrinkage based on possibility theory lack research. Thus, this paper is significant to evaluate the TDR of structures subjected to the aforementioned effects based on possibility theory. However, it requires a rigorous procedure, a single-loop optimization method (SLOM) is employed to evaluate the TDR of structures involving epistemic uncertainty. To enhance the validation of the proposed approach, several examples are thoroughly carried out.

Pengaruh Deskripsi Kerja, Spesifikasi Kerja dan Disiplin Kerja Terhadap Kinerja Karyawan Pada PT. Goldwell Plastic Batam

implementation of all agency activities or activities, so that they are managed properly through Human Resource Management. Employee performance is what employees do or don't do that affect how much they contribute to the organization, including output quality, output quantity, output period, presence at work, and cooperative attitude. The aim of this study was to determine the effect of job descriptions, job specifications, and work discipline on employee performance at PT Goldwell Plastic Batam. The sample for this study used 110 respondents to employees at PT Goldwell Plastic Batam using the sampling technique. By using SPSS 25 software, data analysis techniques include descriptive statistical tests, data quality tests, classical assumption tests, influence tests, and hypothesis testing. Based on the results of the study, it can be concluded that job descriptions, job specifications and work discipline jointly have a significant effect on employee performance at PT Goldwell Plastic Batam

Quantifying uncertainties in direct numerical simulations of a turbulent channel flow

Direct numerical simulation (DNS) provides unrivalled levels of detail and accuracy for simulating turbulent flows. However, like all numerical methods, DNS is subject to uncertainties arising from the numerical scheme and input parameters (e.g. mesh resolution). While uncertainty quantification (UQ) techniques are being employed more and more to provide a systematic analysis of uncertainty for lower-fidelity models, their application to DNS is still relatively rare. In light of this, the aim of this work is to apply UQ and sensitivity analysis to the DNS of a canonical wall-bounded turbulent channel flow at low Reynolds number (Reτ=180). To compute the DNS, Incompact3d – a highly scalable open-source framework based on high-order compact finite differences and a spectral Poisson solver – is used as a black-box solver. Stochastic collocation is used to propagate the input uncertainties through Incompact3d to the output quantities of interest (QOIs). To facilitate the non-intrusive forward UQ analysis, the open-source EasyVVUQ package is used to provide integrated capability for sampling, pre-processing, execution, post-processing, and analysis of the computational campaign. Three separate UQ campaigns are conducted. The first two examine the effect of domain size and the numerical parameters (e.g. mesh resolution, time step, sample time), respectively, and adopt Gaussian quadrature rules combined via tensor products to sample the multi-dimensional input space. Finally, the third campaign investigates the performance of a dimension-adaptive sampling strategy that significantly reduces the computational cost compared to the full tensor product approach. The analysis focuses on the cross-channel statistical moments of the QOIs, as well as local and global sensitivity analyses to assess the sensitivity of each QOI with respect to each individual input. This enables an assessment of the robustness and sensitivity of DNS to the user-defined numerical parameters for wall-bounded turbulent flows, and provides an indication of suitable ranges for defining the values of these parameters.

Photovoltaic/Hydrokinetic/Hydrogen Energy System Sizing Considering Uncertainty: A Stochastic Approach Using Two-Point Estimate Method and Improved Gradient-Based Optimizer

In this paper, stochastic sizing of a stand-alone Photovoltaic/Hydrokinetic/Hydrogen storage energy system is performed with aim of minimizing the cost of project life span (COPL) and satisfying the reliability index as probability of load shortage (POLS). The stochastic sizing is implemented using a novel framework considering two-point estimate method (2m+1 PEM) and improved gradient-based optimizer (IGBO). The 2m+1 PEM is used to evaluate the impact of uncertainties of energy resource generation and system demand on sizing problem. The 2m+1 PEM utilizes the approximate method to account for these uncertainties. In order to avoid premature convergence, the gradient-based optimizer (GBO), a meta-heuristic algorithm influenced by Newtonian concepts, is enhanced using a dynamic lens-imaging learning approach. The size of the system devices, which is determined utilizing the IGBO with the COPL minimization and optimally satisfying the POLS, is one of the optimization variables. The results of three hPV/HKT/FC, hPV/FC, and hHKT/FC configurations of the system are presented in two situations of deterministic and stochastic sizing without and with taking uncertainty into consideration. The findings showed that the hPV/HKT/FC configuration and the IGBO performed better than other configurations and techniques like conventional GBO, particle swarm optimization (PSO), and artificial electric field algorithm (AEFA) to achieve the lowest COPL and POLS (higher reliability) in various cases. Additionally, the COPL for the hPV/HKT/FC, hPV/FC, and hHKT/FC configurations increased by 7.63%, 7.57%, and 7.65%, respectively, while the POLS fell by 5.01%, 4.48%, and 4.59%, respectively, contrasted to the deterministic sizing, according to the results of stochastic sizing based on 2m+1 PEM. As a result, the findings indicate that in the deterministic sizing model, the quantity of output and energy storage is insufficient to meet demand under unknown circumstances. Applying stochastic sizing while taking into account the volatility of both supply and demand can, therefore, be an economically sound way to meet demand.

Effect of uptake of improved seed potato and donor‐funded training on smallholder farmers’ market participation

AbstractEnhancing the productivity of crops through the adoption and uptake of technologies improves farmers' socioeconomic status through increased market participation. However, smallholder farmers in Sub‐Saharan Africa (SSA) face a myriad of challenges that make it difficult for them to access and participate in the output markets. Like most SSA countries, the uptake of improved technologies is still low in Malawi. This study examines the relationship between the uptake of improved potato seed and donor‐funded training on market participation. Using secondary data from 393 potato farmers in Malawi, the study applied a triple‐hurdle model to examine the extent to which such interventions and other socioeconomic and institutional factors influence market participation. The results showed that male‐headed farm households, more experienced farmers, and those who participated in International Potato Center (CIP)‐led training were likely to market their potato output. Further, farmers closer to urban centers, those with off‐farm income, those who had participated in potato training, use of improved potato seed, and quantity of output positively influenced the decision to sell in high‐value markets such as supermarkets. On the other hand, land size, ownership of transport equipment, group membership, and access to credit had positive effects on the amount of potatoes sold, while the distance to the markets reduced the quantity sold. Based on these findings, there is a need to set up a seed system within the local communities where farmers can easily access the seeds. Additionally, there is a need for a collaborative effort between public and private sector stakeholders to invest in the production of improved potato seeds that would help to address the shortfall in supply. Finally, institutional support interventions that utilize farmer groups as a key entry point in sustaining training on improved potato seed, relaxing credit constraints, and promoting diversification into off‐farm enterprises would enhance potato production and marketing.

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation.

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as those based on the finite element method, provide valuable information on the cardiac mechanical function, accurate numerical results can be obtained at the price of very fine spatio-temporal discretizations. As a matter of fact, simulating even just a few heartbeats can require up to hours of wall time on high-performance computing architectures. In addition, cardiac models usually depend on a set of input parameters that are calibrated in order to explore multiple virtual scenarios. To compute reliable solutions at a greatly reduced computational cost, we rely on a reduced basis method empowered with a new deep learning-based operator approximation, which we refer to as Deep-HyROMnet technique. Our strategy combines a projection-based POD-Galerkin method with deep neural networks for the approximation of (reduced) nonlinear operators, overcoming the typical computational bottleneck associated with standard hyper-reduction techniques employed in reduced-order models (ROMs) for nonlinear parametrized systems. This method can provide extremely accurate approximations to parametrized cardiac mechanics problems, such as in the case of the complete cardiac cycle in a patient-specific left ventricle geometry. In this respect, a 3D model for tissue mechanics is coupled with a 0D model for external blood circulation; active force generation is provided through an adjustable parameter-dependent surrogate model as input to the tissue 3D model. The proposed strategy is shown to outperform classical projection-based ROMs, in terms of orders of magnitude of computational speed-up, and to return accurate pressure-volume loops in both physiological and pathological cases. Finally, an application to a forward uncertainty quantification analysis, unaffordable if relying on a FOM, is considered, involving output quantities of interest such as, for example, the ejection fraction or the maximal rate of change in pressure in the left ventricle.

Excellence in forensic psychiatry services: international survey of qualities and correlates.

Excellence is that quality that drives continuously improving outcomes for patients. Excellence must be measurable. We set out to measure excellence in forensic mental health services according to four levels of organisation and complexity (basic, standard, progressive and excellent) across seven domains: values and rights; clinical organisation; consistency; timescale; specialisation; routine outcome measures; research and development. To validate the psychometric properties of a measurement scale to test which objective features of forensic services might relate to excellence: for example, university linkages, service size and integrated patient pathways across levels of therapeutic security. A survey instrument was devised by a modified Delphi process. Forensic leads, either clinical or academic, in 48 forensic services across 5 jurisdictions completed the questionnaire. Regression analysis found that the number of security levels, linked patient pathways, number of in-patient teams and joint university appointments predicted total excellence score. Larger services organised according to stratified therapeutic security and with strong university and research links scored higher on this measure of excellence. A weakness is that these were self-ratings. Reliability could be improved with peer review and with objective measures such as quality and quantity of research output. For the future, studies are needed of the determinants of other objective measures of better outcomes for patients, including shorter lengths of stay, reduced recidivism and readmission, and improved physical and mental health and quality of life.

Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning

We propose derivative-informed neural operators (DINOs), a general family of neural networks to approximate operators as infinite-dimensional mappings from input function spaces to output function spaces or quantities of interest. After discretizations both inputs and outputs are high-dimensional. We aim to approximate not only the operators with improved accuracy but also their derivatives (Jacobians) with respect to the input function-valued parameter to empower derivative-based algorithms in many applications, e.g., Bayesian inverse problems, optimization under parameter uncertainty, and optimal experimental design. The major difficulties include the computational cost of generating derivative training data and the high dimensionality of the problem leading to large training cost. To address these challenges, we exploit the intrinsic low-dimensionality of the derivatives and develop algorithms for compressing derivative information and efficiently imposing it in neural operator training yielding derivative-informed neural operators. We demonstrate that these advances can significantly reduce the costs of both data generation and training for large classes of problems (e.g., nonlinear steady state parametric PDE maps), making the costs marginal or comparable to the costs without using derivatives, and in particular independent of the discretization dimension of the input and output functions. Moreover, we show that the proposed DINO achieves significantly higher accuracy than neural operators trained without derivative information, for both function approximation and derivative approximation (e.g., Gauss–Newton Hessian), especially when the training data are limited.

Comparing the effect of different agricultural land-use systems on biodiversity

This article develops a calculation method to compare the total effect of different agricultural land-use systems on biodiversity. The results of the calculations offer a clear indication of which land-use system provides the highest biodiversity possible to produce a desired quantity of agricultural output. Furthermore, the method can be used to compare all types of farming systems. To give a numerical example, the method is used here to compare the data currently available for conventional agriculture and organic agriculture. Given that the currently available data lack information regarding second moments, a statistical test is not possible. Under this caveat, the results indicate that conventional agriculture allows for more biodiversity in the production of a desired quantity of agricultural goods because its higher productivity helps to spare land, which can then be used as a natural habitat.

Academic Staff Research Productivity in Private Universities in Southwestern Nigeria

Research productivity (teaching, research, and community service) in any university is the totality of academic staff academic achievement within a given period of time which is partly used for university ranking. There has been a renewed interest in the debate about the quality and quantity of research output and the factors which influence the output of university lecturers at the same time. Therefore, this study assessed the level of academic staff research productivity in private universities in Southwestern, Nigeria. The survey design of the correlational type was adopted. Proportional to size and stratified random sampling techniques were used to select 30% of academic staff across the various ranks in the selected universities, making a total of 657. Academic staff research productivity (= 2.02) was low as against the norm test of 3.00. Low research productivity can be overcome if investment in research at private universities is increased and academic staff utilise them in line with the emerging digital trend in universities around the world.

Designing structures with polymorphic uncertainty: Enhanced decision making using information reduction measures to quantify robustness

AbstractThe application of information reduction measures (IRMs) can provide valuable insight and enhance the process of design optimization when dealing with data uncertainty. For the engineering task of designing structures or products, an adequate modeling of data uncertainty is required. Therefore, a consideration of both aleatoric and epistemic uncertainty in combined form as polymorphic uncertain input variables is utilized. The resulting uncertain output quantities are post‐processed to provide relevant insights into robustness and performance for the design optimization. To this end, IRMs are applied, categorized into representative and uncertainty quantifying measures. Various IRMs exist, but clear recommendations or explanations of why certain uncertainty quantifying measures are chosen are scarce, although different features of uncertain quantities are considered with different measures. The aim of this contribution is to give an insight to commonly applied IRMs and the specific information of the uncertain quantity they reflect. Additionally, handling results of nested uncertainty analyses of polymorphic uncertain quantities regarding robustness towards aleatoric and epistemic uncertainty is investigated.

Can the development of digital finance stimulate enterprise innovation? Empirical evidence from China

ABSTRACT Based on the data of China A-share listed enterprises, this paper examines the actual effect and mechanism of the development of digital finance on different innovation behaviors of enterprises, by using the panel data model and regression-based mediation analysis. It is found that the development of digital finance not only promotes the R&D investment of enterprises but also improves the quantity and quality of enterprise innovation output. The incentive effect of digital finance on enterprise R&D investment is stronger than that on innovation output, while the incentive effect of digital finance on enterprise breakthrough innovation is stronger than that of incremental innovation. Both the ‘broadening’ and the ‘deepening’ of digital finance have a significant positive effect on enterprise innovation, while the ‘digitalization degree’ of digital finance has no significant effect on enterprise innovation, and even may hinder the improvement of innovation quality. The incentive effect of digital finance on the innovation output of state-owned enterprises is reflected in ‘quantity’, while the incentive effect on innovation of non-state-owned enterprises is reflected in ‘quality’. Digital finance can stimulate enterprise innovation by easing the financing constraints of enterprises, optimizing the government subsidy system, and improving the business environment.

Gradient-based optimisation of the conditional-value-at-risk using the multi-level Monte Carlo method

In this work, we tackle the problem of minimising the Conditional-Value-at-Risk (CVaR) of output quantities of complex differential models with random input data, using gradient-based approaches in combination with the Multi-Level Monte Carlo (MLMC) method. In particular, we consider the framework of multi-level Monte Carlo for parametric expectations introduced in [24] and propose modifications of the MLMC estimator, error estimation procedure, and adaptive MLMC parameter selection to ensure the estimation of the CVaR and sensitivities for a given design with a prescribed accuracy. We then propose combining the MLMC framework with an alternating inexact minimisation-gradient descent algorithm, for which we prove Q-linear convergence in the optimisation iterations under the assumptions of strong convexity and Lipschitz continuity of the gradient of the objective function. We demonstrate the performance of our approach on two numerical examples of practical relevance, which evidence the same optimal asymptotic cost-tolerance behaviour as standard MLMC methods for fixed design computations of output expectations.

Enhancing energy efficiency in rural CCHP systems with optimal gas engine size selection and Improved Coyote Optimizer

This study introduces a novel tactic to efficiently size the selection of gas engines for Combined Cooling, Heating, and Power (CCHP) systems in rural areas. This method is based on an adapted bio-inspired method, called Improved Coyote Optimizer (ICO). The proposed ICO technique uses a combination of metaheuristic optimization techniques and the system model to determine an optimal overall engine size while satisfying given operating constraints and minimizing the total cost. The analysis revealed that the fuel consumption of the gas engine and boiler in the proposed system is dependent on the power level. At 40 kW, the engine consumed 1558 units of fuel, while the boiler consumed 1430 units, with this difference increasing to 411 units at 110 kW. These findings emphasize the importance of improving the fuel efficiency of the engine, particularly at higher power levels. Furthermore, the study assessed the system’s CO2 emissions, finding that the minimum yearly emissions of 220,600 kg occurred at an engine power of 1 kW, while the maximum CO2 reduction rate of 40% was achieved at 120 kW. This suggests that larger engines are more efficient, resulting in lower CO2 emissions due to reduced fuel consumption. In terms of financial considerations, the engine power dimension of 118.9 kW yielded the highest cash flow value of 49,840 $. However, variations in engine power between 60 and 100 kW indicate a wider range of production costs and output quantities. To accurately evaluate the return on investment, more detailed information on production costs and output within this range is necessary. Lastly, the study compared different optimization methods and found that the Improved Coyote Optimizer (ICO) demonstrated the fastest convergence, reaching its ideal values in 3000 iterations. Both the ICO-based method and the ABC-based technique successfully minimized the cost function, with the ICO-based method achieving the best value of 0.20. Furthermore, the proposed algorithm is practical to a wide variety of CCHP system configurations, making it a valuable tool for practitioners.

Human-Computer Collaborative Visual Design Creation Assisted by Artificial Intelligence

With the support and promotion of big data and cloud computing, AI has penetrated into every field of people's lives more and more deeply, with its characteristics of sustainable work, extremely fast computing speed, and a certain intelligence. This is an effective way to solve the general lack of demand and productivity of visual design, and relieve the pressure off designers to deal with relatively low-quality and high-demand designs. Therefore, the combination of design and artificial intelligence technology is a necessity. Research on the application of artificial intelligence technology for visual design is also in full swing at home and abroad However, at present, teams at home and abroad are in the exploratory stage. This paper considers whether it is possible to build an intelligent visual design and creation system by using artificial intelligence technology to help graphic communication designers achieve high-quality, high-efficiency, and high-quantity design output. Additionally, this paperexplores how to combine artificial intelligence technology with designers' design workflow so as to form a complementary human-computer cooperation mode. We will explore how to integrate AI technology with designers' design workflow and then create a human-machine collaboration model with complementary advantages to achieve the high quality, high efficiency, and high quantity of design output required by the intelligent visual design creation system being built. Finally, a basic framework of a generative smart human-computer collaborative visual design creation system based on a subset of neural network expert systems in multiple domains and an aggregate of different modules supported by the system is formed, and the working principle and usage process of the system are further elaborated with the example of packaging design.