Improve Design Quality Research Articles

Application and renovation evaluation of Dalian's industrial architectural heritage based on AHP and AIGC.

This paper takes the example of industrial architectural heritage in Dalian to explore design scheme generation methods based on generative artificial intelligence (AIGC). The study compares the design effects of three different tools using the Analytic Hierarchy Process (AHP). It first establishes the key indicator weights for the renovation of industrial architectural heritage, with the criterion layer weights as follows: building renovation 0.230, environmental landscape 0.223, economic benefits 0.190, and socio-cultural value 0.356. Among the goal layer weights, the highest weight is for the improvement of living quality at 0.129, followed by resident satisfaction at 0.096, and educational and display functions at 0.088, while the lowest is for renovation costs at only 0.035. The design schemes are generated using Stable Diffusion, Mid Journey, and Adobe Firefly tools, and evaluated using a weighted scoring method. The results show that Stable Diffusion excels in overall image control, Mid Journey demonstrates strong artistic effects, while Adobe Firefly stands out in generation efficiency and ease of use. In the overall score, Stable Diffusion leads the other two tools with scores of 6.1 and 6.3, respectively. Compared to traditional design processes, these tools significantly shorten the design workflow and cycle, improving design quality and efficiency while also providing rich creative inspiration. Overall, although current generative artificial intelligence tools still have limitations in understanding human emotions and cultural differences, with continuous technological iteration, this method is expected to play a larger role in the design field, offering more innovative solutions for the renovation of industrial architectural heritage.

A Study on Guesthouse Interior Space Planning Method Based on BIM Topological Relationships

Building Information Modeling (BIM) technology has revolutionized the architectural and construction industries by providing detailed digital representations of buildings, enhancing design efficiency and project management. Despite its widespread application, the utilization of BIM in optimizing interior space planning, particularly in boutique accommodations like guesthouses, remains underexplored. This research addresses the gap by introducing a novel method for interior environment space planning of guesthouses based on the topological relationships derived from BIM data. This study developed a new algorithm that utilizes detailed topology information from BIM in order to make space planning decisions more efficiently, thereby improving space utilization efficiency and guest experience. Through a comprehensive analysis of BIM topological relationships and the application of advanced optimization techniques, our method aims to optimize the use of space while considering the unique constraints and requirements of guesthouse environments. The proposed algorithm demonstrates significant improvements in spatial efficiency and design quality when tested against traditional planning methods on real-world BIM datasets. This research not only contributes a novel approach to the field of architectural design and planning but also offers practical implications for the enhancement of interior spaces in guesthouses, potentially influencing future applications of BIM technology in the hospitality industry.

Research on Collaborative Management of BIM (Building Information Modeling) in Engineering Projects

Abstract: The research on collaborative management of Building Information Modeling (BIM) in engineering projects aims to explore the application of BIM technology in construction engineering and its impact on collaborative management. BIM technology creates digital 3D models, centrally manages project information, promotes information sharing and collaboration, and improves project management efficiency. In construction projects, BIM technology can effectively solve the problems of difficult information sharing and poor information transmission, thereby making collaborative management more efficient. In addition, the application of BIM technology in the design, bidding, and construction stages can ensure the smooth transmission of information and improve the efficiency of collaborative management. Research has shown that the application of BIM technology in structural design significantly improves design quality and demonstrates its wide applicability. However, despite the significant advantages of BIM technology in collaborative management, there are still some challenges, such as trust issues and technical operational difficulties. Through in-depth research and application of BIM technology, more efficient collaborative management models can be provided for future engineering projects, promoting the development of the construction industry.

AI-Driven DRC Routing Convergence in IC Design : A Paradigm Shift in Semiconductor Development

This article explores the transformative impact of Artificial Intelligence (AI) on Integrated Circuit (IC) design, particularly in Design Rule Checking (DRC) and routing convergence. As semiconductor technology advances to smaller nodes, traditional design methodologies struggle with increasing complexity, time-to-market pressures, and stringent manufacturing constraints. Integrating AI-driven approaches offers promising solutions, dramatically reducing design cycles, improving yield, and enabling continued scaling. The article discusses the challenges of modern IC design, the critical role of DRC, and the AI revolution in routing optimization. It examines the benefits of AI-driven DRC routing convergence, including reduced time-to-market, improved design quality, cost reduction, enhanced scalability, and adaptability to new processes. Finally, it addresses current challenges and future research directions in this rapidly evolving field.

A critical review of the Iranian National Olympic Committee's performance in designing the opening parade costumes: signs of inefficiency and damage to the national identity

This research aims to evaluate and critique the performance of the Iranian National Olympic Committee in designing the costumes for the Olympic opening parade. The study seeks to identify the strengths and weaknesses of these designs and compare them with successful global examples to help improve future designs. A review method was used to achieve this goal. This method involved examining previous research related to the design of Olympic uniforms and the criticisms they received. The analysis included public and media critiques, athlete surveys, and an examination of the characteristics of successful international designs.The findings indicate that Iran's Olympic uniforms have struggled to make a positive impact internationally due to weaknesses in aligning with global standards, a lack of attention to cultural symbols, and inadequate use of modern technologies. Compared to successful global designs, these uniforms have faced challenges in representing national identity and effectively communicating with audiences. The study concludes that to improve future designs, the Iranian National Olympic Committee should focus on enhancing collaboration with cultural and social experts, using international consultants to align with global standards, and establishing continuous evaluation processes to identify and address weaknesses. Additionally, developing specialized teams and providing training for designers are recommended to improve design quality and elevate national prestige on the international stage.

Automated design and optimization of distributed filter circuits using reinforcement learning

Abstract Designing distributed filter circuits (DFCs) is complex and time-consuming, involving setting and optimizing multiple hyperparameters. Traditional optimization methods, such as using the commercial finite element solver HFSS (High-Frequency Structure Simulator) to enumerate all parameter combinations with fixed steps and then simulate each combination, are not only time-consuming and labor-intensive but also rely heavily on the expertise and experience of electronics engineers, making it difficult to adapt to rapidly changing design requirements. Additionally, these commercial tools struggle with precise adjustments when parameters are sensitive to numerical changes, resulting in limited optimization effectiveness. This study proposes a novel end-to-end automated method for DFC design. The proposed method harnesses reinforcement learning (RL) algorithms, eliminating the dependence on the design experience of engineers. Thus, it significantly reduces the subjectivity and constraints associated with circuit design. The experimental findings demonstrate clear improvements in design efficiency and quality when comparing the proposed method with traditional engineer-driven methods. Furthermore, the proposed method achieves superior performance when designing complex or rapidly evolving DFCs, highlighting the substantial potential of RL in circuit design automation. In particular, compared to the existing DFC automation design method CircuitGNN, our method achieves an average performance improvement of 8.72%. Additionally, the execution efficiency of our method is 2000 times higher than CircuitGNN on the CPU and 241 times higher on the GPU.

Virtual Acoustic Characterization of a Multipurpose Sports Facility: A Case Study of the Coliseum at “Unidad Educativa Municipal Sebastián de Benalcázar”, Quito, Ecuador

Abstract This study presents a virtual acoustic characterization of a multipurpose sports facility, focusing on a case study of the coliseum at “Unidad Educativa Municipal Sebastián de Benalcázar” in Quito, Ecuador. Utilizing a non-intrusive, simulation-based approach, an initial detailed survey of the coliseum’s interior was conducted. AutoCAD and SketchUp software were utilized to generate a three-dimensional (3D) model based on the acquired data. This model was then exported to ODEON software for acoustic simulation. Important acoustic parameters, including reverberation duration, clarity, STI (Speech Transmission Index), and RASTI (Rapid Speech Transmission Index), were obtained from the simulation. These parameters were instrumental in characterizing the sports facility’s acoustic quality and comparing it with established reference values. Special emphasis was given to the initial parameters of reverberation time and STI, crucial for assessing the acoustic quality of the space. This virtual characterization provides significant insights into the acoustical properties of the coliseum, highlighting its potential and limitations as a multifunctional sports venue. The present study makes a significant contribution to the field of virtual acoustic assessments by providing a comprehensive framework for future improvements in the acoustic design and sound quality of similar environments. The study specifically addresses the distinct geographical and architectural context of Quito, Ecuador.

Research on key technology of smart grid based on holographic data

Abstract With the increasing scale and complexity of power projects, designers face more and more challenges in terms of design quality and efficiency. In order to improve design quality and reduce human-caused errors, utilizing technologies such as big data and strong artificial intelligence, this manuscript proposes a key technology for smart grid based on holographic data. Firstly, the concept and key technologies of holographic intelligent design were proposed. Then, the overall plan of holographic smart grid was constructed, and the implementation methods of the main functions of holographic smart grid were elaborated. Finally, the construction process of the holographic digital twin platform was explored.

Implementasi Metode Agile Scrum Dalam Perancangan UI/UX Design Command Control Map Service Di PT. Len Industri (Persero)

Implementasi Metode Agile Scrum Dalam Perancangan UI/UX Design Command Control Map Service Di PT. Len Industri (Persero)

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

Art design has entered a new era with the integration of cloud computing technology, revolutionizing the creative process and expanding artistic possibilities. By leveraging the vast computational power and storage capabilities of cloud platforms, artists can explore innovative techniques, collaborate with peers remotely, and access a wealth of digital resources from anywhere in the world. Cloud computing enables artists to work with large-scale datasets, create complex visualizations, and render high-resolution artworks with ease. Moreover, cloud-based tools and applications offer flexibility and scalability, allowing artists to experiment freely and iterate rapidly. Whether creating digital paintings, 3D animations, or interactive installations, cloud computing empowers artists to push the boundaries of their creativity and bring their visions to life in ways previously unimaginable. This paper presents an innovative application of genetic algorithm (GA) leveraging cloud computing technology for art product design, with a focus on the Centralized Clustering Middle Chain (CCMC) framework. By harnessing the computational power and scalability of cloud platforms, GA facilitates the optimization of design parameters to generate novel and aesthetically pleasing art products. The CCMC framework streamlines the design process by centralizing data clustering and analysis, enabling efficient exploration of design space and identification of optimal solutions. Through simulated experiments and empirical evaluations, the effectiveness of the GA-based approach in art product design is assessed. Results demonstrate significant improvements in design quality and efficiency, with the GA-enabled cloud computing solution achieving a 40% reduction in design iteration time and a 30% increase in product innovation compared to traditional methods.

Simulation and Optimization of Landscape Planning and Design Based on Virtual Reality Technology

Virtual reality (VR) is transforming the landscape design and planning industry by offering immersive and interactive tools for visualization and decision-making. With VR technology, designers and planners can create realistic 3D environments that allow stakeholders to experience proposed designs in a virtual space before implementation. This enables them to explore different design options, evaluate spatial relationships, and assess the impact of various elements such as vegetation, topography, and infrastructure. VR also facilitates collaboration among project stakeholders by providing a shared platform for communication and feedback. By immersing themselves in virtual landscapes, clients, designers, and stakeholders can better understand design concepts and make informed decisions, leading to more efficient and successful projects. This paper introduces a novel approach to simulating and optimizing landscape planning and design using virtual reality (VR) technology, enhanced by Whale Swarm Optimization Classification (WSOC). The proposed framework aims to leverage the immersive and interactive capabilities of VR to facilitate more effective landscape planning and design processes. Through simulated experiments and empirical validations, the efficacy of the WSOC-enhanced VR-based approach is evaluated. Results demonstrate significant improvements in optimization efficiency and landscape design quality compared to traditional methods. For example, the WSOC model achieved a 40% reduction in design iteration time and a 25% increase in landscape sustainability metrics. Additionally, the framework enabled stakeholders to visualize and interact with proposed designs in real-time, leading to more informed decision-making and consensus-building. These findings highlight the potential of VR technology with WSOC in revolutionizing landscape planning and design, fostering sustainable and aesthetically pleasing outdoor environments.

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN.

InGaAs detection systems have been increasingly used in the aerospace field, and due to the high signal-to-noise ratio requirements of short-wave infrared quantitative payloads, there is an urgent need for methods for the rapid and precise evaluation and the optimal design of these systems. The rigid-flex printed circuit board (PCB) is a vital component of InGaAs detectors, as its grid ground plane design parameters impact parasitic capacitance and thus affect weak infrared analog signals. To address the time-intensive and costly nature of design optimization achieved with simulations and experimental measurements, we propose an innovative method based on a neural network to predict the scattering parameters of rigid-flex boards for InGaAs detection links. This is the first study in which the effects of rigid-flex boards on weak infrared detection signals have been considered. We first obtained sufficient samples with software simulation. A backpropagation (BP) neural network prediction model was trained on existing sample sets and then verified on a rigid-flex board used in a crucial aerospace short-wave infrared quantitative mission. The model efficiently and accurately predicted high-speed interconnect scattering parameters under various rigid-flex board grid plane parameter conditions. The prediction error was less than 1% compared with a 3D field solver, indicating an overcoming of the iterative optimization inefficiency and showing improved design quality for InGaAs detection circuits.

Development of a Residential Space Design Module Based on Virtual Reality (VR) Applications

Abstract The adoption of virtual reality (VR) in architectural design is increasing, driven by its ability to create immersive simulated environments. This is particularly advantageous in residential space design, where understanding and evaluating solutions is critical. Although extensive research has focused on basic VR applications, few have explored VR modules tailored for residential design. Our study fills this gap by developing a VR module designed specifically for this purpose. We use a mixed-methods approach, blending quantitative analysis with qualitative feedback, to demonstrate how this VR module can improve design quality, efficiency, and user engagement.

Analyzing the Development History of Chinese Ceramic Packaging Containers and Their Modern Design Innovation Based on Multivariate Statistics

Abstract This paper explores the development history and modern design innovation of Chinese ceramic packaging containers through multivariate statistical analysis, revealing their essential role in the market and cultural heritage. The development history and contemporary design innovation of Chinese ceramic packaging containers are studied in depth. The study finds that ceramic packaging containers have evolved from prehistoric hand-kneaded small pieces to the present, constantly incorporating new crafts and cultural elements. Regarding formal aesthetics, the use of rhythm and rhyme in ceramic packaging containers grew at 81.13% between 2015 and 2020, suggesting that designs tend to be more dynamic and expressive. Data analysis shows that the key factors influencing the design of ceramic packaging containers include process technology, trade development, cultural concepts and religious beliefs. Regression analysis indicates that these factors significantly positively impact the design quality of ceramic packaging containers, with a model fit of 63.52%, revealing that these factors jointly promote the improvement of design quality. Innovative combination of traditional elements and modern technology is the key to fostering innovation of ceramic packaging container design and cultural heritage. The market competitiveness and cultural value of ceramic packaging containers can be effectively promoted by emphasizing the development of craft technology and the integration of cultural elements.

Mitigating the Effects of Design for Manufacturability on Design Iteration Cycles in Advanced Integrated Circuit Design

In integrated circuit (IC) design for advanced manufacturing processes, iterative improvement processes are an approach commonly used for improving design quality, particularly for low-energy, high-frequency circuits. Layout-dependent effects (LDEs) affect IC design and fabrication, resulting in differences in circuit functionality and performance in presimulation versus postsimulation. These differences can greatly increase the length of design iteration cycles. Foundries have attempted to accelerate the iterative design process by providing process design kit libraries to IC designers. However, these kits can neither fully mitigate the negative affect of LDEs on design for manufacturability (DFM) nor eliminate the difference between presimulation and postsimulation results. To address this problem, this study proposed a novel algorithm-based design process in which an IC designer can use layout parasitic extraction to extract the DFM parameters of all components in a circuit prior to the routing process; these parameters include netlists that describe the internal connectivity of components as well as their interconnectivity with other components. Accordingly, the IC designer can determine the effects of DFM parameters on the circuit and modify them in advance if necessary. When the LDEs generated by device placement have been confirmed to not affect circuit properties, physical verification of the routing of metal wires in the IC layout can be performed. In this verification, postsimulations only need to focus on problems regarding the wire loading and timing effects of metal routing. The proposed design process is useful for mitigating LDEs in IC design and considerably reduces the time required for iterative improvement processes. Specifically, this study provides a method of enhancing design iteration cycles and provides guidelines for analyzing factors that hinder IC device functionality and performance. In sum, various problems can be resolved by separately extracting the DFM parameters associated with LDEs and parasitic parameters associated with routing.

Developing a resilient and sustainable non-linear closed-loop supply chain management framework for the automotive sector industry using a gaussian fuzzy optimization based non-linear model predictive control approach

ABSTRACT Efforts to merge sustainability and resilience within the automotive industry’s supply chain models have proven challenging. This paper proposes a novel non-linear closed-loop supply chain management framework tailored to the tire industry supply chain from the automotive sector to address the issue of exploring interrelationships. Framework employs trapezoidal linguistic cubic fuzzy Z-score technique for order of preference by similarity to the ideal solution ranking approach to prioritize resilience strategies to maintain sustainability performance during sudden disturbances. Furthermore, Gaussian fuzzy optimization-based non-linear model predictive control acts as a feedback controller to integrate sustainability and resilience by providing a stable output based on the objective function related to sustainability dimensions. An experimental study assesses the impact of resilience strategies on total supply chain costs, highlighting significant cost savings. Adopting strategies like multiple sourcing, information sharing, and improved design quality of the supply chain keeps total expected costs optimal for various sustainability levels.

Determination of wind load using wind tunnel test for building design in Vietnam

Buildings in Vietnam are increasingly tall and complex in shape, so it is not feasible to apply current Vietnamese and international wind standards in many cases. The wind tunnel test is a modern and increasingly applied method to determine wind loads for design work in Vietnam. In addition, this test has many meanings in improving design quality and reducing construction costs because the wind load determined by this method is usually about 20% to 30% smaller than the load when determined by the standard. This paper presents the results of applying wind tunnel test over the past 10 years at the Vietnam Institute of Building Science and Technology.

Erratum to “Improving design quality using meta‐pattern transformations: a metric‐based approach”

Erratum to “Improving design quality using meta‐pattern transformations: a metric‐based approach”

Exploring the impact of set-based concurrent engineering through multi-agent system simulation

Abstract Set-based concurrent engineering (SBCE), a process that develops sets of many design candidates for each subproblem throughout a design project, proposes several benefits compared to point-based processes, where only one design candidate for each subproblem is chosen for further development. These benefits include reduced rework, improved design quality, and retention of knowledge to use in future projects. Previous studies that introduce SBCE in practice achieved success and had very positive future outlooks, but SBCE encounters opposition because its core procedures appear wasteful as designers must divide their time among many designs throughout the process, most of which are ultimately not used. The impacts of these procedures can be explored in detail through open-source computational tools, but currently few exist to do this. This work introduces the Point/Set-Organized Research Teams (PSORT) modeling platform to simulate and analyze a set-based design process. The approach is used to verify statements made about SBCE and investigate its effects on project quality. Such an SBCE platform enables process exploration without needing to commit many projects and resources to any given design.

Building Information Modeling (BIM) Process and Assessment methods

The construction sector has seen the potential of Building Information Modeling (BIM) to assist a transformation in the design and construction processes. The use of BIM throughout a building project for quality control and effective information use has received very little investigation, even though it can improve design quality by reducing dispute and rework. The promise of BIM application in quality management rests in its capacity to offer multi-dimensional data including design data and time sequence due to the compatibility of design data with quality and construction process with quality control. An evolutionary method of transforming the entire building process is the 5D BIM approach. This paper introduces BIM as a strong instrument for "model analysis, detection of clashes, simulation and evaluation." In addition, BIM offers a digital machine-readable representation of building data and various other processes to enhance design, development, operating processes and boost life-cycle building functions.