Advanced Design Research最新文献

Emotional fidelity in AI-Generated music: A comparative analysis of conveyance and induction across models and modalities 人工智能生成音乐中的情感保真度：跨模型和模态的传递和归纳的比较分析

Advanced Design Research

Pub Date : 2025-12-01 DOI: 10.1016/j.ijadr.2025.11.001

Xinwei Gao , Dengkai Chen , Zhiming Gou , Jaap Ham , Raymond H. Cuijpers

The introduction of Generative Adversarial Networks has accelerated the development of AIGC, enabling creative assistance or even replacement in various fields including music. However, while AIGC-based music generation systems allowed users to input specific prompts to describe emotions, these models did not explicitly address or evaluate whether the generated emotions matched the emotion labels. This study aims to investigate the ability of AI music generated technology in emotion conveyance and induction. Three music generated software (Google MusicLM, Stable Audio, MusicGen) and two generated forms (text-to-music, music-to-music) were used to generate 75 AI music clips with four typical emotion labels (Energetic, Distressed, Sluggish, Peaceful) as prompts in 22 participants. The results show that Energetic AI music had significantly higher accuracy of conveyance than Sluggish and Peaceful in text-to-music, and its accuracy in text-to-music was higher than in music-to-music. The accuracy of Google MusicLM is remarkably higher than MusicGen. The Sluggish has the lowest accuracy in both forms. For emotion induction, interaction effects between Software and Prompt were observed, with Google MusicLM significantly higher than MusicGen for Energetic, and Energetic was significantly higher than Sluggish within Google MusicLM. These suggest that AI music can convey all emotions, but it may lead to significantly higher accuracy in conveying Energetic AI music as humans' higher sensitivity to positive-valence positive-arousal emotions. AI music's emotion conveyance ability may further influence its emotion induction. Compared to the other two software, Google MusicLM's higher accuracy of conveyance allows it to more effectively induce emotional responses in both forms. Ultimately, these findings inform the design of more anthropomorphic and emotionally intelligent systems, where AI-generated music can serve as a crucial non-verbal cue to foster deeper and more effective human-machine emotional connections.

生成对抗网络的引入加速了AIGC的发展，在包括音乐在内的各个领域实现了创造性的帮助甚至替代。然而，尽管基于aigc的音乐生成系统允许用户输入特定的提示来描述情绪，但这些模型并没有明确地处理或评估生成的情绪是否与情绪标签相匹配。本研究旨在探讨人工智能音乐生成技术在情感传递和诱导方面的能力。使用三个音乐生成软件（谷歌MusicLM， Stable Audio, MusicGen）和两个生成表单（文本到音乐，音乐到音乐），根据22名参与者的提示，生成75个具有四种典型情绪标签（精力充沛，痛苦，懒散，和平）的AI音乐片段。结果表明，在文本到音乐的传递中，Energetic音乐的传递准确率显著高于slow和Peaceful音乐，并且在文本到音乐的传递准确率高于music到音乐的传递准确率。谷歌MusicLM的准确率明显高于MusicGen。在这两种形式中，迟缓的准确率最低。在情绪诱导方面，观察到Software与Prompt之间的交互作用，谷歌MusicLM中Energetic显著高于MusicGen， Energetic显著高于呆滞。这表明AI音乐可以传达所有情绪，但由于人类对正价正唤醒情绪的敏感度更高，因此它可能会导致传达Energetic AI音乐的准确性显著提高。人工智能音乐的情感传递能力可能会进一步影响其情感诱导。与其他两款软件相比，谷歌MusicLM更高的传递准确性使其能够更有效地诱导两种形式的情绪反应。最终，这些发现为更多拟人化和情感智能系统的设计提供了信息，在这些系统中，人工智能生成的音乐可以作为重要的非语言线索，促进更深入、更有效的人机情感联系。

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引用次数: 0

Orchestrating acts of kindness: An exploratory framework for designing beneficial kindness interventions 策划善意行为：设计有益善意干预的探索性框架

Advanced Design Research

Pub Date : 2025-12-01 DOI: 10.1016/j.ijadr.2025.08.003

Michelle K.P. Johnson , Pieter M.A. Desmet , Haian Xue , Paul Hekkert , Anna E. Pohlmeyer

Acts of kindness can enhance well-being for both actors and recipients. Consequently, numerous products and technologies have been created to foster such behaviors. However, existing design interventions often assume that any kind act will positively impact well-being, neglecting factors that determine whether acts will actually enhance actor and recipient well-being. To address this gap, we conducted an explorative investigation into everyday kindness dynamics. Through a diary study capturing 137 everyday acts of kindness, we identified 13 factors across three categories (context, characteristics, and outcomes) that influence actor and recipient well-being. We organized these insights into an exploratory framework and tested its application in a workshop with designers that explored the practical applications of these factors in intervention design. Our findings demonstrate that kindness impacts depend on complex interrelationships between factors such as timeliness and fit with individuals. These insights support designers in moving beyond encouraging random acts to orchestrating interventions that consider both actor and recipient experiences, creating more meaningful and impactful kindness interventions.

善举可以增强行为者和接受者的幸福感。因此，许多产品和技术被创造出来，以促进这种行为。然而，现有的设计干预通常假设任何一种行为都会对幸福感产生积极影响，而忽略了决定行为是否真的会提高行为者和接受者幸福感的因素。为了解决这一差距，我们对日常善意动态进行了探索性调查。通过一项记录137个日常善意行为的日记研究，我们确定了影响行为者和接受者幸福感的13个因素，分为三类（背景、特征和结果）。我们将这些见解组织到一个探索性框架中，并在与设计师一起探讨这些因素在干预设计中的实际应用的研讨会上对其应用进行了测试。我们的研究结果表明，善意的影响取决于时间性和与个体的契合等因素之间复杂的相互关系。这些见解支持设计师从鼓励随机行为转向考虑行动者和接受者经验的协调干预，创造更有意义和更有影响力的善意干预。

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引用次数: 0

An intelligent school bus safety system: Integrating customer journey map and semiotic approach to product architecture design 智能校车安全系统：将客户旅程图与符号学方法结合到产品架构设计中

Advanced Design Research

Pub Date : 2025-12-01 DOI: 10.1016/j.ijadr.2025.11.002

Wen Cao , Qiuhan Lin , Xuerui Li , Junhui Lin , Qing Li , Yonggu Kim

Ensuring children's safety and improving their riding experience on school buses is a critical challenge for intelligent transportation systems worldwide. However, existing school bus safety designs often suffer from isolated functionalities and a lack of a holistic user journey perspective. To address these limitations, this paper proposes the Customer Journey Map and Semiotic Approach to Product Architecture Design (CJM-SAPAD) method. This approach bridges macro-level user journey insights with micro-level system functions, forming a closed-loop process from user needs to functional implementation. Through field studies, cross-regional operational comparisons, and user behavior analyses, we mapped the end-to-end journey of children and parents to identify core needs. By applying the SAPAD model to map and cluster “behavior–object–meaning” relationships, we distilled 9 key requirements. These findings informed the design of an intelligent school bus safety system, structured around three modules: interaction, safety, and information communication. Simulation results and expert evaluations demonstrate that the proposed system significantly improves operational efficiency, safety assurance, and school–family communication. This study presents a comprehensive methodology for the intelligent upgrading of school bus systems, from need identification to functional implementation, while contributing fresh perspectives for transforming public intelligent care systems.

确保儿童的安全并改善他们在校车上的乘坐体验是全球智能交通系统面临的关键挑战。然而，现有的校车安全设计往往受到孤立的功能和缺乏整体的用户旅程角度的影响。为了解决这些局限性，本文提出了客户旅程图和产品架构设计符号学方法（CJM-SAPAD）方法。这种方法将宏观层面的用户旅程洞察与微观层面的系统功能连接起来，形成了从用户需求到功能实现的闭环过程。通过实地研究、跨区域运营比较和用户行为分析，我们绘制了儿童和家长的端到端旅程，以确定核心需求。通过应用SAPAD模型来映射和聚类“行为-对象-意义”关系，我们提炼出9个关键需求。这些发现为智能校车安全系统的设计提供了依据，该系统围绕三个模块构建：交互、安全和信息通信。仿真结果和专家评价表明，该系统显著提高了运行效率、安全保障和学校与家庭的沟通。本研究为校车系统的智能升级提供了一个全面的方法，从需求识别到功能实施，同时为公共智能护理系统的转型提供了新的视角。

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引用次数: 0

Investigating the impact of crowded train commuting on cognitive load: A pilot study 研究拥挤的火车通勤对认知负荷的影响：一项初步研究

Advanced Design Research

Pub Date : 2025-12-01 DOI: 10.1016/j.ijadr.2025.10.001

Yitao Yang , Xinyi Guo , Nick Tyler

Overcrowding is a common challenge faced by public transport systems in many megacities around the world. Commuting in a crowded environment will not only generate physiological discomfort but also affect passengers’ psychological and neurophysiological state, leading to an increase in the level of anxiety. This pilot study investigates the neurophysiological impact of crowded train commuting using functional near-infrared spectroscopy (fNIRS) in an ecologically valid environment. Brain activity was recorded from the prefrontal cortex (PFC). Results show a progressive increase in oxygenated haemoglobin (HbO) concentration—indicating rising cognitive load—when commuting in the crowded condition, but not in the uncrowded condition. Moreover, significant and continuous increases in HbO were observed during deceleration in the crowded condition, suggesting that elevated mental effort is more likely to occur towards deceleration and getting off. These findings provide early neurophysiological evidence of crowding-induced cognitive strain and demonstrate the feasibility of fNIRS in simulating real-world commuting scenarios. The study highlights the potential of neuroimaging to inform transport design and urban mobility policy.

过度拥挤是世界上许多大城市公共交通系统面临的共同挑战。在拥挤的环境中通勤不仅会产生生理上的不适，还会影响乘客的心理和神经生理状态，导致焦虑程度的增加。本初步研究利用功能性近红外光谱（fNIRS）在生态有效环境中研究拥挤的火车通勤对神经生理的影响。从前额皮质（PFC）记录大脑活动。结果显示，在拥挤的情况下通勤时，氧合血红蛋白（HbO）浓度逐渐增加，表明认知负荷上升，而在不拥挤的情况下则没有。此外，在拥挤条件下减速时，观察到HbO显著且持续增加，这表明在减速和下车时更有可能发生更高的精神努力。这些发现提供了拥挤导致认知紧张的早期神经生理学证据，并证明了fNIRS在模拟现实通勤场景中的可行性。这项研究强调了神经成像在交通设计和城市交通政策方面的潜力。

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引用次数: 0

Get older adults involved: Participatory design of assistive robots for older adults using the Context-based Design Toolkit (CoDeT) 让老年人参与进来：使用基于情境的设计工具包（CoDeT）为老年人设计辅助机器人

Advanced Design Research

Pub Date : 2025-06-01 DOI: 10.1016/j.ijadr.2025.04.001

Ela Liberman-Pincu, Tal Oron-Gilad

Socially assistive robots (SARs) have the potential to significantly enhance the quality of life for older adults by addressing their unique needs and challenges. This study employs a Context-based Design Toolkit (CoDeT) to gather input from older adults for three types of SARs: a Medical Assistant Robot (MAR), a COVID-19 Officer Robot (COR), and a Personal Assistant Robot (PAR). CoDeT, which includes tangible cutouts of robotic components, facilitates participatory design (PD) sessions, allowing users to express their needs and concerns effectively. Three PD groups engaged with the toolkit to create new robot models, while two PD evaluation groups assigned each model to specific use cases. The tangible nature of the toolkit improved engagement and communication, leading to user-centered designs. The findings underscore the importance of context-driven design, with distinct recommendations for each use case to ensure the robots meet user needs and contextual requirements. The study involved five sessions with older adults, including three PD workshops and two evaluation sessions. Participants aged 75–92 contributed to the design process by selecting visual qualities such as structure, morphology, and color. The outcomes were analyzed to form a design space model for each use case, highlighting the significance of user-centered and context-driven design in developing effective SARs. This research demonstrates the value of involving older adults in the design process to create practical and empathetic SARs that address their specific needs and preferences.

社会辅助机器人（sar）通过解决老年人的独特需求和挑战，有可能显著提高老年人的生活质量。本研究采用基于情境的设计工具包（CoDeT）收集老年人对三种类型SARs的输入：医疗助理机器人（MAR）、COVID-19官员机器人（COR）和个人助理机器人（PAR）。CoDeT包括机器人部件的有形切割，促进参与式设计（PD）会议，允许用户有效地表达他们的需求和关注。三个PD小组使用工具包创建新的机器人模型，而两个PD评估小组将每个模型分配给特定的用例。该工具包的有形特性提高了用户粘性和沟通能力，从而实现了以用户为中心的设计。研究结果强调了上下文驱动设计的重要性，为每个用例提供了不同的建议，以确保机器人满足用户需求和上下文要求。该研究涉及老年人的五次会议，包括三次PD研讨会和两次评估会议。年龄在75-92岁之间的参与者通过选择结构、形态和颜色等视觉品质来参与设计过程。对结果进行分析，形成每个用例的设计空间模型，强调以用户为中心和上下文驱动的设计在开发有效的sar中的重要性。这项研究证明了让老年人参与设计过程的价值，以创建切合实际和富有同情心的sar，满足他们的特定需求和偏好。

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引用次数: 0

Short-term effects of static and dynamic lighting on sleepiness and task performance in simulated enclosed spaces 模拟封闭空间中静态和动态照明对睡意和工作表现的短期影响

Advanced Design Research

Pub Date : 2025-06-01 DOI: 10.1016/j.ijadr.2025.08.002

Xian Zhang , Ali Ghahramani , Ziteng Wei , Jingluan Wang , Lei Wang , Dengkai Chen

With the rapid advancement of manned spaceflight and deep-sea exploration and the development of planetary habitats and future underground facilities, research on environmental factors within enclosed spaces has become increasingly important. Beyond visual perception, light exerts significant non-visual effects (NVEs) on human physiology and behavior, such as sleepiness, arousal, and task performance. However, the short-term effects of lighting strategies in enclosed spaces remain underexplored, particularly across static and dynamic illumination patterns. We conducted a controlled experiment analyzing five distinct lighting conditions: two static (300 lx and 500 lx), one dynamic (transitioning from 300 lx to 500 lx), and two combined conditions (integrating static and dynamic elements, transitioning from 300 lx to 500 lx). Participants’ subjective sleepiness was assessed using a visual analog scale at the beginning and end of each lighting condition. Results revealed significant differences in sleepiness scores, with the lowest sleepiness observed under the first combined lighting condition and the highest sleepiness observed under the second combined condition. For task performance, participants exhibited the shortest reaction times under the first combined lighting condition, followed by the second static and dynamic lighting conditions. Notably, 500 lx led to faster responses compared with 300 lx. This finding highlighted the importance of illuminance in improving cognitive efficiency. Electrocardiogram monitoring further revealed increased heart rate and heart rate variability during the first combined lighting condition. These findings indicate that incorporating dynamic lighting elements into static lighting strategies may effectively reduce subjective sleepiness and improve cognitive performance in enclosed spaces. Future work should further explore long-term and circadian-related NVEs to inform intelligent lighting design.

随着载人航天和深海探测的快速推进，以及行星栖息地和未来地下设施的发展，对封闭空间内环境因素的研究变得越来越重要。除了视觉感知，光对人的生理和行为也有显著的非视觉影响，如嗜睡、觉醒和任务表现。然而，照明策略在封闭空间中的短期影响仍未得到充分探索，特别是在静态和动态照明模式中。我们进行了一项对照实验，分析了五种不同的照明条件：两种静态（300 lx和500 lx），一种动态（从300 lx过渡到500 lx），以及两种组合条件（整合静态和动态元素，从300 lx过渡到500 lx）。在每个照明条件的开始和结束时，使用视觉模拟量表评估参与者的主观困倦程度。结果显示困倦得分有显著差异，在第一种组合照明条件下观察到的困倦最低，在第二种组合照明条件下观察到的困倦最高。在第一种组合照明条件下，参与者的反应时间最短，其次是第二种静态和动态照明条件。值得注意的是，与300 lx相比，500 lx的响应速度更快。这一发现强调了光照对提高认知效率的重要性。心电图监测进一步显示，在第一次联合照明条件下，心率和心率变异性增加。这些发现表明，将动态照明元素与静态照明策略相结合，可以有效地减少主观困倦，提高封闭空间中的认知表现。未来的工作应进一步探索长期和昼夜节律相关的神经网络，为智能照明设计提供信息。

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引用次数: 0

Comparative analysis of AIGC-assisted and conventional design approaches in car seat design aigc辅助设计方法与常规设计方法在汽车座椅设计中的比较分析

Advanced Design Research

Pub Date : 2025-06-01 DOI: 10.1016/j.ijadr.2025.05.002

Yunpeng Bai , Yuanjun Li , Min Zhao , Chenjie Zhao , Bingjun Liu , Dengkai Chen

With the rapid advancement of artificial intelligence technology, the application of Artificial Intelligence Generated Content (AIGC) in the realms of creativity and design is becoming increasingly prevalent. This paper seeks to explore a comparative study between AIGC-assisted and traditional methods in automotive seat design. The design of automotive seats is a complex process that integrates considerations of ergonomics, material science, safety, and comfort. Traditional design approaches rely on the experience of designers and preliminary user research to iteratively refine design solutions, a process that is time-consuming and contingent upon the skill level of the designers. By utilizing the Pole Position (ABE) automotive seat design project as a case study and employing the SWOT analysis model, this research compares traditional design methods with those augmented by AIGC. The findings indicate that AIGC-assisted design excels in reducing design timeframes, enhancing design diversity, and increasing user satisfaction. However, traditional methods still hold an edge in deeply understanding and integrating user needs to achieve emotional design. Consequently, this study recommends integration of AIGC-assisted with traditional design approaches, leveraging the strengths of AI to supplement the deficiencies of conventional methods, and harnessing the creative thinking of designers to realize more humanized and personalized automotive seat designs.

随着人工智能技术的飞速发展，人工智能生成内容（AIGC）在创意和设计领域的应用越来越普遍。本文旨在探讨aigc辅助方法与传统方法在汽车座椅设计中的比较研究。汽车座椅的设计是一个综合考虑人体工程学、材料科学、安全性和舒适性的复杂过程。传统的设计方法依赖于设计师的经验和初步的用户研究来迭代地改进设计解决方案，这是一个耗时的过程，并且取决于设计师的技能水平。本研究以ABE汽车座椅设计项目为例，运用SWOT分析模型，对传统设计方法与AIGC增强的设计方法进行比较。研究结果表明，aigc辅助设计在缩短设计时间、增强设计多样性和提高用户满意度方面表现出色。然而，传统方法在深入理解和整合用户需求以实现情感化设计方面仍然具有优势。因此，本研究建议将人工智能辅助的汽车座椅设计方法与传统设计方法相结合，利用人工智能的优势弥补传统方法的不足，利用设计师的创造性思维，实现更加人性化、个性化的汽车座椅设计。

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引用次数: 0

Influence of color and icon encoding themed HMl on trust calibration in automated vehicles 主题为html的颜色和图标编码对自动驾驶车辆信任校准的影响

Advanced Design Research

Pub Date : 2025-06-01 DOI: 10.1016/j.ijadr.2025.03.004

Qi Guo, Yu Wang, Yan Chen

Conditional driving automation, also known as SAE Level 3 automated driving, allows drivers to perform non-driving related tasks NDRT when certain conditions are met without the need for constant monitoring. However, these automated systems require human drivers to be prepared to take over control when faced with operational constraints, and in an emergency, the automated system will send a take-over request (TOR) to the driver via the human-machine interface (HMI). As a result, the in-vehicle HMI is becoming an increasingly complex and important information interaction system. This study systematically investigates the combined effects of color and icon coding in human-machine interfaces HMIs on trust calibration during SAE Level 3 automated driving scenarios, with a focus on emergencies. Twelve females and thirteen males made up the 25 valid data samples. The sample driving experience range was 0 to 5 years (Mean = 1.56, SD = 0.77), with a maximum age of 30 and a minimum age of 20 (Mean = 22.68, SD = 2.19). A one-way experimental design using a combination of subjective and objective data was used to study subjects' driving trust and NDRT performance under three different static driving interfaces. Distinct from previous works focusing on unimodal encoding effects, our research pioneers in examining the synergistic relationship between color semantics and icon semantics in emergency scenarios. Additionally, we propose a novel dynamic trust assessment framework integrating both subjective scales and ocular metrics (fixation count/dwell time) validated through psychophysiological literature. The study used three sections of the experimental road, and participants had to complete the non-driving related task in each section. It was found that (1) colour coding of information in the driving interface affects driving trust, and (2) the combined effect of color coding and icon coding led to higher subjective trust than either coding method alone.

条件驾驶自动化，也被称为SAE 3级自动驾驶，允许驾驶员在满足某些条件时执行与驾驶无关的NDRT任务，而无需持续监控。然而，这些自动化系统要求人类驾驶员在面临操作限制时做好接管控制的准备，并且在紧急情况下，自动化系统将通过人机界面（HMI）向驾驶员发送接管请求（TOR）。因此，车载人机界面正成为一个日益复杂和重要的信息交互系统。本研究系统地研究了SAE 3级自动驾驶场景中人机界面hmi中颜色和图标编码对信任校准的综合影响，重点是紧急情况。12名女性和13名男性组成25个有效数据样本。样本驾驶经验范围为0 ~ 5年（Mean = 1.56, SD = 0.77），最大年龄为30岁，最小年龄为20岁（Mean = 22.68, SD = 2.19）。采用主客观数据相结合的单向实验设计，研究了三种不同静态驾驶界面下被试的驾驶信任和NDRT性能。与以往关注单模编码效应的研究不同，我们的研究率先研究了紧急情况下颜色语义和图标语义之间的协同关系。此外，我们提出了一个新的动态信任评估框架，整合了主观量表和通过心理生理学文献验证的眼部指标（注视计数/停留时间）。该研究使用了三段实验道路，参与者必须在每个路段完成与驾驶无关的任务。研究发现：(1)驾驶界面信息的颜色编码影响驾驶信任；(2)颜色编码和图标编码的联合作用比单独使用任何一种编码方式都能产生更高的主观信任。

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引用次数: 0

The cognitive acceptance of generative AI image tools based on TPB-TAM model and multi-theory integration 基于TPB-TAM模型和多理论集成的生成式AI图像工具的认知接受度

Advanced Design Research

Pub Date : 2025-06-01 DOI: 10.1016/j.ijadr.2025.08.001

Yao Wang , Xinwei Guan , Yiwei Sun , Hanyu Wang , Dengkai Chen

The rapid development and application of generative AI image tools are profoundly reshaping the landscape of image generation. As a primary user group, designers' acceptance of these tools directly impacts their application effectiveness and industry trends. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), extracts key variables from the Information Systems Success Model (ISSM) and the theory of perceived risk, and introduces the concepts of user experience and technological anxiety to construct a comprehensive model of designers' behavioral intentions to use generative AI image tools across different design types. Using AMOS software and the CB-SEM structural equation model to analyze valid data, this study reveals the significant impact of subjective norms, cognitive attitudes, and perceived behavioral control on usage intention in different design contexts. It also highlights the differentiated influence of external variables, such as system information quality, on intermediary variables like subjective norms. Through these analyses, the study clarifies the specific impact mechanisms of various external variables on behavioral intention. This study offers a new perspective on understanding designers' cognitive acceptance of generative AI image tools and proposes differentiated promotion and training strategies, providing valuable guidance for industry practice.

生成式人工智能图像工具的快速发展和应用正在深刻地重塑图像生成的格局。作为主要的用户群体，设计师对这些工具的接受程度直接影响到他们的应用效果和行业趋势。本研究整合计划行为理论（TPB）和技术接受模型（TAM），从信息系统成功模型（ISSM）和感知风险理论中提取关键变量，并引入用户体验和技术焦虑的概念，构建了设计师在不同设计类型中使用生成式人工智能图像工具的行为意图的综合模型。本研究利用AMOS软件和CB-SEM结构方程模型对有效数据进行分析，揭示了不同设计情境下主观规范、认知态度和感知行为控制对使用意图的显著影响。它还突出了系统信息质量等外部变量对主观规范等中介变量的差异化影响。通过这些分析，本研究明确了各种外部变量对行为意愿的具体影响机制。本研究为理解设计师对生成式AI图像工具的认知接受度提供了新的视角，并提出了差异化的推广和培训策略，为行业实践提供了有价值的指导。

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引用次数: 0

Optimization method for control panel layout of manned submarine vehicle based on genetic-ant colony algorithm 基于遗传蚁群算法的载人潜艇控制板布局优化方法

Advanced Design Research

Pub Date : 2025-06-01 DOI: 10.1016/j.ijadr.2025.03.003

Ning Xie , Yidan Qiao , Hongqi Shi , Dengkai Chen

Traditional computer-aided design methods for the control panel layout of manned submersibles commonly use interactive input and modification, which cannot support a global optimization process. To overcome this limitation, this study introduces the genetic-ant colony algorithm to design the control panel layout of manned submersibles. Based on the cognitive characteristics of humans, layout optimization principles are summarized and incorporated into the objective function variables during the optimization process. The fitness function, pheromone, and heuristic information expressions for optimizing the control panel layout of manned submersibles are studied to establish the genetic-ant colony algorithm flow during optimization design. Taking the control panel layout design of a certain manned submersible type as an example, this study performs multi-objective optimization calculations to quantify the layout optimization principles and combine them with the algorithm, verifying the feasibility of the proposed design method.

传统的载人潜水器控制板布局计算机辅助设计方法多采用交互式输入和修改，无法支持全局优化过程。为了克服这一局限性，本研究引入遗传蚁群算法来设计载人潜水器的控制面板布局。根据人的认知特点，总结布局优化原则，并将其纳入优化过程中的目标函数变量中。研究了载人潜水器控制板布局优化的适应度函数、信息素和启发式信息表达式，建立了优化设计过程中的遗传蚁群算法流程。以某型载人潜水器控制板布置图设计为例，进行多目标优化计算，量化布置图优化原则，并与算法相结合，验证所提设计方法的可行性。

引用次数: 0