用户行为分析视角下的产品人机优化设计

目的 以提升用户体验为前提,结合产品的实际功能,从使用过程、用户行为分析、人机工程等多个方面探索产品的设计方法。方法 以医疗保健类产品手持式头部按摩器为例,从用户使用产品时的行为出发,以非参与式方法,观察用户使用产品的整个过程。基于SAPAD架构,完成行为—意义—对象三个维度之间的映射分析,得到手持式头部按摩器在整个使用过程中的关键部件和用户内心潜在需求。再以人机工程学原理和方法为依据,提出了基于用户需求的产品人机设计原则,为设计研发提供参考并最终得出设计结果。结论 提出了一种手持式头部按摩器的设计方法,优化了用户体验,提高了产品的工作效率。为产品的人机优化设计提供了一种新思路,对相关实体产品的工业设计具有一定的参考。     

基于用户行为的产品开发研究

从需求多样化的消费者与纷繁复杂的产品开发现实出发,提出了"基于用户行为的产品开发"这一创新理念;分析了用户行为过程在完成优秀产品开发中的地位和重要性,发展了两者关系;通过对多个创新设计案例的归类与分析,从单个用户行为到多个用户行为的系统整合,探讨了基于用户行为分析的理念、思路与方法,为设计师创造令人值得期待的产品提供了一种创新的研究思路。     

基于用户行为的适老型智能卫浴产品设计研究

目的探寻老年人卫浴行为的本质需求,获取该群体卫浴行为的共性及差异性特征。同时将智能技术与卫浴产品结合,引导人机交互向更加便捷、合理、自然的交互方式演变,以适应老龄化需求,提升老年人对卫浴空间的使用体验。方法采用行为观察法研究老年人淋浴、如厕的行为模拟过程,主要关注其行为流程、模式及对卫浴产品的使用情况。分析用户的卫浴行为方式和典型动作姿态,归纳和总结出卫浴产品的适老性、智能化设计策略,用以指导设计实践。结论将行为研究作为用户需求投射到产品属性的媒介来引导设计方向,不仅可以反映用户的使用习惯和操作特点,而且有助于直观有效地探寻用户行为过程中的直接和潜在需求,以设计出顺应用户行为逻辑、符合用户使用习惯的产品,提升产品属性与用户需求的匹配程度。     

基于用户行为的智能家居产品设计方法研究与应用

目的 通过研究用户的操作行为和认知行为,获取用户对智能家居产品的功能和操作需求,提出基于用户行为的设计研究方法,有利于提高智能家居产品可用性、降低用户的认知负荷.方法 首先将智能家居产品的重要功能操作流程进行拆解,结合可用性研究方法并计算检验每项操作任务的可用性水平,将产品用户体验问题进行量化,采用任务分析法、出声思维方法和访谈法对可用性水平较低的操作任务进行行为采集;其次结合行动七阶段方法对每个任务流程进行具体的行为分析,发现用户行为与产品概念模型之间的"鸿沟".然后,提出用户行为"鸿沟"解决方法并结合智能家居产品的特点提出优化方向.最后,进行产品迭代设计与方案评估.结果 以即将上市的某品牌扫地机器人产品为例,通过用户行为采集和行为数据分析,验证了该研究方法的可行性和有效性.结论 基于用户行为的产品设计研究方法能够发掘产品痛点,提出改进方案,并为智能家居产品的用户体验设计研究提供参考.     

基于用户行为的产品界面姿态选择与设计研究

目的研究用户行为对产品界面姿态选择与设计的影响因素。方法基于Alan Cooper关于产品界面姿态划分理论,通过对比多个界面姿态,以手机APP设计为案例开展相关研究。结论交互设计中的多种产品界面姿态本质都是从"暂时姿态"和"独占姿态"发展出来的,两者的融合是交互设计的发展趋势之一。     

用户创新行为的演化博弈分析

基于委托-代理理论和消费者剩余理论等理论框架对用户创新的动机和目的进行分析,认为用户创新行为是对制造商创新行为的补充,也是用户坚持实现个人差异化需求的有效途径。同时,基于有限理性假设,运用演化博弈理论对用户群体与企业群体的合作创新行为进行解析。研究表明,用户群体与企业（制造商）群体的演化博弈模型存在2个演化稳定策略（ESS）,来自企业群体和系统外部（例如政府）的激励因素对用户创新均具有积极的刺激作用。     

基于贝叶斯网络的用户需求分析方法研究

目的 尝试在设计研究中引入贝叶斯网络分析工具,结合案例的统计数据计算,分析用户需求与行为的映射关系,系统设计快递包装回收的解决方案。方法 通过调查问卷法、观察法、访谈法对用户行为进行数据采集,结合生活方式转变机制模型,运用贝叶斯网络进行用户研究,产出需求理解,指导产品服务系统设计工作的开展。结果 快递包装再设计的产品基础属性、使用便捷性、系统完整程度会在很大程度上影响用户行为可持续性以及设计认同度。结论 通过研究形成了可持续的快递包装使用系统,帮助引导用户可持续行为,从产品全生命周期的理论角度为快递包装回收的产品服务系统设计提供了面向用户研究的全新设计思考方法,证明了贝叶斯网络在用户研究中解决不确定性问题的积极意义。     

Energy Prediction in IoT Systems Using Machine Learning Models

The Internet of Things (IoT) technology has been developed for directing and maintaining the atmosphere in smart buildings in real time. In order to optimise the power generation sector and schedule routine maintenance, it is crucial to predict future energy demand. Electricity demand forecasting is difficult because of the complexity of the available demand patterns. Establishing a perfect prediction of energy consumption at the building’s level is vital and significant to efficiently managing the consumed energy by utilising a strong predictive model. Low forecast accuracy is just one of the reasons why energy consumption and prediction models have failed to advance. Therefore, the purpose of this study is to create an IoT-based energy prediction (IoT-EP) model that can reliably estimate the energy consumption of smart buildings. A real-world test case on power predictions is conducted on a local electricity grid to test the practicality of the approach. The proposed (IoT-EP) model selects the significant features as input neurons, the predictable data is selected as output nodes, and a multi-layer perceptron is constructed along with the features of the Convolution Neural Network (CNN) algorithm. The analysis of the proposed IoT-EP model has higher accuracy of 90%, correlation of 89%, and variance of 16% in less training time of 29.2 s, and with a higher prediction speed of 396 (observation/sec). When compared to existing models, the results showed that the proposed (IoT-EP) model outperforms with a satisfactory level of accuracy in predicting energy consumption in smart buildings.     

基于八角行为分析法的学习类APP游戏化驱动设计研究

目的 研究八角行为的分析方法,并探索该方法在学习类APP设计中的应用.只有细化用户在学习体验中的自我需求和学习动机,才能为提升学习类APP的趣味性提供更多视角.方法 从八角行为分析法出发,探索八大核心驱动力对不同类型玩家用户行为的触发,通过对学习类APP用户类型的总结,得出目标型、探索型、互动型、竞争型4种学习者类型的学习乐趣和适用于4种类型学习者的游戏驱动机制.基于用户学习过程的前、中、后3个阶段,梳理得出激励学习者学习行为的游戏化驱动设计方法和模型.结论 以交互体验为中心的设计方法是设计开发中的重要环节,基于八角行为分析法的游戏化驱动设计模型,能够激发设计师的设计灵感和创新意识,突破其固有思维模式,寻找驱动学习者行为习惯的游戏化设计方法.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.     

Handling Uncertainties in Fault Tree Analysis by a Hybrid Probabilistic–Possibilistic Framework

Fault tree analysis is a method largely used in probabilistic risk assessment. Uncertainties should be properly handled in fault tree analyses to support a robust decision making. While many sources of uncertainties are considered, dependence uncertainties are not much explored. Such uncertainties can be labeled as ‘epistemic’ because of the way dependence is modeled. In practice, despite probability theory, alternative mathematical structures, including possibility theory and fuzzy set theory, for the representation of epistemic uncertainty can be used. In this article, a fuzzy β factor is considered to represent the failure dependence uncertainties among basic events. The relationship between β factor and system failure probability is analyzed to support the use of a hybrid probabilistic–possibilistic approach. As a result, a complete hybrid probabilistic–possibilistic framework is constructed. A case study of a high integrity pressure protection system is discussed. The results show that the proposed method provides decision makers a more accurate understanding of the system under analysis when failure dependencies are involved. Copyright © 2015 John Wiley & Sons, Ltd.     

随机潜蚤病模型的动力学行为分析

潜蚤病是贫困地区的一种人畜共患病,其发病过程极易受到随机波动环境因素的影响。因此,建立并讨论了一类以正确卫生习惯为控制策略的随机潜蚤病模型。首先,通过构造恰当的Lyapunov函数并利用It$\hat{\rm o}$公式证明了随机系统全局正解的存在唯一性。其次,在一定的条件下证明了随机系统的正解围绕在确定性系统平衡点附近的振荡行为。最后,通过数值模拟验证了理论结果的正确性。数值结果表明,当随机干扰强度足够大时将会导致疾病灭绝。     

Analysis of the Bipolar Resistive Switching Behavior of a Biocompatible Glucose Film for Resistive Random Access Memory

Resistive random access memory (RRAM) devices are fabricated through a simple solution process using glucose, which is a natural biomaterial for the switching layer of RRAM. The fabricated glucose‐based RRAM device shows nonvolatile bipolar resistive switching behavior, with a switching window of 10³. In addition, the endurance and data retention capability of glucose‐based RRAM exhibit stable characteristics up to 100 consecutive cycles and 10⁴ s under constant voltage stress at 0.3 V. The interface between the top electrode and the glucose film is carefully investigated to demonstrate the bipolar switching mechanism of the glucose‐based RRAM device. The glucose based‐RRAM is also evaluated on a polyimide film to verify the possibility of a flexible platform. Additionally, a cross‐bar array structure with a magnesium electrode is prepared on various substrates to assess the degradability and biocompatibility for the implantable bioelectronic devices, which are harmless and nontoxic to the human body. It is expected that this research can provide meaningful insights for developing the future bioelectronic devices.     

Using Discourse Analysis to Study a Cross‐Disciplinary Learning Community: Insights from an IGERT Training Program

A major challenge for fostering integrative cross‐disciplinary collaborations at the graduate level arises from the divergent exposure and training of students from uni‐disciplinary graduate programs. In this report, we present the design and preliminary analysis of an experimental forum to facilitate cross‐disciplinary discourse within a NSF‐sponsored Integrative Graduate Education and Research Traineeship Program (IGERT) at Rutgers. This forum brings together IGERT Graduate Training Fellows and faculty from four diverse graduate programs in the engineering area and four related programs in life sciences and physical sciences for structured seminars and interchanges. Our report offers methodological and analytical tools grounded within a conceptual framework for promoting discourse that integrates content across diverse disciplines as well as across levels of inquiry. Both the theoretical framework and the research tools may be valuable to others seeking to develop integrative training environments for coalescing learning communities between engineers and their collaborators.