基于亲生物理论的医疗空间色彩设计研究

人类在漫长的进化过程中形成了对自然的本能需求,亲生物设计从生物进化的角度以更系统和更合理的方法将自然重新融入现代建筑环境,构建人与场所之间的积极交互。源于光、水、风、植物、动物等自然元素的亲生物色彩设计具有令人愉悦的艺术感染力,动态的视觉效果暗示着蓬勃的生命力,能够系统、清晰地传递复杂的环境信息,引发观者的情感共鸣和情感依恋。从环境疗愈的角度看,具有亲生物特征的色彩设计对改善患者在医疗空间中的体验,激发患者的自我康复能力具有重要意义。亲生物设计是可持续发展设计中不可或缺的一环,两者的结合有助于创造兼具低环境影响和高情感联系的设计,以实现真正和持久的可持续性发展。     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

Information system ecology: An application of dataphoric ascendancy

Information systems, like biological systems, are susceptible to external perturbations. Similar to flora and fauna in a biome, species of data can be classified within a dataphora. While entropic properties and data geometries can be used to describe local species of data within a dataphora, they are not designed to describe the global properties of an information system or evaluate its stability. Ecologists have used Information Theories to describe macro-level properties of biological ecosystems and statistical tools to evaluate biological systems. This research leverages an ecological perspective to model information systems as a living system. Our findings support the theory of dataphoric ascendancy with Wikipedia having a Diversity Index value of 0.68, within the range of 0.65 and 0.80 that indicates a balanced state. We further support our findings with additional evaluations of other ecosystems including the predicted collapse of the information service known as the Digital Universe. This research allows for an information system’s stability to be (a) characterized and (b) predicted using ecological measures specific to the diversity of data within the ecosystem.     

瞬变电磁井间勘探的全空间几何因子

井间是剩余油的主要分布区域,为探测井间剩余油,提高采收率,提出了基于全空间几何因子的瞬变电磁井间勘探方法。在本井使用线圈发射、邻井使用线圈接收,根据瞬变电磁场理论,在阶跃信号的激励下发射线圈在地层中激发出沿圆周方向的闭合瞬变电场,该电场在导电地层中产生与地层电导率呈正比的涡流。由Doll地层环模型可知,地层中的涡流在空间任意点激发得到与地层电导率成正比的二次场响应信号(有用信号),并可表示为空间各点电导率的加权平均值,其权重即为井间瞬变电磁勘探的全空间几何因子;全空间几何因子集中分布在发射线圈和接收线圈附近,其它区域分布较少,在发射线圈和接收线圈两侧呈现不同的极性;对瞬变电磁响应与地层电导率、井间距和源距的变化规律研究可知,瞬变电磁井间勘探有用信号随着地层电导率的增大而增大,随着井间距的增加单调减小,在发射线圈和接收线圈处于同一深度时该响应信号幅度最大。     

基于GFU和分层LSTM的组群行为识别研究方法

提出一种以"关键人物"为核心，使用门控融合单元（GFU，Gated Fusion Unit）进行特征融合的组群行为识别框架，旨在解决两个问题：①组群行为信息冗余，重点关注关键人物行为特征，忽略无关人员对组群行为的影响；②组群内部交互行为复杂，使用GFU有效融合以关键人物为核心的交互特征，再通过LSTM时序建模成为表征能力更强的组群特征.最终，通过softmax分类器进行组群行为类别分类.该算法在排球数据集上取得了86.7%的平均识别率.     

生物氧化预处理中进气量实时预测数学模型研究

精准预测生物氧化预处理中的进气量对提高黄金提取率和节能降耗具有重要意义。以气体管流连续性方程和运动方程为控制方程，采用Preissmann隐格式法作为差分方法。同时，根据集合卡尔曼滤波(Ensemble Kalman filter，EnKF)算法原理，构造进气量、压强的状态空间模型。结果表明，基于气体管流控制方程建立的进气量模型预测结果与实际进气量观测值具有较好的一致性；与传统静态预测方法相比，EnKF同化方法引入实时观测值和模型参数的更新，有效提高了进气量的预测精度，其平均绝对误差、平均相对误差和均方根误差有明显的降低。可见，基于气体管流控制方程建立的预测模型结合EnKF同化方法是提高生物氧化槽进气量预测精度的有效手段。     

中医学视角下城市物质空间的生命要素探析

通过借鉴中医学整体观和生命观的相关理论,从认识城市有机体的生命属性入手,发现并提炼能够产生和传递城市运营所需各种能量的生命要素:廊道和功能体。阐释了其多层级、多性质和多形态的特点,提出了功能体有动力、廊道成系统、敏感点有活力的疏通策略,旨在促进城市各种能量的有机循环,从而维护城市的健康与安全,提高城市生活的效率与质量。     

Continuous space maximal coverage: Insights,advances and challenges

Siting facilities in continuous space to maximize coverage of regional demand is an important planning problem. It is challenging to solve because demand is continuously distributed in a region and facilities may be sited anywhere in the region, suggesting that an infinite number of locations must be considered both as demand for service as well as a potential facility. An ingenious approach for solving continuous space coverage problems is abstracting continuously distributed demand as discrete spatial objects (points, lines or polygons), generating a finite dominating set from continuous space as candidate facility sites, and applying a discrete location coverage model. However, abstracting continuous demand and potential facility sites often introduces errors and uncertainties in coverage of regional demand, rendering findings (service and optimal facility locations) less than definitive. In this paper we examine the continuous space maximal coverage problem. Studies focused on locating antennas in Paradise Valley, Arizona and emergency warning sirens in Dublin, Ohio, are used to highlight issues and challenges in continuous space modeling. Potential paths forward are suggested based on empirical insights.