Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

Benchmark experiment of large-angle scattering reaction cross section of iron at 14 MeV using two shadow bars – Comparison of experimental results with ENDF/B-VIII –

ABSTRACT

It is important to perform neutron transport simulations with accurate nuclear data in the neutronics design of a fusion reactor. However, absolute values of large-angle scattering cross sections vary among nuclear data libraries even for well-examined nuclide of iron. Benchmark experiments focusing on large-angle scattering cross sections were thus performed to confirm the correctness of nuclear data libraries. The series benchmark experiments were performed at a DT neutron source facility, OKTAVIAN of Osaka University, Japan, by the unique experimental system established by the authors’ group, which can extract only the contribution of large-angle scattering reactions. This system consists of two shadow bars, target plate (iron), and neutron detector (niobium). Two types of shadow bars were used and four irradiations were conducted for one experiment, so that contribution of room-return neutrons was effectively removed and only large-angle scattering neutrons were extracted from the measured four Nb reaction rates. The obtained experimental results were compared with calculations for five nuclear data libraries including JENDL-4.0, JEFF.-3.3, FENDL-3.1, ENDF/B- VII, and recently released ENDF/B-VIII. It was found from the comparison that ENDF/B-VIII showed the best result, though ENDF/B-VII showed overestimation and others are in large underestimation at 14 MeV.     

基于K-means和SOM的水下传感器数据采集方法

随着海洋资源勘探和海洋污染物监控工作的开展，水文数据的监测和采集等已经成为重要的研究方向。其中，水下无线传感器网络在水文数据采集过程中起着举足轻重的作用。本文研究的是水下无线传感器二维监测网络模型中，传感器节点数据采集的问题，其设计方法是通过自组织映射（Self-organizing mapping，SOM）对传感器节点进行路径最优化处理，结合优化的路径图形和K-means算法找到路径内部聚合点，利用聚合点和传感器的节点得到传感器通信半径内的数据采集点，最后通过SOM得到水下机器人（Autonomous underwater vehicle，AUV）到各个数据采集点采集数据的最优路径。经过实验验证，在水下1 200 m×1 750 m范围内布置52个传感器节点的情景下，数据采集点相比于传感器节点路径规划采用相同的采集顺序得到的路径优化了6.7%；对数据采集点重新进行自组织路径规划得到的路径比传感器结点路径的最优解提高了12.2%。增加传感器节点的数量，其结果也大致相同，因此采用该方法可以提高水下机器人采集数据的效率。     

高稳高效海上风电安装平台关键制造技术研究

海上风电作为可再生清洁能源之一,受到世界各国的高度重视与大力发展。我国将海上风电提升至解决能源危机、减缓气候变化、调整能源结构的国家战略高度,到2030年我国单位国内生产总值二氧化碳排放将比2005年下降65%以上,非化石能源占一次能源消费比重将达到25%左右。安装平台不足将是我国海上风电场无法如期建成投产的主要障碍。对自升自航式海上风电安装平台系列高端装备及其设计制造的三大技术难题——腿站立作业易“失稳”、大平台大跨距大倾覆力矩自升易“失控”、高空吊装巨型叶片逾百螺栓精准定位易“失准”,以及焊缝缺陷修复和局部裂纹损伤的激光锻造修复再制造进行了介绍,研制的具有不同规格的系列装备在中国、英国、丹麦、德国等国家的著名海上风电场建设应用情况良好。     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Patterns in environmental priorities revealed through government open data portals

The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance--the proliferation of government open data portals (ODPs)--has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.