Img2Logo: Generating Golden Ratio Logos from Images

Logos are one of the most important graphic design forms that use an abstracted shape to clearly represent the spirit of a community. Among various styles of abstraction, a particular golden-ratio design is frequently employed by designers to create a concise and regular logo. In this context, designers utilize a set of circular arcs with golden ratios (i.e., all arcs are taken from circles whose radii form a geometric series based on the golden ratio) as the design elements to manually approximate a target shape. This error-prone process requires a large amount of time and effort, posing a significant challenge for design space exploration. In this work, we present a novel computational framework that can automatically generate golden ratio logo abstractions from an input image. Our framework is based on a set of carefully identified design principles and a constrained optimization formulation respecting these principles. We also propose a progressive approach that can efficiently solve the optimization problem, resulting in a sequence of abstractions that approximate the input at decreasing levels of detail. We evaluate our work by testing on images with different formats including real photos, clip arts, and line drawings. We also extensively validate the key components and compare our results with manual results by designers to demonstrate the effectiveness of our framework. Moreover, our framework can largely benefit design space exploration via easy specification of design parameters such as abstraction levels, golden circle sizes, etc.     

A novel cause analysis approach of grey reasoning Petri net based on matrix operations

Cause analysis makes great contributions to identifying the priorities of the causes in fault diagnosis system. A fuzzy Petri net (FPN) is a preferable model for knowledge representation and reasoning and has become an effective fault diagnosis tool. However, the existing FPN has some limitations in cause analysis. It is criticized for the inability to fully consider incomplete and unknown knowledge in uncertain situations. In this paper, an enhanced grey reasoning Petri net (EGRPN) based on matrix operations is presented to address the limitations and improves the flexibility of the existing FPN. The proposed EGRPN model uses grey numbers to handle the greyness and inaccuracy of uncertain knowledge. Then, the EGRPN inference algorithm is executed based on the matrix operations, which can express the relevance of uncertain events in the form of grey numbers and improve the reliability of the knowledge reasoning process. Finally, industrial examples of cause diagnosis are used to illustrate the feasibility and reliability of the EGRPN model. The experimental results show that the new EGRPN model is promising for cause analysis.

     

微波辅助萃取-超高效液相色谱/静电场轨道阱高分辨质谱法快速测定纺织品中有害有机溶剂残留量

建立了1个快速测定纺织品中有害有机溶剂残留量的超高效液相色谱/静电场轨道阱高分辨质谱(UPLC/Orbitrap HRMS)方法,该方法以乙醇为萃取溶剂,100℃下微波萃取纺织品中残留的有害有机溶剂,萃取液直接进行UPLC/Orbitrap HRMS,提取离子色谱峰面积外标法定量。对样品的提取条件、分析条件进行了优化。该方法的定量限为0.5μg/kg~10.0μg/kg。在3个不同加标浓度水平下,方法的平均加标回收率为81.8~94.5%,相对偏差偏差为3.8%~10.5%(n=9)。该方法简便快速,灵敏度高,定量限低,可有效地解决纺织品中残留的有害有机溶剂的快速测定问题。     

生物模板法合成锂离子电池电极材料研究进展

锂离子电池是一类极具潜力的新型二次化学储能器件,被广泛应用于便携式电子设备、电动交通工具和智能电网等领域。高性能电极材料的设计和合成是获得高能量密度、长循环寿命、高安全性锂离子电池的关键。文章针对锂离子电池电极材料存在制备工艺复杂、结构难以控制、活性物质利用率低、循环稳定性和倍率性能差等问题,从生物资源高效利用角度出发,结合生物材料尺寸均匀、形态多变、结构精密、环境友好等优点,综述了生物模板法合成锂离子电池电极材料的研究进展,并对该领域的发展方向进行了展望。     

Construction of irreducible polynomials using cubic transformation

The transformationf(x)f(x ³–3x) is studied. We show that for allF _q of characteristic >3 andn1, there existsf(x)F _q [x] of degreen which generates an infinite sequence of irreducible polynomials of degree 3 ⁱ n by the iteration of this transformation.     

微波视频类电子装备通用测试系统的实现与应用

为了实现小批量多品种电子装备的通用测试,通过对微波视频类电子装备的测试指标、测试方法进行共性分析,将不同测试指标的仪器控制和测试方法封装成独立的测试模块,并采用XML技术和IVI技术,使测试仪器具有互换性,使用户可以对测试系统的测试仪器进行组合配置,自由选择测试方法,自由订制测试流程,并能生成测试报告,实现了微波视频类电子装备的通用测试;该通用测试系统已应用于多种电子装备测试中,减少了自动测试系统的重复开发,提高了测试效率,取得了良好的生产效益。     

Using ubiquitous games in an English listening and speaking course: Impact on learning outcomes and motivation

This paper reports the results of a study which aimed to investigate how ubiquitous games influence English learning achievement and motivation through a context-aware ubiquitous learning environment. An English curriculum was conducted on a school campus by using a context-aware ubiquitous learning environment called the Handheld English Language Learning Organization (HELLO). HELLO helps students to engage in learning activities based on the ARCS motivation theory, involving various educational strategies, including ubiquitous game-based learning, collaborative learning, and context-aware learning. Two groups of students participated in the learning activities prescribed in a curriculum by separately using ubiquitous game-based learning and non-gaming learning. The curriculum, entitled ‘My Campus’, included three learning activities, namely ‘Campus Environment’, ‘Campus Life’ and ‘Campus Story’. Participants included high school teachers and juniors. During the experiment, tests, a survey, and interviews were conducted for the students. The evaluation results of the learning outcomes and learning motivation demonstrated that incorporating ubiquitous games into the English learning process could achieve a better learning outcomes and motivation than using non-gaming method. They further revealed a positive relationship between learning outcomes and motivation.