基于灰色预测理论的软件缺陷预测模型研究

软件缺陷是软件产品预期属性的偏离现象.妥善处理软件中的缺陷关系软件质量以及软件组织的生存与发展.利用开发过程中收集的软件缺陷的相关数据,依据灰色预测理论的核心:GM(1,1)来建立预测模型,对后续软件开发中缺陷的存在情况作出相应预测.实验表明该预测结果能指导软件组织较好地把握软件质量、合理分配测试资源,并在一定程度上帮助软件组织度量软件过程.     

GM(n,h)模型建模序列数据数乘变换特性研究

基于数乘变换是灰色系统建模过程中数据处理的基础,讨论了ＧＭ（n,h）模型与其他几类灰色模型的内在联系．将各类灰色模型统一于共同的分析体系,并在此基础上研究了数乘变换对ＧＭ（n,h）模型参数取值的影响．指出了模型的模拟和预测值只与因变量的数乘变换有关,而与自变量的变换无关．最后分析了几个特殊灰色模型的数乘变换性质,该结果对研究系列灰色模型参数特征有重要意义．     

基于一类新的强化缓冲算子的GM(1,1)预测精度研究

根据灰色系统理论中新息优先利用原理,在对缓冲算子和已有强化缓冲算子研究的基础上,构造了一类新的强化缓冲算子,有效解决了冲击扰动数据序列在建模预测过程中常常出现的定量预测结果与定性分析结论不符的问题.实例分析结果表明,这类新的强化缓冲算子能显著提高GM(1,1)模型的预测精度.     

基于GM模型的软件可靠性建模

将灰色理论应用于软件可靠性建模研究,通过对历史失效数据进行建模,对软件的下一步失效时刻进行预测。为验证该模型的预计精度,将其应用于多组广泛被认可的数据,通过计算度量元短期相对误差（SRE）与经典的可靠性模型进行比较,结果表明,基于GM的模型对将来失效时间间隔的预测更加准确。     

An optimum design of robotic food handling by using Burger model

This paper discusses an optimum design approach on robotic food handling by considering the characteristics of viscoelasticity of object. We pick up a traditional Japanese food, “Norimaki” as a typical example with the viscoelastic characteristics. We first show that the dynamic characteristics of Norimaki can be expressed by utilizing the Burger model. After testing the parameter sensitivity, we show an example of the optimum design for determining the combination of the hand stiffness and the operating velocity. We further show that the resultant plastic deformation can be formulated with the exact solution.     

基于灰色预测的氯化聚乙烯生产过程控制

氯化聚乙烯作为一种新型的高分子材料,被广泛地应用于塑料和橡胶的生产中。反应釜温度是其过程控制的主要被控参数,但是由于其存在纯滞后和大惯性等特点,普通的串级控制方式很难得到良好的控制效果。本文基于灰色模型的原理,设计了灰色PID控制器。仿真表明,该控制器具有良好的抗干扰性和稳定的鲁棒性,可有效改善系统的控制效果。     

基于灰色预测模型的个人建房分析

灰色模型具有所需数据少、预测精度高和无需先验信息的特点.本文通过建立GM(1,1)模型对某区的农村个人建房进行预测,为相关职能部门提供科学的决策依据.结果表明灰色预测模型精度较高、预测误差较小、简捷实用.     

Deep ensemble-based hard sample mining for food recognition

Deep neural networks represent a compelling technique to tackle complex real-world problems, but are over-parameterized and often suffer from over- or under-confident estimates. Deep ensembles have shown better parameter estimations and often provide reliable uncertainty estimates that contribute to the robustness of the results. In this work, we propose a new metric to identify samples that are hard to classify. Our metric is defined as coincidence score for deep ensembles which measures the agreement of its individual models. The main hypothesis we rely on is that deep learning algorithms learn the low-loss samples better compared to large-loss samples. In order to compensate for this, we use controlled over-sampling on the identified ”hard” samples using proper data augmentation schemes to enable the models to learn those samples better. We validate the proposed metric using two public food datasets on different backbone architectures and show the improvements compared to the conventional deep neural network training using different performance metrics.     

Health-guided recipe recommendation over knowledge graphs

While the availability of large-scale online recipe collections presents opportunities for health consumers to access a wide variety of recipes, it can be challenging for them to discover relevant recipes. Whereas most recommender systems are designed to offer selections consistent with users’ past behavior, it remains an open problem to offer selections that can help users’ transition from one type of behavior to another, intentionally. In this paper, we introduce health-guided recipe recommendation as a way to incrementally shift users towards healthier recipe options while respecting the preferences reflected in their past choices. Introducing a knowledge graph (KG) into recommender systems as side information has attracted great interest, but its use in recipe recommendation has not been studied. To fill this gap, we consider the task of recipe recommendation over knowledge graphs. In particular, we jointly learn recipe representations via graph neural networks over two graphs extracted from a large-scale Food KG, which capture different semantic relationships, namely, user preferences and recipe healthiness, respectively. To integrate the nutritional aspects into recipe representations and the recommendation task, instead of simple fusion, we utilize a knowledge transfer scheme to enable the transfer of useful semantic information across the preferences and healthiness aspects. Experimental results on two large real-world recipe datasets showcase our model’s ability to recommend tasty as well as healthy recipes to users.     

基于单片机和GM8125的多路串口收发器设计

介绍了串口扩展芯片GM8125的特性,设计了基于该芯片和8051单片机的多路串口收发器,程序算法及硬件电路实现了多达4台子设备(电子天平)到主设备(打印机)数据标号传输,并有效地解决了GM8125芯片多路并行工作时的出现的乱码和数据混乱的问题。系统使用方便,实时性好,扩展方便,取得了良好的实际应用效果。