一种求取物体凹点的算法研究

在图像识别的应用中,物体凹凸情况的分析是非常重要的,它既是描述物体形状的一个特征参数,也是一些重叠物体分离算法的前提。目前,求取物体边界凹点的算法已有多种,但都存在一些不足之处,为此,本文介绍了一种简便算法,该算法在我们开发的微生物细胞图像自动判读系统中已得到应用。     

函数凹凸性质在不等式中的推广及应用

从凹凸函数的基本不等式出发,将其推广,可解决某些不等式问题。     

A biomechanical shoulder strain index based on stabilizing demand of shoulder joint

Abstract

Work-related shoulder joint disorders contribute considerably to absenteeism in the workplace. To identify the tasks that are stressful to the shoulder joint, a strain index was formulated based on the concept of concavity compression—a shoulder stabilizing mechanism. The magnitude and direction of the shoulder joint reaction forces were used in formulating the strain index. A two phase experiment was conducted. In Phase 1, participants performed 30 different manual handling tasks. The tasks were categorized into low, medium and high strain tasks based on their strain index values. In Phase 2, out of the 30 tasks, repetitive exertions of three tasks (low, medium and high strain index values) were simulated using three external loads (0.91, 1.81 and 2.72?kg). The muscle activity data recorded from eight shoulder muscles showed that tasks with higher strain index values induced significantly greater activation and muscle fatigue than tasks with lower strain index values.Practitioner Summary: The strain index developed in this study is a conclusive estimation of the concavity compression required for shoulder joint stabilization. It can be used to identify the activities that may contribute to the risks of shoulder disorders. Abbreviation BLS Bureau of the Labor Statistics

     

石油化工拱顶罐缺陷原因分析及对策

针对拱顶罐在生产使用过程中发生的凹瘪问题,通过10000m3渣油罐罐顶凹瘪实例,分析了拱顶罐凹瘪的原因.介绍了采用充水升压法使凹瘪的罐顶修复的方法.从设计、安装、操作使用及维护管理等方面提出了预防和改进措施.     

LEARNING MONOTONIC-CONCAVE INTERVAL CONCEPTS USING THE BACK-PROPAGATION NEURAL NETWORKS

Monotonicity and concavity play important roles in human cognition, reasoning, and decision making. This paper shows that neural networks can learn monotonic-concave interval concepts based on real-world data, Traditionally, the training of neural networks has been based only on raw data. In cases where the training samples carry statistical fluctuations, the products of the training have often suffered. This paper suggests that global knowledge about monotonicity and concavity of a problem domain can be incorporated in neural network training. This paper proposes a learning scheme for the back-propagation layered neural networks in learning monotonic-concave interval concepts and provides an example to show its application.     

Product line optimisation based on semiparametric choice model

Consumer choice behaviour is important in the product line optimisation problem. The extant literature on product line optimisation is mostly based on traditional consumer choice models, such as the multinomial logit and multinomial probit models. These models either assume that the utility errors are independent from irrelevant alternatives (IIA) or are limited by complex calculation processes or pre-given, specific distributions of measuring errors. The marginal moment model (MMM), which is classified as a semiparametric choice model, does not require specific distributions of errors; thus, it can overcome the IIA shortcoming. This study focuses on the concavity of the profit functions of a product line optimisation model based on MMM. We prove that the profit function based on MMM is concave in market share under a monopoly or oligopoly. Numerical experiments show that the choice probabilities obtained from the MMM, multinomial logit, and multinomial probit models are similar although they are obtained under different assumptions. Experimental results under monopolistic, Cournot, and Bertrand competition based on MMM are compared. Some interesting managerial insights are summarised based on the sensitivity analysis of the various model parameters.     

改进的定性形状表示中凹处同异侧判断方法

指出了Cohn定性形状表示法中凹处同侧判断存在的两种不足之处,给出改进的同侧谓词SameSide*。基于SameSide*,提出一种基于凹处变换的同异侧判断方法CTS,并给出其ADL算法描述。通过实例说明使用方法CTS判断凹处的同异侧关系,能够解决Cohn原方法中存在的不足,能得到符合人们直观认知的判断结果,在一定程度上提高了定性形状表示的合理性和准确性。     

基于改进A~*算法的仿生机器鱼全局路径规划

针对传统A*算法搜索速度慢和规划路径不够优化的缺点,引入可扩展节点,对A*算法流程进行改进,以减少时间和空间复杂度,提高搜索效率,并对规划路径进行优化处理,以有效的缩短路径。对存在凹形障碍物的地图,采用后退—尝试的方法解决路径规划的失败问题,使之绕过凹形障碍物取向目标点,达到输出最短路径的目标。     

利用七宫格的遮挡车辆凹性检测与分割

目的:交通场景中车辆间的距离过近或相互遮挡容易造成识别上的粘连,增加了准确检测目标车辆的难度,因此需要建立有效、可靠的遮挡车辆分割机制。方法:首先在图像分块的基础上确定出车辆区域,根据车辆区域的长宽比和占空比进行多车判断;然后提出了一种基于“七宫格”的凹陷区域检测算法,用以找出车辆间的凹陷区域,通过匹配对应的凹陷区域得到遮挡区域;最后,将遮挡区域内检测出的车辆边缘轮廓作为分割曲线,从而分割遮挡车辆。结果:实验结果表明,在满足实时性的前提下算法具有较高的识别率,且能够按车辆的边缘轮廓准确分割多个相互遮挡的车辆。与其他算法相比,该算法提高了分割成功率和分割精度,查全率和查准率均可达到90%。结论:提出了一种新的遮挡车辆分割方法,有效解决了遮挡车辆不宜分割和分割不准确的问题,具有较强的适应性。