基于2D激光扫描的基准检测技术研究

在机器人自动制孔系统中,基准孔检测的准确性会直接影响整个机器人制孔过程的位置精度。为获取基准孔孔位准确信息,采用激光扫描的方式对基准孔进行检测。设计了2D线激光扫描在基准平面内点云的三维转化方法,通过分析基准孔在扫描仪坐标系下点云的分布特点,提出了一种基于坐标差值的基准孔边界提取算法。通过设定相邻点云在线激光扫描仪坐标系下z轴的坐标差值获取边界点,实验验证该算法能有效地去除点云中的噪声点,获取准确的基准孔边缘特征信息,进而得到准确的基准孔孔位信息。     

Frequency Insertion Strategy for Channel Assignment Problem

This paper presents a new heuristic method for quickly finding a good feasible solution to the channel assignment problem (CAP). Like many other greedy-type heuristics for CAP, the proposed method also assigns a frequency to a call, one at a time. Hence, the method requires computational time that increases only linear to the number of calls. However, what distinguishes the method from others is that it starts with a narrow enough frequency band so as to provoke violations of constraints that we need to comply with in order to avoid radio interference. Each violation is then resolved by inserting frequencies at the most appropriate positions so that the band of frequencies expands minimally. An extensive computational experiment using a set of randomly generated problems as well as the Philadelphia benchmark instances shows that the proposed method perform statistically better than existing methods of its kind and even yields optimum solutions to most of Philadelphia benchmark instances among which two cases are reported for the first time ever, in this paper. Won-Young Shin was born in Busan, Korea in 1978. He received B.S. in industrial engineering from Pohang University of Science and Technology (POSTECH) in 2001 and M.S in operation research and applied statistics from POSTECH in 2003. Since 2003 he has been a researcher of Agency for Defense Development (ADD) in Korea. He is interested in optimization of communication system and applied statistics. Soo Y. Chang is an associate professor in the Department of Industrial Engineering at Pohang University of Science and Technology (POSTECH), Pohang, Korea. He teaches linear programming, discrete optimization, network flows and operations research courses. His research interests include mathematical programming and scheduling. He has published in several journals including Discrete Applied Mathematics, Computers and Mathematics with Application, IIE Transactions, International Journal of Production Research, and so on. He is a member of Korean IIE, and ORMSS. Jaewook Lee is an assistant professor in the Department of Industrial Engineering at Pohang University of Science and Technology (POSTECH), Pohang, Korea. He received the B.S. degree in mathematics with honors from Seoul National University, and the Ph.D. degree from Cornell University in applied mathematics in 1993 and 1999, respectively. He is currently an assistant professor in the department of industrial engineering at the Pohang University of Science and Technology (POSTECH). His research interests include nonlinear systems, neural networks, nonlinear optimization, and their applications to data mining and financial engineering. Chi-Hyuck Jun was born in Seoul, Korea in 1954. He received B.S. in mineral and petroleum engineering from Seoul National University in 1977, M.S. in industrial engineering from Korea Advanced Institute of Science and Technology in 1979 and Ph.D. in operations research from University of California, Berkeley, in 1986. Since 1987 he has been with the department of industrial engineering, Pohang University of Science and Technology (POSTECH) and he is now a professor and the department head. He is interested in performance analysis of communication and production systems. He has published in several journals including IIE Transactions, IEEE Transactions, Queueing Systems and Chemometrics and Intelligent Laboratory Systems. He is a member of IEEE, INFORMS and ASQ.     

格式化文本水印性能评估基准研究

针对目前没有测试基准对格式化文本水印算法鲁棒性进行评估和比较,提出了格式化文本水印性能评估基准.回顾了水印系统通用的框架,分析并确定了影响水印系统性能的参数和文本文档的视觉质量评级标准.借鉴图像水印性能评估基准的攻击分类,提出了Word特有的去除攻击、几何攻击、密码攻击和协议攻击的攻击分类方法,同时阐述了Word特有的攻击模式.对两种不同的文本水印算法进行了攻击强度—鲁棒性、攻击强度—视觉质量和嵌入数据量—鲁棒性测试,实验结果表明,该系统在文本水印算法评估和设计中具有有效性和实用性.     

混沌精英哈里斯鹰优化算法

针对哈里斯鹰优化（HHO）算法存在的收敛精度低、收敛速度慢、易于陷入局部最优的不足,提出了一种混沌精英哈里斯鹰优化（CEHHO）算法。首先,引入精英等级制度策略,以充分利用优势种群来增强种群多样性以及提升算法收敛速度和精度;其次,利用Tent混沌映射调整算法关键参数;然后,使用一种非线性能量因子调节策略来平衡算法的开发与探索;最后,使用高斯随机游走策略对最优个体施加扰动,并在算法停滞时,利用随机游走策略使算法有效跳出局部最优。通过对20个基准测试函数在不同维度下进行仿真实验,来评估算法的寻优能力。实验结果表明,改进算法的表现优于鲸鱼优化算法（WOA）、灰狼优化（GWO）算法、粒子群优化（PSO）算法和生物地理优化（BBO）算法,性能较原始HHO算法有明显提升,验证了改进算法的有效性。     

IB网上CPU-GPU异构超算平台容器性能评估及优化

为了实现资源和系统环境的隔离,近年来新兴了多种虚拟化工具,容器便是其中之一。在超算资源上运行的问题通常是由软件配置引起的。容器的一个作用就是将依赖打包进轻量级可移植的环境中,这样可以提高超算应用程序的部署效率。为了解基于IB网的CPU-GPU异构超算平台上容器虚拟化技术的性能特征,使用标准基准测试工具对Docker容器进行了全面的性能评估。该方法能够评估容器在虚拟化宿主机过程中产生的性能开销,包括文件系统访问性能、并行通信性能及GPU计算性能。结果表明,容器具备近乎原生宿主机的性能,文件系统I/O开销及GPU计算开销与原生宿主机差别不大。随着网络负载的增大,容器的并行通信开销也相应增大。根据评估结果,提出了一种能够发挥超算平台容器性能的方法,为使用者有针对性地进行系统配置、合理设计应用程序提供依据。     

一种高效强K—栅栏覆盖构建算法

K－栅栏覆盖是无线传感器网络覆盖控制的研究热点之一。本文构建了强栅栏覆盖模型,提出了分区强K－栅栏覆盖构建算法PMNSB,用最少的节点形成强栅栏。首先把监控区域分成多个子区域,通过匈牙利算法选用移动距离之和最少的网格集合为基准1－栅栏覆盖,缺少移动节点的子区域,选择附近区域的剩余移动节点修补形成1－栅栏覆盖。水平相邻的两个子区域之间构建竖直栅栏,这些1－栅栏合起来构成强K－栅栏覆盖。仿真结果证明了该方法的有效性,本文的研究对提升无线传感器网络的性能具有重要的理论与实际意义。     

四水箱控制系统的性能评价

针对基于最小方差的性能评价准则由于只考虑时滞引起的性能限制,不适合对PID控制回路进行性能评价的问题,本文采用PID能实现最小方差控制准则,对四水箱控制系统进行性能分析,得到的性能指标比传统最小方差准则明显提高,控制器参数大大改善了过程的输出方差。实验表明,PID能实现最小方差准则能够为PID控制器的性能评价提供一个合理的评价基准,更说明其对特定类型控制器性能评价所具有的实际意义。     

Quantifying requirements volatility effects

In an organization operating in the bancassurance sector we identified a low-risk IT subportfolio of 84 IT projects comprising together 16,500 function points, each project varying in size and duration, for which we were able to quantify its requirements volatility. This representative portfolio stems from a much larger portfolio of IT projects. We calculated the volatility from the function point countings that were available to us. These figures were aggregated into a requirements volatility benchmark. We found that maximum requirements volatility rates depend on size and duration, which refutes currently known industrial averages. For instance, a monthly growth rate of 5% is considered a critical failure factor, but in our low-risk portfolio we found more than 21% of successful projects with a volatility larger than 5%. We proposed a mathematical model taking size and duration into account that provides a maximum healthy volatility rate that is more in line with the reality of low-risk IT portfolios. Based on the model, we proposed a tolerance factor expressing the maximal volatility tolerance for a project or portfolio. For a low-risk portfolio its empirically found tolerance is apparently acceptable, and values exceeding this tolerance are used to trigger IT decision makers. We derived two volatility ratios from this model, the π-ratio and the ρ-ratio. These ratios express how close the volatility of a project has approached the danger zone when requirements volatility reaches a critical failure rate. The volatility data of a governmental IT portfolio were juxtaposed to our bancassurance benchmark, immediately exposing a problematic project, which was corroborated by its actual failure. When function points are less common, e.g. in the embedded industry, we used daily source code size measures and illustrated how to govern the volatility of a software product line of a hardware manufacturer. With the three real-world portfolios we illustrated that our results serve the purpose of an early warning system for projects that are bound to fail due to excessive volatility. Moreover, we developed essential requirements volatility metrics that belong on an IT governance dashboard and presented such a volatility dashboard.     

HSWOA: An ensemble of hunger games search and whale optimization algorithm for global optimization

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting behavior of humpback whales. However, due to minimum exploration of the search space, WOA has a high chance of trapping into local solutions, and more exploitation leads it towards premature convergence. The concept of hunger from HGS is merged with the food searching techniques of the whale to lessen the inherent drawbacks of WOA. Two weights of HGS are adaptively designed for every whale using the respective hunger level for balancing search strategies. Performance verification of the proposed hunger search-based whale optimization algorithm (HSWOA) is done by comparing it with 10 state-of-the-art algorithms, including three very recently developed algorithms on 30 classical benchmark functions. Comparison with some basic algorithms, recently modified algorithms, and WOA variants is performed using IEEE CEC 2019 function set. Statistical performance of the proposed algorithm is verified with Friedman's test, boxplot analysis, and Nemenyi multiple comparison test. The operating speed of the algorithm is determined and tested with complexity analysis and convergence analysis. Finally, seven real-world engineering problems are solved and compared with a list of metaheuristic algorithms. Numerical and statistical performance comparison with state-of-the-art algorithms confirms the efficacy of the newly designed algorithm.