Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.     

Nonlinear state feedback design for continuous polynomial systems

The objective of this paper is to simplify the complexity of practical implementation of the input-state feedback linearization technique for the control of input-affine systems. A polynomial approach which makes use of the Taylor series expansion and the Kronecker product is developed. Our work aims to address the problem of synthesizing a polynomial control via a nonlinear analytical coordinates transformation. To check the effectiveness of the investigated approach, we consider the control problem of a series DC motor. A comparative study with the input-state feedback linearization control is developed.     

Volume-based large dynamic graph analysis supported by evolution provenance

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.

     

Recovering disk storage metrics from low‐level trace events

Block devices such as magnetic disks are nonvolatile data storage devices that transfer data in fixed‐size chunks. They are the main nonvolatile memory that holds the file system, and they are also used in virtual memory mechanisms such swapping and page fault handling. Investigating storage performance issues requires a full insight into the operating system internals. Kernel tracing offers an efficient mechanism to gather information about the storage subsystem at runtime. Still, the tracing output is often huge and difficult to analyze manually. In this paper, we introduce a framework to compute meaningful storage performance metrics from low‐level trace events generated by LTTng. A stateful approach is used to model the state of the storage subsystem. Efficient data structures and algorithms are proposed to offer a reasonable response time, allowing the user to navigate throughout the trace and to retrieve metrics from any time range. The framework includes a visualization system that provides different graphical views that represent the collected information in a convenient way. These views are synchronized together, forming a comprehensive perspective that makes storage performance investigation a much more comfortable task. Different use cases are presented to show the usefulness of the framework in real‐world applications.     

Identification of magnetic deposits in 2-D axisymmetric eddy current models via shape optimization

The non-destructive control of steam generators is an essential task for the safe and failure-free operation of nuclear power plants. Due to magnetite particles in the cooling water of the plants, a frequent source for failures are magnetic deposits in the cooling loop of steam generators. From eddy current signals measured inside a U-tube in the steam generator, we propose and analyze a regularized shap optimization algorithm to identify magnetic deposits outside the U-tube with either known or unknown physical properties. Motivated by the cylindrical geometry of the U-tubes we assume an axisymmetric problem setting, reducing Maxwell’s equations to a 2-D elliptic eddy current problem. The feasibility of the proposed algorithms is illustrated via numerical examples demonstrating in particular the stability of the method under noise.     

Performance analysis of distributed storage clusters based on kernel and userspace traces

Distributed storage systems are commonly used in modern computing. They are highly scalable and offer data replication and fault tolerance. The complexity of those systems makes them difficult to debug using traditional tools. The existing tools are able to evaluate the overall performance of such systems but they do not provide enough information to find the root cause of performance issues. In this article, we propose a tracing‐based performance analysis framework for storage clusters. We use a tracing strategy that reduces the tracing overhead in production systems. The traces collected from the different storage nodes are correlated and used to generate a data model that represents the cluster. Userspace tracing is used to gather data from the storage daemons, while Kernel tracing is used to provide detailed information about operating system internals such as disk queues, network queues and process scheduling. Efficient data structures are used to store the model and to generate metrics and graphical views. Our tool is used in different real world scenarios and is able to investigate interesting performance problems including I/O latencies, data replication and storage nodes failures.     

热声制冷机声功的测量

将一种较为实用的两传感器的声功测量方法，应用于热声制机声功的测量中，分析和研究了制冷机声压和声功率随频率的变化特性，使用网络模型计算的结果与实验结果能较好地吻合，本研究也是对作者所开发的热声热机网络模拟计算程序和两传感器声功测量方法的验证与应用。     

A Chromatographic Study of Shear Stability of Poly(styrene-co-alkyl methacrylate) Viscosity Modifier for Lubricating Oils

Shear stability of styrene, dodecyl methacrylate, and octadecyl methacrylate terpolymer of various molecular weights, as the rheology modifier of improved solubility, thermal and oxidation stability in mineral base oil, was investigated. Viscosity loss of the 5 wt% solutions caused by scission of macromolecules subjected to the high shear forces ranges between 15% and 27% for weight average molecular weights from 156000 to 252000. After the shear tests the number average molecular weights of polymer increases while weight and Z-average molecular weights decrease. As macromolecules of molecular weights above 700000 fully break the molar-mass dispersity decreases significantly, from starting at 2.34–2.79 down to below 1.8. A linear dependence between viscosity loss of terpolymer solutions and molecular weights was observed. The advantage of using the size exclusion chromatography to determine the stability of the polymer against mechanical shear directly and the viscosity loss indirectly was established.