A Simple, Practical Prioritization Scheme for a Job Shop Processing Multiple Job Types

The maintenance, repair, and overhaul (MRO) process is used to recondition equipment in the railroad, off-shore drilling, aircraft, and shipping industries. In the typical MRO process, the equipment is disassembled into component parts and these parts are routed to back-shops for repair. Repaired parts are returned for reassembling the equipment. Scheduling the back-shop for smooth flow often requires prioritizing the repair of component parts from different original assemblies at different machines. To enable such prioritization, we model the back-shop as a multi-class queueing network with a ConWIP execution system and introduce a new priority scheme to maximize the system performance. In this scheme, we identify the bottleneck machine based on overall workload and classify machines into two categories: the bottleneck machine and the non-bottleneck machine(s). Assemblies with the lowest cycle time receive the highest priority on the bottleneck machine and the lowest priority on non-bottleneck machine(s). Our experimental results show that this priority scheme increases the system performance by lowering the average cycle times without adversely impacting the total throughput. The contribution of this thesis consists primarily of three parts. First, we develop a simple priority scheme for multi-class, multi-server, ConWIP queueing systems with the disassembly/reassembly feature so that schedulers for a job-shop environment would be able to know which part should be given priority, in what order and where. Next, we provide an exact analytical solution to a two-class, two-server closed queueing model with mixed non-preemptive priority scheme. The queueing network model we study has not been analyzed in the literature, and there are no existing models that address the underlying problem of deciding prioritization by job types to maximize the system performance. Finally, we explore conditions under which the non-preemptive priority discipline can be approximated by a preemptive priority discipline

The Application of Computer-assisted Method for Smoke Exhausting System Design in Building Atrium Field

AbstractThis paper is to take the design of smoke exhausting system on the atrium of a building as the research target. On the basis of fire safety engineering principles, it uses the computer-assisted method to adjust the program of smoke exhausting system. On the use of computer simulation, then, it demonstrated the conclusion which is that the program could be scientific, reasonable and safe, although the project is not in accordance with the design code. This research provide the scientific data for the study on the computer-assisted method for smoke exhausting system design, so as to lay the foundation of further development and application on the new digital design method

An Adaptive Compression and Communication Framework for Wireless Federated Learning

Federated learning (FL) is a distributed privacy-preserving paradigm of machine learning that enables efficient and secure model training through the collaboration of multiple clients. However, imperfect channel estimation and resource constraints of edge devices severely hinder the convergence of typical wireless FL, while the trade-off between communications and computation still lacks in-depth exploration. These factors lead to inefficient communications and hinder the full potential of FL from being unleashed. In this regard, we formulate a joint optimization problem of communications and learning in wireless networks subject to dynamic channel variations. For addressing the formulated problem, we propose an integrated adaptive $n$ -ary compression and resource management framework (ANC) that is capable of adjusting the selection of edge devices and compression schemes, and allocates the optimal resource blocks and transmit power to each participating device, which effectively improves the energy efficiency and scalability of FL in resource-constrained environments. Furthermore, an upper bound on the expected global convergence rate is derived in this paper to quantify the impacts of transmitted data volume and wireless propagation on the convergence of FL. Simulation results demonstrate that the proposed adaptive framework achieves much faster convergence while maintaining considerably low communication overhead

Noble Metal-Based Nanocomposites for Fuel Cells

Noble metal-based nanocomposites are attractive for a rich variety of electrocatalytic applications as they can exhibit not only a combination of the properties associated with each component but also synergy due to a strong coupling between different constituents. Using noble metal as the base component, a plenty of methods have been recently demonstrated for the synthesis of noble metal-based nanocomposites with novel structures (e.g., alloys, core-shell, skin and 1D/2D structures). In this chapter, an account of recent advances of synthetic approaches to noble metal-based nanocomposites with controlled structures, compositions and sizes are reviewed. The relationship between structures and electrochemical properties of these nanocomposites in fuel cell field is discussed. The potential future directions of research in the field are also addressed

Mapping the surface water storage variation in densely impounded semi-arid NE Brazil with satellite remote sensing approach

Surface water bodies provide vital support to the society and fundamentally affect ecosystems in various manners. Precise knowledge of the spatial extent of surface water bodies (e.g. reservoirs) as well as of the quantity of water they store is necessary for efficient water deployment and understanding of the local hydrology. Remote sensing provides broad opportunities for surface water mapping. The main objectives of this thesis are: 1) delineating surface water area of partly vegetated water bodies only from remote sensing data without field data input; 2) obtaining the surface water storage, and 3) analyzing its spatio-temporal variations for northeastern (NE) Brazil as a representative for a densely dammed semi-arid region. At first, I investigated the potential of digital elevation models (DEMs) generated from TanDEM-X data, which were acquired during the low water level stage, for reservoirs’ bathymetry derivation. I found that the accuracy of such DEMs can reach one meter, both in the absolute and relative respects. It has shown that DEMs derived from TanDEM-X data have great potentials for representing the reservoirs’ bathymetry of temporally dried-out reservoirs. Subsequently, I targeted at developing a method for mapping the water surface beneath canopy independent of field data for further delineation of the effective water surface. Instead of the commonly used backscattering coefficients, I investigated the capability of the Gray-Level Co-Occurrence Matrix (GLCM) texture index to distinguish different types of Radar backscattering taking place in (partly) vegetated reservoirs. This experiment demonstrated that different types of backscattering at the vegetated water surface show distinct statistical characteristics on GLCM variance derived from TerraSAR-X satellite time series data. Furthermore, with the threshold established based on the statistics of the sub-populations dominated by different types of backscattering, the vegetated water surfaces were effectively mapped, and the effective water surface areas were further delineated with an accuracy of 77% to 95%. ii Based on the investigation of the DEMs generated from TanDEM-X data, I derived the formerly unknown bathymetry for 2 105 reservoirs of various sizes in four representative regions of an overall area of 10 000 km2. The spatial distributions of surface water storage capacities in the four regions were subsequently extracted from the combination of the reservoir bathymetry and the water surface extents provided by RapidEye satellite time series. Furthermore, the spatio-temporal variations of surface water storage were derived for the four representative regions on an annual basis in the period of 2009-2017. This study showed that 1) The density of reservoirs in NE Brazil amounts to 0.04-0.23 reservoirs per km2, the corresponding water surface and surface water storage are 1.18-4.13 ha/km2 and 0.01-0.04 hm3 m/km², respectively; 2) On the spatial unit of 5×5 km2, the surface water storage in the region constantly decreased due to a prolonged drought with a rate of 105 m3/year from 2009 to 2017, with a slight increase from 2016 to 2017 in a few reservoirs; 3) Local precipitation deficit controls the variation of the overall surface water storage in the region. In this thesis I demonstrated the great potential of the great potential of SAR and optical satellite time series data for hydrological applications. The method I developed for delineating the effective water extent from the vegetated reservoirs has shown high potential transferability for other similar regions. The data gaps of bathymetry and surface waters storage capacity were filled for 2 105 reservoirs in NE Brazil. The results of the spatio-temporal variations of surface water storage in four representative regions from 2009-2016 can support future water management and improve hydrological prediction in NE Brazil