Most of the existing demand-side management programs focus primarily on the interactions between a utility company and its customers/users. In this paper, we present an autonomous and distributed demand-side energy management system among users that takes advantage of a two-way digital communication infrastructure which is envisioned in the future smart grid. We use game theory and formulate an energy consumption scheduling game, where the players are the users and their strategies are the daily schedules of their household appliances and loads. It is assumed that the utility company can adopt adequate pricing tariffs that differentiate the energy usage in time and level. We show that for a common scenario, with a single utility company serving multiple customers, the global optimal performance in terms of minimizing the energy costs is achieved at the Nash equilibrium of the formulated energy consumption scheduling game. The proposed distributed demand-side energy management strategy requires each user to simply apply its best response strategy to the current total load and tariffs in the power distribution system. The users can maintain privacy and do not need to reveal the details on their energy consumption schedules to other users. We also show that users will have the incentives to participate in the energy consumption scheduling game and subscribing to such services. Simulation results confirm that the proposed approach can reduce the peak-to-average ratio of the total energy demand, the total energy costs, as well as each user's individual daily electricity charges.

/pdf/autonomous-demand-side-management-based-on-game-theoretic-2xyp3ii3r8.pdf

Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid

The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.

Demand response and smart grids—A survey

Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a "positive feedback loop" effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the "print-it-all" paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration. The fundamental attributes and challenges/barriers of Additive Manufacturing (AM).The evolution of research on AM with a focus on engineering capabilities.The affordances enabled by AM such as geometry, material and tools design.The developments in industry, intellectual property, and education-related aspects.The important future trends of AM technologies.

The status, challenges, and future of additive manufacturing in engineering

Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an optimal and automatic residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each appliance in household in presence of a real-time pricing tariff combined with inclining block rates. Our design requires minimum effort from the users and is based on simple linear programming computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires price prediction capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient weighted average price prediction filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the coefficients for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting peak-to-average ratio in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.

/pdf/optimal-residential-load-control-with-price-prediction-in-2g95w0kebu.pdf

Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments

Leu-M

The Log-structured File System (LFS), introduced in 1991 [8], has received much attention for its potential order-of-magnitude improvement in file system performance. Early research results [9] showed that small file performance could scale with processor speed and that cleaning costs could be kept low, allowing LFS to write at an effective bandwidth of 62 to 83% of the maximum. Later work showed that the presence of synchronous disk operations could degrade performance by as much as 62% and that cleaning overhead could become prohibitive in transaction processing workloads, reducing performance by as much as 40% [10]. The same work showed that the addition of clustered reads and writes in the Berkeley Fast File System [6] (FFS) made it competitive with LFS in large-file handling and software development environments as approximated by the Andrew benchmark [4].

These seemingly inconsistent results have caused confusion in the file system research community. This paper presents a detailed performance comparison of the 4.4BSD Log-structured File System and the 4.4BSD Fast File System. Ignoring cleaner overhead, our results show that the order-of-magnitude improvement in performance claimed for LFS applies only to meta-data intensive activities, specifically the creation of files one-kilobyte or less and deletion of files 64 kilobytes or less.

For small files, both systems provide comparable read performance, but LFS offers superior performance on writes. For large files (one megabyte and larger), the performance of the two file systems is comparable. When FFS is tuned for writing, its large-file write performance is approximately 15% better than LFS, but its read performance is 25% worse. When FFS is optimized for reading, its large-file read and write performance is comparable to LFS.

Both LFS and FFS can suffer performance degradation, due to cleaning and disk fragmentation respectively. We find that active FFS file systems function at approximately 85-95% of their maximum performance after two to three years. We examine LFS cleaner performance in a transaction processing environment and find that cleaner overhead reduces LFS performance by more than 33% when the disk is 50% full.

/pdf/file-system-logging-versus-clustering-a-performance-3k1rwjemqv.pdf

File system logging versus clustering: a performance comparison

Adaptive cruise control is one of the most widely used vehicle driver assistance systems. However, uncertainty about drivers' lane change maneuvers in surrounding vehicles, such as unexpected cut-in, remains a challenge. We propose a novel adaptive cruise control framework combining convolution neural network (CNN)-based lane-change-intention inference and a predictive controller. We transform real-world driving data, collected on public roads with only standard production sensors, to a simplified bird's-eye view. This enables a CNN-based inference approach with low computational cost and robustness to noisy input. The predicted inference of traffic participants' lane change intention is utilized to improve safety and ride comfort with model predictive control. Simulation results based on driving scene reconstruction demonstrate the superior performance of inference using the proposed CNN-based approach, as well as enhanced safety and ride comfort.

Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC

DOI: 10.1002/admt.201800136 variability in the powder properties,[9] bed thickness nonuniformity,[10] and laser parameters and scan paths that result in improper power melting.[11] Thus, even after optimizing LPBF operating para­ meters and identifying suitable processing windows,[12] rapid build qualification, improved quality, and higher production yields require methods of monitoring the melt pool and/or powder bed in situ, i.e., during a build, that enable real­time process feedback and automated quality detection.[13,14] The majority of LPBF process moni­ toring approaches rely on noncontact sensing[15] from optical, thermal,[16] and/ or acoustic[17,18] sensors. These sen­ sors provide assessments of spatial and spectral features of the melt pool,[19,20] process plume,[21] degree of spatter,[22–25] overhang layers,[26] or print bed. High­ speed image sequences of the melting process,[27] scans of the powder bed,[10,28] beam quality,[29] and/or thermal monitoring[30] are all routinely collected forms of in situ monitoring data. Making use of this data requires methods that can extract rel­ evant diagnostic information. For instance, before initiating laser melting, automated computer vision algorithms can characterize metal powder feedstocks,[31] and image analysis of newly spread powder can reveal nonuniformities in the powder bed thickness.[32] Aminzadeh et al. demonstrated layer­by­layer detection of fusion defects from images using a Bayesian clas­ sifier.[33] Real­time events such as material ejecta are detectable by applying manually set thresholds to high­speed near­infrared images of the melt pool. Also, increasing the μm pixel−1 image resolution relative to the standard deviation of measured track width, σmeasured, may result in improved predictions of the final track width, δpredicted, and topography.[34] Reducing laser power proportionally to an integrated signal from a photodiode cali­ brated against a camera results in smoother overhang struc­ tures.[35] Using images of the print bed taken after laser melting, a level sets method can detect intentionally created defects,[36] machine vision algorithms can identify pore defects,[37] and multifractal image analysis can characterize layers with balling, cracks and pores, and no defects.[38] Visual imaging equipment is appealing to LPBF monitoring systems because it is relatively inexpensive and provides noncontact sensing.[13] As with most additive manufacturing systems, analysis of LPBF sensor data currently occurs postbuild, rendering A two-step machine learning approach to monitoring laser powder bed fusion (LPBF) additive manufacturing is demonstrated that enables on-the-fly assessments of laser track welds. First, in situ video melt pool data acquired during LPBF is labeled according to the (1) average and (2) standard deviation of individual track width and also (3) whether or not the track is continuous, measured postbuild through an ex situ height map analysis algorithm. This procedure generates three ground truth labeled datasets for supervised machine learning. Using a portion of the labeled 10 ms video clips, a single convolutional neural network architecture is trained to generate three distinct networks. With the remaining in situ LPBF data, the trained neural networks are tested and evaluated and found to predict track width, standard deviation, and continuity without the need for ex situ measurements. This two-step approach should benefit any LPBF system – or any additive manufacturing technology – where height-map-derived properties can serve as useful labels for in situ sensor data.

Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

We present new programmable graphics hardware accelerated algorithms to test the 2-moldability of geometric parts and assist with part redesign. These algorithms efficiently identify and graphically display undercuts as well as minimum and insufficient draft angles. Their running times grow only linearly with respect to the number of facets in the solid model, making them efficient subroutines for our algorithms that test whether a tessellated CAD model can be manufactured in a two-part mold. We have developed and implemented two such algorithms to choose candidate directions to test for 2-moldability using accessibility analysis and Gauss maps. The efficiency of these algorithms lies in the fact that they identify groups of candidate directions such that if any one direction in the group is undercut-free, all are, or if any one is not undercut-free, none are. We examine trade-offs between the algorithms' speed, accuracy, and whether they guarantee that an undercut-free direction will be found for a part if one exists.

/pdf/finding-feasible-mold-parting-directions-using-graphics-1sogyed4oj.pdf

Finding feasible mold parting directions using graphics hardware

“CyberCut TM ” is a testbed for an Internet-based CAD/CAM system. It was specifically designed to be a networked, automated system, with a seamless communication flow from a client-side designer to a server-side machining service. The creation of CyberCut required several new software modules. These include: a) a Web-based design tool in which Design-forManufacturing information and machining rules constrain the designer to manufacturable parts; b) a geometric representation called SIF-DSG, for unambiguous communication between the client-side designer and the server-side process planner; c) an automated process planning system with several sub-modules that convert an incoming design to a set of tool-paths for execution on a 3-axis CNC milling machine. Using this software-pipeline, a CyberCut service, modeled on the MOSIS service for VLSI chips, has been now been launched for limited studentuse at a group of cooperating universities.

Sara McMains

Papers

File system logging versus clustering: a performance comparison

Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC

Machine-Learning-Based Monitoring of Laser Powder Bed Fusion

Finding feasible mold parting directions using graphics hardware

CyberCut: an internet-based CAD/CAM system