TagBook: A Semantic Video Representation without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.Comment: accepted for publication as a regular paper in the IEEE Transactions on Multimedi

Computer Generation of Metal Components by Simultaneous Deposition of Mould, Cores and Part

A new solid freeforming method based on co-delivery of mould powder materials and part powder materials using vibration-controlled, dry powder valves is presented in this paper. Thin layers of stainless steel powder are delivered to the forming area according to the cross-section of the CAD file to produce the component. Mould powder which has low sinterability is delivered to the non-forming areas of the same layer. All powders are delivered by computer-controlled, acoustic powder valves. The flow rate and switching of the valves provides the composition and shape control during fabrication. The stacked layers of loose powder are then sintered in a conventional furnace. The mould materials are removed after sintering. This method avoids the high thermal stress problem in selective laser sintering, avoids high capitalisation, makes use of conventional furnaces and allows for the incorporation of three dimensional function gradients. Test pieces including step wedge and Spierpinski’s cube were fabricated. Advantages, limitations and problems are discussed.Mechanical Engineerin

Individual characteristics, identity styles, identity commitment, and teacher's academic optimism

AbstractThe academic optimism construct is an individual belief in teachers consisting of three components: sense of efficacy, teacher trust in parents and students and academic emphasis which through creating an active and positive learning environment leads to the academic progress and success of the students. The aim of this study was to determine the relation between individual characteristics, identity styles, and identity commitment and the teacher's academic optimism. A sample consisting of 303 primary and middle school teachers (172 female and 131 male) were selected by stratified sampling and completed the revised version of Identity Styles Inventory (ISI-4) (Smits et al., Unpublished) and Teacher's Academic Optimism Questionnaire (Woolfolk Hoy, Hoy & Kurz, 2008). The data were analyzed by a stepwise regression analysis and the results indicated that the informational identity style was the main predictor of the teacher's academic optimism

Reinforcement Learning in Deep Structured Teams: Initial Results with Finite and Infinite Valued Features

In this paper, we consider Markov chain and linear quadratic models for deep structured teams with discounted and time-average cost functions under two non-classical information structures, namely, deep state sharing and no sharing. In deep structured teams, agents are coupled in dynamics and cost functions through deep state, where deep state refers to a set of orthogonal linear regressions of the states. In this article, we consider a homogeneous linear regression for Markov chain models (i.e., empirical distribution of states) and a few orthonormal linear regressions for linear quadratic models (i.e., weighted average of states). Some planning algorithms are developed for the case when the model is known, and some reinforcement learning algorithms are proposed for the case when the model is not known completely. The convergence of two model-free (reinforcement learning) algorithms, one for Markov chain models and one for linear quadratic models, is established. The results are then applied to a smart grid.Comment: This version corrects some typographical error

Risk-Constrained Control of Mean-Field Linear Quadratic Systems

The risk-neutral LQR controller is optimal for stochastic linear dynamical systems. However, the classical optimal controller performs inefficiently in the presence of low-probability yet statistically significant (risky) events. The present research focuses on infinite-horizon risk-constrained linear quadratic regulators in a mean-field setting. We address the risk constraint by bounding the cumulative one-stage variance of the state penalty of all players. It is shown that the optimal controller is affine in the state of each player with an additive term that controls the risk constraint. In addition, we propose a solution independent of the number of players. Finally, simulations are presented to verify the theoretical findings.Comment: Accepted at 62nd IEEE Conference on Decision and Contro

Energy management of virtual power plant considering distributed generation sizing and pricing

UID/EMS/00667/2019The energy management of virtual power plants faces some fundamental challenges that make it complicated compared to conventional power plants, such as uncertainty in production, consumption, energy price, and availability of network components. Continuous monitoring and scaling of network gain status, using smart grids provides valuable instantaneous information about network conditions such as production, consumption, power lines, and network availability. Therefore, by creating a bidirectional communication between the energy management system and the grid users such as producers or energy applicants, it will afford a suitable platform to develop more efficient vector of the virtual power plant. The paper is treated with optimal sizing of DG units and the price of their electricity sales to achieve security issues and other technical considerations in the system. The ultimate goal in this study to determine the active demand power required to increase system loading capability and to withstand disturbances. The effect of different types of DG units in simulations is considered and then the efficiency of each equipment such as converters, wind turbines, electrolyzers, etc., is achieved to minimize the total operation cost and losses, improve voltage profiles, and address other security issues and reliability. The simulations are done in three cases and compared with HOMER software to validate the ability of proposed model.publishersversionpublishe