The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

/pdf/mastering-the-game-of-go-with-deep-neural-networks-and-tree-3viuxo6uxm.pdf

Mastering the game of Go with deep neural networks and tree search

(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

The recovery of objects obscured by scattering is an important goal in imaging and has been approached by exploiting, for example, coherence properties, ballistic photons or penetrating wavelengths. Common methods use scattered light transmitted through an occluding material, although these fail if the occluder is opaque. Light is scattered not only by transmission through objects, but also by multiple reflection from diffuse surfaces in a scene. This reflected light contains information about the scene that becomes mixed by the diffuse reflections before reaching the image sensor. This mixing is difficult to decode using traditional cameras. Here we report the combination of a time-of-flight technique and computational reconstruction algorithms to untangle image information mixed by diffuse reflection. We demonstrate a three-dimensional range camera able to look around a corner using diffusely reflected light that achieves sub-millimetre depth precision and centimetre lateral precision over 40 cm×40 cm×40 cm of hidden space. An important goal in optics is to image objects hidden by turbid media, although line-of-sight techniques fail when the obscuring medium becomes opaque. Veltenet al. use ultrafast imaging techniques to recover three-dimensional shapes of non-line-of-sight objects after reflection from diffuse surfaces.

/pdf/recovering-three-dimensional-shape-around-a-corner-using-3j2ptae4fl.pdf

Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging.

electrical and computer engineering ece courses ece 257a multiuser communication systems 4 congestion control convex programming and dual controller fair end end rate allocation max min fair vs proportional, electrical systems engineering washington university arye nehorai eugene and martha lohman professor of electrical engineering phd stanford university signal processing imaging biomedicine communications, ieee transactions on aerospace and electronic systems ieee transactions on aerospace and electronic systems focuses on the organization design development integration and operation of complex systems for space air, department of electrical engineering and computer science h kumar wickramsinghe department chair 2213 engineering hall 949 824 4821 http www eng uci edu dept eecs overview electrical engineering and computer science is, download electrical and electronics engineering ebooks syst mes temps discret commande num rique des proc d s pdf 499 ko terminology and symbols in control engineering pdf 326 ko the best of thomas, publications stream wise list iit kanpur papers published in journals in 2016 dutta s patchaikani p k behera l near optimal controller for nonlinear continuous time systems with unknown dynamics, resolve a doi name type or paste a doi name into the text box click go your browser will take you to a web page url associated with that doi name send questions or comments to doi, peer reviewed journal ijera com international journal of engineering research and applications ijera is an open access online peer reviewed international journal that publishes research, dod sbir 2016 2 sbir gov note the solicitations and topics listed on this site are copies from the various sbir agency solicitations and are not necessarily the latest and most up, an english japanese dictionary of electrical engineering c 2952 9 691 c band c c contact c c maccs centre for mathematical modelling and computer simulation, the of and to a in that is was he for it with as his on be most common text click on the icon to return to www berro com and to enjoy and benefit the of and to a in that is was he for it with as his on be at by i this had

Detection, Estimation, and Modulation Theory

In recent years, the rapid development of cryptocurrencies and their underlying blockchain technology has revived Szabo’s original idea of smart contracts, i.e., computer protocols that are designed to automatically facilitate, verify, and enforce the negotiation and implementation of digital contracts without central authorities. Smart contracts can find a wide spectrum of potential application scenarios in the digital economy and intelligent industries, including financial services, management, healthcare, and Internet of Things, among others, and also have been integrated into the mainstream blockchain-based development platforms, such as Ethereum and Hyperledger. However, smart contracts are still far from mature, and major technical challenges such as security and privacy issues are still awaiting further research efforts. For instance, the most notorious case might be “The DAO Attack” in June 2016, which led to more than $50 million Ether transferred into an adversary’s account. In this paper, we strive to present a systematic and comprehensive overview of blockchain-enabled smart contracts, aiming at stimulating further research toward this emerging research area. We first introduced the operating mechanism and mainstream platforms of blockchain-enabled smart contracts, and proposed a research framework for smart contracts based on a novel six-layer architecture. Second, both the technical and legal challenges, as well as the recent research progresses, are listed. Third, we presented several typical application scenarios. Toward the end, we discussed the future development trends of smart contracts. This paper is aimed at providing helpful guidance and reference for future research efforts.

Blockchain-Enabled Smart Contracts: Architecture, Applications, and Future Trends

An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.

Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.

A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization

A fault detection, identification, and location approach is proposed and studied in this paper. This approach is based on matching pursuit decomposition (MPD) using Gaussian atom dictionary, hidden Markov model (HMM) of real-time frequency and voltage variation features, and fault contour maps generated by machine learning algorithms in smart grid (SG) systems. Specifically, the time-frequency features are extracted by MPD from the frequency and voltage signals, which are sampled by the frequency disturbance recorders in SG. A hybrid clustering algorithm is then developed and used to cluster the frequency and voltage signal features into various symbols. Using the symbols, two detection HMMs are trained for fault detection to distinguish between normal and abnormal SG operation conditions. Also, several identification HMMs are trained under different system fault scenarios, and if a fault occurs, the trained identification HMMs are used to identify different fault types. In the meantime, if the fault is detected by the detection HMMs, a fault contour map will be generated using the feature extracted by the MPD from the voltage signals and topology information of SG. The numerical results demonstrate the feasibility, effectiveness, and accuracy of the proposed approach for the diagnosis of various types of faults with different measurement signal-to-noise ratios in SG systems.

Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods

Creating the feedback loop: closed-loop neurostimulation.

We propose a new multiple-input, multiple-output (MIMO) radar system with colocated antennas that can greatly reduce target fluctuations. This is achieved by employing frequency diversity. The proposed MIMO radar system results in a radar array with frequency-division multiplexing that can also incorporate beamforming to design the transmission beam pattern. After presenting an overview of existing MIMO radar systems and their related signal models, we provide a detailed description of the proposed frequency diversity technique and the corresponding transmitter and receiver structures. Furthermore, we present a maximum likelihood estimation algorithm that incorporates frequency diversity with MIMO radar, derive the corresponding Cramer-Rao lower bound, and demonstrate its improved performance using numerical results.

https://www.ese.wustl.edu/~nehorai/MURI/publications/wdd5.pdf

Jun Jason Zhang

Papers

Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond

A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization

Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods

Creating the feedback loop: closed-loop neurostimulation.

MIMO Radar with Frequency Diversity