In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO’s profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.

The wireless communication is making it easier for smart devices to communicate with one another in terms of the network of the Internet of Things. Smart devices are automatically linked and built up a network on their own. But there are more obstacles to safe access within the network itself. Mobile devices such as smart home automation access point, smart washing machines, mobile boards, temperature sensors, color-changing smart lighting, smartphones, wearable devices, and smart appliances, etc. are widespread in our daily lives and is becoming valuable tools with wireless communication abilities that are using specific wireless standards that are commonly used with IEEE 802.11 access points. On the realism of the Internet, security has been perceived as a prominent inhibitor of embracing the cloud paradigm. It is resource storage and management that may lay in any since the cloud environment is a distributed architecture, which place of the world, many concerns have been raised over its vulnerabilities, security threats and challenges. The involvement of various parties has widened these concerns based on each party’s perspective and objective. The Cloud point of view we mainly discuss the causes of obstacles and challenges related to security, reliability, privacy and service availability. The wireless communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in constant growth in the cloud’s use and popularity. Our purpose of this study is to create a framework of mobile ad hoc network mobility model using cloud computing for providing secure communication among smart devices network for the internet of things in 5G heterogeneous networks. Our main contribution links a new methodology for providing secure communication on the internet of smart devices in 5G. Our methodology uses the correct and efficient simulation of the desired study and can be implemented in a framework of the Internet of Things in 5G.

Job shops are an important production environment for low-volume high-variety manufacturing. Its scheduling has recently been formulated as an Integer Linear Programming (ILP) problem to take advantages of popular Mixed-Integer Linear Programming (MILP) methods, e.g., branch-and-cut. When considering a large number of parts, MILP methods may experience difficulties. To address this, a critical but much overlooked issue is formulation tightening. The idea is that if problem constraints can be transformed to directly delineate the problem convex hull in the data preprocessing stage, then a solution can be obtained by using linear programming methods without much difficulty. The tightening process, however, is fundamentally challenging because of the existence of integer variables. In this paper, an innovative and systematic approach is established for the first time to tighten the formulations of individual parts, each with multiple operations, in the data preprocessing stage. It is a major advancement of our previous work on problems with binary and continuous variables to integer variables. The idea is to first link integer variables to binary variables by innovatively combining constraints so that the integer variables are uniquely determined by the binary variables. With binary and continuous variables only, it is proved that the vertices of the convex hull can be obtained based on vertices of the linear problem after relaxing binary requirements. These vertices are then converted to tight constraints for general use. This approach significantly improves our previous results on tightening individual operations. Numerical results demonstrate significant benefits on solution quality and computational efficiency. This approach also applies to other ILP problems with similar characteristics and fundamentally changes the way how such problems are formulated and solved.

This paper focuses on the common mistakes made by students at the early stages of research when writing scientific papers, thesis and reports. It points out mistakes to be avoided and the basic rules that can be inferred by reading a few scientific documents but are not usually clearly written and that we, as professors, end up teaching over and over again. The outcome of this paper will be, hopefully, that we will not need to correct the same common mistakes again and you, as a student, will have a faster lane to publishing. Starting with the abstract: it is composed of a single paragraph, does not contain acronyms or references and describes in short the work, main highlights and points out the results or main conclusions obtained from the work being presented. The abstract is an independent part of the paper and commonly has a character or word limit that you need to respect. It can be read as a “stand-alone” and the paper starts in the introduction, meaning that the introduction is not the sequence of the abstract and it can have some text in common if needed.

Digital healthcare technologies are transforming the face of prosthetic care. Millions of amputees around the world do not currently have access to any form of prosthetic healthcare. However, digital technologies provide a promising solution. Digital healthcare technologies have the potential to augment the range and efficiency of prosthetists so they can reach more patients. These technologies will enable affordable prostheses to be built on a scale larger than currently possible with today’s clinical practices. In this paper, we explore the social aspects of amputation as a global issue, describe current practices for designing and manufacturing prosthetic sockets, and examine shifting trends towards virtual care models. Importantly, we assess the technologies used in these virtual health workflows to understand their critical needs. Large technological gaps need to be overcome in order to enable the mass production and distribution of prostheses digitally. However, recent advances in computational methods and CAD/CAM technologies are bridging this gap faster than ever before. We foresee that these technologies will return mobility and economic opportunity to amputees on a global scale in the near future.

Knowing the physical properties and scattering mechanisms contributes to Synthetic Aperture Radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the ’image’ domain. Based on TFA theory, we propose to generate the sub-band scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering algorithm based on time-frequency analysis (HDEC-TFA) is proposed to learn the embedded fea- tures and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single- polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen- 3 quad-polarized SAR data for experiments and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex- valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications.

High quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMU measurements, measurement-based approach for model validation has gained significant prominence. Currently, the model validation results are analyzed by visually comparing real--world PMU measurements with the model-based response measurements, and parameter adjustments rely mostly on engineering experience. This paper proposes advanced performance metrics to systematically quantify the generator dynamic model validation results by separately taking into consideration slow governor response and comparatively fast oscillatory response. The performance metric for governor response is based on the step response characteristics of a system and the metric for oscillatory response is based on the response of generator to each system mode calculated using modal analysis. The proposed metrics in this paper is aimed at providing critical information to help with the selection of parameters to be tuned for model calibration by performing enhanced sensitivity analysis, and also help with rule-based model calibration. Results obtained using both simulated and real-world measurements validate the effectiveness of the proposed performance metrics and sensitivity analysis for model validation and calibration.

Wu and Verdú developed a theory of almost lossless analog compression, where one imposes various regularity conditions on the compressor and the decompressor with the input signal being modelled by a (typically infinite-entropy) stationary stochastic process. In this work we consider all stationary stochastic processes with trajectories in a prescribed set of (bi-)infinite sequences and find uniform lower and upper bounds for certain compression rates in terms of metric mean dimension and mean box dimension. An essential tool is the recent Lindenstrauss-Tsukamoto variational principle expressing metric mean dimension in terms of rate-distortion functions. We obtain also lower bounds on compression rates for a fixed stationary process in terms of the rate-distortion dimension rates and study several examples.

Prognostics predicts future performance progression and remaining useful life (RUL) of in-service systems based on historical/contemporary data. One of the challenges for prognostics is the development of methods which are capable of handling real-world uncertainties that typically lead to inaccurate predictions. To alleviate the impacts of uncertainties and to achieve accurate degradation trajectory and RUL predictions, a novel sequence-to-sequence predictive model is proposed based on a variational auto-encoder (VAE) that is trained with Generative Adversarial Networks (GAN). A Long Short-Term Memory (LSTM) network and Gaussian mixture model are utilized as building blocks so that the model is capable of providing probabilistic predictions. Correlative and monotonic metrics are applied to identify sensitive features in the degradation progress, in order to reduce the uncertainty induced by raw data. Then, the extracted features are concatenated with one-hot health state indicators as training data for the model to learn the end-of-life (EoL) without the need for prior knowledge of failure thresholds. The performance of the proposed model is validated by health monitoring data collected from real-world aero-engines, wind turbines, and lithium-ion batteries. The results demonstrate that significant performance improvement can be achieved in long-term degradation progress and RUL prediction tasks.

A document by Tanweer Alam. Click on the document to view its contents.

We report photonic RF fractional Hilbert transformers and filters based on a 49GHz soliton crystal micro-comb source. By employing up to 80 wavelengths and controlling the channel weights, diverse transfer functions are achieved.

This paper show how the novelty search (NS) algorithm can be used for similarity-based software product line testing

We present a method that operates an electrolyzer to meet the demand of a hydrogen refueling station in a cost-effective manner by solving a model-based optimal control problem. To formulate the underlying problem, we first conduct an experimental characterization of a Siemens SILYZER 100 polymer electrolyte membrane electrolyzer with \SI{100}{\kilo \watt} of rated power. We run experiments to determine the electrolyzer's conversion efficiency and thermal dynamics as well as the overload-limiting algorithm used in the electrolyzer. The resulting detailed nonlinear models are used to design a real-time optimal controller, which is then implemented on the actual system. Each minute, the controller solves a deterministic, receding-horizon problem which seeks to minimize the cost of satisfying a given hydrogen demand, while using a storage tank to take advantage of time-varying electricity prices and photovoltaic inflow. We illustrate in simulation the significant cost reduction achieved by our method compared to others in the literature, and then validate our method by demonstrating it in real-time operation on the actual system.

A technical report discussing some unexpected results when using off-the-shelf convolutional neural networks for image-to-image problems.

This paper presents an electromagnetic inversion algorithm for the design of cascaded metasurfaces that enables the design process to begin from more practical output field specifications such as a desired power pattern or far-field performance criteria. Thus, this method combines the greater field transformation support of multiple metasurfaces with the flexibility of the electromagnetic inverse source framework. To this end, two optimization problems are formed: one associated with the interior space between two metasurfaces, and the other for the exterior space. The cost functionals corresponding to each of these two optimization problems are minimized using the nonlinear conjugate gradient algorithm with analytic expressions for the gradient operators. A total variation regularizer is incorporated into the optimization procedure to favour smooth field variations from one unit cell to the next. The numerical implementation of the developed design procedure is presented in detail along with several two-dimensional (2D) simulated examples to demonstrate the capabilities of the method.

Tucker decomposition is a standard method for processing multi-way (tensor) measurements and finds many applications in machine learning and data mining, among other fields. When tensor measurements arrive in a streaming fashion or are too many to jointly decompose, incremental Tucker analysis is preferred. In addition, dynamic basis adaptation is desired when the nominal data subspaces change. At the same time, it has been documented that outliers in the data can significantly compromise the performance of existing methods for dynamic Tucker analysis. In this work, we present Dynamic L1-Tucker: an algorithm for dynamic and outlier-resistant Tucker analysis of tensor data. Our experimental studies on both real and synthetic datasets corroborate that the proposed method (i) attains high basis estimation performance, (ii) identifies/rejects outliers, and (iii) adapts to nominal subspace changes.

This paper introduces a new equivalent circuit for linear direct current networks consisting of independent voltage and current sources, and resistors, which represents all the power dissipated internally in the resistors. It is demonstrated that the internal losses of any network have two components. One is variable and dependent on the internal resistances of the actual circuit and the power transferred to the pair of accessible terminals. The other is constant and dependent only on the internal voltage and current sources and the resistances of the actual network. It is also demonstrated theoretically and validated by numerical simulation that the traditional Thevenin and Norton equivalent circuits are particular cases of the proposed equivalent circuit in this paper. The proposed equivalent circuit can be used to analyze power and efficiencies of the actual network.

Allprimescanbeindexedbyk,asprimesmustbeintheform of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k.