Search

An average medium-sized organisation logs approx. 10 to 500 mln events per day on the system. Only less than 5% of threat alerts are being investigated by the specialised staff, leaving the security hole open for potential attacks. Insufficient information in alert message produced in machine-friendly rather than human-friendly format causes cognitive overload on currently limited cybersecurity resources. In this paper, the model that generates the report in natural language by means of applying novel storytelling techniques from security logs is proposed. The solution caters for different levels of reader expertise and preference by providing adjustable templates, filled from both local and global knowledge base. The validation is performed on case study from Security Operations Centre (SOC) at educational institution. The report generated proves superior to existing approach in terms of comprehension (increased cognition) and completeness (enriched context). The evaluation demonstrates power of storytelling in potential threats interpretation in cybersecurity context.

Degradable implants occupy the significant position in orthopedic industry. Magnesium is an appropriate degradable material due to its good biocompatibility and degradability properties in addition to advantageous mechanical properties. Improving the material quality, especially corrosion resistance and surface active, could be occur through the chemicals and parameters of the Plasma Electrolytic Oxidation process. Voltage reduction could reduce cost involved and make more environmentally friendly. Doubling the concentration of the sodium phosphate worked at low voltage. The surface morphology showed well-developed structure. Obtained coatings were without cracks and with good thickness size. Increasing wettability point to enhancing bioactive properties.

Thermostatically Controlled Loads (TCL) have a significant impact on Cold Load Pick-up (CLPU) during distribution system service restoration. Widespread deployment of smart meter devices opens up new opportunities for data-driven load modelling. In this paper, we compare several linear regression approaches to robust short term prediction of hourly energy consumption as a function of the outdoor temperature during the low-temperature season. The goal is to estimate the energy that will not be delivered during an outage for the purpose of a further estimation of the CLPU peak and duration for consumers with TCLs. The prediction is based on smart meter load data and outdoor temperature data. The performance of the proposed regression approaches is analyzed for 25 residential homes from real measured data. Prediction is performed on an hourly basis. The quality of the regression results is compared with the Naive forecast benchmark method. The results show that autoregression approach outperforms the other methods, however, since this approach is highly depended on the existence of the sequence of the previous load measurements, as an alternative approach, ENS prediction is successfully performed using only temperature data.

Distribution system reconfiguration (DSR) studies are gaining increased attention. DSR deals with minimizing the power loss by optimally reconfiguring the power system lines. However, the original protection coordination may not be valid for the reconfigured system. This paper presents an integrated approach that considers both distribution system reconfiguration and protection coordination. The DSR problem is formulated as a mixed integer nonlinear programming (MINLP) problem to determine the optimal configuration. Meanwhile, the protection coordination problem is a nonlinear programming (NLP) problem to determine the Directional Overcurrent Relay (DOCR) settings. The methodology is tested on the 119-bus radial distribution test systems, considering different load and generation scenarios. The results show that the proposed protection scheme can ensure proper protection for reconfigurable distribution systems.

It is shown how the kernel approach to joint parameter and state estimation can be improved to handle large measurement noise. High accuracy of estimation results from combining the powers of the kernel representation of the differential invariance in the system, a feasible recursive version of the generalized least squares with covariance weighting to eliminate regression dilution and suitable choices of instrumental variables to compensate for the error-in-the-variable in the stochastic regression formulation.

In this paper, a novel robust backstepping-based approach combined with sliding mode control is proposed for trajectory tracking of a quadrotor UAV subject to external disturbances and parameter uncertainties associated with the presence of aerodynamic forces and possible wind force. To enhance robustness, a nonlinear disturbance observer (NDO) is employed alongside the controller. A sliding surface is introduced, which shares intermediate control goals with a conventional backstepping scheme. The closed-loop system comprising the sliding mode and backstepping controllers is finally combined with the NDO to track the desired position and attitude trajectories. Good tracking is achieved in the closed loop if the controller and observer gains are selected correctly. The system performance exhibits much better robustness than the existing backstepping control methods, which are not equipped with nonlinear disturbance estimators. The simulation results are confirmed in terms of real laboratory experiments. Prior to the implementation of the control method, the real system has been identified and calibrated.

Wearables like smartwatches or wrist bands equipped with pervasive sensors enable us to monitor our physiological signals. In this study, we address the question whether they can help us to recognize our emotions in our everyday life for ubiquitous computing. Using the systematic literature review, we identified crucial research steps and discussed the main limitations and problems in the domain.

A kernel-based approach is explored to enhance robustness of flatness-based nonlinear tracking control design for a synchronous generator machine. The design involves full system identification and nonlinear filtering of the system state, to permit effective implementation of a nonlinear controller based on differential flatness of the model. The difficulty associated with robust implementations of flatness-based controllers resides in the necessity of fast and accurate estimation of higher order derivatives of the noisy, observed flat output. The recently developed forward-backward kernel estimation methods [1], lend themselves powerfully for this task. Two LTI surrogate models are used with the nonlinear model of the machine to serve identification and filtering of the state, and are switched seamlessly to generate persistent excitation for the purpose of a complex nonlinear identification of all the system parameters. The approach does not require a separate start-up phase for identification purposes. The need for on-line adaptive identification and associated re-tuning of the controller is detected and implemented during full operation of the machine. Neither the identification nor the state estimation procedures need any re-initialization while rendering improved accuracy of derivative estimates due to the forward-backward smoothing feature of the kernels involved.

This note extends previous results pertaining to algebraic state and parameter estimation of linear systems based on a special construction of kernel system representations that incorporate system differential invariants. Main results include explicit expressions for the kernel functions for single-input, single-output LTI systems of arbitrary order. A recursive regression type algorithm is also proposed for the purpose of joint system identification and finite interval filtering. As compared with previous results the proposed non-asymptotic estimation method proves remarkably robust to Gaussian noise in output measurements. The approach has been shown to extend to linear time-varying and linear parameter-varying systems in a multivariate setting. The idea of system-related kernels can further be employed to enhance convergence properties of moving-window and minimum energy nonlinear filtering methods.

This note explains the advantage of employing integral system representations of linear systems in application to state and input estimation for a broad class of LPV systems. Integral (kernel) representations of linear systems are seen as vehicles for retaining local information about the system's input-output behaviour in which the notion of initial or boundary conditions play no role. An important by-product of kernel system representations is their capacity for reconstruction of time derivatives of the measured system output by using the kernel derivatives. These attributes immediately lead to the construction of kernel-based dead-beat state and parameter estimators for linear systems. The approach is extended here to LPV systems with measured scheduling parameters, or else LPV systems in which the scheduling parameters cannot be measured directly, but whose values may be inferred from the output observations of other, possibly nonlinear, dynamical systems. It is shown that the lack of knowledge of the system input does not prejudice successful state estimation provided that the system has strong observability properties that effectively permit input reconstruction in a suitable B-spline basis. The method also applies to nonlinear smooth systems that can be transformed to LPV systems with dynamically varying parameters. An example of a strongly nonlinear system is presented for which the extended Kalman filter is known to fail.

Necessary conditions of optimality for several types of problems with constraints on the terminal control, state and its time derivatives are obtained using a direct variational approach under strong regularity conditions. The properties of attainable sets for selected classes of admissible controls are discussed for the case of linear time-invariant systems. The results are used to achieve control allocation in an over-actuated linear quadratic tracking problem that accomplishes gear-shifting in a multi-speed transmission system (MSTS) of an electric vehicle. The application of the results extends to optimal control problems in which imposing terminal constraints on the state, its time derivative, or controls is necessary to guarantee smooth system operation over extended periods of time, or else seamless blending of operational modes.

A kernel functional representation of linear time-varying systems is employed in conjunction with B-spline functional approximation techniques to construct non-asymptotic state and parameter estimators for LTV systems. Total observability of the estimated system must be assumed. Practical identifiability conditions for parametric estimation are also stated. In the absence of output measurement noise the observer provides almost exact reconstruction of the system state and delivers high fidelity functional estimates of the time varying system parameters. It also shares the usual superior features of algebraic observers such as independence of the initial conditions of the system and good noise attenuation properties. Other advantages of the kernel and B-spline based identification of linear time-varying systems are elucidated.

An approach similar to variational data assimilation is presented for simultaneous model and state estimation of linear systems from output measurement perturbed by large noise of unknown characteristic. Estimation can be carried out over time windows of arbitrary length and is based on a special kernel representation of a hypothetical linear model. A functional criterion is employed to assess the distance of a model to a data cloud. The reproducing kernel representation of the model is particularly advantageous as it permits a Hilbert subspace representation of the model sought. The approach is similar to variational data assimilation as it relies on the adjoint method for calculation of the gradient of the cost functional involved.

The effect of reflection probe displacement on human skin spectra in the different points of hand in the range of 400–1000 nm was investigated. It was found that absorption in the human skin decreases with pressure increasing due to the optical and physiological properties of the skin. During conducting spectrophotometric research of human skin the influence of pressure should be taken into account.

A double-sided input-output kernel functional representation is developed for the class of totally observable linear time-varying systems with inputs. The double-sided kernel representation is immediately applicable as part of a non-asymptotic state observer for observable LTV systems. In the absence of output measurement noise the observer provides exact state values of the system state in arbitrarily short time. It also shares the usual superior features of algebraic observers such as independence of the initial conditions of the system and good noise attenuation properties. Other advantages of the double-sided input-output kernel functional representation of linear systems are elucidated as the concept can be employed to construct state and parameter estimators for flat nonlinear systems.

The novel adaptive multiple-model target tracking algorithm presented here employs a non-asymptotic state and parameter estimator whose design hinges on a non-standard integral system representation. The same estimator can be used for target maneuver detection and isolation and hence constitutes the principal ingredient of the tracking algorithm. The algorithm does not maintain a model bank, but creates and identifies new models in an attempt to best track the measurement data. Such an approach is rendered uniquely possible by the fact that the state and parameter estimator is essentially dead-beat. Practical model identifiability, persistent excitation condition for the measured signal are discussed. Although this first version of the algorithm is deterministic and employs threshold-based maneuver detection, it exhibits good robustness with respect to Gaussian measurement noise.

This paper presents a design of a robust tracking controller for a quadrotor UAV. A sliding mode controller (SMC) is employed in which the unmatched perturbations and uncertainties are estimated by a nonlinear disturbance observer (NDO). The NDO is employed to estimate the effect of wind perturbations and unknown aerodynamical forces. Several sliding surfaces are designed to explicitly depend on the estimate of the perturbation. Global asymptotic tracking of the desired position and yaw angle of the quadrotor is achieved in closed loop where the controller gains are selected to satisfy conditions that follow from stability analysis of the system. The proposed method has two important properties. First, the chattering phenomenon is considerably attenuated by selecting the switching gains to exceed the bound on the disturbance estimation error rather than a prior bound for the disturbance. Second, the nominal performance of the introduced method is close to the performance of a traditional sliding mode controller in the absence of disturbances. The system performance exhibits much better robustness than the conventional SMC method without the nonlinear disturbance observer.

In this paper, we evaluate a multi-objective-moving-horizon-optimization (MO-MHO) approach as an instrument for improvement of economic dispatch in microgrids. In particular, we investigate the effect of adaptation of the multi-objective optimization strategy used in the moving horizon framework on the end result of the economic dispatch. Power dispatch in microgrids is inherently a high-dimensional problem often cast as a mixed-integer stochastic program with conflicting objectives. Implied is the fact that exhaustive exploration of the whole Pareto front in a related multi-objective approach is not a computationally tractable decision tool. It is thus argued that it is preferable to represent the problem as a bi-objective optimization problem with the two costs grouping the least conflicting components. The solution method for the optimization problem over a window “looking into the future” can then be selected or adjusted in real time by employing an independent set of assessment functions evaluated along the trajectories of optimal multi-objective solutions already implemented over a window in the past. The proposed strategy is applied to a case study of a remote microgrid. Its performance is evaluated based on simulation results that suggest that choosing the compromise solution of the MO-MHO problem may be superior to the usual scalarization methods.

It is shown how differential invariance can be used to extract an underlying signal from its noisy measurement towards constructing a non-asymptotic state estimator for linear systems. While the model of the system is assumed known, the noise can have arbitrary characteristics. The differential invariance is rendered by the Cayley-Hamilton theorem and the system is represented in terms of a output reproducing functional on a Hilbert subspace. High accuracy, full state estimation of the system is achieved over arbitrary time intervals by way of orthogonal projection onto the subspace that represents the system invariance. Although the results are presented here primarily with reference to SISO LTI systems they readily extend to LTV systems with multiple outputs.

A novel robust tracking controller for a quadrotor UAV is designed that is capable to compensate the unknown unmatched uncertainties in the system model. A backstepping approach is employed in which such perturbations are estimated on-line by a nonlinear disturbance observer (NDO). The NDO is employed to estimate the effect of wind perturbations and unknown aerodynamical forces. Global asymptotic tracking of the desired position and yaw angle of the quadrotor is achieved in closed loop where the controller gains are selected to satify conditions that follow from stability analysis of the system. The system performance exhibits much better robustness than the existing backstepping control methods which are not equipped with nonlinear disturbance estimators.

We present mid-infrared (mid-IR) supercontinuum (SC) generation from a stepindex fluorozirconate fiber pumped at 2.25 mu m. When 70 ps pulses with a peak power of 128 kW were launched into a ZBLAN fiber with a zero dispersion wavelength at 1.603 mu m, SC was extended to 4695 nm, with spectral flatness below 10 dB, in the 2.00-4.56 mu m range. To the best of our knowledge, this is the first time such a broad and flat mid-IR SC spectrum from a ZBLAN fiber pumped with ps pulses has been achieved. The SC evolution as a function of pump wavelength and pump pulse energy was also experimentally demonstrated. Web of Science 9 2 art. no. 3200207

Application performance on these processor array platforms is highly sensitive to how functionality is physically placed on the device, as this choice crucially determines communication latencies and congestion patterns of the on-chip inter-core communication. The problem of identifying the best, or just a good enough, partitioning and placement does not, in general, admit to an analytic solution, and its combinatorial nature makes solving it by pure experimentation impractical. This paper presents an approach that maps stream programs onto processor arrays using trace analysis as a technique for evaluating candidate solutions and for suggesting alternatives.

An important challenge for a dataflow designer is to efficiently explore the design space in order to find a set of configurations that satisfy the defined objective function. The exploration directions may involve the partitioning, scheduling and buffer dimensioning, and all together should drive the designer to maximally benefit from the potential parallelism of an application. Successful exploration can be strongly facilitated by means of performance estimation. This paper presents a tool that allows a high-precision estimation of a program execution on a given platform, when various sets of configurations can be applied. It demonstrates which information related to the multi-core program execution can be extracted and successfully used to drive the optimization procedures. The experimental results are confirmed by an actual execution on different types of platforms.

The exploration of different design configurations of dynamic dataflow programs executed on many-core or multi-core platforms is, in general, a very difficult task. Determining a close-to-optimal partitioning, scheduling and buffer dimensioning configuration, when associated with a performance optimization function, belongs to the class of NP-complete problems. In order to explore the space of feasible solutions with efficient heuristics looking for solutions of good quality, it is important to be able to evaluate the design points in terms of the performance optimization function with sufficient precision without having to physically execute the program on the platform. This paper presents a performance estimation approach and an associated SW tool capable of exploring, with a high level of accuracy, the space of feasible solutions by using only a limited set of measurements from the physical processing platform. Moreover, the estimation model allows an identification of possible improvements that can be applied to different configurations. The results reported validate the accuracy of the methodology using examples of dataflow implementations of dynamic video codec designs for two different classes of platforms: Transport Triggered Architecture and Intel platforms.

In this work ion-selective arrays based on spray-coated electrolyte-gated carbon-nanotube field-effect transistors will be used in an example application for water quality assessment. Several cations like Na+, K+, Ca2+ and anions like Cl− and NO 3 − are detected with the ion-selective sensor arrays in different bottled mineral waters. For the ion-selective arrays the sensing results of two different array configurations will be compared: an array with six transistor structures and one common gate electrode or six different planar gate electrodes with one common read-out CNT-FET. These ion-selective sensor array will have various applications in the fields of biomedical diagnostics, soil-quality testing and water-control measurements.

We will present details of optical properties of silver nanoparticles in titanium dioxide host (Ag-TiO 2 ) plasmonic absorbers. These nanocomposites were fabricated using electron beam physical vapour deposition technique. Components were deposited sequentially. We varied several parameters of the structures and fabrication process including silver and TiO 2 layer thickness, number of layers, deposition temperature and post fabrication annealing. Utilizing the semicontinuous nature of thin silver films we achieved structures with broadband absorption spanning across visible and near infrared. The level and spectral width of absorption can be tuned by design. Such composites could be used as efficient absorbers in sunlight to heat conversion applications.

We report on an all-fiber, fast gain-switched, Tmdoped fiber laser and amplifier system operating at 1994.4 nm. For 26 kHz repetition rate, stable 25-ns pulses with an energy of 0.28 mJ corresponding to a peak-power of 10.5 kW were achieved. Gain-switched pulses with simultaneous mode-lockedlike operation were also demonstrated. The peak-power of the highest subpulses from the train was over 30 kW. Keywords—fiber lasers, gain-switching, pulse amplification

We report on mid-infrared supercontinuum generation in a step-index fluorozirconate fiber. The output power of over 5 W in a 3-octave spanning spectrum is presented. Keywords—supercontinuum generation, fiber-based systems