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Electrical devices in transmission and distribution networks are submitted to fast front transients. These transients are often a cause of a failure in the power system. To simulate these phenomena, it is necessary to detailly model all the components of the power system. This paper concentrates on studding transformer internal failures due to voltage stress that can occur when fast front electromagnetic wave travels through the transformer. To prevent a failure, an internal insulation of the transformer has to be dimensioned to sustain these transient phenomena. Therefore, it is necessary to use advanced wideband transformer models for its dimensioning. In this paper, a wide band transformer model based on limited transformer geometry is used to simulate evaluate the stresses on internal transformer insulation in the case of fast front transients. The model is validated for calculation of internal overvoltages with the test case from CIGRE brochure.

This paper describes the possibility of analytical friction factor generation using a Grey Box modelling approach. A preprocessor is used, which automatically collects measured data via a Smart Production Network and calculates the effective friction coefficient as a function of all significant process parameters of the Equal-Channel-Angular-Pressing (ECAP) process using a data-driven evaluation algorithm. The results of this calculation are then automatically implemented in an efficiently programmed finite element analysis. This Digital Shadow then serves as the basis for the implementation of a Digital Twin, which is described shortly at the end.

A grey-box model of an automated people mover system (APMS) is constructed in this paper. APMS is one of the urban transit constructions that is being constructed in Indonesia and used for connecting between terminals in the airport. The APMS model can be identified to obtain the mathematical representation of the full system by finding unknown parameters through the data measured from the installed system at the airport. Generalized reduced gradient (GRG) algorithm is operated to identify the model parameters. The model can then be used for verification and testing controller designs such as proportional-integralderivative (PID) or model predictive control (MPC), and energy-saving strategies. The obtained model is able to follow the dynamic of the real APMS with RMSE value 0.013.

Modelling the effects of solar irradiation plays an important role in various applications. This paper describes a semi-parametric (combined grey-box and spline-based), data-driven technique that can be used to model systems in which the solar gain depends on the sun position. The solar gain factor is introduced, i.e. the absorbed fraction of measured solar irradiation, and estimated as a continuous non-parametric function of the sun position. The implementation of the spline-based solar gain factor in a grey-box model framework is described. The method is tested in two case studies—in a model of the internal temperature of a dwelling in Aalborg, Denmark, and a model of the return temperature of a solar collector field in Solrod, Denmark. It is shown that the solar gain factor as a function of sun position is able to account for structural variations in solar gain that may occur due to factors such as shading obstacles and window or absorber orientation. In both test cases, the spline-based solar gain function improved the model accuracy significantly, and largely reduced structural errors in prediction residuals. In addition, the shape of the estimated function provided insight into the dynamics of the system and the local solar input characteristics. Accurate representation of such site characteristics was not possible with any data-driven method found in the literature. Besides the grey-box models used in this study, the solar gain factor can be used in a variety of data-driven models, for example in linear regression models.

This study investigates which product characteristics and sourcing location conditions (global or local) support efficient supplier integration in New Product Development (NPD) processes. A research model is suggested to analyse the relationship between procurement product characteristics, buyer-supplier project organisation and NPD results. Moreover, the moderating impact of the geographical sourcing location on the causal relationships in the model is assessed. Data from 209 research-intensive firms in German-speaking countries were used to test the research hypotheses using structural equation modelling. The findings propose that the complexity and importance of the product procured are crucial to the form of project organisation between the parties. Buyers and suppliers tend to collaborate to a greater extent when the complexity and importance of the procurement object are high. Collaborative project organisation influences the result of NPD, and this relationship is more effective when a local supplier is involved in the process.

The physiomic approach is now widely used in the diagnosis of cardiovascular diseases. There are two possible methods for cardiovascular physiome: the traditional mathematical model and the machine learning (ML) algorithm. ML is used in almost every area of society for various tasks formerly performed by humans. Specifically, various ML techniques in cardiovascular medicine are being developed and improved at unprecedented speed. The benefits of using ML for various tasks is that the inner working mechanism of the system does not need to be known, which can prove convenient in situations where determining the inner workings of the system can be difficult. The computation speed is also often higher than that of the traditional mathematical models. The limitations with ML are that it inherently leads to an approximation, and special care must be taken in cases where a high accuracy is required. Traditional mathematical models are, however, constructed based on underlying laws either proven or assumed. The results from the mathematical models are accurate as long as the model is. Combining the advantages of both the mathematical models and ML would increase both the accuracy and efficiency of the simulation for many problems. In this review, examples of cardiovascular physiome where approaches of mathematical modeling and ML can be combined are introduced.

We design and implement from scratch a new fuzzer called SIVO that refines multiple stages of grey-box fuzzing. First, SIVO refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic in the input size number of tests to infer the dependency of all program branches on the input bytes, and (b) it deploys a novel method for inverting branches by solving directly and efficiently systems of inequalities. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, SIVO refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that SIVO scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.

We design and implement from scratch a new fuzzer called Sivo that refines multiple stages of grey-box fuzzing. First, Sivo refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic number of tests in the input size to infer dependency of many program branches on the input bytes, and (b) it employs a novel method for inverting branches by solving a systems of inequalities efficiently. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, Sivo refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that Sivo scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.

We introduce a grey-box adversarial attack and defence framework for sentiment classification. We address the issues of differentiability, label preservation and input reconstruction for adversarial attack and defence in one unified framework. Our results show that once trained, the attacking model is capable of generating high-quality adversarial examples substantially faster (one order of magnitude less in time) than state-of-the-art attacking methods. These examples also preserve the original sentiment according to human evaluation. Additionally, our framework produces an improved classifier that is robust in defending against multiple adversarial attacking methods. Code is available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.

The 16th International Building Simulation Association (Building Simulation 2019), Rome, Italy, 2-4 September 2019

Fuzzing technologies have evolved at a fast pace in recent years, revealing bugs in programs with ever increasing depth and speed. Applications working with complex formats are however more difficult to take on, as inputs need to meet certain format-specific characteristics to get through the initial parsing stage and reach deeper behaviors of the program. Unlike prior proposals based on manually written format specifications, in this paper we present a technique to automatically generate and mutate inputs for unknown chunk-based binary formats. We propose a technique to identify dependencies between input bytes and comparison instructions, and later use them to assign tags that characterize the processing logic of the program. Tags become the building block for structure-aware mutations involving chunks and fields of the input. We show that our techniques performs comparably to structure-aware fuzzing proposals that require human assistance. Our prototype implementation WEIZZ revealed 16 unknown bugs in widely used programs.

Fuzzing is an effective technology in software testing and security vulnerability detection. Unfortunately, fuzzing is an extremely compute-intensive job, which may cause thousands of computing hours to find a bug. Current novel works generally improve fuzzing efficiency by developing delicate algorithms. In this paper, we propose another direction of improvement in this field, i.e., leveraging parallel computing to improve fuzzing efficiency. In this way, we develop P-fuzz, a parallel fuzzing framework that can utilize massive, distributed computing resources to fuzz. P-fuzz uses a database to share the fuzzing status such as seeds, the coverage information, etc. All fuzzing nodes get tasks from the database and update their fuzzing status to the database. Also, P-fuzz handles some data races and exceptions in parallel fuzzing. We compare P-fuzz with AFL and a parallel fuzzing framework Roving in our experiment. The result shows that P-fuzz can easily speed up AFL about 2.59&times, and Roving about 1.66&times, on average by using 4 nodes.

This paper presents a hybrid building modelling method with a reduced modelling and calibration effort. The method combines a physics-based model, which describes the general behaviour of the system, with a machine learning algorithm trained to correct the physics-based model’s systematic errors. To exemplify the method, a highly simplified grey-box model is used as the physics-based part and a Gaussian process as the machine learning part. It is shown that the hybrid model improves the temperature and energy predictions of the grey-box model while having a lower generalization error than the pure Gaussian process. Specifically, the hybrid approach achieved a day-ahead zone temperature prediction error ca. 0.1 K (RMSE) lower than the grey-box model. As for the energy prediction, the hybrid model obtained an error of 3% compared to 8% for the grey-box model. In comparison to the Gaussian process, the hybrid approach achieved better predictions in all cases. The improvements were especially high when the models were trained with small datasets: 0.7 K in the temperature prediction and 25 percentage points in the energy prediction.

With the need for automatic control based supervisory controllers for complex energy systems, comes the need for reduced order system models representing not only the non-linear behaviour of the components but also certain unknown process dynamics like their internal control logic. At the Institute of Energy Systems Technology in Offenburg we have built a real-life microscale trigeneration plant and present in this paper a rational modelling procedure that satisfies the necessary characteristics for models to be applied in model predictive control for grid-reactive optimal scheduling of this complex energy system. These models are validated against experimental data and the efficacy of the methodology is discussed. Their application in the future for the optimal scheduling problem is also briefly motivated.

In the software integration testing, the combined exploding problem was appeared for GUI (Graphic User Interface) software with many advantages. Based on the engineering thinking of staging and the feature of GUI software, the grey technique of integration testing for GUI Software based on message is proposed to solve this problem, in which, the white-box method is used to deal with the consistent and unified part and black-box method is mainly engaged to dispose the difficult and varied part of software testing. By analysis of basic GUI software, the application of this grey technique will improve the testing efficiency with 333% according to testing executed number, and the efficiency in terms of testing executed time will be 438%. An actual example in product quality monitoring software has verified the approach and its efficiency of the grey technique.

Low order grey-box models are suitable to be used in predictive controls. In real buildings in which the measured quantities are few the reliability of these models is crucial for the control performance. In this paper an identification procedure is analyzed to investigate the accuracy of different order grey-box models for short-term thermal behavior prediction in a real building, part of a living smart district. The building has a low number of zones and a single indoor temperature measuring point. The models are identified on the data acquired in 31 days during the winter 2015. The second order model shows the best performance with a root-mean-square error (RMSE) less than 0.5°C for a prediction horizon of 1-hour and a RMSE less than 1°C for a prediction horizon of 3-hours. ispartof: pages:2107-2112 ispartof: 8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016) vol:2016 pages:2107-2112 ispartof: The 8th International Conference on Applied Energy – ICAE2016 location:Beijing date:8 Oct - 11 Oct 2016 status: published

The study presents an attempt to develop a model for the management of indoor air quality based on real-time sensing of particulate matter (nano and micro particles) and volatile organic compounds. The development of the model used a grey box approach where the initial data on pollutant variation was collected during the experimental phase, and further applied to the pollutant mass balance model. The pollution sources have been analyzed in a controlled environment to obtain patterns of temporal variation, which have been approximated by mathematical functions. Approximations allowed the employment of pollutant mass balance model for determining the variation of pollutant source and further to modeling variation of pollutant concentration with changing air change rate. The proposed management approach can be applied to control indoor air quality in homes, assuring optimal utilization of the air handling unit in order to achieve the acceptable indoor air quality in the lowest time span and optimal energy use.

The use of grey-box models for short-time forecasting of buildings&rsquo, thermal behavior requires the determination of the models&rsquo, order since this order could influence the grey-box models&rsquo, performance. This paper presents an analysis of the optimal order of these models for different thermal conditions. The novelty of this work consists of considering the influence of the heating conditions on the determination of the performances of grey-box models. The analysis is based on experimental tests that were conducted in a room with different thermal conditions, related to the variation of the heating power. Experimental results were used for the determination of the optimal grey-box models&rsquo, order that minimizes the gap between the experimental results and the grey-box forecasting. Results show that the optimal grey-box models&rsquo, order depends on the buildings&rsquo, thermal conditions, but generally lies between two and three with an error less than 0.2 &deg, C and a fit percent greater than 90%.

Building energy management systems can reduce energy consumption for space heating in existing buildings, by utilising Model Predictive Control. In such applications, good models of building thermal behaviour is important. A popular method for creating such models is creating Thermal networks, based cognitively on naive physical information about the building thermal behaviour. Such models have lumped parameters which must be calibrated from measured temperatures and weather conditions. Since the parameters are calibrated, it is important to study the identi?ability of the parameters, prior to analysing them as physical constants derived from the building structure. By utilising a statistically founded parameter estimation method based on maximizing the likelihood function, identi?ability analysis can be performed using the Pro?le Likelihood method. In this paper, the effect of different sensor locations with respect to the buildings physical properties is studied by utilising likelihood pro?les for identi?ability analysis. The extended 2D pro?le likelihood method is used to compute two-dimensional pro?les which allows diagnosing parameter inter-dependence, in addition to analyzing the identi?ability. The 2D pro?les are compared with con?dence regions computed based on the Hessian.

This article presents a novel methodology for the determination of soiling appearance on photovoltaic panels by means of data analysis of their energy production and operating conditions. The proposed methodology is based on the generation of a daily-based grey box model for each supervised panel, fitted through the sequential quadratic programming optimization approach, and the evolution analysis of the fitted coefficients to determine the appearance of soiling through the calculation of its monotonic drift and slope. The presented approach has been developed in the framework of a CORFO R&D project and validated under real operating conditions in a utility-scale photovoltaic power plant of one axis mount, located in Chile.

Paper aims To shed light on how knowledge is shared between companies during a collaborative new product development (NPD) process. Originality It is the first study of different knowledge sharing levels for each stage of the NPD process in an inter-organizational context. Research method A research framework was created based on the literature. Guided by this framework, an in-depth case study was performed in a multinational company analyzing the knowledge sharing with the company’s supplier during a collaborative NPD process. Main findings Different levels of knowledge sharing were found at each stage of the NPD process. Implications for theory and practice The results complement previous works demonstrating that this phenomenon should not be considered as a binary state. By clearly presenting how knowledge sharing occurs at each phase of the NPD process, managers can be prepared and correctly implement tools to support NPD teams from both buyer and supplier companies.

An obfuscator $$\mathcal {O}$$O is Virtual Grey Box (VGB) for a class $$\mathcal {C}$$C of circuits if, for any $$C\in \mathcal {C}$$CźC and any predicate $$\pi $$ź, deducing $$\pi (C)$$ź(C) given ...

Optimisation based economic despatch of real-world complex energy systems demands reduced order and continuously differentiable component models that can represent their part-load behaviour and dynamic responses. A literature study of existing modelling methods and the necessary characteristics the models should meet for their successful application in model predictive control of a polygeneration system are presented. Deriving from that, a rational modelling procedure using engineering principles and assumptions to develop simplified component models is applied. The models are quantitatively and qualitatively evaluated against experimental data and their efficacy for application in a building automation and control architecture is established.

We study the application of Adaptive Control to estimate external disturbances in a Water Level System model, taking advantage of a constructed nonlinear grey box identification that allows to exploit its dynamics. The external disturbances will be specially related to valve manipulations considering limited sensing information using only water height sensors. Our methodology is twofold: (1) Using a grey box model to identify the plant under an operating point and, (2) using adaptive control to react against changes in the system or perturbations on the valve and still lead to a convergent and asymptotically stable controller.

Producción Científica, This paper presents a methodology for developing grey models of process systems, that is, models that, being based on fundamental principles and laws of nature, combine them with sub-models obtained from experimental data. The method follows two steps: the first one takes advantage of what is known, while the second uses the data and mixed-integer optimization algorithms to identify the structure and parameters of the remaining parts of the model. The method is illustrated in a challenging biotechnological process: the Acetone-Butanol-Ethanol (ABE) fermentation process., MINECO/FEDER Grant DPI2015-70975 (INOPTCON), EU H2020-SPIRE Grant Agreement nº 723575 (CoPro)

The dynamic model for HVAC (Heating, Ventilating and Air Conditioning) electricity consumption is estimated for a campus building in Daido University. In order to construct energy management system...

System identification is now a well established approach for the development of control-oriented models in the rotorcraft field (see, e.g., the survey paper Hamel and Kaletka (1997), the recent books Tischler and Remple (2006), Jategaonkar (2006) and the references therein). Though the application to full scale rotorcraft is by now fairly mature, less experience has been gathered on small-scale vehicles, such as, e.g., quadrotors. This paper deals with the problem of characterizing the attitude dynamics of a variable-pitch quadrotor from data and presents the results obtained in an experimental identification campaign. More precisely, on-line and off-line methods have been considered and the performance of black-box versus grey-box models has been compared.

There exists various ways of modeling and forecasting photo-voltaic (PV) systems. These methods can be categorized, in board-way, under either definite equations models (white or clear-box) or heuristic data-driven artificial intelligence models (black-box). The two directions of modeling pose a number of drawbacks. To benefit from both worlds, this paper proposes a novel method where clear-box model is extended to a grey-box model by modeling uncertainities using focused time-delay neural network models. The grey-box or semi-definite model was shown to exhibit enhanced forecasting capabilities.

Specific insulation coordination studies might require an accurate modeling of power transformers. This is the case for instance when transfers of fast front overvoltages from the high voltage side to the low voltage side of a transformer are involved. Modeling accurately the transformer’s electromagnetic behavior for the frequency range of fast front overvoltages requires special care. There are numerous existing transformer models which are intended to model the wide band transformer’s behavior. However, many of them are too complex or require confidential information about the transformer design. This makes them not suitable for power engineering applications. In this paper a wide band transformer model based on limited information about the transformer design and compatible with an electromagnetic transients program is presented. Then, its application on calculating internal overvoltages distribution along transformer windings, taking into account different conditions on the outer transformer terminals, is shown for lightning impulse.

The design of countermeasure works to mitigate landslide risk needs to deal with the multiple unknowns that are linked with soil properties, distribution and rheology. Most of the time, the degree of definition of all these elements is low. Through landslide monitoring, it is possible to acquire signals from the landslide that carry synthetic information about its dynamic. Thus, if it is possible to define a model that is able to link the landslide displacements with the triggering factors and to predict them consistently, that model may be used to evaluate the effect of a countermeasure work directly, bypassing the geomechanical uncertainty. In this paper, an example application of this approach is described. The displacements of a landslide located in North East Italy are connected with the water discharge of the small stream the crosses the landslide body. A countermeasure work that intercepts the discharge of the torrent is expected to reduce the landslide displacements of approximately 70%, with lower costs of construction and smaller impacts on the environment and landscape with respect of other types of structural mitigation works such as slope reprofiling and large retaining walls.

In the present paper, a flexible and parsimonious model of the vibrations of nonlinear mechanical systems is introduced in the form of state-space equations. It is shown that the nonlinear model terms can be formed using a limited number of output measurements. A two-step identification procedure is derived for this grey-box model, integrating nonlinear subspace initialisation and maximum likelihood optimisation. The complete procedure is demonstrated on the Silverbox benchmark, which is an electrical mimicry of a single-degree-of-freedom mechanical system with one displacement-dependent nonlinearity., Comment: Presented at the 17th IFAC Symposium on System Identification SYSID 2015 - Beijing, China, 19-21 October 2015