Many tools support exploration and analysis of various run-time properties of programs. However, the results produced by these tools are often discarded or stored in custom external files. We discuss an approach where data obtained when executing the program are written as Java annotations over program elements. First, the viability of this approach is demonstrated by a feature annotator, which assigns features to corresponding methods. Second, we describe other types of run-time information that we consider suitable for inclusion in the source code. We also suggest two possible workflows how generated annotations can be used in the development process.

The article describes and compares two different approaches to haiku poetry generation. First approach uses interactive evolution to create poems and the second composes poems from haiku models. The goal is to create poems, considered by humans as understandable and with aesthetic value. Two systems were created, one implementing each approach.

This article describes the non-linear phenomenon SPM (Self Phase Modulation) located in all-optical communication systems. WDM systems are commercially available and providers use it without simulation. Also some experimental setups are available and simulation is not necessary because it offers real tests. The most important WDM components are AWG (Arrayed Waveguide Grating) multiplexer and demultiplexer where the multiplexing and demultiplexing of the optical signals happens. 10 Gbit/s optical line of the DWDM (Dense Wavelength Division Multiplex) system in accordance with ITU-G.694.1 was created and the SPM phenomenon is observed at it.

A new numerical method to calculate the inverse hysteresis model using few experimental data extracted from the inverse first magnetization curve and based on scalar Preisach model is described. This modeling approach is applied to the case of soft ferromagnetic materials and allows the prediction of the necessary applied magnetic field value in order to obtain a desired magnetization function. In this paper, we first show how to model the inverse magnetic hysteresis behavior using an approach of the scalar Preisach type and the first magnetization curve [1-2] and then, the determination of the inverse density function using few experimental data extracted from inverse first magnetization curve. Mathematical developments revealed a parameter denoted  used in the positioning and whose variation affects significantly the magnetic properties. Comparisons of the obtained results with experimental data allowed the identification of the  factor as the tenth of the ratio of induction (magnetization) at the bend of saturation to the induction (magnetization) of saturation. The inverse model is then integrated into a finite element code using an electrical device formed of a ferromagnetic core surrounded by a coil which is supplied by a sinusoidal current. The efficiency and applicability of the developed method have been tested by numerical simulations and comparisons with available experimental data.

The induction machine will play a role very important in the industry, but the existence of a certain defect returns their use limited as the defects rotor (broken bar). this article makes a comparative study of two Controller: adaptive Neural Fuzzy inference system (ANFIS) and neuronal with the existence of a rotor defect on the one hand and another hand of a defect of switch of the seven level inverter to see the influence of these two defects on the physical parameters of the machine. The application of ANFIS control with the existence of a broken bar in the motor allows us to see the effect of this fault on the motor parameters (speed, electromagnetic torque and current), to control itself is also used in existence of seven-level inverter fault (delay of blocking the switch) to give the results shown the swelling of this fault on the engine. After we passed to use the second control it is the neural control with the existence on one hand of the defect broken bar and on the other hand inverter fault, with this controller each fault influenced the parameters of Engine and can notice it from the simulation results.

This paper presents space vector based random discontinuous PWM techniques for two level three phase inverter fed induction motor drive. The pulse width modulation (PWM) based induction motor drives often produces objectionable acoustic noise apart from harmonic distortion. The proposed modulation techniques not only concentrate on harmonic reduction through reduced switching but also aim at acoustic noise reduction which causes inconvenience in certain applications such as electric propulsion for the drivers and passengers. The proposed algorithm is developed by using the imaginary switching times, which does not require angle and sector information. In order to get the randomization effect, the proposed algorithm uses discontinuous pulse width modulation algorithm in conjunction with the variable delay pulse width modulation (VDRPWM) algorithm. The proposed PWM algorithm uses 012 and 721 sequences results 33.33% reduction in inverter switching loss, as the zero state time is varied randomly according to the operating sequence, randomization effect will occur, which results in reduced total harmonics distortion and hence gives reduced acoustical noise. To validate the proposed PWM algorithm, the numerical simulation studies have been carried out and results are presented and compared. The simulation results confirm the effectiveness of the proposed algorithm.

Many induction motor speed controllers contain a speed or position sensor. This last is required to provide accurate measurement. A faulty speed sensor decreases the controller performance dramatically. Hence, a fault diagnosis and detection technique is necessary. This paper deals with several faults which affect the speed sensor used in an induction motor vector controlled drive. Three different faults are considered; offset fault, uncertainty of measurement, and total loss of feedback information. Then a detection strategy is suggested based on the computation of the energy of the average standard deviation of speed data. Simulation on Matlab/Simulink and experiments were carried out to show the effect of each fault on the vector control performance and to verify the effectiveness of the proposed detection scheme.

We present the implementation of intelligent software for image classification on the IBM Bluemix platform using the IBM Watson cognitive services. Our system is voice or text controlled cloud-based service accessible via implemented RESTful API. The core idea behind this system is the ability to learn new classifiers without the need of acquiring training data from the user. To this end, we developed multiple methods aiming to maximize classification accuracy of the system. The system uses web search services to download data used for training the classifiers. We have experimentally evaluated the service and proved that our software is capable of learning and creating new classifiers based upon the user entered keyword.