Scientia Iranica
Volume:31 Issue: 2, Jan-Feb 2024

To address the problems of automatic repair techniques, we present Doctor Code, a new APR technique that chooses repair operators by systematically learning from the features of the most common bugs in different programs, using machine learning. The wise selection of repair operators reduces the number of candidate patches. We compare our technique against Mutation repair, a test suite-based APR technique, using the Siemens suite. The experiment results indicate that our technique can fix 41 bugs while the baseline only repairs 22. In addition, Doctor Code can produce patches that do not exist in the search space of the three test suite-based techniques called SPR, Prophet, and SemFix. We also experiment with Doctor Code utilizing three buggy versions of a program called Space (9K LOC), to indicate its capability of repairing large-sized programs. In addition, we compare Doctor Code against 7 state-of-the-art APR tools like Elixir, using the Defects4j dataset. The experiment results indicate that our technique outperforms the other tools regarding the number of fixed bugs and overfitted patches.Comparing Doctor Code with RAPR as the baseline indicates that using machine learning reduces the number of overfitted patches and the time of patch production by 33.33% and 82.68%, respectively.

Human behavior analysis and visual anomaly detection are important applications in fields such as video surveillance, security systems, intelligent houses, and elderly care. People re-identification is one of the main steps in a surveillance system that directly affects system performance; and variations in appearance, pose and scene illumination may be challenging issues for such system. Previous re-identification approaches faced limitations while considering appearance changes in their tracking task. This paper proposes a new approach for people re-identification using a descriptor which is robust to appearance changes. In our proposed method the enhanced Gaussian of Gaussian (GOG) and the Hierarchical Gaussian Descriptors (HGDs) are employed to extract feature vectors from images. Experimental results on a number of commonly used people re-identification databases imply the superiority of the proposed approach in people re-identification compared to other existing approaches.

Computational creativity modeling, including concept combination, enables us to foster deeper abilities of AI agents. Although concept combination has been addressed in a lot of computational creativity studies, findings show incompatibility amongst empirical data of concept combination and the results of the used methods. In addition, even though recent neuroscientific studies show the crucial impact of retrieving concepts’ relations explicitly stored in episodic memory, it has been underestimated in modeling creative processes. In this paper, a quantum cognition-based approach is used to more effectively consider the context and resolve logical inconsistencies. Also, episodic memory is leveraged as the basis for the concept combination modeling process based on the created context. The result of the proposed process is a set of meaningful concepts and expressions as a combination of stimuli and related episodes which are used to depict a visual collage as an image. The significant improvement in the quality of results in comparison with the existing methods suggests that quantum-like modeling can be considered as the foundation for developing AI agents capable of creating artistic images or assisting a person during a creative process.

This paper establishes a model-free finite-time tracking control of nonlinear robotic manipulator systems. The proposed controller incorporates both time delay estimation (TDE) and an enhanced terminal sliding mode control (TSMC). The improved TSMC scheme is devised using fractional-order TSMC (FOTSMC) and proportional-integral-derivative (PID) control to obtain robust tracking and high control performance. The TDE is designed to estimate the unknown nonlinear dynamics of robotic manipulators including the Stribeck friction and the external disturbances. Due to Stribeck friction, the effect of TDE error may fail to obtain the desired error performance; thus, another TDE loop is devised to compensate for TDE error generated by non-smooth frictions. The Lyapunov criterion is used to investigate the finite-time stability to analyze the behavior of the designed approach. Finally, computer simulations of the proposed method on PUMA 560 robotic manipulators are performed in contrast with FOTSMC and adaptive fractional-order nonsingular terminal sliding mode control (AFONTSM).

The purpose of this paper is to present a new hybrid analytical model (HAM) based on winding function theory (WFT) for electromagnetic analysis of performance of one typical unbalanced two-phase induction motor (UTPIM). Different indexes of electromagnetic modeling such as winding distribution, slotting effect, and magnetic saturation can be accurately considered by using the proposed HAM. For obtaining this new hybrid technique, the winding function theory is reformulated for considering the magnetic saturation in addition to the influence of slotting and winding distribution. The conformal mappings (CMs) are used to accurately calculate the slotted air-gap length. The magnetic equivalent circuit (MEC) model is used to consider the magneto-motive force (MMF) drop in iron parts of stator and rotor due to the excitation of one phase-winding. The obtained results of CMs and MEC are then utilized in reformulated WFT to calculate the inductances of respective phase-winding. Transient analysis is then done to calculate the indexes of performance such as air-gap magnetic field, phase currents, electromagnetic torque, and rotor speed by using the lookup table of inductances while considering different capacitors in auxiliary phase. In each step, the accuracy of analytical results is verified by comparing with corresponding results obtained through FEM.

Penetration of harmonics from downstream low voltage networks and resonance phenomenon has caused the level of harmonics to increase in transmission networks. Compensation of these harmonics at high voltage levels is neither recommended nor cost effective. This is due to the fact that there is no single large non-linear load which is causing these harmonics, yet filtering at low voltage non-linear load sites cannot completely compensate the harmonics and the remaining harmonic at standard levels still flow into the upstream networks. The idea of controlling harmonics at a medium/high voltage level caused by the remaining harmonics flown from low voltage networks and possibly amplified by resonance conditions with the use of series active power filters has been proposed by the authors in a published work. Since, this application of a series active filter was new, it was preferred to assign a new name for such device, i.e. harmonic power flow controller or simply HPFC. This work looks into the control strategies applicable to a HPFC in more details. The shortcomings of the previous methods are now removed resulting in better performance of a HPFC.