AFRC’s image processing platform : a high speed user-friendly architecture for real time object detection in forging processes

Real-time detection and measurements of hot parts is desirable to permit real-time process control and automated part handling in harsh environments like forging and forming. The image processing platform described provides improved performance of Graphical User Interface (GUI), fast processing speed and integrity over industrial packages which are typically used in process control of forging processes or harsh manufacturing environments. A flexible, image processing software package for detecting objects in manufacturing environments has been developed at the Advanced Forming Research Centre (AFRC). The software consists of a set of image processing tools, written in MFC/C++ and OpenCV for Windows platforms. The software provides a powerful flowchart-based GUI for designing image processing algorithms. AFRC’s Image Processing Platform can be easily integrated with other industrial software packages like GE Proficy® using ActiveX technology. The package was successfully tested for real-time hot object detection inside a hot furnace in a forging environment

Reinforcement Learning for Test Case Prioritization

Continuous Integration (CI) significantly reduces integration problems, speeds up development time, and shortens release time. However, it also introduces new challenges for quality assurance activities, including regression testing, which is the focus of this work. Though various approaches for test case prioritization have shown to be very promising in the context of regression testing, specific techniques must be designed to deal with the dynamic nature and timing constraints of CI. Recently, Reinforcement Learning (RL) has shown great potential in various challenging scenarios that require continuous adaptation, such as game playing, real-time ads bidding, and recommender systems. Inspired by this line of work and building on initial efforts in supporting test case prioritization with RL techniques, we perform here a comprehensive investigation of RL-based test case prioritization in a CI context. To this end, taking test case prioritization as a ranking problem, we model the sequential interactions between the CI environment and a test case prioritization agent as an RL problem, using three alternative ranking models. We then rely on carefully selected and tailored state-of-the-art RL techniques to automatically and continuously learn a test case prioritization strategy, whose objective is to be as close as possible to the optimal one. Our extensive experimental analysis shows that the best RL solutions provide a significant accuracy improvement over previous RL-based work, with prioritization strategies getting close to being optimal, thus paving the way for using RL to prioritize test cases in a CI context

A novel framework for Shot number minimization in Quantum Variational Algorithms

Variational Quantum Algorithms (VQAs) have gained significant attention as a potential solution for various quantum computing applications in the near term. However, implementing these algorithms on quantum devices often necessitates a substantial number of measurements, resulting in time-consuming and resource-intensive processes. This paper presents a generalized framework for optimization algorithms aiming to reduce the number of shot evaluations in VQAs. The proposed framework combines an estimator and an optimizer. We investigate two specific case studies within this framework. In the first case, we pair a sample mean estimator with a simulated annealing optimizer, while in the second case, we combine a recursive estimator with a gradient descent optimizer. In both instances, we demonstrate that our proposed approach yields notable performance enhancements compared to conventional methods.Comment: 20 pages, 11 figure

Pre-Abortion Decision-Making Conflict in Pregnant Women Seeking Legal Abortion

Background: Woman's decision-making for abortion entails understanding and assessing those options in the context of her unique situation, feelings, aspirations and beliefs. The objective of this study was to examine decision–making conflict and all relevant factors, among women seeking legal abortion authorization letter, referred to Legal Medical Centre in Tehran.Materials and Methods: In this cross-sectional study, decision-making conflict assessed using the decisional conflict scale (DCS) among 282 pregnant women in their first trimester. Descriptive and logistic regression analyses were undertaken to describe and explore collected data.Results: Eligible women requesting legal abortion were mostly in age group 25-34 years old (50.4% .142, M=31.55, SD=6.1, ranging from 17 - 46 years). They were mostly in gestational age<16 weeks, (212, 75.2%), with average 14.67 (SD=3.51), range 15.0 weeks (4-19 weeks). Some decision conflict (DCS score 25 or greater) was experienced by 182 (64.5%) participants.Conclusion: Women seeking legal abortion may go against their own sense of right and wrong. They deserve pre-abortion consulting to deal with conflict and negative effects in decision-making

Dynamic maximum power point tracking and robust voltage regulation for photovoltaic systems

This research proposes a Maximum Power Point Tracking (MPPT) and voltage regulation method based on model reference adaptive control (MRAC). The MPPT algorithm which is presented in this work is a modified perturb and observe (P&O) algorithm. The new algorithm prevents oscillation around maximum power point (MPP) by approximating the peak of photovoltaic (PV) array power curve. This goal is achieved by comparing the change in output power during each cycle with change in array terminal power during the previous cycle. When array terminal power decreases following an increase in the previous cycle or the opposite, a decrease in array terminal power is followed by an increase, it means the power curve has reached its peak. Therefore, the duty cycle of the boost converter should remain the same. When irradiance changes, the proposed technique produces an MPPT algorithm's average efficiency ( MPPT ) of nearly 3.1 percent greater than the conventional P&O and the Incremental conductance (InC) algorithm. In addition, under strong partial shading conditions (PSC) and drift avoidance tests, the proposed technique produces an average MPPT of nearly 9 percent and 8 percent greater than the conventional algorithms, respectively. To inject the generated PV power into the grid with high quality, this work designs voltage regulation controller based on MRAC to ensure the output voltage of the PV system is at the desired level. To achieve this goal, we propose a DC–DC boost converter that stabilizes output voltage variations by using MIT rule controllers. An output voltage is stabilized using two control loops, PID controllers are capable of regulating output voltage at fixed levels, and for the outer loop, it's intended to implement the direct model reference adaptive controller (DMRAC) MIT rule. In comparison with DC–DC boost converters connected to the micro-grid (MG), the controller presented here, manages disturbances and unknown parameter fluctuations more effectively. The proposed controller and the model are tested in MATLAB/SIMULINK for load disturbances. The load was changed by ~50% of its original value, and the worst-case settling time and maximum overshoot were less than ~0.1 s and 0.5 V (0.3%), respectively. Comparison with the PID methods, the lowest overshoot among three different PID tuning methods, namely the Ziegler–Nichol’s frequency-domain method, damped oscillation method, and Good Gain method, is 34%. Therefore, it is evident from results that the proposed algorithm has better performance in dealing with the maximum overshoot issues. The hardware validation is also carried out to show the performance of the proposed controller