An Adaptive Sliding Mode Control for a Dual Active Bridge Converter With Extended Phase Shift Modulation by Farzaneh Bagheri (17947811)

“…The conventional single-phase shift (SPS) modulation-based DAB converter is known to be inefficient. Hence, an optimization algorithm based on the Lagrange multiplier method (LMM) is proposed to minimize both backflow power and inductor current stress simultaneously. …”

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain by Farshad Rahimi Ghashghaei (20880995)

“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …”

Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems by Khaled Dhibi (16891524)

Scatter search for homology modeling by Mansour, Nashat

“…Homology modeling is an effective technique in protein structure prediction (PSP). …”
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Scatter search metaheuristic for homology based protein structure prediction. (c2015) by Stamboulian, Mouses Hrag

“…To improve homology based PSP, we propose a scatter search (SS) metaheuristic algorithm. Our algorithm optimizes the initial poor alignments, generated by a dynamic programming method. …”
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Multidimensional Gains for Stochastic Approximation by Saab, Samer S.

“…The two algorithms assume full knowledge of the Jacobian. The recursive algorithms are proposed for generating the optimal iterative-dependent matrix gain. …”
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Improving Active Resonance Damping and Unbalanced Voltage Mitigation Based on Combined DDSRF and Washout Filter in Islanded Microgrids by Ehsan Akbari (10803914)

“…The presented control algorithm can perform optimally under dynamic changes of active and reactive power of nonlinear and imbalanced loads. …”

The bus sightseeing problem by Qian Hu (205735)

“…A mixed-integer programming formulation for the BSP is provided and solved by a Benders decomposition algorithm. For large-scale instances, an iterated local search based metaheuristic algorithm is developed with some tailored neighborhood operators. …”

SDODV. (c2018) by Assi, Maram

“…Our proposed adaptive algorithm increases effectively the lifetime of the built network by considering the network topology when establishing a route. …”
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State-of-Charge Estimation Using Triple Forgetting Factor Adaptive Extended Kalman Filter for Battery Energy Storage Systems in Electric Bus Applications by Mena S. ElMenshawy (17983807)

“…Therefore, accurate battery state-of-charge (SoC) estimation is essential in effectively monitoring and controlling the BESS stability. …”

Design and analysis of entropy-constrained reflected residual vector quantization by Mousa, W.A.H.

“…Residual vector quantization (RVQ) is a vector quantization (VQ) paradigm which imposes structural constraints on the encoder in order to reduce the encoding search burden and memory storage requirements of an unconstrained VQ. Jointly optimized RVQ (JORVQ) is an effective design algorithm for minimizing the overall quantization error. …”
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Optimisation of PV Cleaning Practices: Comparison Between Performance Based and Periodic Based Approaches by ALHAJERI, RASHED ABDULLA

“…It was also important to note that higher soiling did not correlate to higher cleaning requirements, as the cleaning was dependent not only on the soiling but on the PV output and cleaning costs. The algorithm was found to be an effective yet simple tool to help PV owners decide on cleaning procedures, however it does not take into account the effect of natural cleaning events. …”
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Framework for rapid design and optimisation of immersive battery cooling system by Ali Almshahy (23544823)

“…Two key parameters are optimised, namely: battery gap spacing (3–10 mm) and inlet/outlet width (5–15 mm), via Optimal Latin Hypercube Sampling, Support Vector Regression, and GDE3 algorithm. …”

Enhancement of power system dynamic performance through an on-line self-tuning adaptive SVC controller by Rahim, A.H.M.A.

“…The controller has been tested for ranges of operating conditions and for various disturbances. The effectiveness of the adaptive damping controller was also evaluated through an ‘optimized’ PI controller.…”
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Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes by Muhammad Mohsin Khan (22150360)

“…Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”

Studying cooperative multi-agent reinforcement learning in networks by Oudah, Mayada Mohamed

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Performance Modeling of Rooftop PV Systems in Arid Climate, a Case Study for Qatar: Impact of Soiling Losses and Albedo Using PVsyst and SAM by Sachin Jain (19161721)

“…The research addresses a key limitation in existing PV modeling practice: the restricted capability of standard software to represent site-specific soiling and dynamic albedo effects under arid climatic conditions. To overcome these limitations, the Humboldt State University (HSU) soiling model was calibrated using field measurements from a DustIQ sensor, and its parameters, rainfall cleaning threshold and particulate deposition velocity were optimized through a Differential Evolution algorithm. …”

Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

“…They often lack the adaptability required to respond to the dynamic and complex nature of healthcare environments, struggle with optimizing latency, and fail to address specific node capacity constraints key factors that impacts the effectiveness of healthcare applications. …”

Morphology for Planar Hexagonal Modular Self-Reconfigurable Robotic Systems by Sadjadi, Hossein

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A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security by S. Shitharth (12017480)

“…Then, the Conjugate Self-Organizing Migration (CSOM) optimization algorithm is deployed to select the most relevant features to train the classifier, which also supports increased detection accuracy. …”