University regulations and announcements at Bursa Technical University (BTU) are dispersed across PDF files, web pages, and notice boards, causing information retrieval inefficiencies for students and administrative staff. This study presents the development and quantitative evaluation of BTU-Chatbot, an Agentic Retrieval-Augmented Generation (RAG) powered conversational assistant that consolidates fragmented institutional information into a single citation-aware dialogue interface. Ninety-seven PDF documents were processed using PyPDFLoader and regular-expression-based preprocessing, then embedded into 1536-dimensional vectors using OpenAI’s text-embedding-ada-v2 model and stored in a 29 MB ChromaDB collection. The vector-based retrieval layer selects the three most relevant passages per query using cosine similarity search. The upper layer implements a LangChain-orchestrated multi-agent ReAct loop, in which the retrieve tool accesses the vector database while the Google_search_univ tool performs domain-restricted searches limited to the “*.btu.edu.tr” domain. GPT-4o-mini, with a 128k context window, serves as the generative backbone. System reliability was measured using the RAGAS metric suite. The best performing run achieved Context Recall = 0.97, Context Precision = 0.99, and F1- RP (the F1 score of Context Recall and Precision) = 0.954, demonstrating nearperfect retrieval accuracy. The average cost per query was 6.6×10⁻⁵ USD, with 7.6 s of latency for typical 124-token exchanges. BTU-Chatbot demonstrates that an Agentic RAG pipeline can deliver source-grounded, citation-attributed answers to university-specific queries at low operating cost, although further improvements in generation faithfulness are needed before full deployment.
This paper presents a two-stage adaptive noise cancellation system for noise cancellation of Hindi speech signals corrupted by babble and factory noise, under different input signal-to-noise ratio (SNR) levels. The corrupted signals with different SNR levels are passed through three different noise cancellation systems: an adaptive noise cancellation system with a fast block least mean squares algorithm (FBLMS) algorithm; a fully connected convolutional deep neural network (FCCDNN); and a two-stage system with an adaptive noise cancellation system consisting of the FBLMS algorithm followed by FCCDNN. The performance of each noise cancellation system is evaluated and compared. The parameters such as output SNR, perceptual evaluation of speech quality and shorttime objective intelligibility are used for testing the intelligibility and quality of the recovered speech signals. The proposed system shows a maximum SNR improvement of 13.997 and 15.2965 dB at an input SNR of −5 dB for factory noise and babble noise, respectively, with significant increments in both of the evaluation metrics. The proposed system outperforms speech enhancement systems based on FCCDNN and FBLMS in terms of all performance parameters.
This paper presents an analysis comparing theoretical model predictions and experimental measurements of the number of pixels on target (PoT) for unmanned aerial vehicles (UAVs) using multi-sensor electro-optical and infrared (EO/IR) systems. The primary objective is to evaluate the fidelity of theoretical range prediction models that use the number of PoT as a metric, comparing these calculated values against experimental field measurements. The data used in this analysis were acquired using both visible (VIS) and mid-wave infrared (MWIR) channels, with the target UAV positioned at known reference distances up to 900 m. The results quantitatively demonstrate the deviations between the two methodologies, providing critical insights into the practical limitations of performance prediction models for early drone detection systems operating under field conditions.
This article presents Pelican Optimization Algorithm (POA) for Load Frequency Control (LFC) in two area interconnected power systems. Extensive simulations in MATLindicate that the POA surpasses numerous established metaheuristic optimization algorithms, involving the Grey Wolf Optimizer (GWO), Hippopotamus Optimization Algorithm (HOA), Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), Teaching-Learning Optimization Algorithm (TLOA) and Whale Optimization Algorithm (WOA) regarding the mitigation of frequency deviations and improvement of dynamic response. The robustness of the POA based LFC is validated under scenarios of random load disturbances, varying system parameters, typical power system nonlinearities like generation rate constraints (GRC) and governor deadband (GDB), and with renewable energy sources. The studies highlighted in the article demonstrate superior adaptability and effectiveness of POA to optimally tune the gains of PID controller. The statistical analysis demonstrate that the proposed POA achieves convergence to the global optimum with reduced execution time ( : 12% less than the second best HOA) and exhibits lower mean (~15.2% less than the second best SSA) and standard deviation (~3.09% less than the second best HOA) over multiple independent runs. Stability analysis using Bode plots confirms that the proposed control strategy maintains adequate stability margins.
This paper utilizes the Parametric Magneto-Dynamic (PMD) model, enhanced with an inverse hysteresis formulation, to estimate iron losses in the time domain under arbitrary excitation waveforms. The proposed PMD model reproduces the intricate magnetization dynamics of advanced steel grades, facilitating a systematic investigation into how metallurgical and geometric material properties - such as lamination thickness, alloy composition, and grain morphology - influence the required spatial and temporal discretisation for accurate loss estimation. The paper evaluates the consequences of discretisation parameters in the PMD model on both the accuracy of the iron loss prediction accuracy and computational overhead. The findings establish rigorous guidelines on the required discretisation schemes for purely sinusoidal excitation and for nonsinusoidal regimes with significant harmonic content across a broad frequency spectrum.
