Precision air conditioning (PAC) systems are prone to various types of failures, leading to inefficiencies, increased energy consumption, and possible reductions in equipment performance. This study proposes an automatic real-time fault detection and diagnosis system. It classifies events as either faulty or normal by analyzing key status signals such as pressure, temperature, current, and voltage. This research is based on data-driven models and machine learning, where a specific strategy is proposed for five types of system failures. The work was carried out on a Rittal PAC, model SK3328.500 (cooling unit), installing capacitive pressure sensors, Hall effect current sensors, electromagnetic induction voltage sensors, infrared temperature sensors, and thermocouple-type sensors. For the implementation of the system, a dataset of PAC status signals was obtained, initially consisting of 31,057 samples after a preprocessing step using the Random Under-Sampler (RUS) module. A database with 20,000 samples was obtained, which includes normal and failed operating events generated in the PAC. The selection of the models is based on accuracy criteria, evaluated by testing in both offline (database) and real-time conditions. The Support Vector Machine (SVM) model achieved 93%, Decision Tree (DT) 93%, Gradient Boosting (GB) 91%, K-Nearest Neighbors (KNN) 83%, and Naive Bayes (NB) 77%, while the Random Forest (RF) model stood out, having an accuracy of 96% in deferred tests and 95.28% in real-time. Finally, a validation test was performed with the best-selected model in real time, simulating a real environment for the PAC system, achieving an accuracy rate of 93.49%.

Hydroponics in recirculation systems faces significant challenges in regulating critical parameters, such as pH and electrical conductivity (EC), especially in adverse environmental conditions, such as high altitudes. This paper presents the design and validation of a fuzzy controller integrated with IoT for NFT-type hydroponic systems, implemented on low-cost hardware and tested in the city of Cusco (3339 m.a.s.l.). Unlike previous studies that are limited to simulations or laboratory tests, the proposal was validated under real growing conditions, demonstrating its practical viability. The system incorporates a fuzzy controller based on simple rules, an IoT module with ESP32 for remote monitoring via Blynk, and an accessible and replicable architecture. The results demonstrate stable performance in pH and EC regulation, with adequate response times, minimal overshoot, and reduced errors, achieving levels comparable to those of higher-cost commercial solutions. The main contribution of this study is the demonstration that an intelligent, economical, and replicable system can be applied in agricultural environments with limited resources, offering a viable alternative for improving productivity in high-altitude hydroponic systems.

This article presents the design, simulation, and real-time implementation of an intelligent water level control system using Deep Reinforcement Learning (DRL) with the Deep Deterministic Policy Gradient (DDPG) algorithm. The control policy was initially trained in a MATLAB-based simulation environment, where actor–critic neural networks were trained and optimized to ensure accurate and robust performance under dynamic and nonlinear conditions. The trained policy was subsequently deployed on a low-cost embedded platform (Arduino Uno), demonstrating its feasibility for real-time embedded applications. Experimental results confirm the controller’s ability to adapt to external disturbances. Quantitatively, the proposed controller achieved a steady-state error of less than 0.05 cm and an overshoot of 16% in the physical implementation, outperforming conventional proportional–integral–derivative (PID) control by 22% in tracking accuracy. The combination of the DDPG algorithm and low-cost hardware implementation demonstrates the feasibility of real-time deep learning-based control for intelligent water management. Furthermore, the proposed architecture is directly applicable to low-cost Internet of Things (IoT)-based water management systems, enabling autonomous and adaptive control in real-world hydraulic infrastructures. This proposal demonstrates its potential for smart agriculture, distributed sensor networks, and scalable and resource-efficient water systems. Finally, the main novelty of this work is the deployment of a DRL-based controller on a resource-constrained microcontroller, validated under real-world perturbations and sensor noise.

Conventional scalar voltage–frequency (V/f) control is widely adopted in industrial drives due to its simplicity and low cost; however, at very low operating frequencies, it often suffers from poor transients, speed oscillations, and excessive start-up currents when a proportional–integral controller (PIC) is used in the speed loop. These limitations hinder stable operation and reliable start-up under load, motivating the need for a more robust low-speed control scheme. This paper presents the design and experimental validation of an improved V/f scalar control based on a Mamdani fuzzy logic controller tuned by particle swarm optimization (PSO). A physical prototype was implemented using speed and current sensing and LN CO3636-3B that enables the control strategies, updates K_p and K_i through Mamdani inference in a discrete PI law, and drives a self-commutated inverter via PWM. Experiments were performed under variable conditions, demonstrating that the proposed controller at 6.06% of rated speed achieves stable regulation with M_p = 10% and t_s = 0.445 s, whereas the PIC exhibits instability with steady oscillations and M_p up to 44%. The proposed controller keeps the inrush current within the design limit below 1.615 A. Overall, the PSO-tuned Mamdani fuzzy controller improves low-speed robustness while respecting transient design constraints.

COVID-19 can be detected by molecular tests or medical imaging. Doctors use medical images such as chest X-rays of patients to detect COVID-19, however, it is not so easy to detect the disease just by looking at an X-ray, in this work a system based on Convolutional Neural Networks (CNN) capable of classifying X-ray images of healthy patients, pneumonia patients and COVID-19 patients. The images were taken from the Kaggle open access datasets, 6279 images were taken in total, 4175 for training, 949 for validation and 1155 for testing. All the images were transformed at a resolution of 224x224 pixels and each pixel value was normalized, then the images were equalized by histogram. In the training stage, a CNN with MobileNet architecture was used, which has 50176 inputs (pixels), a hidden layer of 1000 neurons and an output layer of three neurons corresponding to the classes healthy lung, lung with pneumonia and lung with pneumonia by coronavirus. The Grad-CAM tool was used for visualization. For the test set, a batch size of 1 was used, this for the model to evaluate and classify each image, in this set a accuracy of 99.48 % was obtained on average for all categories. Where the objective is to contribute with an auxiliary tool for the detection of COVID-19 based on CNN. Thus, the proposed system is capable of differentiating and classifying chest X-ray images of healthy patients, with pneumonia and with COVID-19, so the system is capable of detecting COVID-19 with good precision.

This paper presents the design and optimization, based on electromagnetic simulation, of a fifth-order bandpass filter centered at 30 GHz, implemented using Gap Waveguide (GWG) technology and pole-type cylindrical resonators, intended for applications in 5G communication systems and high-frequency satellite links. Unlike conventional Chebyshev designs reported in the literature, this study proposes an integrated methodology that combines theoretical synthesis, full-wave electromagnetic modeling, and multivariable optimization, while accounting for manufacturing constraints. The developed method encompasses the electromagnetic characterization of individual resonators, the extraction of cavity–cavity coupling parameters, and the complete implementation of the filter using full-wave electromagnetic simulations. A distinctive aspect of the proposed approach is the explicit incorporation of practical manufacturing effects, such as rounded corners induced by machining processes, alongside analytical and numerical geometric compensation procedures that preserve the device’s electrical response. Furthermore, a combination of gradient-based optimization algorithms and evolutionary strategies is employed to align the electromagnetic response with the target theoretical behavior. The results obtained through electromagnetic simulation indicate that the designed filter achieves return losses exceeding 20 dB and a fractional bandwidth of 4.95%, consistent with the reference Chebyshev response. Finally, the potential extension of the proposed approach to higher frequency bands is discussed conceptually, laying the groundwork for future work that includes experimental validation.