Currently, medical imaging has largely supplanted traditional methods in the realm of diagnosis and treatment planning. This shift is primarily attributable to the non-invasive nature, rapidity, and user-friendliness of medical-imaging techniques. The widespread adoption of medical imaging, however, has shifted the bottleneck to healthcare professionals who must analyze each case post-image acquisition. This process is characterized by its sluggishness and subjectivity, making it susceptible to errors. The anterior cruciate ligament (ACL), a frequently injured knee ligament, predominantly affects a youthful and sports-active demographic. ACL injuries often leave patients with substantial disabilities and alter knee mechanics. Since some of these cases necessitate surgery, it is crucial to accurately classify and detect ACL injury. This paper investigates the utilization of pre-trained convolutional neural networks featuring residual connections (ResNet) along with image-processing methods to identify ACL injury and differentiate between various tear levels. The ResNet employed in this study is not the standard ResNet but rather an adapted version capable of processing 3D volumes constructed from 2D image slices. Achieving a peak accuracy of 97.15% with a custom split, 96.32% through Monte-Carlo cross-validation, and 93.22% via five-fold cross-validation, our approach enhances the performance of three-class classifiers by over 7% in terms of raw accuracy. Moreover, we achieved an improvement of more than 1% across all types of evaluation. It is quite clear that the model’s output can effectively serve as an initial diagnostic baseline for radiologists with minimal effort and nearly instantaneous results. This advancement underscores the paper’s focus on harnessing deep learning for the nuanced detection and classification of ACL tears, demonstrating a significant leap toward automating and refining diagnostic accuracy in sports medicine and orthopedics.

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance R_s and combined inertia of rotor and load J) and changes in load torque T_L can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.

This work addresses the analysis and design of various Proportional-Integral-Derivative (PID) control techniques for a three-level inverter. Multilevel power converters are modern and basic elements of high-voltage electric drive and power supply systems. By using simulations and specific computer-aided design tools, the overall functional characteristics of multilevel converters, as well as the electrical demands of the components, can be accurately assessed to obtain an appropriate control solution. An innovative and detailed software model of a three-level inverter is developed and then used for the implementation of control techniques. Several tuning methods are used to tune PID controllers for two specific cases: the multilevel inverter with a linear load and with an asynchronous motor load, respectively. A detailed analysis and comparisons of the quality criteria and control performance are achieved. This analysis shows that the choice of controller type depends on the inverter load. For the linear load, proper results are obtained with a PI Nichols-tuned controller, and for the asynchronous load, with a PI controller tuned via a modified Hokushin method. The computer-aided design tools can be further used for the simulation of the equipment in various operating conditions, normal and fault, following all functional parameters.

The design and implementation of a simulator, as a real-time application, for a complex process from the biological treatment stage of a wastewater treatment plant (WWTP), is addressed. More precisely, this emulator was achieved as a software tool that can be later integrated into a more complex SCADA (supervisory control and data acquisition) system of the WWTP Făcăi, Romania. The basic idea is to implement and validate a reduced-order model of the activated sludge process (ASP), initially simulated in the Matlab/Simulink environment (The MathWorks, Inc., Natick, MA, USA). Moreover, an advanced multivariable adaptive control scheme of the ASP is addressed. This software tool can be made to work in parallel with the evolution of the process and can have as input signals measured directly at the process level, possibly following parametric or model adaptations. The software emulator is developed in the LabWindows/CVI programming environment (National Instruments), which offers low-level access to hardware or software systems that have minimal open-architecture facilities. This environment provides versatile drivers and software packages that can facilitate the interaction with software tools developed within some earlier SCADA systems. The structure and the graphical interface of the emulator, some functionalities, experiments, and evolution of main variables are presented.

As proved by the studies carried out but also from practical situations, the acoustic behaviour of an enclosed performance hall is strongly dependent on occupancy level. This happens because human bodies and upholstered seats contribute significantly to sound absorption and in addition modify the modal structure of the acoustic field. This work presents a complex finite element method (FEM) simulation, conducted in ANSYS Workbench 2023, which compares the acoustic performance of a concert hall under three discrete occupancy scenarios: 57, 43, and 28 attendees. Seven humanoid solid models were used to represent audience members and extract the air volume accurately from the hall geometry. Key acoustic metrics were evaluated, including acoustic pressure distribution, Sound Pressure Level (SPL), A-weighted SPL (dBA), far-field SPL, frequency band SPL, and frequency response phase angle, at representative frequencies of 400 Hz, 10,800 Hz, and 20,000 Hz. The obtained results demonstrate that occupancy reduction from 57 to 28 persons increases the effective modal density, elevates low-frequency standing wave contributions, reduces broadband absorption, and shifts the acoustic pressure distribution toward higher spatial non-uniformity. The results obtained provide quantitative assistance for the design of adaptive acoustic scenarios in spaces with variable occupancy.