Images of brain tumors may only show up in a small subset of scans, so important details may be missed. Further, because labeling is typically a labor-intensive and time-consuming task, there are typically only a small number of medical imaging datasets available for analysis. The focus of this research is on the MRI images of the human brain, and an attempt has been made to propose a method for the accurate segmentation of these images to identify the correct location of tumors. In this study, GAN is utilized as a classification network to detect and segment of 3D MRI images. The 3D GAN network model provides dense connectivity, followed by rapid network convergence and improved information extraction. Mutual training in a generative adversarial network can bring the segmentation results closer to the labeled data to improve image segmentation. The BraTS 2021 dataset of 3D images was used to compare two experimental models. Each of the mentioned imaging techniques is specific to certain tissues in the body. The MRI technique provides clinicians with crucial information on the type, size, shape, and location of brain tumors without exposing the patient to dangerous radiation. Meningioma and glioma are fatal brain tumors that can be discovered via magnetic resonance imaging studies . If tumoGlioma is the most common kind of brain cancer in humans. The World Health Organization (WHO) divides tumors into four types . Low-levThe present categorization of methods for brain tumor segmentation can be classified into many groups according to unique conceptual frameworks. The categorization of brain tumor segmentation methods is commonly based on the level of human involvement, resulting in three distinct categories: manual, semi-automated, and fully automatic . ProficiDeep learning is a subfield within the broader discipline of Machine Learning (ML) that is employed to model complex problems and abstract notions. Deep learning (DL) facilitates the training of models that consist of numerous layers of processing, known as deep neural networks, which have the ability to acquire abstract representations of data. The assessment of conceptual qualities as nonlinear functions of low-level features is facilitated by the multilayer nature of DL networks. Convolutional Neural Networks (CNN), Restricted Boltzmann Machines (RBM), Deep Belief Networks (DBN), Deep Auto-Encoders (DAE), Recurrent Neural Networks (RNN), and their derivatives, including Long Short-term Memory (LSTM), are considered to be very valuable Deep Neural Networks (DNNs). These networks are frequently employed to execute extensive operations for a multitude of objectives. The important difference between conventional machine learning (ML) and deep learning (DL) algorithms is due to the aspect of feature engineering. Traditional machine learning (ML) algorithms perform classification tasks by utilizing a predefined set of characteristics. In contrast, deep learning (DL) techniques possess the ability to automatically extract features, resulting in higher accuracy compared to conventional ML models. The effectiveness of these models in addressing large-scale data issues surpasses that of shallow machine learning techniques due to the increased depth of the processing layers . The stuGAN networks are generative models that generate high-quality data from a small collection of training datasets. The GAN network is made up of two components: the generator and the discriminator. The generator attempts to learn the model built from the data and, as a result, generates graphics from random noise inputs. The discriminator is a CNN network that attempts to discriminate between actual data (training data) and data generated by the generator, and it assesses the likelihood of a mistake using this approach. Two of the most well-known GAN architectures are the semi-supervised GAN and Laplacian pyramid GAN. Since GAN networks are trained using only non-lesion images, it is expected that the error probability assigned by the network to images with lesions will be significantly different from that assigned to non-lesion images. The general structure of a standard GAN network with a generator and discriminator is shown in This research is organized into the following sections: The generation of labeled data to train a deep network for brain tumor segmentation in MRI scans is time-consuming and requires the contribution of experienced radiologists.Dong et al. demonstrated an FCN network for detecting and segmenting brain tumors. Some instances of low-grade glioma were less successful. The U-Net hybrid pyramid model was proposed by Kong et al. . The U-NIn 2015, Ronberger et al. , inspireInspired by U-Net, Miltri et al.  presenteLow- and high-grade gliomas have different architectures. Post-processing volume threshold limitations eliminated the inaccurate cluster categorization error. During training and assessment, the following methods clustered medical scans. SegNet was created by Drozdahl et al. Instead of long connections, they used identity mappings (short-hop connections) . These iAs a result of the preceding, a model that takes all the images as the input and utilizes local and global features to balance the data’s class imbalance is required. To produce high-quality results when analyzing medical images, methods based on GAN networks are frequently used. The following studies, each of which has advantages and disadvantages that will be discussed below, are related to and are like the subject of this study. Different architectures characterized low- and high-grade gliomas, post-processing volume thresholds eliminated cluster categorization errors, and cluster medical scans were carried out during training and assessment. Zhu et al.  demonstrIn , a GAN-bIn , Kiani KA neural network is proposed and developed based on the electrochemical activities of neurons. The task of these networks is to model human neural activities in learning knowledge and apply the knowledge obtained in similar situations. Various models have been proposed for modeling neural structures, among which structures like CNN have performed better in modeling the activity of the human visual system in relation to the visual cortex of the brain. The proposed method of this research attempted to design and improve the optimal structure of this combination of these two types of networks for the segmentation of brain MRI images by utilizing innovations related to CNN and GAN networks in various topics of machine vision over the last few years.This approach develops a GAN framework where one neural network (the generator) tries to generate a model that resembles real neuron activity. In contrast, the other network (the discriminator) tries to distinguish between real and generated data. In addition, it defines a loss function that incorporates both the traditional GAN loss  and a loss related to the generated model to the real data and fine-tunes the network architecture and hyperparameters based on the performance of a validation set.The term “data preprocessing” is used to describe the process of preparing data for use in training a network. Image resizing, or scaling volumetric images to a specified dimension, is a useful preprocessing technique in this situation. As datasets might contain information on a wide range of dimensions, it is vital to standardize the data across all of them. So, resampling the images to get a consistent image resolution is important. This easy technique can also assist in keeping the number of calculations from increasing excessively when large images are delivered over a network. The term “normalization” refers to a crucial step in the pre-training phase of neural network image processing. Normalization is used to adapt measurements to a standard scale. When the inputs to a neural network have significantly different scales , normalization is necessary. Standard normalization procedures include min–max , which sA fully convolutional network (FCN) is combined with a generative adversarial network (GAN) in the suggested method. The proposed GAN network design has two major nodes: a generator and a discriminator.Generator: A segmentation network that automatically creates segmented images of brain tumors. This is referred to as the algorithm’s core module.Discriminator: the classification network validates the input, which indirectly improves segmentation accuracy.Loss function: adversarial training coordination between generator and discriminator, combination of conventional segmentation and adversarial training optimization objectives.Once the network is randomly set up, its parameters stay the same. The generative network used for segmentation is then repeated to create a generative network that does not need adversarial training. The parameters of the generator network are then modified, and the discriminator network is repeated. The alternative loop iteration method can generate segmentation results while enhancing classification accuracy.As previously stated, the suggested 3D CNN is based on the FCN approach and is employed in two structures: the generator and the discriminator. FCN networks such as , 3D U-NeA distinct Fully Convolutional Network (FCN) architecture has been developed for the purpose of segmenting brain tumors. This architecture is specifically designed for the two components of the proposed Generative Adversarial Network (GAN), namely the generator and discriminator. The suggested Generative Adversarial Network (GAN) incorporates the ideas of mirroring and residual. The design provided for the generating structure in our research involves the utilization of a three-dimensional fully convolutional network (3D FCN) to perform the multi-class segmentation of magnetic resonance imaging (MRI) images showing brain tumors. The FCN architecture we propose integrates the U-Net model with the Residual Network. The suggested Fully Convolutional Network (FCN) is designed to accept three-dimensional (3D) Magnetic Resonance Imaging (MRI) pictures of varying dimensions as its input. The segmentation map generated offers the probability distribution of the voxel classes at each spatial position. The quantity of channels depicted on the map is equivalent to the quantity of labels present. The network depicted in The generator network under consideration comprises three distinct building blocks, namely a convolutional block, a residual block, and an output block. The composition of each CB block consists of a three-dimensional extended convolution layer, a group normalization (GN) layer, and an exponential linear unit (ELU) activation function. In contrast, every database block consists of a three-dimensional extended deconvolution (Deconv) layer, a group normalization (GN) layer, and an exponential linear unit (ELU) activation function. In order to achieve feature compression sampling, the integration of residual blocks is implemented at intermediate phases of the contraction process. The aforementioned blocks are designed to tackle the issue of gradient fading that arises in the process of back-propagation. Within the framework of the expansion pipeline, it is observed that an intermediate stage exists whereby an upsampling layer is implemented prior to the subsequent appending layer. The layer serves as a connection between the expansion path feature maps and the contraction path feature maps, as their quantities may vary. Additionally, the incorporation of this topology adds a sense of symmetry to the network under consideration. To address the problem of resolution loss, a concatenation layer is included between the symmetric feature maps obtained from the contraction path and the output of each Deconv layer in the expansion path. The information pertaining to the quantity and sizes of feature maps, which provides a more detailed understanding of the network’s intended functionality, is shown in The generative network generates a segmentation volume that matches the dimensions of the input volume. The number of final convolutional features in the generated volume is determined by the desired number of classes for prediction, achieved through the use of 1 × 1 × 1 convolutions. It is worth mentioning that the study of volumetric images necessitates the utilization of 3D operations, such as convolution, deconvolution, and fusion layers. These operations are presently supported by some machine learning frameworks, such as TensorFlow. The aim of the subsequent sections is to provide clear definitions of these notions in order to enhance the comprehension of the proposed network.(1)Experts manually label original images and segmented images. In other words, these categories are segmentation masks and represent the real value and the category with the value.(2)The generator automatically labels both the original image and the segmented image with the generated value. The category is 0.The utilization of the 3D FCN network in the GAN network involves the categorization of images as a discriminator, which in turn generates the generative model. The primary purpose of this network is to differentiate real input from fake input. The discriminator segment inputs a pair of images consisting of the initial segmentation images and the images segmented by the generator segment. Based on the generated segmented images, it is apparent that this pair of images may be classified into two separate groups:The convolutional layer is used as the primary component within the Convolutional Neural Network (CNN) architecture, responsible for conducting convolutions between numerous filters, commonly referred to as “kernels,” and the input image. The filters execute the convolution process by traversing the input with respect to its dimensions. The result derived from this process is sometimes referred to as a “feature map” or an “activation map.” In a more precise style, the convolutional layers of a neural network receive the unprocessed image as the input in order to compute the resulting signal. This enables the deep learning model to circumvent the need for manual techniques in the segmentation of brain tumors within MRI brain images. The resolution of intricate abstract learning patterns can be effectively achieved by the utilization of non-linear algorithms and varying degrees. The objective of utilizing this layer is to extract features from input images. This is achieved by convolving the pixels of the input image with a small region known as the receptive field, which exhibits local connectivity. Indeed, it might be posited that this stratum imparts a sense of profundity to the visual representation. The hyperparameters of the system consist of a collection of trainable filters, which encompass the dimensions (F) and step size (S) of each filter. The term “step size” (S) denotes the quantity of pixels by which the window is displaced following each action. In the majority of instances, SF is regarded as being significant. It is noteworthy to emphasize that the convolution process can be executed in one-dimensional, two-dimensional, and three-dimensional formats. In the network under consideration, the utilization of a 3D convolution operation is necessitated by the inherent three-dimensional structure of the input images. As previously stated, a specialized convolutional module known as extended convolution has been employed for conducting 3D convolution operations. One notable advantage of this method in comparison to the traditional convolution operation is its ability to provide a broader perspective with a reduced number of parameters. This enables the extraction of a greater amount of content information from the input, resulting in enhanced efficiency. Next, the difference between extended convolution and standard convolution will be explained.(1)Let (2)Let (3)If it is assumed that these conditions are true, then the discrete convolution encoder can be defined as a relation:l is the expansion coefficient and l is equal to one.Now, the generalized state of this operator can be defined as relation:This operator is based on the notion that extended convolution expands the region of impact exponentially without sacrificing coverage or image resolution.i from 0 to n − 2 in the form of the relation:If p in p in Define the receptive field of an element The next phase is to use the features acquired from the convolutional layer for the purpose of classification. There are no discernible benefits associated with employing a classifier for the purpose of categorizing an extensive array of features. Furthermore, it is possible for the classifier to experience the issue of overfitting. The integration layer is employed as an approach to address this issue, effectively reducing the spatial dimensions and partially mitigating the computational burden associated with network training. It is noteworthy to mention that the merging procedure is executed individually on every image channel. There are two types of integration: maximum integration and mean integration. Peak integration involves determining the highest value inside a certain area by employing an integration window, often a 2 × 2 filter. The practice of average pooling involves computing the mean value within a specified region, and it was initially implemented in the LeNet convolutional neural network. In practical applications, it has been observed that max pooling tends to yield superior results compared to mean pooling due to its ability to retain the discovered characteristics. As a result, the suggested network, which encompasses both the generator and discriminator, employs a strategy of maximum integration.Normalization is a widely employed technique in deep neural networks, which has the potential to enhance the generalizability of the network and facilitate the convergence of the cost function. Normalization is a common practice that is typically carried out subsequent to a fully connected layer, prior to a convolutional layer, or prior to a nonlinear layer. This feature facilitates an accelerated pace of learning and mitigates the model’s dependence on its original configuration. Batch normalization (BN) is a fundamental and valuable technique in the advancement of deep learning, enabling the training of various networks. On the contrary, normalization in relation to the category dimension gives rise to challenges. As the size of the cluster shrinks, there is a significant increase in the BN error. This increase can be attributed to the inaccurate estimation of cluster statistics. As a consequence of this constraint, Bayesian Networks (BNs) are not suitable for training larger models or transferring data to computer vision applications that need the classification of small classes due to their reduced memory utilization. Consequently, the GN approach  was chosx is the feature calculated by a layer and i is the index. For example, i in a two-dimensional image is a four-dimensional vector as Normalization procedures, such as Batch Normalization (BN), Instance Normalization (IN), Layer Normalization (LN), and Group Normalization (GN), typically involve the computation of relational equivalents:ϵ is a small constant to maintain numerical stability, µ and σ; in other words, the number of their estimated probabilities is different. This is shown in µ and σ. The set G is a default hyperparameter indicating the number of groups, i and k are in the same group of channels. This, of course, is assuming that each group of channels is stored sequentially along the C axis. GN calculates µ and σ values along H and W and a group of channels  in the form of equation:V  for a given generator G has the interfaces of the type.Here, the bias is omitted for the simplicity of the calculations. The final output of the differentiator can be considered as the relation:D=AfAccording to the DL literature, the performance space from which the discriminators are chosen influences the GAN performance. Some studies such as ,25,26) s,26 s25,2Also, its derivative is calculated as equation:f(x).This derivative can be infinite or even uncomputable. This prompts us to apply some regularization conditions to the derivative of x. In SN, we followed in their footsteps and sought for the discriminant D from continuous K-Lipschitz functions:Refs. ,26 suggeSince input-based systematization approaches offer relatively simple formulae based on examples, they suffer from the difficulty that systematization cannot be applied to the space beyond the data generator and distribution without the use of heuristics. The SN technique circumvents this issue by normalizing the weight matrices, as proposed by Yoshida et al. . FollowiThe SN method controls the Lipschitz constant of the discriminant function f by limiting the spectral softness of each layer  = Wh, the smooth is obtained using This is equivalent to the largest single value of The SN method normalizes the spectral smoothness of the weight matrix W so that it satisfies the constraint σ(W) = 1 for the Lipschitz constant:If we normalize each approach  is that D” is used to denote the correlation or association between two binary volumes: N voxels, a predicted binary segmentation volume Assuming that the network projections consist of two volumes with the identical resolution as the original input data, it frequently appears that the region of interest occupies a relatively small portion of the volumetric medical images, such as the 3D MRI images employed in this investigation. In this study, the region of interest undergoes processing via a SoftMax layer, which generates the probability of each voxel belonging to either the foreground or background. This occurrence often leads to the learning process getting encased in the local minima of the loss function, which in turn produces a network that exhibits highly biased predictions compared to the background. Consequently, the primary subject matter is typically obstructed or only partially discernible. Previous studies employed loss functions that incorporated sample reweighting, wherein greater emphasis was placed on foreground instances compared to background instances throughout the learning process. Mi-lhtari et al.  introducj. Using this connection, it is not essential to add weights to samples of different classes in order to obtain a reasonable balance of foreground and background voxels, and the experimentally observed results are generated. One of the binary Dice loss function’s key shortcomings is its inability to be used for multi-class segmentation. The mean of the binary Dice function is used as the relation to derive a unique measure for multiclass segmentation:GDL) of this function. The GDL form has a relationship of the form:y calculated with a tensor of ones. The second part, the L2 error of the discriminator output between the original image and the prediction of the corresponding segmentation The prediction is made using voxel e et al.  have prox and the corresponding segmentation prediction On the other hand, the productive cost function (LG) consists of two parts. The first part is the total L2 error of the discriminator output between the original image DL error , is calcThe approach used in this research involved the utilization of the brain tumor segmentation challenge dataset (BraTs 2021). The dataset was publicly released during the MICCAI conference and was subsequently utilized in a competitive setting, allowing the participating groups to evaluate their novel methodologies using this dataset. The BraTs 2021 dataset comprises three-dimensional magnetic resonance imaging scans of individuals. The Hold-Out strategy was employed to partition the dataset into two distinct subsets, namely the training set and the test set. The training set constituted 80% of the dataset, while the remaining 20% was allocated to the test set. The representation of three-dimensional (3D) images on paper is often challenging, thus necessitating the display of the two-dimensional (2D) parts of these images. . For each patient, 3D images are available to the researchers; there are four images related to different imaging channels , and one image of a real situation in which different parts of the brain tumor are identified by medical experts. The BraTS2021 dataset files consist of patients with the highest grade of glioma brain tumor and patients with the lowest grade of glioma brain tumor. There is a separate folder for each patient. The number of patients in the training data set is 1251, and the validation data set includes 219 casesIn general, the goal of neural networks is to provide a highly accurate estimate, and in fact, each neural network is an estimator. The closer the value estimated by the network is to the ideal value, the more successful the network is. The type of the estimated value is determined according to the type of ideal labels in the data set. In this research, our ideal labels are three-dimensional images that we call true state images; these images are used in the form of tensors. As a result, the output of our network will be a 3D tensor.To evaluate an estimator, statistical evaluation criteria are used. The type of statistical criterion is determined according to the nature of the network output and must be chosen in such a way that it can correctly measure the behavior of the estimator to be evaluated.Our goal in this research is to find the tumor areas in the 3D real-state images. These areas in the real-state tensor are very limited in number because the healthy brain tissues in the real-state image are marked with a zero label and occupy a very large volume of this image. As a result, the estimation of the elements that have a zero label has no value and considering these textures in the evaluation of the results will lead to wrong conclusions. Therefore, the Dice criterion is used in medical image segmentation methods to obtain estimation accuracy because this criterion does not value true negative samples.LD) consists of two parts. In other words, we have interfaces in the form:Since the proposed GAN network includes two independent generator and discriminator structures, we have used two loss functions. The discriminant cost function (LG) consists of two parts, and we have a relation of:On the other hand, the productive cost function (® Xeon (R) E5-2683 v4 @ 2.10 GHz 52 CPU, and a PNY NVIDIA Quadro P6000 PCI-E Pro 24 GB GPU. Each model took approximately 5–6 days to run. While Google Colab offers the opportunity to utilize the graphics processing unit (GPU) at no cost, it is worth noting that this research also employed its Pro Plus version. However, it is important to acknowledge that the utilization of Google Colab is accompanied by certain obstacles that notably impact the duration of network training. The network was trained using the cross-software framework implemented on the TensorFlow platform. TensorFlow is an open-source software framework that was initially developed by Google and has since been collaboratively enhanced by a global community of contributors. It is primarily implemented using the C programming language. During the implementation phase, the suggested architecture was trained using two different approaches. Deep neural networks have the capability to undergo training utilizing a central processing unit (CPU). However, due to the substantial computational demands associated with these networks, the training process executed on a CPU tends to be somewhat sluggish and time intensive. The utilization of graphics processing units (GPUs) is prevalent in the training process of the deepest neural networks due to this underlying rationale. The training and testing of the proposed network were conducted on a computer system with 128 GB of RAM, an IntelThe initial approach involved utilizing images with a higher count of convolutional layers following each normalization. Subsequently, in the subsequent training phase, these layers were omitted in both architectures. Additionally, data augmentation techniques were employed throughout the experiments.In each training iteration, a systematic process is followed where all the training samples are inputted into the neural network. Subsequently, the network generates an output and evaluates the estimation error. The optimizer function then adjusts the network’s variable parameters. The network’s training duration, when employing data augmentation, averaged approximately 25 min for each training iteration. The training process, which encompassed both methods, required 500 training rounds to achieve the necessary response. Without the inclusion of data augmentation techniques, the full training process lasted around 4–5 days. The network configurations are effectively presented in In this study, following the necessary preprocessing procedures specific to magnetic resonance imaging, the obtained data were utilized to generate image segmentation and classification. These outputs were subsequently employed as the input to a proposed network model, aiming to extract either a feature or category. Classifications are employed for categorization purposes. Several studies have demonstrated the potential impact of utilizing deep learning models in conjunction with neural imaging data ,34. AddiTo assess the quality of the suggested approaches, we compared the findings generated from the proposed model with other comparable studies on the BraTS dataset, as shown in Recent research in the field of medical imaging, specifically focusing on MRI scans, aims to explore various methodologies and their use for the precise extraction of tumor location and classification. This study aimed to implement measures to enhance the following within this field of study. Based on the experimental results of this study, it is recommended that the following methodologies be employed to enhance the precision and caliber of upcoming research attempts. The 3D GAN model that has been proposed exhibits potential for utilization in the upcoming BraTS2021 challenges through multiple avenues. These include training the model to enhance the optimization of the BraTS2021 evaluation criteria, as opposed to relying solely on the criteria provided within the dataset. Additionally, optimizing the proposed architecture can be achieved through the application of algorithmic optimization techniques for hyperparameters. Furthermore, the integration of various data augmentation methods can be explored to enhance the performance of the trained model. The investigation of the multi-task learning approach presents an additional path for inquiry, as it has the potential to be combined with the proposed architecture in order to enhance precision. To enhance the detection and differentiation of tumors, this network has the potential to utilize data from several sources, thereby constructing a more comprehensive representation. The proposed architectural framework can also be assessed using alternative imaging modalities, such as CT scans, and segmentation software designed for general purposes. In conclusion, it can be asserted that the proposed 3D GAN model has the potential to be employed for both image segmentation and image data augmentation. The proposed model has the potential to be employed in conjunction with a 3D noise-to-image GAN model, such as those referenced in citations ,39,40. T


The paper introduces the development stages of a MOSFET-based controller for a DC brush motor. The main objective was to design a controller that could be integrated with the existing telemetry system, offering full configurability through an Android application. This controller aims to provide real-time analysis of data collected from the measurement system, including motor revolutions and current draw. Based on the analyzed data and the conditions set in the Android application, the controller adjusts the motor’s operational characteristics accordingly. The paper provides a comprehensive description of the controller system’s functioning. The proposed control system is particularly relevant in applications where minimizing energy consumption for driving a DC motor is of utmost importance. In today’s fast-changing world, saving energy and improving how things work is vitally important. One area where this could have the greatest impact is in the electric motors used in various industries like transportation, manufacturing, and renewable energy ,2,3. WitThis paper presents a motor controller capable of optimizing motor performance in real time, with a particular focus on integrating electric motor controllers with telemetric systems to enhance energy consumption ,6.The telemetric system connected to an Android application plays a major role in that integration. This powerful combination provides the means of monitoring critical motor parameters, such as the current drawn from the motor and the revolutions per minute (RPM) of the motor, with the utmost precision and accuracy. The telemetric system acts as a digital conduit, transmitting invaluable information to the motor controller in real time, enabling seamless adjustments and optimizations ,8.Despite the promising advancements in electric motor controllers and telemetry systems for optimizing energy consumption, there remains a significant research gap in exploring the full potential of their integration within the context of electric car competitions. While the existing literature acknowledges the benefits of using PWM signals for controlling motor behavior and enhancing energy efficiency, there is limited research on customizing these systems for specific predefined conditions. One of the primary objectives of an electric motor controller is to achieve the highest level of energy efficiency. By dynamically adjusting the control signals sent to the motor, the controller optimizes the motor’s power consumption, ensuring that energy is utilized effectively without compromising performance. The key to accomplishing this lies in the implementation of Pulse Width Modulation (PWM) signals ,10,11.The controller configures the PWM signal according to the real-time data received from the telemetric system, carefully balancing power output and motor speed. By modulating the duty cycle of the PWM waveform, the controller can achieve precise control over the motor’s behavior. This ability to fine-tune the power delivered to the motor results in reduced energy waste and increased overall efficiency ,11,12.The FRACMO 624-65-112 motor is used in the electric car used by the Silesian Greenpower team. This team participates in international competitions for electric cars. The goal of the competition is to drive as great a distance as possible in one hour, using only two 12 V batteries with a capacity of 36 Ah each. Therefore, a motor controller that can adapt to the actual track conditions is a key element ,8,13. In conclusion, the electric motor controller, when paired with a telemetric system and harnessed through an Android application, represents a potent synergistic device for achieving optimal energy consumption and maximizing the performance of electric motors. By addressing the specific requirements of electric car competitions, this research not only advances the understanding of energy-efficient systems but also offers practical implications for real-world applications. The findings of this study are expected to have broader implications for electric vehicle technology and sustainable transportation, paving the way for further advancements in the field. Ultimately, this article serves as a comprehensive guide, unravelling the complexities of this integration, and highlighting the pivotal role played by the controller in driving the future of energy-efficient systems.Step-down voltage converter set to 15 V output voltage;MCP1407 MOSFET control circuit;EL817C optocoupler;MOSFET circuit.In order to control a DC motor, a system consisting of the following components was designed:The wiring diagram for the above components is presented in The EL817C optocoupler was added to the circuit due to the need to separate the ground of the measurement and control circuit. The DC motor generates significant interference, which caused blocking of the USB OTG port on the phone connected to the measuring circuit. Stalica et al. [An existing telemetry solution was used to generate the PWM signal controlling the MCP1407 chip. The finished circuit is shown in The figure shows the individual elements of the circuit described earlier in the wiring diagram. The numbers 1, 3, and 4 indicate the connector. Pin 1-GR-MOS corresponds to the signal output to the MOSFET. Pin 3-OR-24V is responsible for the added pole of the supply voltage. Pin 4-YE-0V corresponds to the negative pole of the power supply. The B-PWM designation refers to the blue wire connecting the measurement circuit , while G-GND refers to the ground wire from the telemetry circuit.The C++ code utilizes the data obtained from the telemetric system, including the RPM and current measurements, to calculate and update the PWM value in real time. On the basis of the acquired RPM and current values, the code calculates the desired PWM value. This calculation can be customized based on the specific control algorithm and optimization strategy used. The calculated PWM value is then sent to the motor controller to update the PWM signal. This ensures that the motor receives the appropriate power input based on the current operating conditions. The code snippet presented in Conditions were implanted in the code to prevent the user from drawing more than 18A of current during any particular trip. This value is conventional and can be dynamically changed, requiring further analysis and customization for a specific user’s needs. The software also checks the actual speed of the motor, by which it tries to keep it within the optimal RPM range according to the motor manufacturer’s data sheet .To investigate the performance and efficiency of the electric motor controller, a dedicated measuring station was employed. A schematic view of the dyno test stand is presented in PC and router;MDX61B frequency converter with DFE11B expansion card;DRE80M4 three-phase asynchronous motor with incremental encoder;DC electric motor (used in a vehicle);Shaft with mass wheel;NCTE2200-75 Nm torque sensor with CompactRIO platform NI9149;Vehicle wheel;Toothed belts.The elements presented in the schematic view are described below:This station comprised a DC motor coupled with an SEW drive acting as a load, allowing for precise measurements of the key parameters. The SEW drive played a crucial role in capturing the torque exerted by the motor on a roller, which was connected via a toothed belt and wheel mechanism.The SEW drive utilized in the measuring station offered a reliable and accurate method of quantifying the torque generated by the DC motor. By measuring the torque, it was possible to assess the motor’s output power and its ability to drive various loads. A view of the SEW drive dyno test stand is presented in The timing belt and wheel arrangement served as the mechanical connection between the motor and the SEW drive. As the motor rotated, the toothed belt transmitted the rotational force to the wheel, which, in turn, applied torque to the roller. This configuration allowed the SEW drive to measure the torque accurately and in real time.To simulate different load scenarios and evaluate the performance of the electric motor controller under various conditions, a LabVIEW application was employed. LabVIEW, a powerful graphical programming environment, allows for the creation of custom control interfaces and data acquisition systems. The LabVIEW application utilized in this study facilitated the configuration of the load diagram, allowing precise control over the load parameters. In the application, it is possible to define and adjust the load characteristics, such as torque levels and variations, to replicate real-world operating conditions and assess the controller’s ability to optimize energy consumption accordingly. A view of the application window is presented in The application allows us to generate an additional load on a DC motor using the aforementioned SEW motor. By assigning a ‘Setpoint’ value, it is possible to change the torque value with which the motor loads the test stand. Generating a variable load allows us to verify the behavior of the DC motor control system in the event of a change in the current drawn from the batteries.The data collected were subsequently processed and analyzed using statistical and graphical tools. These findings served as a basis for evaluating the controller’s efficiency in optimizing energy consumption and its ability to adapt to varying load demands. It should be noted that this specific DC motor has the highest efficiency at approximately 1800 RPM, as presented in During the experiments, different load profiles were applied using the LabVIEW application, mimicking various operational scenarios. The motor’s performance was continuously monitored, and the corresponding data were recorded for subsequent analysis. Through this set-up, the torque exerted by the motor and the load characteristics were precisely controlled and measured, while the telemetric system and the motor controller enabled real-time adjustments to optimize energy consumption.In this chapter, we discuss the findings and outcomes of our research on the adaptive electric motor controller. As the demand for efficient and sustainable transportation continues to increase, electric motor controllers play a crucial role in optimizing the performance and energy consumption of electric vehicles and industrial machinery. The results are presented and discussed below.One of the key parameters monitored during the experimental trials was the current drawn from the motor over time. Current values were recorded using the telemetric system connected to the motor controller and were recorded at regular intervals throughout the testing period. The measurements taken show the stage where the motor has already accelerated to its nominal speed. The acceleration stage itself should be a stage of separate analysis and testing.According to the program, the current value did not exceed 18A.Another critical parameter monitored during the experiments was the revolutions per minute (RPM) of the motor. RPM values were also recorded using the telemetric system connected to the motor controller and logged at regular intervals throughout the testing period. In the above graph, it can be seen that the controller attempted to keep the motor within the optimal speed range.The purpose of the present study was to enable the PWM signal controlling the motor to be dynamically varied in fill, based on measured values in real time. This correlation signifies the motor controller’s ability to optimize power delivery, ensuring that the motor responds to load variations by adjusting its rotational speed accordingly.Overall, the results presented here provide a comprehensive understanding of the motor’s performance in terms of the current draw and RPM. The analysis of these parameters is crucial in evaluating the effectiveness of the electric motor controller and its ability to achieve optimal energy consumption and performance. Test time: 120 s;Load applied: 0.4 Nm;Intervals of inflicted load: 30:10 .The final step compared the battery current during the test with the same set variable load. For the test, the controller designed in the present work and a purchased controller available on the market were considered. The test parameters were as follows:The load scheme is based on real, albeit simplified, movement on a section of track where, during a race, the car makes three turns with a 3:1 ratio of load (current is drawn from the batteries) to no load .The power consumption in the test conducted showed that the designed controller is more energy efficient. Almost 10% less energy was consumed, which, for the electric car in which the controller will be used, is a clear outcome that could affect the final result.In this chapter, the results of the research on an adaptive motor controller are discussed. This specific controller is capable of adjusting itself as the motor’s conditions change, allowing the electric motor to work in the optimal RPM range and more reliably in general. By looking at the experiment results, studying the advantages and limitations, and exploring how it can be used, it can be understood how adaptive motor controllers can really change the way we control electric motors.The results obtained from the current and RPM measurements provide valuable insight into the performance of the electric motor controller and its impact on energy consumption. These findings can be interpreted within the context of previous studies and work hypotheses, shedding light on the effectiveness of the controller in achieving optimal efficiency.Previous studies have highlighted the importance of fine-tuning the control signals sent to the motor to minimize energy waste and maximize performance. The current measurements demonstrate that the motor controller successfully adjusts the power delivery based on the load demands. As the current draw increases, the RPM of the motor also increases, indicating that the controller effectively modulates the PWM signal to optimize energy consumption while meeting the required load.Furthermore, the observed correlation between the current and RPM aligns with the working hypotheses, reinforcing the notion that the motor controller adjusts the motor’s speed in response to varying load conditions. This dynamic adjustment ensures that the motor operates in an optimal RPM range, allowing for efficient energy utilization while maintaining performance.The results of this study show that electric motor controllers have great potential to improve energy efficiency solutions in many industries. By integrating telemetric systems and smart control algorithms, the controller can optimize energy consumption and improve electric motors’ work overall. These improvements could be beneficial for various fields and industries.Optimizing energy consumption in electric motors is crucial to reduce greenhouse gas emissions and promote sustainability. The results demonstrate that the motor controller, through real-time adjustments of the PWM signal, can effectively balance power output and motor speed to achieve optimal energy efficiency. This has significant implications for applications ranging from electric vehicles to industrial machinery, where energy conservation plays a vital role.Furthermore, the successful integration of telemetric systems and the LabVIEW application highlights the importance of real-time data acquisition and analysis. This enables researchers and motorists to monitor and fine-tune motor performance, facilitating continuous improvements in energy efficiency and operational effectiveness.Based on the findings of this study, several avenues for future research can be identified. First, further investigations could focus on optimizing control algorithms within the motor controller. To improve the energy optimization algorithm, optimization techniques such as ALO or EHO could be compared. Fine-tuning these algorithms could lead to even more precise adjustments of the PWM signal, resulting in enhanced energy efficiency and performance.Additionally, exploring the integration of machine learning and artificial intelligence techniques into the motor controller could provide additional opportunities for efficiency improvement. Using data-driven approaches, the controller’s performance could be adapted and optimized based on real-time feedback and predictive models, leading to more sophisticated and intelligent motor control systems.Furthermore, expanding the scope of the study to evaluate the controller performance under different load profiles and operating conditions could provide a comprehensive understanding of its capabilities. Examining the controller’s response to dynamic and transient loads could further enhance its performance and energy efficiency.Lastly, investigating the scalability and applicability of the motor controller in larger-scale systems and complex network environments would be a valuable area of research. Understanding how the controller interacts with multiple motors and its integration into smart grid systems could unlock further possibilities for energy optimization and system-wide efficiency improvements.The proposed circuit board on the MCP1407 chip fulfilled its role and was successfully implemented.The EL817C optocoupler is necessary to avoid the interference caused by the DC brush motor.The assumed method of cooperation of the telemetry system together with the motor controller was successfully validated. The controller is able to change the motor’s operating characteristics in real time, which has a beneficial effect on saving the current drawn from the batteries. The test carried out showed that almost 10% lower power consumption was achieved during the interval loading of the motor on the designed controller.As part of the next research, a simulation should be carried out considering the entire 1 h race.In conclusion, the findings of this study demonstrate the effectiveness of the electric motor controller in optimizing energy consumption and enhancing motor performance. By integrating telemetric systems, advanced control algorithms, and the LabVIEW application, the controller successfully adjusts the PWM signal based on real-time data, achieving optimal energy efficiency and adapting to varying load demands. Furthermore, the successful implementation of the electric motor controller and its ability to optimize energy consumption contribute to social welfare by promoting sustainability and reducing environmental impact. As electric vehicles become more prevalent, the adoption of energy-efficient motor control technologies could significantly reduce greenhouse gas emissions and decrease the overall carbon footprint. By enhancing the performance and energy efficiency of electric motors, this research can foster a broader societal transition towards cleaner and greener transportation solutions. Additionally, the findings of this study hold relevance beyond the electric vehicle industry, as improved motor control technologies can be applied in various sectors, such as industrial automation and renewable energy systems, further benefiting society as a whole. The positive impact on the environment and the potential for wider application underscore the importance of advancing motor control technologies to align with the global goals of sustainable development and responsible resource management. The conclusions can be summarized in the following points:


This case report describes a particular application of endodontic microsurgery with a palatal approach in the presence of a radiopaque lesion inside the maxillary sinus. The patient presented complaining of pain related to the first maxillary molar and events of nasal obstruction and facial pain in the cheek and nasal area. The endodontic orthograde treatment and retreatment were done, respectively, 7 and 4 years earlier. The cone-beam computed tomography (CBCT) scan taken before the treatment showed two separate lesions: one associated with the palatine root of the molar and another one inside the maxillary sinus. The patient agreed to solve both problems in one surgical step: endodontic surgery of the palatine root with palatal access with the simultaneous asportation of a lesion from the maxillary sinus floor. Complete bone healing of the periapical area and the maxillary sinus was visualized on intra-oral radiographs, and CBCT was taken one year after the treatment. As far as the authors know, no one in literature has ever described this approach and solved in such a conservative way both the problems at the tooth and in the maxillary sinus. Over the years, the success rate of endodontic surgery has increased to 90% thanks to the improvement of surgical instruments, materials, and techniques , 2. WhilHowever, some conditions can complicate the surgical treatment, such as the presence of important anatomic structures and limited surgical access. For these reasons, the endodontic surgical management of maxillary molars can be considered one of the most challenging interventions . In partFew studies have focused on the endodontic surgery of the upper molar teeth with a success rate that, depending on the type of surgery and the parameter used, such as a clinical parameter or two-dimensional (2D)/three-dimensional (3D) radiographic healing parameters, ranged between 44% and 88% –13. HoweIn literature, two approaches have been described to reach and manage the palatal root depending on the presence of the maxillary sinus recess between the buccal and palatal roots and in consideration of the divergence between them: the transantral approach or the palatal approach –16.Particularly, the transantral approach is indicated when the maxillary sinus floor has proximity with the palatal root apexes when its recess is located between the buccal and the palatal root or when the palatal root is inside it . WhereasFurthermore, some pathological conditions can affect the maxillary sinus, which can complicate the surgical approach of the posterior maxillary area, for example, the pseudocyst and mucous retention cyst. Both of them are self-limiting and benign lesions that originate inside the maxillary sinus due to the accumulation of liquids –26, and This report described for the first time in literature a case where a palatal approach was used both for the management of a compromised palatal root of a maxillary first molar, and the asportation of a lesion from the maxillary sinus floor. In this way, two problems have been solved in one single and conservative surgical step.A 75-year-old female patient, American Society of Anesthesiologists I, presented complaining of pain related to the right maxillary first molar and events of nasal obstruction, and facial pain in the cheek and nasal area. The patient had a history of non-surgical root canal treatment performed seven and four years earlier with an intact full-coverage crown. According to the patient, the tooth had been treated for deep caries that had caused necrosis of the tooth, and the cone-beam computed tomography (CBCT) requested from the first clinician had shown a cupuliform or “rising-sun” shape radiopaque lesion inside the maxillary sinus above the tooth. Clinical examination revealed no tooth mobility, no swelling, and no pain sensation to percussion and/or chewing . A periaN-acetylcysteine aerosol up to 6 days after surgery, and anti-inflammatory nonsteroidal drugs (ibuprofen 400 mg) twice a day for two days after surgery for swelling and pain control. Sutures were removed 7 days after surgery.The modern microsurgical technique was performed using surgical loupes (5×). To reduce the number of bacteria in the surgical field, the patient was made to rinse with chlorhexidine digluconate (0.12%) before the surgical procedure. Local buccal and palatal anesthesia was gently administered in the maxillary right quadrant with articaine 4% and epinephrine 1 : 100,000. A full-thickness papilla preservation flap without vertical incision from the palatal right maxillary first premolar to the distal part of the second molar was elevated and retracted carefully during the surgical procedure and continuously irrigated with sterile saline solution. After flap elevation, bone was removed in the apical part of the palatine root using round burs under irrigation to expose the root apex. The exact point where to perform the osteotomy was calculated in the CBCT starting from the crown of the tooth and transferred in the mouth with a probe. After the complete removal of the lesion, the palatal root was resected 3 mm from the apex with a fluted fissure bur as perpendicular as possible (0°–15°) relative to the long axis of the root. The apex was extracted very delicately without damaging the maxillary sinus membrane to allow the management of the apical third in a safe situation, avoiding contamination of the maxillary sinus with apical residues or material for root canal obturation. The retrograde preparation of the root was performed using ultrasonic microtips , to allow a parallel preparation . After aAt the 6-month follow-up, a clinical assessment was performed, and a periapical radiograph was taken, whereas, at the 12-month follow-up, both periapical radiograph and CBCT were analyzed. The patient came to a recall appointment at 6 and 12 months after surgery with no symptoms. Soft tissue examination showed no sign of a gingival recession and revealed healthy periodontal tissue with normal probing around the tooth. The periapical radiographs at 6 and 12 months revealed no more apical radiolucency. At the last appointment, the CBCT showed a complete remission of either the periapical lesion or the lesion inside the maxillary sinus . The palOur case report showed a particular application of EMS with a palatal approach in the presence of a radiopaque lesion in the maxillary sinus. Wallace proposed a trans-antral approach to reach the palatine root of the maxillary molars –16; thisAfter performing the EMS and cleaning the cavity, the liquid content in the maxillary sinus lesion was aspirated with a syringe.It had been described in the literature that the mucous retention cyst or the pseudocyst inside the maxillary sinus are common lesions that in most cases remain in the sinus without any symptoms and for that reason, surgical intervention is often not necessary , 36. HowIn this case, the previous CBCT done years earlier was compared with the one required for surgery. It was clear that the lesion had increased in volume. This dimensional change, in addition to the symptoms described by the patient attributable to sinusitis, made us decide to intervene surgically taking advantage of the surgical access made for the endodontic surgery. In this way, we could solve both the apical and sinus pathology in a single surgical session. To manage the lesion inside the maxillary sinus, the procedure first described by Maiorana et al.  and TorrData are lacking about if aspiration without enucleation may expose the patient to an increased risk of relapse. As far as we know, no one has ever described the EMS of the palatal root of a maxillary molar through a palatal approach associated with the suction of a lesion inside the maxillary sinus. With this conservative approach, we have successfully solved both problems. Furthermore, studies are needed to evaluate the effectiveness of this surgical technique.


This work presents a novel methodology for the accurate and efficient elastic deformation reconstruction of thin-walled and stiffened structures from discrete strains. It builds on the inverse finite element method (iFEM), a variationally-based shape-sensing approach that reconstructs structural displacements by matching a set of analytical and experimental strains in a least-squares sense. As iFEM employs the finite element framework to discretize the structural domain and as the displacements and strains are approximated using element shape functions, the kind of element used influences the accuracy and efficiency of the iFEM analysis. This problem is addressed in the present work through a novel discretization scheme that combines beam and shell inverse elements to develop an iFEM model of the structure. Such a hybrid discretization paradigm paves the way for more accurate shape-sensing of geometrically complex structures using fewer sensor measurements and lower computational effort than traditional approaches. The hybrid iFEM is experimentally demonstrated in this work for the shape sensing of bending and torsional deformations of a composite stiffened wing panel instrumented with strain rosettes and fiber-optic sensors. The experimental results are accurate, robust, and computationally efficient, demonstrating the potential of this hybrid scheme for developing an efficient digital twin for online structural monitoring and control. Inverse problems are concerned with estimating unknown characteristics or parameters of a structural system from indirect observations. Such problems are often ill-posed, implying that conditions of the existence and uniqueness of a solution are not satisfied. Additionally, small errors in the measured data can lead to a large error in the estimated model parameters, highlighting the instability of the solution achieved. Nevertheless, inverse problems are of great importance in structural engineering, with structural health monitoring (SHM)  perhaps Solution approaches for the inverse shape-sensing problem have been the focus of numerous works , with thShape sensing using the iFEM is based on the finite element discretization framework, where the structural domain is discretized using finite elements. The element displacement field is approximated by using an interpolation based on shape functions and nodal degrees-of-freedom (DOF), while the strains are obtained using the linear strain-displacement relations. Displacement reconstruction is based on solving an error functional described as the least-squares error between analytically and experimentally evaluated strains ,21. As iPreliminary iFEM development focused on the shape sensing of 2D plate or shell structures . The involations , produciolations . This inolations ,28,29, solations ,31,32, wolations , and marolations ,35. Moreolations ,37. The olations ,39 and colations ,41,42 stolations ,44. A keolations ,46,47 anolations ,49,50,51The inefficiency of the 2D iFEM to accurately analyze beam and frame structures inspired the development of the 1D iFEM by Gherlone et al. . The preAt this stage, it is useful to consider the advantages and limitations of both the 1D and 2D iFEM approaches discussed in this section. The 1D iFEM, using a low-fidelity finite element discretization of inverse beam elements, can reconstruct structural displacements using a relatively low number of strain sensors with low computational effort. However, the results obtained are also of a low resolution or accuracy, i.e., the nodal displacements are reconstructed along the beam axis, and 3D displacement reconstruction depends on the accuracy of the kinematic assumptions. These assumptions can break down in the case of complex structures, such as a wing box, leading to inaccurate results. In contrast, the high-fidelity inverse shell element discretization employed by the 2D iFEM allows for more accurate and precise structural modeling. However, the analysis is computationally intensive, requiring relatively more strain-sensor measurements to produce accurate results. This work combines the 1D and 2D approaches using a hybrid scheme that discretizes the structure using beam and shell finite elements. Such a coupling enlarges the repository of inverse elements available to model a structure, generating greater accuracy than the 1D iFEM but at a lower computational cost and requiring fewer sensors than the 2D iFEM. The novel hybrid iFEM is demonstrated in this work for the experimental shape sensing of a composite stiffened wing panel instrumented with strain rosettes and fiber-optic sensors. The inverse model of the stiffened panel is developed using beam elements to model the stringers and shell elements to model the flat panel or skin. The merits and limitations of the hybrid iFEM are qualified by comparing shape-sensing results to those obtained from a complete shell model. Moreover, this work also assesses the robustness of the iFEM in using a constant strain-sensor configuration to produce accurate results across a variety of panel deformations. A sensor configuration, optimized for a specific load configuration, is selected for this purpose, and experimental results are computed for different panel load cases to test the adaptability of the approach.The paper is organized as follows: The hybrid approach introduced in this work is based on the 1D and 2D iFEM formulations of Gherlone et al.  and Tessx-axis is parallel to the beam axis and coincident with the shear center of the section, while y and z are the two transverse axes. The beam has a length L, cross-sectional area E, shear modulus G, and Poisson’s ratio Consider a prismatic beam defined in the 3D Cartesian coordinates, x, y, and z-axes, respectively. The variables u, v, and w are the beam displacements at the shear center along the x, y, and z-axes, respectively ε*10)). ForUsing the standard finite element assembly procedure, the contributions from all beam and shell elements are assembled to obtain the global set of equations of the structureAs in the direct FEM, nodal boundary conditions are applied to constrain the structure against rigid-body motion and ensure a non-singular system matrix. In the case of overlapping nodes, the associated nodal DOF are also coupled. Finally, Equation  is solveThis section presents the geometric and material specifications of the panel specimen used for the experimental investigation. Additionally, details regarding the loading conditions, sensor configuration instrumented on the structure, and the inverse models used for the iFEM analysis are also discussed.The structure analyzed in this work is a composite stiffened wing panel. The panel is swept and is stiffened by three T-section stringers installed on one face. These stringers lie along the wing span and are not parallel to each other. The geometry of the panel is presented in The entire structure is made of a multilayered composite whose layers are made of a TWILL T-300 carbon-fiber fabric prepreg. The characteristics of the prepreg are reported in he panel . TherefoThe experimental investigation aims to validate the hybrid iFEM approach that combines both beam and shell elements within the same inverse model. Therefore, an inverse model of the wing panel, composed of the two-node 0th-order Timoshenko inverse beam element ,54 and tThe inverse quad elements are used to model the flat panel skin and the stringer caps, while the web of the stringers is modeled using inverse beam elements. The characteristics that have to be defined for these inverse elements are only related to their geometry, since the inverse model does not require any information about the material of the structure. Therefore, only the thickness of the quad elements and the cross-sectional properties of the beam elements need to be defined. The shell elements that simulate portions of the skin without underlying caps have a thickness of 4 mm as a result of the relative stacking sequence’s total thickness. The shell elements that model the regions of the panel where the skin and the caps of the stringers overlap have a thickness of 6 mm (4 mm for the skin and 2 mm for the cap). Finally, the beam elements that simulate the web of the stringers have a rectangular 4 × 20 mm-section . The whoA second inverse model is also developed to evaluate the relative advantages and disadvantages of the hybrid modeling scheme. It is a standard inverse model composed only of shell elements .The panel skin and the stringer caps are simulated similarly to the hybrid model. Instead, the web of the stringers is modeled using two lines of 4 mm thick quad elements. This model is used as a benchmark to compare the accuracy and efficiency of the hybrid model. It is constituted by 960 quad elements and 1035 nodes, resulting in a total of 6210 DOF. The higher number of DOF with respect to the hybrid model indicates a more computationally demanding model to analyze.A high-fidelity direct FE model of the structure is also derived from the second inverse model. It is obtained by splitting each element of the complete shell model into four quad elements, thus obtaining a refined mesh. The FE model is used to simulate the experimental tests and produce numerical data for the preliminary computations. The numerical strains and displacements obtained from the direct FE model are used to identify an optimal strain-sensor configuration for the iFEM analysis of the structure. This process is described in the following sections.Due to its geometric complexity, the stiffened panel is prone to experience complex deformations and is a challenging test for shape sensing. Two loading conditions are considered for the experimental tests to simulate distinct panel behavior, as shown in Both load cases consider simply supported boundary conditions at the wing tips and a concentrated transverse force applied on the flat surface of the panel. The first load case F1, see  is desigThe second load case F2, see  is obtaiTherefore, the two load cases, although applied on the same structure, generate two considerably different problems for the shape-sensing procedure. The experimental setup also includes displacement transducers installed to measure the transverse displacement of the panel. Six LVDTs  are given a value of 1, while k = 4, 5, 6) are set to a small value 14), strax-axis of each element is aligned with the corresponding fiber direction, the weighting coefficients of Equation , resulting in only one non-penalized term in the error functional. For this reason, some shell elements are instrumented with strain rosettes to measure tri-axial strains. Strain rosettes are considered for the two rows of elements across the chord length next to the root section of the panel, where the deformation is expected to be the highest. These rosettes allow the full characterization of the in-plane stretching and bending curvatures of the element. Therefore, for elements instrumented with rosettes, Considering the instrumentation schemes mentioned above and the available sensors, an optimization procedure is used to identify an optimal sensor configuration for the iFEM analysis. Mainly fiber-optic sensors are considered for this application, as they can be used to efficiently sensorize both shell and beam elements. One 20 m fiber-optic sensor is considered. In addition, 16 strain rosettes, for measuring strains close to the wing root, are included in the optimization process.N is the number of nodes of the inverse mesh, The optimization is performed through the application of a genetic algorithm (GA) . The obj follows ,23:(23)%The optimized sensor configuration is shown in The manufactured composite wing panel is shown in The simply supported condition is realized by placing the two sections close to the extreme edges of the panel between two half-cylinder iron bars. Since the curved surfaces of the half cylinders are in contact with the panel, the transverse displacements are constrained, whereas the out-of-plane rotations are not. The concentrated forces for the two load cases are applied on the structure through an iron sphere pressed against the flat surface of the panel see . An ironThe stiffened panel is instrumented with two 10-meter-long optic fiber strain sensors based on Rayleigh scattering and optical frequency domain reflectometry (OFDR). This approach considers the fiber itself as a sensor by detecting changes in the characteristics of the light scattered along the fiber length, caused by the local variation of strain . These hMultiple tests based on the two load cases were performed, and strain and displacement data were collected. Consistency of measurements between successive tests indicated their reliability. Therefore, considering the small variability between the results of each test, the results of only one test per load case are presented here. Moreover, the comparison between results obtained with the hybrid model and the shell-only model are performed considering the same set of data. In Analyzing the errors for the torsional load case highlights the accuracy of the iFEM analysis. In fact, the errors never exceed The primarily bending load case is fundamental in this application for two reasons. The first objective is to assess the adaptability of the iFEM in general to the reconstruction of different deformed shapes using the same sensor configuration. In fact, the same sensor configuration optimized for the first load case is adopted for the second one. The second objective is to consider another application to specifically evaluate the accuracy of the introduced hybrid formulation. For the first objective, the results reported in Comparing the two models for the bending load case leads to similar conclusions as those of the previous one. The maximum error of the hybrid model (This work presented and experimentally validated a novel hybrid formulation of the inverse finite element method. This formulation improves existing modeling capabilities of this shape-sensing method by introducing the possibility of creating hybrid models, i.e., models that discretize the structure using beam and shell inverse elements. Such a scheme is particularly adaptable for modeling thin-walled stiffened panels where the panel can be modeled using shell elements and the stringers using beam elements within the same inverse model. Compared to standard shell-only models, hybrid models are more computationally efficient and require a relatively lower number of strain sensors to produce shape-sensing results of similar accuracy.This hybrid iFEM formulation is evaluated experimentally for the shape sensing of a composite stiffened wing panel. The accuracy of the novel formulation is assessed by comparing the results with those obtained from a standard shell-only model. Two different loading conditions are considered: one that induces combined torsion and bending deformation, and a second that is primarily bending. The panel is instrumented with strain rosettes and fiber-optic sensors whose optimal configuration is obtained through an optimization scheme considering numerical results only from the torsional load case. The experimental results prove that the hybrid model is able to achieve consistently accurate displacement reconstructions for both load cases. Comparison with the shell-only model successfully demonstrated that the hybrid model achieves a comparable, or slightly greater, accuracy while using a more computationally efficient model and relatively lower number of strain sensors. Moreover, the use of a single sensor configuration across the two load cases demonstrated the adaptability of iFEM to variability in the analyzed deformation field.Although the results presented for the hybrid iFEM are promising, future adoption of this shape-sensing methodology relies on its ability to adapt to sparse sensor configurations. In fact, the sensor requirements for the shape-sensing techniques represent a strong limitation for the monitoring of existing structures, either those instrumented with few sensors or those which present difficulties in the installation of new ones. Future research in this field should focus on techniques that further reduce the number of strain sensors required. Additionally, efforts should be directed at demonstrating the applicability of iFEM to real-world scenarios by monitoring increasingly complex structures with reduced availability of strain sensors.