Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blind Deblurring Strategy for Images based on Iterative and Guided Filtering

Image deblurring is an important branch of image restoration tasks. In order to eliminate blurring in a single image, an image deblurring strategy is proposed. Specifically, first enhance the image, then delimit the special area, calculate the initial iterative Point spread function according to the initial state of the special area and the enhanced state, and finally restore the original image according to the blind image Iterative method method combined with guided filtering. Compared with the Iterative method directly used for image blind deblurring, the strategy in this paper can give an initial Point spread function that is closer to the true value. At the same time, due to the existence of guided filtering, the iterative strategy in this paper can achieve the estimation of the original image faster.
“Escape with Pulcinella”: Development of A Gamified Environment And Pilot Study On Escape Rooms For Language Learning And Cultural Knowledge Acquisition

The paper shows a pilot study whose purpose is to evaluate the usability and user satisfaction of a prototype escape room designed with a Mixed Reality (MR) approach. We investigated if it is perceived by participants as an easy, useful, and satisfying tool to use. The methodological framework on the Game-based learning (GBL) and the application of tangible user interfaces in the field of the educational escape rooms applying a Bring Your Own Device (BYOD) approach. The pilot study collect data for the design of an educational escape room for the learning acquisition and the knowledge of the local culture. This pilot research has been inspired by the European project EULALIA (Enhancing University Language courses with an App powered by game-based Learning and tangible user Interfaces Activities) addressed to Erasmus students. It is developed to facilitate learning host country language and sociocultural context with the use of new technologies such as tangible user interfaces. The application made for this project has been called “Escape with Pulcinella”, the goal is to empower the Italian language learning and the improvement of knowledge about Neapolitan culture's learning, applying the paradigm of Tangible User Interface (TUI) using a storytelling that motivates and involves the user. The application proposes clues and riddles proposing a multisensory approach and involving the sense of smell, taste and touch mediated by the digital devices. The users' smartphone represents a sort of magic wand that it is used to browse the room – designed as a learning environment – aimed at completion of the story. The participants are 22 to 32 years old, 8 females and 2 males. Regarding nationalities, 2 are of Croatian nationality, 8 are of Italian nationality, for a total of 10 participants. Scoring System usability scale (SUS) was used to assess if the instrument was effective. The mean and standard deviation was calculated and according to the mean score this instrument is satisfactory.
Spatial-Temporal Attention Mechanism and Graph Convolutional Networks Based Distributed PV Ultra-short-term Power Forecasting

To ensure the efficient operation and healthy development of photovoltaic power generation systems, reliable distributed photovoltaic power prediction is significant. However, the lack of modeling ability of existing distributed photovoltaic power forecasting methods for dynamic spatiotemporal correlation of distributed photovoltaic power data limits the further improvement of prediction accuracy. Therefore, a distributed photovoltaic ultra-short-term power forecasting method based on spatial-temporal attention mechanism and graph convolutional networks is proposed in this paper. Firstly, distributed photovoltaic cluster is divided into sub-regions and graph structured data can be generated with each sub-region being a node on the graph. Then, spatial-temporal attention mechanism is used to adaptively capture spatiotemporal correlation information and dynamically update adjacency matrix. Finally, spatial-temporal convolution is used to extract deep spatiotemporal features of distributed photovoltaic power data and achieve regional power prediction. The validity and progressiveness of the model have been verified in real world data sets.
A Gaussian Mixture Model and Conditional Generative Adversarial Network based Hybrid Method for Short-Term Wind Power Forecasting of Offshore Wind Farms

Improving the accuracy of short-term offshore wind power prediction is extremely important for the integration of wind power generation into the electricity grid. Therefore, a short-term forecasting method of offshore wind power is proposed combining Gaussian mixture model (GMM) with conditional generative adversarial network (CGAN) in this paper. The datasets of an offshore wind farm with 15 wind turbines collected by a supervisory control and data acquisition (SCADA) system are first analyzed and preprocessed. A GMM clustering method based on data distribution is then used to cluster the wind turbines and determine the best grouping scheme, and the forecasting model CGAN designed with the convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed for each group separately. Finally, taking the processed data as a practical example, forecasting results indicate that the proposed GMM-CGAN-based method can effectively predict the day-ahead hourly actual power output of the offshore wind farm, and provide higher accuracy compared with the other forecasting methods.
Tree-Based Elastic Parameter Server to Schedule Resources to Accelerate Distributed 'Training

In the realm of distributed training, the utilization of a parameter server architecture has proven to be highly efficient in reducing training time. However, conventional implementations of parameter server architecture tend to overload a single server, thereby necessitating the introduction of a tree-based parameter server approach. Nevertheless, such parameter server architectures typically require the specification of the number of servers and workers during the initial stages of task execution, lacking the capability for dynamic expansion during runtime. To address this limitation, we propose the implementation of a Tree-Based Elastic Parameter Server (TEPS), which effectively mitigates strain on an individual server and enables real-time modification of server configurations. This dynamic adjustment of server count and worker allocation at runtime significantly enhances system resource utilization. Our implementation of TEPS, coupled with the utilization of ViT for model training, yields promising results, demonstrating a noteworthy 1.28x improvement in training speed compared to traditional parameter server architectures.
U-Net Convolutional Neural Network for Optic Disc Segmentation

This paper proposes a modified U-Net neural network architecture to segment the optic disc from retinal fundus images because it allows for early detection and treatment of glaucoma, a serious eye disease. Since manual segmentation is time-consuming and influenced by operator experience, automated methods have been developed, including image processing and deep learning algorithms. The proposed technique is based on the use of a modified U-net CNN applied on the PAPILA dataset, which includes 244 patient records, achieving an Intersection over Union (IOU) of 91% on the validation set for the optic disc class. Compared to other state-of-art works, this method outperforms them even if it is applied to a smaller dataset, demonstrating its potential for use in clinical practice. Indeed, the proposed method could improve the diagnosis and monitoring of eye diseases and provides valuable assistance to medical professionals.
The Challenges of Implementing a Computerized Maintenance Management System in the South African Railway Sector

To improve maintenance management processes, it is crucial to identify and overcome any potential obstacles to adopting computerized maintenance management systems. That is why this study focuses on examining the common barriers to adoption. By doing so, organizations can streamline their maintenance operations and improve overall efficiency. The survey and questionnaires were used as research methods in this study. The information was gathered from engineering employees over a month. Following exploratory factor analysis, the number of variables in the model was reduced from six to three. According to the study, perceived usefulness, relative advantage, and perceived ease of use are three factors that influence the adoption of computerized maintenance management. People will embrace technology if they believe it will provide useful results and help them perform better. It is suggested that the company expand and provide more technical support so that people can easily see how the computerized maintenance management system can help them. The company should also ensure that the computerized maintenance management system is simple to use and outperforms the old one in terms of benefits and results.
Will Industry 4.0 Applications Help in Designing Sustainable Forest Management? A Conceptual Framework of Connected Networks in Novel Sectors

Using Industry 4.0 technologies creates new opportunities in many fields. This paper examines the potential of such technologies for the forest sector. Existing research mainly proposes solutions to collect and analyze data on specific topics. This research aims to create a model combining different data inputs to draw a comprehensive picture of forest conditions by closing the gap between science and policymaking. With the help of a pre-defined set of indicators, the output is communicable across sectors and countries while maintaining practicability on a local level. The model evaluation has been completed according to the Design Science Research (DSR) Guidelines proposed by Hevner, et al., which prospected good chances of adoptability. With the successful implementation of the model, ways of decision-making for sustainable forest management could be revolutionized.
Using Social Scripts in Human-Robot Interaction

In Psychology, scripts can be defined as representations of regularly repeated sequences of actions that are characterized by socially shared roles and contexts. Scripts allow children to understand what a given situation requires, and this is possible by taking advantage of sequences of actions. Based on these considerations, we used a Compact Prediction Tree (CPT) to test whether a machine can predict certain behaviors from some scripts, exploiting the predictability of sequences. From a Human-Robot Interaction (HRI) point of view, this may enable the robot to understand more quickly what to expect from a specific situation. In addition, in the model we have provided for the possibility of learning new routines. This creates a set of routines customized to the user by which the robot can better understand what to expect from the interactions with the human with whom it interacts.
A Regularized Neural Optimization Approach with Beta Densities for Blind Deconvolution

In the field of image blind deblurring, existing deep learning deblurring networks can learn from a large number of paired training images, but are limited in dealing with a variety of complex large-size blur kernels. The underlying reason is that, those networks are not sufficiently generalized to unseen blurs, and hence, it is necessary to explore a proper regularizer so as to better constrain the problem. In this paper, we are inspired by the Beta probability density function, and empirically find that it has a great potential in discriminating between the blurred images and sharp images. With such an observation, we try to investigate the prior under the maximum-a-posterior framework so as to benefit the deep learning methods. In specific, this paper proposes a regularized neural optimization approach with beta densities for blind deblurring. In difference to most existing deep learning methods, the proposed approach has greatly benefited from the model-based scheme in terms of both hand-crafted and self-learned priors. With amounts of experiments on synthetic datasets as well as real datasets, and we find that the recovered images show good performance, the proposed method is shown comparative or better performance than state-of-art algorithms.
Caractérisation de milieux multiplement diffusants à l'aide de corrélations dans la coda

Les signaux enregistres a la surface du globe sont composes de trois types d'ondes : des ondes directes, des ondes reflechies plus ou moins facilement interpretables, et des ondes multiplement diffusees beaucoup plus complexes a interpreter. Certaines proprietes physiques de ces ondes multiplement diffusees permettent de les assimiler au bruit sismique ambiant. Nous appliquons des techniques de correlation de bruit sismique developpees ces dernieres annees a la coda sismique, afin de tirer des informations sur le caractere multiplement diffusant du milieu. En particulier, nous montrons que le theoreme reliant fonction de Green et champ ambiant peut etre utilise dans la coda. La dynamique temporelle de la reconstruction de la fonction de Green est alors un indicateur de la repartition de l'energie dans le milieu. En reconstruisant la fonction de Green a l'aide de correlations pour plusieurs fenetres de temps dans la coda, nous montrons qu'il est possible de relier la symetrie des parties causales et acausales de la fonction de Green au libre parcours moyen du milieu. Nous developpons ensuite des simulations numeriques de propagation d'onde acoustique en 2D. Nous observons que l'evolution de la reconstruction des fonctions de Green observee dans ce milieu et celle predite par la theorie est proche. La meme approche est ensuite appliquee a des donnees sismologiques de terrain, sans permettre de retrouver la meme dynamique. Nous developpons alors une methode de type MCMC permettant de reconstruire les fonctions de Green du milieu de maniere optimale.
A Novel Optimized Tourism Itinerary Recommender System: A Modified Capacitated Vehicle Routing Problem Approach

This paper proposes an optimized tourist itinerary recommender system (OTIRS) using a capacitated vehicle routing problem with constant service time (CVRPCST) approach, slightly modified from capacitated vehicle routing problem (CVRP). The goal is to maximize tourist destinations around a major city while taking the shortest routes. To achieve this, a comprehensive dataset of major cities and tourist destinations in India is constructed by scraping data from commercial websites. Post data accumulation, we formulate the problem as an Integer Linear Programming (ILP). The CVRPCST has been modified to represent each day of the itinerary as a vehicle, with its capacity determined by the available hours, while the tourist destinations and their respective travel and stay times are analogous to products with varying volumes in the vehicle routing problem. Modifications to the Miller-Tucker-Zemlin (MTZ) sub-tour elimination formulation improve itinerary optimization accuracy and effectiveness. Optimizing tourist itineraries based on user input parameters and accumulated data validates the system, providing efficient and personalized travel recommendations. This optimized tourist itinerary recommender system aims to improve travel and enable the exploration of diverse Indian destinations.
Battery-Less Grid-Forming Power Converter as a Smart Load for Microgrid Operating Mode Transition

Both the transition from grid-tied mode (GTM) to islanded mode (ISM), denoted as GTM-ISM transition, and the transition from ISM to GTM, denoted as ISM-GTM transition, are one of the main topics in AC electrical microgrid (MG) research. Within the MG, the grid-forming power converter (GFRC) is responsible to carry out both the GTM-ISM and ISMGTM transitions. Usually, the GFRC includes a power supply source at the DC bus; in most cases, this DC source is a battery pack. In abnormal mains conditions, the MG must operate in ISM, then, the GFRC uses the battery power to generate the voltage at the point of common coupling (PCC) during the GTM-ISM transition; in the same way, the GFRC uses the power from the battery pack to sustain the PCC voltage during ISM and for synchronizing it when the nominal conditions of the mains are recovered. The transition of MG operating modes with a battery-less GFRC in the literature has not been reported. Thus, this paper presents a control scheme proposal to perform both GTM-ISM and ISM-GTM transitions with a battery-less GFRC. The results showed the capacity of the battery-less GFRC to operate as smart load during both GTM-ISM and ISM-GTM transitions. This battery-less GFRC proposal highlights a contribution within the research of GFRC units, since the GFRC operation has always been reported using a power supply source at the DC bus, mainly battery packs, but not using a battery-less GFRC working as a smart load for the generation of the PCC voltage.
Modified Carrier Discontinuous PWM Technique for Mitigating THD and Capacitor Voltage Balancing Problem of T-type NPC Inverters

Neutral point clamped (NPC) multilevel inverters (MLIs) have been widely adopted than other multilevel inverter topologies due to their simple design and superior power density. However, the performance improvement by mitigating several crucial power quality issues is considered as critical research challenge of these inverters, which heavily depend on the applied modulation technique. Harmonic distortion and neutral point voltage imbalance are the most commonly encountered problems of these inverters. In order to mitigate these problems, a modified carrier discontinuous PWM (DPWM) technique is proposed in this paper. The proposed technique is able to mitigate the total harmonic distortion (THD) of the output line voltage as well as improve the voltage balancing performance of the dc-link capacitors. A three-phase T-type NPC inverter system has been utilized for the evaluation purpose. To prove the superiority of the proposed PWM technique, an extensive comparative study has been conducted with the existing PWM techniques. It offers the lowest THD and capacitor voltage difference for all conditions, which can significantly improve the performance of the inverter. The performance of the proposed PWM technique has been tested through simulation in MATLAB-Simulink platform as well as a laboratory prototype.
Ćuk DC-DC Converter with Multilevel Flying Capacitor Switching Cell

The large industries that work with high power demand increasingly want to optimize their production lines. In this context, several studies and research about the multi-level power converts have been realized in last years, as they have several advantages when compared to traditional two-level topologies: better harmonic performance, filters reduction, lower components voltage stress and higher power process capability. The present work proposes a Cuk DC-DC converter composed of multilevel flying capacitor asymmetric totem-pole switching cell. This structure is achieved by replacing one leg of switch/diode of the traditional two-level asymmetric totem-pole cell by a similar multilevel flying capacitor cell. This work presents a brief analysis of the operation of the proposed converter, as well as the control strategy to keep the output voltage at the desired value and contains simulation results to validate the theoretical approach.
Application of Generalized Predictive Controller and Truncated Singular Value Decomposition for the Control of a LCL Filtered Grid Converter

The current control of a LCL-filtered grid converter is important to optimize the connection of a renewable energy source with the electrical grid. To achieve this objective, a robust and accurate sinusoidal current control is required to transfer power to the grid with a power factor near unity. On the other hand, Generalized predictive control (GPC) was applied to control the output current of the LCL filter connected to an AC grid. GPC control law depends on a Hessian matrix. However, depending on the plant and the GPC structure, this matrix could be ill-conditioned (almost singular), being the GPC control law very sensitive to problems such as noise or numerical representation errors, typical problems in embedded systems. This paper proposes the application of truncated singular value decomposition (TSVD) to get a robust GPC system to control the LCL filter output current. Simulations show that the proposed approach allows robust LCL filter output current control against noise and numerical errors.
Multi-channel data fusion and fault diagnosis of forging press based on GAF-ViT

Because the multi-channel pressure data of forging press has space complexity and time sequence dependence, the data characteristics cannot be fully mined. To solve this problem, a fault diagnosis method based on the Gram Angular Field (GAF) and Vision Transformer (ViT) is proposed in this paper. The original pressure data are encoded by the Gram Angle Field to retain the correlation and dependence of the time series. The four channel images are fused by the pixel weighted average method, and input into the ViT network for fault classification, so as to fully mine the data features to obtain better classification effect. The effectiveness of the proposed method is verified by experiments. And the proposed method is compared with other methods. The experimental results show that the proposed method has the highest accuracy of $99.6{{\% }}$, and can effectively solve the problem of insufficient data feature mining of forging press.
Navel Orange Disease Target Detection Based on Transfer Learning and Improved YOLOv5s

In order to realize the detection of navel orange disease targets in the complex leaf background of target samples of different sizes, transfer learning was introduced, and the YOLOv5s model was pre-trained on the public COCO dataset. On the basis of the YOLOv5s model, a new network was designed by adding an attention mechanism Structure, using navel orange disease pictures to train the model, including the learning rate of the model before and after improvement, the optimizer, selecting the best result. The collected navel orange disease image dataset was divided into training set and test set according to the ratio of 4:1. The original image of the training set is expanded by operations such as random scaling, random translation, flipping, and Mosaic. On the training set before and after the expansion, the two learning methods of the improved model and the transfer learning were compared and tested under different hyperparameters and optimizers. Experiments show that the object detection ability of the model can be improved by data augmentation and training all layers of the model. Under the conditions of training set expansion and overall model transfer learning, the detection of navel orange leaves with yellow dragon disease, canker disease leaves, anthracnose disease leaves, and red spider leaves has an average precision of 0.980, which can realize navel orange disease images under the complex background of fruit forests. The detection provides a basis for the timely prevention and control of navel orange diseases.