Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG-based left-hand/right-hand/rest motor imagery task classification

This paper presents an EEG-based Brain-Computer Interface (BCI) designed for classification of motor imagery tasks. Apart from the imagined right-hand / left-hand movements, a third class - the resting (idle) state - is also included. The classification is based on Linear Discriminant Analysis (LDA) and Common Spatial Patterns (CSP) filtering of the band-limited EEG signal acquired from 10 electrodes placed over the motor cortex area. The system, planned for integration with a virtual reality (VR) environment and designed for future neurorehabilitation applications is tested on an experimental database comprising 52 subjects. The observed variability be-tween individual participants and selected subgroups is further analysed with statistical tools, revealing significant differences with respect to gender, age and individual motor imagery task classes.
Sparse coding based on L2,1-norm and manifold regularization for remote sensing images

Sparse coding is an important tool in image re- construction. In this paper, a novel sparse coding algorithm based on L2,1-norm and manifold regularization is developed to reconstruct remote sensing images. In the proposed algorithm, loss function based on L2,1-norm has the capability of reducing outliers. Dictionary and sparse coding based on L2,1-norm can select features with joint sparsity. Manifold regularization can retain more geometric information. An efficient algorithm for updating dictionary is developed, and the convergence is proved. To this end, we compare the proposed algorithm with other existing methods. Experiments on real remote sensing images demonstrate that the proposed algorithm has promising reconstruction results.
Thyroid Disease Prediction using Random Forest Algorithm

Recent years have seen an increase in the incidence of thyroid conditions. The pituitary hormone is most crucial besides regulating respiration. Two of the least prevalent conditions resulting from abnormalities in the thyroid gland are hyperactivity and hypothermia. Every year, thyroid conditions involving hypothyroidism and hyperthyroidism are found in a large number of people. Both hypothyroidism and hyperthyroidism can be brought on by a deficiency in the thyroid hormones levothyroxine (T4) and triiodothyronine (T3), which are generated by the thyroid parotid. To properly care for the individual just at right time and prevent unnecessary deaths and medical expenses, a strategic thyroid ailment prediction is necessary. Since each thorough health history is known over time, it was possible to predict how each patient would respond to therapy in the future and decide whether to intensify or reduce it based on the pattern of biological indicators and other factors. It is highly desirable to improve healthcare procedures to identify and prevent thyroid problems utilizing cutting-edge technologies. Numerous techniques are recommended in the literature for diagnosing thyroid disease. Machine learning approaches are increasingly used to forecast thyroid diagnoses as a result of improvements in data processing and computation before they are fully formed. The thyroid illness is categorized by using the data from GitHub repository. These algorithms, the same as SVM, KNN, Decision Tree, and Naïve Bayes produced results with an accuracy of up to 90%. Whereas an existing technique like the Random Forest algorithm produced an accuracy of about 70%. In order to improve the accuracy, this study has introduced the dimensionality reduction technique like PCA, which was found to produce an accuracy of about 90%.
A Review on Various Image-Recognition Types and their Efficiency

Now-a-days images and documents play an important role in storing information. Image processing and text extraction are essential to sustain in our digital society. Text extraction and recognition from images and documents is a key step to build efficient indexing methodology for accessing and retrieving of information from any database. Commonly used in online shopping and augmented reality. To put it simple image recognition provides an extensive range of possibilities and services to categorise and validate documents. Image recognition is not optimized for extracting text and information from images with complex backgrounds and fonts. Still the elements or characters cannot be extracted due to character misalignment or writing style. The purpose of this document is to collect information about various techniques and algorithms used for text extraction from images, documents, and their efficiency. This study has reviewed various deep learning methods such as optical character recognition (OCR) and a hybrid technique by combining region-based and texture-based methods for text extraction.
Study on the automatic modeling method of 3D information model for substations

Reverse modeling is a kind of technology that transforms natural scenes into three-dimensional models. The models are generated manually by referring to the point cloud obtained by a laser scanner, which is time-consuming, laborious, and complicated. To address this problem, we propose an automatic modeling method for the point cloud data of substations. First, an algorithm is designed to automatically generate a component model library by referring to the standard structure of the substation equipment. Then, we improve the Euclidean clustering to segment disconnected point cloud data. Finally, corresponding points are found according to the SHOT feature descriptor, and each component is identified with Hough voting. After the location information for each element in the scene is obtained, the models can be transferred to the scene to replace the corresponding part in the point cloud, thus completing the process of automatic modeling. The experiment compares the results of improved Euclidean clustering and traditional Euclidean clustering. The clustering method in this paper has a significant improvement in execution efficiency. In addition, we also give the final modeling result of the method in this paper. Compared with the Delaunay triangulation and Poisson surface reconstruction methods, the model built by this method is more complete.
An Innovative Deep Learning Framework for Healthcare Cost Prediction

$A$ crucial project with many applications, such as firm profiling, responsible care management, and capitated scientific price adjustment, is accurately calculating affected individual healthcare cost. Even while considering a common man, he/she doesn't know how much money to save for health. The proposed model enables the user to know his/her expenses in health care. Existing strategies rely heavily on manually designed points and models that rely on linear regression, that require a vast amount of clinical data and have limited predictive ability. Based on historical claims data, this study provides a deep learning approach for forecasting the future healthcare expenses at the person level. Our technique can accurately model the heterogeneous data, which includes patient demographics. These findings contribute to improved preventative and corrective care in the healthcare area. Here, a prediction model has been developed based on Neural Network - LSTM - Multi-View, Random Forest Regressor, Voting Regressor, Decision Tree Regressor. In which, this study prove that deep learning algorithm LSTM implemented using keras wins the race with less error recordings when compared to others. A web interface is used to collect user data to forecast health expenditure.
Motor Imagery Hand Movement Classification Using Attention Network Integrated Inception Model

In the Brain Computer Interface (BCI) system, classification of hand movements using brain activity is a very significant task. The use of this classification in medical and biomedical applications has expanded, particularly for people with disabilities. In this context, time series data based on electroencephalogram (EEG) are utilized to capture brain signals and perform classification tasks. While several machine learning-based techniques can accurately classify intra-subject data validation, cross-subject validation remains inadequate. To this end, a novel method is proposed in this paper to classify hand movements (cross-subject) by incorporating an attention mechanism (squeeze-and-excitation) with a modified inception-V1 model. In order to filter out less significant channels and data, input data is first simultaneously passed through a squeeze-and-excitation block and a ID convolutional block. The squeeze-and-excitation block is further followed by two ID convolution sub-branches. Moreover, inception modules are utilized to extract local sparse features from the data. The proposed model is evaluated on the well-known BCI-2000 motor imaging dataset. The model demonstrates an accuracy of 84.75% in cross-subject validation, by doing so, the proposed approach substantially outperforms several state-of-the-art methods. In the context of BCI, this research work has the potential to be utilized as an effective resource for future hand movement classification-based applications.
Using Mel-Spectrograms and 2D-CNNs to Detect Murmurs in Variable Length Phonocardiograms

As part of the George B. Moody PhysioNet Challenge 2022, we developed a computational approach for identifying abnormal cardiac valve function from phonocardiograms (PCGs). Our team, uke-cardio, developed a deep learning model that uses melspectograms of up to four different auscultation locations. CutMix and smooth labels were used to simultaneously learn different tasks. On the hidden test set, the classifier achieved a weighted accuracy score of 0.735 for murmur detection (ranked 15 out of 40 teams) and a challenge cost score of 11990 (ranked 8 out of 39 teams) on the outcome detection task. The source code is available at: github.com/msknorr/cinc22-pcg
Hybrid QPSO assisted Incremental Conductance MPPT Algorithm under Extreme Partial Shading Conditions

Photovoltaic (PV) technology has seen rapid growth over the last decade owing to its declining cost and minimal pollution. However, one major drawback of the PV systems is the non-linear characteristics of its output power under partial shading conditions (PSC). Varying weather parameters such as temperature and solar irradiance result in a multi-peak maximum power point tracking (MPPT) problem. To resolve this, many conventional and metaheuristic optimization MPPT algorithms have been proposed, some of which come with problems like slow tracking speed, increased oscillation, and no guarantee of convergence at the global maximum power point (GMPP). This paper presents a hybrid quantum particle swarm optimization assisted variable incremental conductance (QPSOVIC) algorithm which efficiently tracks maximum power (Pmax) under varying weather conditions. This metaheuristic algorithm overcomes the conventional tracking algorithm’s inability to track the GMPP and bypasses premature convergence of the traditional PSO algorithm. MATLAB/Simulink has been used for modeling and demonstration of the proposed algorithm.
Order Reduction of Fixed Coefficient System Using Optimization Techniques

This paper explores order reduction of fixed coefficient system using optimization techniques: Particle swarm optimization (PSO) and Greywolf optimization (GWO) techniques. By using these techniques, the higher-order system can be reduced to a lower-order model. The results obtained by both PSO and GWO are compared to the traditional method to demonstrate how superior the suggested model is based on the step response specification of the system. The key benefit of these PSO and GWO models is that it offers reasonable accuracy more quickly than existing methods. Additionally, the original system time response characteristics are preserved in the reduced model.
Pid Controller Tuning In Magnetic Levitation System Using Giza Pyramid Algorithm

This paper presents the control and stabilization of levitation of the magnetic ball at a certain height; and tracking the trajectory for a time period. Various parameters vis. Rise time, Maximum Overshoot, Settling time, and Steady State Error have been compared with different tuned gain values of PID controllers. The optimization method used to obtain optimal gain values of the PID controller is the recently developed Giza Pyramids Construction (GPC) Algorithm. Simulation and computational results for GPC have been compared with those of Genetic Algorithm(GA) and Particle Swarm optimization(PSO) based PID controller tunning in order to validate the efficacy of the proposed approach.
A Methodology for Maintenance Analysis and Modeling Using Deep Learning

SUMMARY & CONCLUSIONSThis paper presents a methodology for applying topic modelling and deep learning to unstructured maintenance data, for improving availability and cost savings. Learning from textual technical data requires special skills and time. As a result, the valuable insights in such data are often trapped and not fully harnessed when necessary.The approach demonstrated in this work takes texts from service records as input to a Bidirectional Encoder Representations from Transformers (BERT) model, and yields service action topics, according to frequency of occurrence. The yielded service action topics were regarded as failures and linked to the timestamps when they occurred. Failures were selected and analyzed using a 3-parameter Weibull model to determine the reliability profile. The Weibull model parameters from the model were used to determine the optimal repair time, considering the theoretical costs of preventative and corrective maintenance. Additionally, deep learning was applied to the service diagnostic texts to classify them into preventative maintenance and corrective maintenance.The benefit of the approach lies on its applicability to large textual datasets; to identify recurring activities hidden in maintenance records, prioritize cost savings and digitally transform system availability.
Modeling and Accelerating of Mobile Advertisement Dissemination in Vehicular Networks

Mobile advertising is an appealing application in vehicular networks, by which relevant information can be delivered to (and then accessed by) vehicles and pedestrians in different regions. However, due to the complex interplay of mobility and data communication in the dissemination process, it is still not clear how likely an advertisement can be accessed at time t in a particular region, and how to accelerate the dissemination with a limited budget. To solve these problems, we establish a data dissemination model, rigorously define the stage of a dissemination process, and propose a total variance metric to quantitatively differentiate the impact of vehicle mobility from that of V2V communication. Simulations based on two real datasets of taxi traces show that the choice of initial advertisers (i.e., seeds) greatly affects the stage of the dissemination process, and two practical seed selection strategies are proposed as an application of the dissemination model.
AI Training for Thunderstorm Training: Better Situational Awareness for Disaster Tweets Using Context and Emotions

Finding and responding to people caught up in natural disasters is an important and difficult task. The task is usually done by rescue parties. However, it is difficult for such rescue parties to get a good situational awareness of disaster victims. Therefore, improving the situational awareness of such victims with the help of technology, especially language technology has become a popular research topic. A typical approach includes performing an emotion analysis of Twitter messages sent in realtime during the disasters with the assumption that negatively charged tweets are sent by the victims. However, in reality, the obtained results usually contain a mixture of tweets from various, mostly unrelated users. To address this problem, we propose a two-step filtering method to firstly filter out tweets of users least probable to be sent by the victims. We applied this method to tweets by users caught up in thunderstorm training and found that disaster-related tweets containing only first-hand information were the most useful for improving situational awareness.
Prediction of Electric Vehicle's Annual Accessibility to Chargers for Providing Ancillary Services Using an Efficient Random Forest Method

The use of electric vehicles (EVs) in the power system has grown phenomenally, and when combined with smart grids, a wealth of raw data is accessible. It is challenging to plan and schedule for EVs due to the randomness of their driver behavior and their uncertainties. To cope with these uncertainties, a supervised machine-learning framework (Random Forest) is developed using an open-source application (emobpy) that simulates EVs to help EV aggregators and drivers predict annual charger accessibility. Since ML models are complex black boxes to decipher, a game theory method SHAP (SHapley Additive exPlanations), is employed to indicate the impact of each feature on the model outcome. EV aggregators can plan their market participation using this model. A simulation of the proposed framework in the frequency-controlled normal operation reserve market grew EV aggregators' revenue, indicating its effectiveness.
A New Topology of Single-Phase Transformerless Inverter for Reducing Leakage Current of Grid-tied Photovoltaic Systems

This paper is about a novel transformer-less grid connected single-phase photovoltaic inverter. Now-a-days fossil fuels are in scarce. So, renewables are the only way to fulfill the increasing energy crisis. Solar energy, tide energy, wind energy are different sources of renewable energies. Among those, solar energy is most reliable, cost-free, simple processing and mostly available energy source. The inverter topology described in this paper converts the dc power captured in photovoltaic cells to ac power and supplies it directly to the grid. The grid voltage and current THDs are kept below 2%; extensive leakage current flow has been controlled; and power quality is much improved by this unique inverter topology. The power switches have been controlled using SPWM in addition to unipolar control. However, the common mode voltage has remained constant, which has also helped to lower the leakage current to 7.205mA. In this topology with an LCL filter, eight super junction IGBTs and six diodes have been employed. With the help of simulation findings, the leakage current suppression has been thoroughly examined. The switches decreased the leakage current but had little impact on efficiency. This topology has been verified through MATLAB/Simulink software environment.
Reachable workspace and Robot-assisted personalized rehabilitation training of upper limb

The increasing number of patients with upper extremity hemiplegia seriously affects their activities of daily living, and robot-assisted rehabilitation training reduce the burden on therapists. Due to the different conditions of patients, the need for personalized rehabilitation training methods is obvious. This paper proposes a method to meet the needs of personalized rehabilitation training. A 9-DOF upper limb kinematic model is constructed, including 2-DOF at the sternoclavicular joint, 3-DOF at the shoulder, 2-DOF at the elbow and 2-DOF at the wrist. A combination of the geometric method and the Levenberg-Marquardt (LM) algorithm is proposed to analyze the inverse kinematics of the upper limbs, the geometric method is used to calculate the initial value, and the LM is iteratively calculated to optimize the trajectory of the upper limbs. The reachable workspace is mapped to the working space of the robot, and a method of combining the reachable workspace of the upper limbs with the nonlinear potential field function is proposed to adjust the change of the force field. Thus, personalized rehabilitation training based on the motion range of the affected limb is realized to meet the needs of different patients. Finally, the effectiveness of the proposed method is verified by experiments.
Wind Generators Ferroresonance Overvoltage Protection Methods: A Review

Ferroresonance, as a sustained transient phenomenon associated with excessive voltages and currents, can cause severe consequences if not mitigated properly. In that regard, ferroresonance limiters or inhibitor devices can mark promising solutions against such adverse situations. This paper presents a comparative survey of research activities and emerging technologies for ferroresonance protection schemes in wind generation systems. In this article, after mathematically defining the ferroresonance phenomenon in wind generation systems, a short discussion is made on different types of wind generators with a focus on their vulnerability to ferroresonance. Subsequently, a comprehensive comparative study on ferroresonance protection systems is presented based on their technical operational characteristics. It is understood that inductive and resistive overvoltage protection systems possess superior performances and higher operating speeds in comparison with other protection systems.
Multi-Attention Gate Based U-net For Retinal Vessel Segmentation

Retinal blood vessel segmentation images can be used to detect and evaluate various cardiovascular and ophthalmic diseases. However, due to the intricate vessel structures and blurred boundaries of vessels, it is a huge challenge to efficiently and accurately segment blood vessels. To deal with the above problems, this paper improves on the U-net by firstly using multi-scale feature convolution with kernels of varying size for feature extraction. Second, a non-local attention mechanism is applied to obtain richer global semantic information. Then multi-attention gate is used in the skip connection part by inputting feature maps of various scales and dimensions and selectively learning the interrelated regions, which improves the segmentation ability of the network model for the tiny structure of blood vessels. Quantitative and qualitative experimental results on two public datasets, DRIVE and CHASE_DB1, demonstrate the effectiveness of the proposed method.
Decomposition of Petri nets and Lagrangian relaxation for solving routing problems for AGVs

In this paper, we propose a Lagrangian relaxation method for solving routing problems for multiple AGVs by decomposition of timed Petri nets. The AGV routing problem is represented by an optimal transition firing sequence problem for timed Petri nets. The timed Petri net is decomposed into several subnets in which the subproblem for each subnet can be easily solved by Dijkstra's algorithm. We show that each subproblem generated by each subnet is polynomially solvable. The optimality of the solution can be evaluated by the duality gap derived by the Lagrangian relaxation method. The performance of the proposed method is compared with a conventional optimisation algorithm with the penalty function method. The effectiveness of the proposed method is demonstrated.
A Signal-Physical Siamese neural network for Wi-Fi Fingerprint localization

It is important to obtain accurate indoor location information in the Wi-Fi fingerprinting localization system, which suffers from a lot of labour-power to construct the fingerprint database. Improving positioning accuracy with less manual labour is still an issue. In this paper, we propose a signal-physical Siamese neural network SPSNN, consisting of a signal distance matrix encoder and a physical distance matrix encoder to learn features. The main idea of the SPSNN is to use the physical feature to constrain the signal feature. Grid fingerprints are constructed using crowdsourcing samples. We compute the distance difference between the sample and different grid fingerprints in signal space to build the pictures as the input of the Siamese network for further feature extraction. In addition, we exploit the distance distribution information in physical space to modify and constrain the signal feature vector during the network training phase. The position prediction is given by the regression method. Experiments on-field measurements validate the effectiveness and robustness of the proposed model, and the optimal localization accuracy is less than 1 m.