Miracle-dz/UniFAR-FTUs huggingface dataset:
A Combined Projection for Remote Control of a Vehicle Based on Movement Imagination: A Single Trial Brain Computer Interface Study

Disabled patients using brain computer interface (BCI) applications have a more convenient life. The present study implements an electroencephalogram (EEG)-based signal processing algorithm for controlling a wireless mobile vehicle through imagination. The aim is to improve the filtered common spatial pattern (CSP) algorithm for BCI applications. The proposed method is a combination of the CSP projection with a Modified Secondary Projection of the filtered Common Spatial Pattern (MSPCSP). With this algorithm, distinctive differential features are obtained from the combination of the MSPCSP and CSP projection eigenvalues to identify four classes: moving-forward-for-pause, stop-for-pause, moving-forward-continuously, and stopped-continuously. The second contribution is the design of a task to produce clear imaginary movement patterns. The task is a combination of brain stimulation by viewing red and yellow sketches of the right hand that indicate opening the hand and making a fist. Eighteen subjects participated in the experiment for wireless control of a mobile vehicle in offline and real-time modes. The results were then evaluated through an accuracy and paired t-test statistical analysis for offline and real-time signal processing. The results based on the MSPCSP projection showed significant improvements in accuracy in comparison with the CSP projection: 82.16± 9.04% with $p < 0.05$ and 70.83± 8.27% for offline and real-time processing, respectively. In addition, the MSPCSP projection attained higher accuracies of 14.72% and 13.33% for offline and real-time processing, respectively. It was concluded that the MSPCSP projection generates more discriminant differential features than the filtered CSP projection. Further, the MSPCSP projection with the thresholds extend the limitation of CSP-based methods from two- to four-class identification.
An Assessment Method for Smart Grid Functions in Residential Loads

This paper presents a method for evaluating smart grid functions that are implemented to operate residential loads. The proposed assessment method is developed based on the energy not-served (ENS) determined at a point-of-supply feeding residential loads. Smart grid functions can operate energy storage appliances (household water heaters, air conditioners, and heating units) to store thermal energy during the daily off-peak-demand hours. The stored thermal energy is discharged during the daily peak-demand hours, thus reducing the power demands of residential loads. The differences in daily energy demands created by smart grid functions can provide an accurate assessment of the effectiveness of smart grid functions. The ENS-based method is tested for 200 residential households fed from 4 distribution transformers, and are operated by smart grid functions. In these tests, smart grid functions are implemented by the peak-demand management, direct load control, and demand response. Test results demonstrate the accuracy and simplicity of the ENS-based method to assess smart grid functions in terms of the ability to reduce the power demands of residential loads during peak-demand hours.
Onomatopoeia Search System Focused on Attributes Based on Sensibility and Various Sounds

In recent years, several onomatopoeias have been used in food advertisements as words to express texture. When consumers observe onomatopoeias in advertisements, they link them to food sales by associating them with the willingness to purchase a particular product. It is important to select onomatopoeias that appropriately express the product image. If the most suitable onomatopoeias that represent the advertised product or review text can be searched, it will be easier for consumers to understand the product better, and the advertisement, as a result, will be successful. The purpose of this study was to identify the attributes that characterize onomatopoeias from user reviews and to analyze the relationship between the sounds of onomatopoeias and each of their attributes to construct a system that can search for onomatopoeias by matching attribute images. In the proposed system, onomatopoeia words were extracted by arranging the onomatopoeias on a plane using principal component analysis and then clicking on this plane using a mouse. In addition, we confirmed whether the extracted onomatopoeias matched the images of the attributes through verification experiments.
Robust Self-Supervised LiDAR Odometry Via Representative Structure Discovery and 3D Inherent Error Modeling

The correct ego-motion estimation basically relies on the understanding of correspondences between adjacent LiDAR scans. However, given the complex scenarios and the low-resolution LiDAR, finding reliable structures for identifying correspondences can be challenging. In this letter, we delve into structure reliability for accurate self-supervised ego-motion estimation and aim to alleviate the influence of unreliable structures in training, inference and mapping phases. We improve the self-supervised LiDAR odometry substantially from three aspects: 1) A two-stage odometry estimation network is developed, where we obtain the ego-motion by estimating a set of sub-region transformations and averaging them with a motion voting mechanism, to encourage the network focusing on representative structures. 2) The inherent alignment errors, which cannot be eliminated via ego-motion optimization, are down-weighted in losses based on the 3D point covariance estimations. 3) The discovered representative structures and learned point covariances are incorporated in the mapping module to improve the robustness of map construction. Our two-frame odometry outperforms the previous state of the arts by 16%/12% in terms of translational/rotational errors on the KITTI dataset and performs consistently well on the Apollo-Southbay datasets. We can even rival the fully supervised counterparts with our mapping module and more unlabeled training data.
Low speed Performance improvement of Constant Switching Frequency DTC of Five phase Induction Motor

A conventional Hysteresis torque controller based DTC (HTC-DTC) of an induction motor is well known simple control technique with good dynamic performance which has comparable performance with filed oriented control method. The limitations of this control are variable switching frequency over operating speeds and poor low speed performance and higher torque ripples. This paper discuses inverter fed five phase induction motor with a constant switching frequency torque controller (CSFTC-DTC) based DTC to improve low speed performance and maintains constant switching frequency with various operating conditions. However, when compared to the classical direct torque control (HCT-DTC) method, the CSFTC-DTC gives sluggish torque dynamics due to frequent selection of null vectors during load disturbance. This article mainly contribute: to avoid the flux instability and flux droop at low speeds under light load conditions by applying active vectors frequently, to maintain constant switching frequency throughout all possible speeds, and to eliminate null vectors during torque dynamics in order to get fast response in torque transients. The proposed CSFTC-DTC scheme verified through MATLAB Simulink software results and compared with classical HTC-DTC.
High Speed Dynamic Climbing on Rough Exterior Surfaces

Currently, few scansorial robots exist that are capable of operating on rough exterior surfaces. None of these platforms have, as of yet, been able to achieve high speed dynamic climbing on these surfaces. This thesis shows that through the use of compliant micro-spines, a miniature dynamic climber, BOB, is capable of climbing dynamically at high speed on rough exterior surfaces. A new manufacturing method for micro-spines is examined. The effects of micro-spine arrays onto a dynamic climber are examined and how the micro-spines affect compliance, roll, and yaw. Methods to mitigate the effects of these dynamic motions are also examined and presented.
Failure Prediction Based on LSTM and SVM under SDON Architecture

Artificial Intelligence in data center optical network is very popular these years. In this paper, we proposed an optical network failure prediction scheme based on long short-term memory (LSTM) and support vector machine (SVM) under Software Defined Optical Network (SDON) architecture. LSTM is used to predict parameters of the optical network in the future, and then SVM is used to judge whether the network will fail. The prediction accuracy can reach 0.9063 which means this method is efficient. When the failure is predicted to occur, the controller issues instructions to avoid the failure.
Power Electronics Design Implications of Novel Photovoltaic Collector Geometries and Their Application for Increased Energy Harvest

Power Electronics Design Implications of Novel Photovoltaic Collector Geometries and Their Application for Increased Energy Harvest. (August 2011) Amulya Karavadi, B.E, Osmania University Chair of Advisory Committee: Dr. Robert S. Balog The declining cost of photovoltaic (PV) modules has enabled the vision of ubiquitous photovoltaic (PV) power to become feasible. Emerging PV technologies are facilitating the creation of intentionally non-flat PV modules, which create new applications for this sustainable energy generation currently not possible with the traditional rigid, flat silicon-glass modules. However, since the photovoltaic cells are no longer coplanar, there are significant new requirements for the power electronics necessary to convert the native form of electricity into a usable form and ensure maximum energy harvest. Non-uniform insolation from cell-to-cell gives rise to nonuniform current density in the PV material, which limits the ability to create seriesconnected cells without bypass diode or other ways to shunt current, which is well known in the maximum power tracking literature. This thesis presents a modeling approach to determine and quantify the variations in generation of energy due to intentionally non-flat PV geometries. This will enable the power electronics circuitry to be optimized to harvest maximum energy from PV pixel elements – clusters of PV cells with similar operating characteristics.
Shift Invariant Wavelet Denoising of Ultrasonic Traces

Basic wavelet denoising techniques rely on a thresholding of the discrete wavelet transform (DWT) coefficients of the noisy signal. Some improvements in noise reduction efficiency can be obtained by the use of shift-invariant undecimated wavelet transforms (UWT). Ultrasonic grain noise is one of the most usual types of noise present in ultrasonic non-destructive evaluation. It comes from reflections in the material structure, and occupies a frequency band very similar to that of the echosignals of interest. In this work, new advances in the application of redundant wavelet transforms to ultrasonic grain noise reduction are presented. Wavelet denoising is applied to several sets of synthetic ultrasonic traces, which are obtained from a model that includes frequency dependent attenuation for both grain and flaw backscattered echoes, frequency dependent scattering from the grains, and an accurate model for the pulse-echo frequency response of the piezoelectric ultrasonic transducer. Two processors based on traditional DWT and alternative a trous UWT denosing have been implemented and compared, using level dependent thresholds (appropriate for correlated noise), soft thresholding, and Universal, SURE and Minimax threshold selection rules. The performances of the two processors are analyzed in terms of the mean value and standard deviation of the signal-to-noise ratio (SNR) of different sets of ultrasonic traces before and after denoising. It is shown that a trous UWT processing provides better results than DWT with a general tendency to higher quality of the resulting traces and greater robustness of the processing. It is also shown that the better performance of the UWT is mainly related to the redundancy of the representation, since there are not significant variations between the threshold values obtained with each processor.
Automatic Report-Based Labelling of Clinical EEGs for Classifier Training

Machine learning classifiers for detection of abnormal clinical electroencephalography (EEG) signals have advanced signficantly in recent years, largely supported by the carefully curated Temple University Hospital Abnormal EEG Corpus (TUAB). Further progress towards clinically useful tools is likely to require larger volumes of data. In this study, we explore the viability and benefits of fully automated labelling of clinical EEG recordings based on the text in the clinical report, to efficiently exploit larger existing databases. We apply a machine learning classifier to the text reports in the Temple University Hospital EEG Corpus (TUEG) in order to label individual recordings. We show that training a deep convolutional neural network against the resulting dataset yields advantages in the resulting classification performance, namely increased area under the receiver operating characteristic curve and state-of-the-art specificity, albeit with a notable reduction in sensitivity. By demonstrating the viability of automatic report-based labelling, this paper opens the prospect of efficiently utilising the huge amount of historical EEG data in global medical archives to enhance the training of machine learning classifiers, either for enhanced general performance or bespoke training/evaluation for local populations.
Joint Extraction of Clinical Entities and Relations Using Multi-head Selection Method

The extraction of entities and relations from unstructured clinical records has been attracting increasing attention. In addition to the existing traditional methods, deep learning methods have also been proposed for entity and relation extraction. However, previous work on clinical entity and relation extraction did not consider the multiple relations between clinical entities, which often exist in clinical texts. To deal with multiple relations, we propose using a multi-head selection method for clinical entity and relation extraction. As pre-trained language models have been shown to be effective for clinical entity and relation extraction, we integrate a pre-trained language model with a multi-head model to jointly extract clinical entities and relations. The experimental results show that the proposed model is effective for entity and relation extraction on both the i2b2/VA 2010 and n2c2 2018 challenge datasets and outperforms the top-ranking systems in the n2c2 2018 challenge. We also evaluate the impact of four existing pre-trained language models on clinical entity and relation extraction performance. The domain-specific pre-trained language model improves the performance of clinical entity and relation extraction. Between BERT and CharacterBERT, which uses a Character-CNN module instead of BERT's wordpiece system to represent entire words, we find that BERT outperforms CharacterBERT on joint extraction of clinical entities and relations.
Faster Capacitated Arc Routing: A Sequence-to-Sequence Approach

The Capacitated Arc Routing Problem (CARP) is an NP-hard optimization problem that has been investigated for decades. Heuristic search methods are commonly used to solve it. However, given a CARP instance, most heuristic search algorithms require plenty of time to iteratively search for the solution from scratch, and hence may be impractical for emerging applications that need a solution to be obtained in a very short time period. In this work, a novel approach to efficiently solve CARP is presented. The proposed approach replaces the heuristic search process with the inference phase of a trained Deep Neural Network (DNN), which is trained to take a CARP instance as the input and outputs a solution to the instance. In this way, CARP could be solved by a direct mapping rather than by iterative search, and hence could be more efficient and more easily accelerated by the use of GPUs. Empirical study shows that the DNN-based solver can achieve significant speed-up with minor performance loss, and up to hundreds of times acceleration in extreme cases.
Effect of Unequal Clustering Algorithms in WirelessHART networks

The use of Graph Routing in Wireless Highway Addressable Remote Transducer (WirelessHART) networks offers the benefit of increased reliability of communications because of path redundancy and multi-hop network paths. Nonetheless, Graph Routing in a WirelessHART network creates a hotspot challenge resulting from unbalanced energy consumption. This paper proposes the use of unequal clustering algorithms based on Graph Routing in WirelessHART networks to help with balancing energy consumption, maximizing reliability, and reducing the number of hops in the network. Graph Routing is compared with pre-set and probabilistic unequal clustering algorithms in terms of energy consumption, packet delivery ratio, throughput and average end-to-end delay. A simulation test reveals that Graph Routing has improved energy consumption, throughput and reduced average end-to-end delay when conducted using probabilistic unequal clustering algorithms. However, there is no significant change in the packet delivery ratio, as most packets reach their destination successfully anyway.
Exploratory Data Analysis of the N-CMAPSS Dataset for Prognostics

In the recent years, industries such as aeronautical, railway, and petroleum has transitioned from corrective/preventive maintenance to condition based maintenance (CBM). One of the enablers of CBM is Prognostics which primarily deals with prediction of remaining useful life of an engineering asset. Besides physics-based approaches, data driven methods are widely used for prognostics purposes, however the latter technique requires availability of run to failure datasets. In this manuscript authors have aimed at performing exploratory data analysis (EDA) on the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset published by NASA. Although 8 datasets are publicly available, authors have chosen dataset 3 (DS03) for EDA in this paper which consists of 9.8 million instances and 47 features. The main aim of doing EDA is to gain better understanding of the dataset as it would facilitate in building a deep learning model that can be used for predicting RUL of the aircraft engines.
Twenty Years of Configuration Knowledge Modeling Research. Main Works, What To Do Next?

A configuration software (configurator) associates a knowledge base (KB) with a knowledge processing unit (PU). The KB describes all possible combinations of components while the PU overlays this knowledge with the customer requirements. Our work deals with the KB and the approaches, models, or tools for modeling configuration knowledge. Our goal is to present a small quantitative literature survey highlighting two work streams: the first one gathers modeling works dealing with constraint-based approaches while the second deals with ontologies, description logic, or object-oriented modeling approach. We will also consider hybrid approaches. We will present a quantitative analysis of published materials in Web of science over the last twenty years. The keywords occurrence versus time will also be studied in detail to identify tendencies in configuration knowledge modeling.
6G, LIFI and WIFI Wireless Systems: Challenges, Development and Prospects

While 5G is being deployed commercially worldwide, scientists have carried out research for 6G as well as WIFI 6G bands, and LIFI are also tested. Both advantages and disadvantages of these three wireless communication methods are focused and the respective application scenarios are described as well as the difficulties and challenges to be overcome. An integrated network system of space and earth is proposed to provide users with ubiquitous wireless network connection. Technologies and challenges required by three communication methods are sorted out and the way they can be combined and applied are analyzed through extensive research and analysis.
Innovative and Sustainable Supply Chain Model in Industry 4.0 Based on Moroccan Industrial Field

Moroccan industrial enterprises are leading many changes inside their supply chain to take advantage of industry 4.0 transformation. This optic aims to ensure sustainability and innovation simultaneously. Or, one of the most crucial operations is to enhance innovation inside supply chain processes and to improve supply chain resilience and sustainability regarding the changeable environment. Therefore, in this paper, we develop an integrated business model considering six constructs: supply chain integration, supply chain orientation, competition capability, business continuity management, environmental sustainability, and finally human and social approach. Then we apply and validate the business model to the case of large and medium Moroccan enterprises. The main objective is to study the impact of the considered constructs with the mediation of industry 4.0 technological systems in achieving supply chain innovation and sustainability. Our paper is built of four sections, the first lists previous studies performed in some countries, the second presents the proposed business model, the third describes the experimental data collection, and in the last part, we discuss the results of the data validation process, followed by a conclusion and work perspectives.
Accelerating Classification on Resource-Constrained Edge Nodes Towards Automated Structural Health Monitoring

Resource-constrained edge nodes are ubiquitous in industrial settings yet are challenged by limited computing resources. Leveraging computational advantage and perceptual awareness of Gabor filters, a hybrid classifier to mitigate computational requirements in the context of classification of key components of civil structures which are essential in the proactive structural assessment, specific repairs, and maintenance post-construction is given. Deployment of the hybrid classifier using the CoreML framework exhibits favorable classification accuracy and robustness as compared to contemporary state-of-the-art classifiers.
Performance Analysis of Train-Ground Communication System for Vacuum Tube High-Speed Flying Train

With the development of conventional wheel high speed railway, the Vacuum Tube High-speed Flying Train (high-speed flying train) has gradually entered people's vision. In order to guarantee the safe operation of the train, train-ground communication is of vital important. However, there are few research about the communication performance over 500km/h. In this paper, the system-level simulator is used to evaluate system performance of 5G networks for the maglev in ultra-high speed scenarios (1000 km/h). Based on position-based results of Block-Error Ratio (BLER), spectral efficiency and throughput, we analysis the performance of train-ground communication system for Vacuum Tube High-speed Flying Train. Simulation results show that 5G system presents satisfying performance results for the train-ground wireless communication in terms of SE and throughput, but the BLER rises rapidly as the vacuum tube high-speed flying train moves away from the base station and the quality of train-ground communication cannot be effectively guaranteed