Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the potential public health impact of fibre enrichment: a UK modelling study

Abstract For improving human health, reformulation can be a tool as it allows individuals to consume products of choice while reducing intake of less desirable nutrients, such as sugars and fats, and potentially increasing intake of beneficial nutrients such as fibre. The potential effects of reformulating foods with increased fibre on diet and health need to be better understood. The objective of this statistical modelling study was to understand how fibre enrichment can affect the diet and health of consumers. The UK National Diet and Nutrition Survey datasets from 2014 to 2015 and 2015 to 2016 were utilised to evaluate intakes of fibre and kilocalories with a dietary intake model. Foods and beverages eligible for fibre enrichment were identified (n 915) based on EU legislation for fibre content claims. Those people who meet dietary reference values and fibre enrichment health outcomes such as weight, CVD and type 2 diabetes risk reductions were quantified pre- and post-fibre reformulation via Reynolds et al., D’Agostino et al. and QDiabetes algorithms, respectively. The fibre enrichment intervention showed a mean fibre intake of 19·9 g/d in the UK, signifying a 2·2 g/d increase from baseline. Modelling suggested that 5·9 % of subjects could achieve a weight reduction, 72·2 % a reduction in cardiovascular risk and 71·7 % a reduced risk of type 2 diabetes with fibre fortification (all Ps ≤ 0·05). This study gives a good overview of the potential public health benefits of reformulating food products using a straightforward enrichment scenario.
Foot-Based 6-DOF Haptic Interface with Force Feedback Capability for Third Arm Manipulation

In this paper, we propose a 6-DOF haptic interface with force feedback capability for foot-based interaction. To direct a "third arm" to any position that the operator wants to reach while both hands are busy, the controller needs 6-DOF input from a modality that does not rely on the hands. We focus on foot-based operation and propose a device that is composed of a parallel link and omni wheel. We evaluated the operating performance of this device during a robot manipulation task, an obstacle avoidance task, and a Fitts’ Law task. The results show the precision of the 6-DOF manipulations to be 1.83 cm. The performance of the obstacle avoidance task is significantly higher with force feedback. The performance measure of the Fitts’ Law task, the throughput, differed depending on the plane of operation. In comparison with prior study, the throughput of 1.21 bits/s, the average of all planes, was higher than the prior study. The proposed device could be effective as an interface to control the third arm.
Impact of e-mobility participation in the ancillary service market on the operation of high-density urban low voltage distribution networks

The present paper investigates the opportunities given by the participation of electric mobility in the ancillary service (AS) market, including the analysis of the impact of the charging process on the operation of urban distribution networks. Requested ASs are supplied by electric vehicles (EVs) through the optimal scheduling of their charging requests based on a heuristic algorithm. The impact of such ASs provision on the operation of the distribution grid is assessed. If grid technical limits are exceeded, the flexibility offered by the resources other than those provided by EVs (i.e., distributed generators, active customers, storage, etc.) is evaluated for solving the operation criticalities. Such evaluation is assessed by deploying a hierarchical multi-agent control system capable of handling the flexibility provided by the small resources managed through an aggregator. The validity of the proposed approach has been proved on a realistic scenario, modeling the Italian high-density urban low voltage distribution networks.
A Plane-based Map for Wheel-legged Rover Efficient Motion Planning during Planetary exploration*

This paper proposes an algorithm for wheel-legged robot mapping in rocky terrain environments. The proposed plane-based mapping method employs two density-based machine learning algorithms to determine approximate flat surfaces from the point cloud. It models the terrain as an elevation map but sacrifices its elevation accuracy to detect planar surfaces in the scenery. The plane-based terrain modeling method is a submap of the whole environment. It maps the robot's desired area to pre-plane the following motion patterns, providing a solution for wheel-legged robots taking advantage of wheels and legs. In cooperation with an extra motion library, the wheel-legged robot's transit speed and moving efficiency could be accelerated.
Dragon Rider - An Integrated Unmanned Quadruped-Hexarotor System for Flight-Impeded Area Exploration

Flight-impeded zones like forests, caves, and indoor areas have limited aerial space and a complex environment. Exploration in such zones requires terrestrial vehicles, which are usually not rapidly deployable. In this article, a rapidly deployable and low-cost integrated system of the quadruped robot and the hexarotor is built to address the exploration tasks in the flight-impeded zone. The two agents share one set of sensing and computing resources when concatenated and work independently when separated. The design utilizes the high motility of the hexarotor and the adaptability of the quadruped robot in a complex environment. Compared to the hybrid paradigm design, this design reduces the weight during exploration by 54.5% to improve endurance. The hexarotor is in charge of transportation while the quadruped robot conducts the exploration. The system can be automatically conjunct with a self-adaptive docking structure and the positioning and control algorithms. The docking structure also provides a stable wired connection between the two agents. The docking of the system is proved to be valid, and the feasibility of sharing resources is verified as well.
Semi-supervised 3D Object Detection via Temporal Graph Neural Networks

3D object detection plays an important role in autonomous driving and other robotics applications. However, these detectors usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging large amounts of unlabeled point cloud videos by semi-supervised learning of 3D object detectors via temporal graph neural networks. Our insight is that temporal smoothing can create more accurate detection results on unlabeled data, and these smoothed detections can then be used to retrain the detector. We learn to perform this temporal reasoning with a graph neural network, where edges represent the relationship between candidate detections in different time frames. After semi-supervised learning, our method achieves state-of-the-art detection performance on the challenging nuScenes [3] and H3D [19] benchmarks, compared to baselines trained on the same amount of labeled data. Project and code are released at https://www.jianrenw. com/SOD-TGNN/.
Comprehensive Design and Analysis of an Interior Permanent Magnet Synchronous Machine for Light-Duty Passenger EVs

This paper presents a detailed process for the design of interior permanent magnet synchronous machines (IPMSMs) for electric vehicle (EV) applications. First, a comprehensive design process featuring two objectives and three constraints is proposed to realize an optimal IPMSM design that can satisfy the key performance requirements for an EV application. The objectives and constraints were carefully selected and designed to obtain a balance between performance, efficiency, and cost. Because the inductances and magnet flux linkage of the machine are key design parameters for attaining optimal flux-weakening capability, the impact of the machine dimensions on these two parameters is thoroughly investigated and explained using both analytical and numerical techniques. Additional analyses including normalized key performance metrics and the impact of short-circuit faults in PM machines with three different characteristic current values (1, 1.3, and 2.6 pu) are also provided, highlighting the advantages and limitations of the three baseline designs.
Asynchronous SSMVEP BCI and Influence of Dynamic Background in Augmented Reality

One of the important challenges in designing an SSMVEP-based Brain-computer interface (BCI) under any augmented reality (AR) environment is the stability of the system under dynamically changing background conditions. In this study, a novel AR optical see-through (OST) light-weight headset was incorporated into a BCI system based on steady-state-motion-visually evoked potential (SSMVEP). The AR-BCI system was evaluated in a five-class asynchronous scenario, with four intentional control (IC) states and one no-control (NC) state. The system was tested under two background conditions: active (AB) and non-active (NB) background. Offline analysis was performed to evaluate the IC vs. NC classification performance using the complex-spectrum based convolutional neural network (C-CNN). The average performance metrics for 1-second window length were (NB vs. AB): Accuracy: 83%±9% vs. 81%±8%, F1-score: 0.82±0.1 vs. 0.80±0.09 and False Activation Rate: 16%±11% vs. 17%±10%. The results suggested that the C-CNN method applied on the SSMVEP responses in AR provides high IC vs. NC classification performance, and more importantly, is robust to change in dynamically changing background conditions.
A Recommender System Based on Model Regularization Wasserstein Generative Adversarial Network*

A recommender system (RS) commonly adopts a High-dimensional and sparse (HiDS) matrix to describe user-item preferences. Collaborative Filtering (CF)-based models have been widely adopted to address such an HiDS matrix. However, a CF-based model is unable to learn the property distribution characteristic of user’s preference from an HiDS matrix, thereby its representation ability is limited. To address this issue, this paper proposes a Model Regularization Wasserstein GAN(MRWGAN) to extract the distribution of user’s preferences. Its main ideas are two-fold: a) adopting an auto-encoder to implement the generator model of GAN; b) proposing a model-regularized Wasserstein distance as an objective function to training a GAN model. Empirical studies on four HiDS matrices from industrial applications demonstrate that compared with state-of-the-art models, the proposed model achieves higher prediction accuracy for missing data of an HiDS matrix.
TS-Net: Texture and Shape Stream Network for Retinal Vessel Segmentation

Retinal blood vessels are the only blood vessels that can be directly observed in the human body through non-invasive methods. The precise segmentation of retinal blood vessels helps us diagnose fundus diseases. We propose a network model-TS-Net (Texture and Shape Stream Net) that combines shape information and texture information. The shape stream is used to capture the shape and orientation of the vascular tree. Shape stream is composed of Residual Block, Gated Convolutional Layers (GCL) and vascular orientation modules. The texture stream is used to learn dense pixel information and features. In order to prevent the network from overfitting, we modify the convolutional layer network and get the Improved Dropout Block. We fuse the dense pixel information features from the texture stream and the shape trend features from the shape stream to produce a refined segmentation result. We use the color fundus DRIVE dataset and the CHASE_DB1 dataset to evaluate TS-Net. Through the comparison of experimental results, we find that TS-Net is effective and achieves advanced performance.
Cross-Subject Driving Fatigue Detection Model Based on Feature Selection of Critical Functional Connectivity

Driving fatigue in monotonous or demanding driving situations has been a major concern for modern traffic and public safety. Continuous efforts have been made to develop a practical method for driving fatigue detection. Because of its non-invasive and high-temporal resolution, EEG has long been considered as a salient biomarker for driving fatigue detection in recent studies. It is noteworthy that most of the studies up to date focused on single-subject fatigue detection, scattered studies have attempted to develop feasible method for driving fatigue detection across different subjects. In the current work, we used critical functional connectivity (FC) as features to build both single-subject and cross-subject mental fatigue detection models. EEG data were recorded from 19 subjects during a 90-min simulated driving experiment. Multi-band FC matrices were established using Phase Lag Index. Three feature selection methods (including SVM-RFE, Sparse Regression, and PCA) were then used to determine the most contributing FC features for driving fatigue detection. We achieved a superior classification accuracy (99.02%) in single-subject driving fatigue detection using the discriminative connection features from 3 frequency bands (i.e., θ, α, and β) and a comparable accuracy of 97.34% in cross-subject driving fatigue detection. Our results demonstrated that critical connection is an efficient feature for cross-subject driving fatigue detection, and the feature selection results may help build a practical EEG-based driving fatigue detection system.
Flexible Strain Sensors Fabricated by Fused Deposition Modeling-Based Multimaterial 3D Printing with Conductive Polyurethane Composites*

Flexible strain sensors have attracted much attention due to their excellent stretchability, conformability and ability to measure large deformations. These sensors have found a wide range of applications, including health tracking, human-machine interaction, and human motion monitoring. However, current mass-production methods do not support the small-batch customization of flexible strain sensors, and the additional mounting step reduces the structural integrity. This paper presents a widely accessible approach for the rapid fabrication of flexible strain sensors that are directly printed onto objects in an on-demand and reliable manner by exploring the multimaterial fused deposition modeling techniques and optimizing the composition of functional filaments. Furthermore, the feasibility and effectiveness of the proposed method are demonstrated in the development of customized, flexible strain sensors that have complex sensing patterns and geometries.
Identification of Locomotion Modes Based on Artificial Intelligence Algorithms Using Surface Electromyography Signals

Today, Electromyography (EMG) signals have many applications in the engineering sciences, especially in biomechanics and prostheses, which normalizes the lives of people with disabilities. The purpose of this work is to evaluate the accuracy of various Machine Learning algorithms for classifying different locomotion modes using the EMG signal, from which the signal features including Mean absolute value (MAV), Root mean square(RMS), Waveform length(WL), Variance(VA) and Zero crossing(ZC) are extracted. K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multi Lyer Perceptron (MLP) are employed as classifiers. Our features that extracted from dataset are devided into the parts: Training, Validation, and Test. The results yield the MLP algorithm with adam optimizer had the highest accuracy among other algorithms with an accuracy of 0.992. This satisfying accuracy along with the fast prediction speed of MLP algorithm make this method an appropriate candidate for online applications such as control of rehabilitation devices.
Continuous Estimation of Knee Angles from Decomposition of Single Channel Surface Electromyography Signals

Multiple surface electromyography (sEMG) channels used in motion estimation would increase implementation complexity. However, reduced sEMG channels would lead to insufficient information and deteriorate the estimation performance. This paper studies the problem of motion estimation based on single-channel sEMG signals by combining a decomposition method into a state-space model. More precisely, the state-space model consisted of an sEMG-driven motion model used to estimate joint angles directly and observation functions applied to correct the estimation, in which the observation functions were constructed by features of sub-channel sEMG signals from decomposed single-channel sEMG signals. We tested the effectiveness of the proposed method by estimating the knee joint angles based on six kinds of single-channel sEMG signals. Each kind of sEMG signal was decomposed into m sub-channel, where m ranged from 2 to 10, respectively. The results showed that values of normal root-mean-squared error (NMRSE) and correlation coefficient (CC) were below 0.16 and over 0.85 based on decomposed signals of the gastrocnemius. Compared to those results without applying the decomposition method, the NRMSE and CC value were decreased at least 33.31 % and increased at least 11.01%, respectively. The results verified the effectiveness of the proposed method.
Detection Method for Broken Rail Based on Rate of Change of Strain Mode

In order to quickly and accurately detect the rail damage and the position and extent of damage, a new rail damage detection method which is based on the rate of change of strain mode is put forward. Taking five cross rail as an example, through the numerical simulation analysis and laboratory model test, the results show that: Based on the frequency change, it can determine the rail damage occurs, but is unable to determine the location and extent of damage; based on strain modal changes,it can determine the rail damage, the location and extent of injury; based on strain modal changes in the rate of change, it can determine the rail damage, the location and extent of injury; injury severity influences damage identification greatly.Injury severity is greater, the rate of change of strain mode of the injuried element is bigger. The two order strain mode change rate of the rail can determine injury occurrence, damage location and damage degree.
Plane-based Accurate Registration of Real-world Point Clouds

Traditional 3D point clouds registration algorithms, based on Iterative Closest Point (ICP), rely on point matching of large point clouds. In well-structured environments, such as buildings, planes can be segmented and used for registration, similarly to the classical point-based ICP approach. Using planes tremendously reduces the number of inputs.In this article, an efficient plane-based registration algorithm is presented. The optimal transformation is estimated through a two-step approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) benchmark dataset show that the proposed method enables to successfully register 100% of the scans from the three indoor sequences. Experiments also show that the proposed method is robust in large motion scenarios and more accurate than other state-of-the-art algorithms. Moreover, a new challenging dataset, LOOP’IN, is provided. It is composed of two loops in real-world indoor scenes, with a large number of scans captured with a 3D LiDAR. Tests led on this dataset show that the algorithm is able to register long sequences, to close loops and to build an incremental map of the explored environment.
Fast and robust template matching based on ring projection and pseudo-Zernike moments for automatic optical inspection*

The traditional template matching methods are very time-consuming and cannot deal with the problems of rotation, scaling, and multi objects matching at the same time. In this paper, we propose a two-stage fast and robust template matching method based on ring projection transformation and pseudo-Zernike moments, which can be used in automatic optical inspection. In the first stage, we use dynamic time warping to estimate the scaling factors and obtain the preliminary candidates according to the extracted ring projection vectors. And then the candidates of the multi objects are obtained by non-maximum suppression. In the second stage, the fine matching process based on pseudo-Zernike moments is performed. Besides, we apply the image pyramid search strategy to improve the computational efficiency. The rotation angle is estimated by the improved ring-shifted method. The experimental results show that the mean errors of rotation, scaling, and translation for the image with or without noise or illumination changes are less than 0.2412°, 0.0037, and 1.0826 pixels, respectively. Compared with the conventional search method, the image pyramid search strategy increases the computational efficiency by 50 times. The experiments of robustness demonstrate that the template matching method we proposed can still obtain high accuracy even in the case of noise or illumination changes. In addition, it can be used for multi objects detection.
VMD-WSST: A Combined BCI Algorithm to Predict Self-paced Gait Intention*

Prediction of movement intention is of paramount importance to enable volitional control of assistive devices. The performances of current Brain-Computer Interfaces (BCI) are yet to reach the desired degree of accuracy necessary for real-life assistive technologies. Therefore, the prediction of gait intention remains a critical topic of research. This paper proposes an algorithm by leveraging two powerful empirical time-frequency analysis tools: Variational Mode Decomposition (VMD) and Wavelet Synchrosqueezed Transform (WSST). A combination of VMD and WSST was implemented to extract high-quality features in the time-frequency plane from Electroencephalography (EEG) data collected from six healthy individuals. The data were collected while they performed self-paced repetitions of gait initiations and terminations without any external audio or visual cue. The extracted features were later used to train a Support Vector Machine (SVM) classifier with a radial basis kernel to predict the intention to start or stop the gait cycle from gait-related EEG data. The combined VMD-WSST approach reached 83.36±1.75% accuracy, 82.83±2.99% sensitivity, and 83.45±3.59% specificity in starting intention detection. While, in the case of stopping intention prediction, the classification accuracy, sensitivity, and specificity were 81.57±1.70%, 81.14±3.06%, and 82.06±3.28%, respectively. The performances obtained by the proposed methodology were better than or competitive with those obtained by numerous state-of-the-art BCI methodologies. The results of this study show promise in predicting intention to start or stop walking from EEG, which could potentially enable assistive devices to be controlled volitionally.