Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Sizing of IPM Machines for Automotive Traction Application

This paper presents the implementation of an electrical machine design tool including its overall architecture. The tool is flexible and can be employed for the sizing of any permanent magnet synchronous machine. The case study considered in this work is the design of V-shaped interior permanent magnet synchronous machine for automotive applications. The preliminary design stage is considered with the aim of selecting the optimum machine pole pairs number and rotation speed. A multi-objective genetic algorithm is linked to the design tool and adopted to maximize the machine power density in terms of kW/L as well as to minimize the machine cost. The outcomes of this work are showing the variation of the above objective functions with respect to the pole-pair number and gearbox ratio as well as their effect on the machine efficiency.
Semi-Analytical Calculation of Field Distribution in Yoke/Tooth Transition in Electrical Machines

This paper investigates a novel approach of calculating the spatial field distribution in the yoke and the transition of yoke and teeth. First, an analytical field calculation approach based on the Laplace equation is presented and compared to a FEM calculation. To increase the agreement of FEA and analytical method, a Schwarz-Christoffel mapping is introduced. By the use of a conformal mapping in combination with analytical field calculation, the transition area of tooth and yoke can be modelled. At last, the results are compared to a 2D FEA.
Depthwise Separable Convolutions for Short Utterance Speaker Identification

At present, the traditional speaker identification methods cannot perform well under short test speech conditions. In addition, speaker identification based on deep learning often requires a large amount of data for training when the speaker set is relatively large. In order to solve these two issues, this paper presented a novel deep convolutional neural networks (CNN) model based on depthwise separable convolutions for text-independent speaker identification in case of limited training speech and short test utterances. The CNN model automatically learned the deep features of the speaker from the spectrogram generated by the speech segment. In the case of taking 1-min speech from each speaker for training, the proposed model achieved the highest recognition rate compared with the other two classic models and obtained the accuracy of 99.08% on the test sets consisting of 4s speech segments. In addition, we proposed a method to expand effective region of spectrogram generated by test speech less than 4s, which generated a new spectrum by copying the effective part of the original spectrum to the blank part. The proposed speaker identification system significantly improved the classification effect of short utterances (< 4s) by using this method.
Instance Segmentation of Anatomical Structures in Chest Radiographs

Automatic and accurate segmentation of anatomical structures in chest radiographs is fundamental and essential for computer-aided diagnosis system. We introduce Mask R-CNN for instance segmentation of lung fields, heart and clavicles. This method efficiently detects different structures and generates accurate segmentation mask for each instance. To the best of our knowledge, we are the first to implement instance segmentation of these three anatomical structures in chest radiographs. We have done extensive experiments on a common benchmark dataset. Results show that the best of our models achieves the state-of-the-art segmentation performance on image resolution of 512 × 512. The Dice and Ω similarity are 0.976 and 0.953 for lung fields, 0.949 and 0.904 for heart, 0.920 and 0.852 for clavicles. And the average contour distance outperforms human observer on both lungs and heart with image resolution of 256 × 256. In addition, it takes only 0.16 and 0.12 seconds per image for the above two resolutions during inference, which is comparable to or even better than current methods.
Multi-view Clustering via Simultaneously Learning Graph Regularized Low-Rank Tensor Representation and Affinity Matrix

Low-rank tensor representation-based multi-view clustering has become an efficient method for data clustering due to the robustness to noise and the preservation of the high order correlation. However, existing algorithms may suffer from two common problems: (1) the local view-specific geometrical structures and the various importance of features in different views are neglected; (2) the low-rank representation tensor and the affinity matrix are learned separately. To address these issues, we propose a novel framework to learn the Graph regularized Low-rank Tensor representation and the Affinity matrix (GLTA) in a unified manner. Besides, the manifold regularization is exploited to preserve the view-specific geometrical structures, and the various importance of different features is automatically calculated when constructing the final affinity matrix. An efficient algorithm is designed to solve GLTA using the augmented Lagrangian multiplier. Extensive experiments on six real datasets demonstrate the superiority of GLTA over the state-of-the-arts.
Characterization of Diseases Based on Phenotypic Information Through Knowledge Extraction using Public Sources

Despite the huge findings made by the study of the behaviour of diseases, there are currently many non-cure or non-treatment diseases and only some of their symptoms can be beaten. Understanding how the diseases behave implies a complex analysis that together with the new technologies provide researchers with more calculation and observational capabilities, as well as novel approaches that allow us to observe how the diseases behave and relate in different environments with distinct factors. Current research aims to find new ways of characterizing the diseases based on phenotypic manifestations using knowledge extraction techniques from public sources. With the characterization of the diseases, a better understanding about the diseases and how similar they are can be achieved, leading for example to find new drugs that can be applied to different diseases. In order to carry out the present research we have made use of our own dataset of symptoms and diseases developed using an approach that allows us to generate phenotypic knowledge from the extraction of medical information from several data sources.
Alzheimer's disease diagnosis from structural MRI using Siamese convolutional neural network

Deep learning (DL) methods have been recently utilized in medical imaging diagnosis and prognosis, which have significantly improved the performance of algorithms. As Alzheimer's Disease (AD) is one of the most financial costly diseases, many researchers have concentrated on introducing a high accuracy automated algorithm for classifying the AD and the Normal Control (NC) cases. In this paper we proposed a new deep learning based automated method for Alzheimer's disease diagnosis. Among the DL networks, the Siamese Convolutional Neural Network (SCNN) is implemented with three branches of ResNet-34 to discriminate between the AD and NC from the Structural Magnetic Resonance Imaging (sMRI). We selected 235 subjects from OASIS dataset. The proposed method achieved the accuracy of 98.72%. The proposed method has the best performance compared with the previous state of the art methods.
Identity Recognition Scheme Based on User Access Behavior

Users generate a large amount of access data in the process of network interaction, and their access behavior contains rich personal information that can represent the user identities. How to effectively extract personality characteristics from these access behaviors, and establish a user access model, is the key to recognize the user identities. The existing recognition methods are mainly based on face, fingerprint, gait and other biological characteristics, with high recognition accuracy but high requirements for data acquisition. This paper was based on user history behavior data that is easily available, proposed the method of time window partition and feature extraction based on fuzzy representation, fusion of multiple features to obtain semantically rich web usage context for model training, and finally establishes a personal access model with multi-layer perception for identity recognition. Experimental results show that the proposed method is accurate in real data sets.
Correlation Filter Tracking with Adaptive Proposal Selection for Accurate Scale Estimation

Recently, some correlation filter based trackers with detection proposals have achieved state-of-the-art tracking results. However, a large number of redundant proposals given by the proposal generator may degrade the performance and speed of these trackers. In this paper, we propose an adaptive proposal selection algorithm which can generate a small number of high-quality proposals to handle the problem of scale variations for visual object tracking. Specifically, we firstly utilize the color histograms in the HSV color space to represent the instances (i.e., the initial target in the first frame and the predicted target in the previous frame) and proposals. Then, an adaptive strategy based on the color similarity is formulated to select high-quality proposals. We further integrate the proposed adaptive proposal selection algorithm with coarse-to-fine deep features to validate the generalization and efficiency of the proposed tracker. Experiments on two benchmark datasets demonstrate that the proposed algorithm performs favorably against several state-of-the-art trackers.
Verification on the Feasibility of A Vision-Based Swallowing Detection Method

During the irradiation of laryngeal cancer, the motion occur from swallowing can cause the area to be irradiated to detach from the clinical target volume. Therefore, it is necessary to detect the number and time of the patient’s swallowing during radiotherapy and take corresponding remedial measures afterward. This paper proposes a non-contact swallowing detection method based on the visual sensor which monitors swallowing by detecting the movement of the thyroid cartilage. In our method, first the video images of the thyroid cartilage are taken by a visual sensor, then an improved template matching algorithm is adopted for signals extraction from video images, finally the number of swallowing and the movement time of swallowing can be obtained by analyzing the signals. To verify the feasibility and accuracy of this method, some experiments are made in ten normal subjects. The results show that this method is feasible for detecting the number and time of swallowing movement.
Design and Evaluation of a Factorization-Based Grasp Myoelectric Control Founded on Synergies

In this article we present a factorization-based myoelectric proportional control that uses surface skin electromyographic (sEMG) measurements to estimate the hand closure level of a user for telemanipulation purposes. The sEMG-based proportional control design is presented and the results of an experimental session are reported. In particular, involving one healthy subject, four different factorization algorithms are tested (Factor Analysis, Fast Independent Component Analysis, Non-negative Matrix Factorization and Principal Component Analysis) and quantitative evaluated along four different daily session using four different error metrics (Root-Mean-Square Error, Normalized Root-Mean-Square Error, cross-correlation coefficient and Dynamic Time Warping measurement). The metrics are computed comparing the sEMG-based estimation of the hand closure level with a ground-truth signal obtained through a motion tracking system. The results report for better performances of the Non-negative Matrix Factorization algorithm, that can be used for controlling robotic hands in a real telemanipulation scenario. Therefore, the proposed myoelectric proportional control was finally tested in a simple validation grasping scenario using a real robotic hand, reporting for user's simplicity and intuitiveness in regulating the grasp closure in accordance with different objects.
Analysis and Identification of Dermatological Diseases Using Gaussian Mixture Modeling

Skin diseases are common and are mainly caused by virus, bacteria, fungus, or chemical disturbances. Timely analysis and identification are of utmost importance in order to control the further spread of these diseases. Control of these diseases is even more difficult in rural and resource-poor environments due to a lack of expertise in primary health centers. Hence, there is a need for providing self-assisting and innovative measures for the appropriate diagnosis of skin diseases. Use of mobile applications may provide inexpensive, simple, and efficient solutions for early diagnosis and treatment. This paper investigates the application of the Gaussian mixture model (GMM) based on the analysis and classification of skin diseases from their visual images using a Mahalanobis distance measure. The GMM has been preferred over the convolution neural network (CNN) because of limited resources available within the mobile device. Gray-level co-occurrence matrix (GLCM) parameters contrast, correlation, energy, and homogeneity derived from skin images have been used as the input data for the GMM. The analysis of the results showed that the proposed method is able to predict the classification of skin diseases with satisfactory efficiency. It was also observed that different diseases occupy distinct spatial positions in multidimensional space clustered using the Mahalanobis distance measure.
Advanced Uncertainty Calculation Method for Converter-Fed Motor Loss Determining

There is only a single practical method to determine the losses of converted-fed electrical machines. The method is based on mechanical output and electrical input power measurements. When this method is used, the loss measurement uncertainty is a function of the efficiency. The electrical motor efficiency increases non-linearly with increasing rated power. Therefore, the higher the rated power of the motor is the more care should be laid in the analysis of the efficiency measurement results. Here, a workflow starting from measurement instruments' data sheet information to final measurement uncertainty result is presented for three converted-fed induction motors with different rated power of 15 kW, 37 kW and 75 kW. The results are applicable for all rotating field machines regardless of technology.
Detecting Pedestrians with YOLOv3 and Semantic Segmentation Infusion

Pedestrian detection has been considered essential for many real-world applications. Continuous progress in the detection accuracy tends to be supported by solutions of increasing complexity, which inflicts scaling processing costs. In this paper, we counter that tendency by exploring the fastest generic object detector, YOLOv3, by applying in the learning phase the weak semantic segmentation infusion technique - inspired in the SD- SRCNN - increasing its accuracy without affecting the inference speed. Experiments are run on the Caltech Pedestrian Detection Benchmark, as well as in a real-world video surveillance scenario with the PTI01 Pedestrian Detection Dataset. Our proposed method demonstrates infusion benefits in some scenarios achieving competitive accuracy on Caltech, staying less than 3% behind the best method on the primary metric, while being 5 faster. We verify that, among the compared methods, YOLOv3 seems to be the most feasible one for practical applications. Lastly, we also contribute by providing improved annotations for the PTI01 dataset.
Simple Mechanical Rotor Position Estimation Method Based on Rotor Eccentricity

A simple mechanical rotor position estimation method is presented in the paper. The proposed method is based on the rotor eccentricity effect which is inevitable due to manufacturing and/or operating tolerance. Mainly because of dynamic rotor eccentricity, a mechanical frequency order ripple can appear in the torque. Based on this phenomenon, a mechanical rotor position estimation method is developed. Instead of using the torque signal which is not easy to obtain, by observing power signals, the mechanical rotor position can be obtained simply. The method can be applied to both interior permanent synchronous machines (IPMSM) and surface-mounted permanent synchronous machines (SPMSM). Based on the dSpace platform, the effectiveness of the proposed method is verified by experiment results.
Large-Scale Datasets for Going Deeper in Image Understanding

Recently, extensive efforts have been devoted to computer vision and machine learning by exploiting big data to explore many practical applications. However, these research fields are still quite limited not only by the sheer volume, but also the versatility and diversity, of the available datasets. In this paper, we target at four challenging and yet important computer vision tasks, namely, human-centered scene classification, attribute based zero-shot learning (recognition), human keypoint detection and image Chinese captioning. Four novel large-scale datasets are collected and annotated to facilitate these tasks of deeper image understanding. Labels, bounding boxes, attributes, keypoints and captions are annotated in corresponding datasets. These rich annotations bridge the semantic gap between low-level images and high-level concepts. Extensive experiments on baseline methods have been implemented and compared, which show that these learning tasks on our datasets are still challenging.
Voltage Control and Load Sharing in a DC Islanded Microgrid based on Disturbance Observer

Increasing DC loads along with DC nature of Distributed Energy Resources (DERs) raised interest to DC microgrids. Conventional droop/non-droop power sharing in microgrids suffers from load dependent voltage deviation, slow transient response, and requires the parameters of the loads, system and DERs connection status. In this paper, a new nonlinear decentralized back-stepping control strategy for voltage control and load sharing of DC islanded microgrids is proposed. The proposed method is robust against to the load variations and uncertainty in microgrid parameters and has excellent dynamic and steady state performance under different operating conditions. The local controller regulates the terminal voltage of dc-dc converter regarding to the local quantities without needs to additional data of other system components. For simplicity, the proposed method is simulated with PSIM software on a DC microgrid with two DGs. Different scenarios are studied to present the proposed method performance under different operating conditions. The results indicate the capability of the proposed method for voltage control and load sharing in DC microgrids.
Radiomics Textural Features Extracted from Subcortical Structures of Grey Matter Probability for Alzheimers Disease Detection.

Alzheimer's disease (AD) is characterized by a progressive deterioration of cognitive and behavioral functions as a result of the atrophy of specific regions of the brain. It is estimated that by 2050 there will be 131.5 million people affected. Thus, there is an urgent need to find biological markers for its early detection and monitoring. In this work, it is present an analysis of textural radiomics features extracted from a gray matter probability volume, in a set of individual subcortical regions, from a number of different atlases, to identify subject with AD in a MRI. Also, significant subcortical regions for AD detection have been identified using a ReliefF relevance test. Experimental results using the ADNI1 database have proven the potential of some of the tested radiomic features as possible biomarkers for AD/CN differentiation.
Video-Based Early ASD Detection via Temporal Pyramid Networks

Autism spectrum disorder (ASD) is a brain-based disorder characterized by social deficits and repetitive behaviors, high-rising in children. In this work, we first build a ASD video dataset, and then introduce the one glimpse early ASD detection (O-GAD) network, an effective and efficient end-to-end deep architecture for video-based early ASD detection. Our network can take arbitrary-length videos as input, detecting ASD typical actions and determining if repetitive behaviours appeared only at one glimpse. The experimental results show that our method outperforms other state-of-the-art video content analysis methods on this task in terms of mAP. Moreover, we conduct extensive ablation experiments to demonstrate the effectiveness and rationality of the designed network structure.
Bonded Magnets in PM-Assisted Synchronous Reluctance Machines: Performance Dependence on the Production Technology

The versatility and robustness with the low costs, characterize the synchronous reluctance SRM machines. On the other hand, some drawbacks limit the potential of such machines. A solution was found in the use of permanent magnets in the flux barriers. The adoption of traditional regular sintered magnets does not allow a complete filling of the flux barriers, therefore the adoption of bonded magnets has been proposed, and two different production technologies have been analyzed. Three prototype machines have been prepared: Reference SRM and two PM-assisted reluctance. Interesting results have been obtained from experimental tests and compared to FE simulation ones.
F-Number Adaptation for Maximizing the Sensor Usage of Light Field Cameras

Since the effective imaging area in micro lens array (MLA) of the light field camera cannot completely cover the MLA plane, the imaging response of MLA cannot fully cover the sensor plane, which decreases the sensor usage. Hence, an f-number adaptation model is proposed to maximize the sensor usage without changing the structure of light field cameras. By deriving the relationship between the main lens f-number, the macropixel diameter and the sensor usage, a constrained concave optimization model is proposed. A pixel extraction method is also proposed to make a full use of the increased effective pixels. The experimental results demonstrate the effectiveness of the proposed model in terms of the effective pixel number, the subaperture number and improving 3D reconstruction quality. It is also robust to different light field camera architectures and different arrangements of MLA.
Directly Follows-Based Process Mining: Exploration & a Case Study

Many organisations now seek to analyse and improve their processes using event logs from various IT systems supporting their operations. Process mining aims to obtain insights from such process data, using process discovery, conformance checking and performance measures. While many commercial process mining tools feature user-friendly directly follows-based process maps, they typically do not offer a way to assess the quality of the model, leaving users with potentially unreliable insights, which could lead to the wrong conclusion being drawn from these insights. In contrast, academic tools typically provide verifiable results, but are often difficult to use and understand for stakeholders, sometimes overgeneralising behaviour to fit more extensive process model formalisms. In this paper, we bridge this well-known gap between commercial and academic tools by combining sound process discovery, conformance checking and performance capabilities with user-friendly directly follows-based process models. We implemented these techniques in a new process mining tool and applied them to analyse several business processes in a Queensland Government department. We discovered sound directly follows-based process models from their logs, compared them with prescribed models and analysed the performance of these processes. In particular, our conformance checking techniques allowed to pinpoint deviations between prescribed processes and actual recorded behaviour. The outcomes of this case study are now being used to document, review, improve and automate processes.