Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting and Residual Learning Scheme with Pseudoinverse Learners

The traditional gradient descent based optimization algorithms for neural network are subjected too many vulnerabilities, such as slow convergent rate, gradient vanishing and falling into local minima. Therefore, the alternative non-gradient descent learning algorithm was proposed and prevalently applied in kinds of domains, such as pseudoinverse learning algorithm (PIL). However, when a special variant of the PIL, taking the random configuration of weight parameters, is adopted, the generalization ability needs further improvement although it has excellent training efficiency. Thus, on consideration of integrating the idea of ensemble learning, we proposes two methods to enhance basic PIL. One method is equivalent to an additive model, which can raise the network’s performance by introducing boosting mechanism, and the other is to adopt a recursive way to rectify the hidden layer output of the neural network, then the relative better model is used in the subsequent prediction. Comprehensive evaluating experiments are conducted on several datasets, and the experimental results illustrate that the our proposed methods are effective on the classification accuracy.
Wearable CSRR-based Sensor for Monitoring Glycemic Levels for Diabetics

Monitoring glycemia levels in people with diabetes has developed rapidly over the last decade. A broad range of easy-to-use systems of reliable accuracies are now deployed in the market following the introduction of the invasive self-monitoring blood glucose meters (i.e. glucometers) that utilize the capillary blood samples from the fingertips of diabetic patients. However, the limitations and discomforts associated with these painful finger pricking devices have established a new demand for non-invasive pain-free blood glucose monitors to encourage more frequent glucose checks and thereby contribute more generously to diabetes care and prevention. In this study, a novel microwave biosensor is developed in a wearable format to enable non-invasive real-time monitoring of blood glucose level. The design comprises three cells of circular complementary split ring resonators (CSRRs) incorporated in the ground plane of an FR4 dielectric substrate. The passive sensing elements (CSRRs) are excited remotely via a coupled antenna to enable the wearable sensing in a reader/tag configuration. The CSSR-sensor is numerically modeled and analyzed for sensing the glucose concentrations relevant to diabetes condition (60-500mg/dL) by tracking the resonant amplitude variations in the frequency range 1-4GHz. The sensitivity performance of the TP-CSRR tag is practically demonstrated through in-lab measurements using a VNA setup.
Comprehensive Thermal Design of A 250 MVA Oil-Immersed Auto-Transformer Based on Taguchi Method and Multi-Physics Coupled Approach

For large oil-immersed transformers, the thermal design is an essential issue to guarantee the operation safety. Since the outer sizes of the large scale transformers are typically 103~104 times greater than that of the small scale components, such as oil channels, windings, insulation sheets, etc., the integrated thermal design of the whole transformer is a difficult task. In this paper, the thermal designs of the external and internal components are conducted separately and comprehensively. An electromagnetic-fluid-thermal coupled method for the accurate transformer temperature rise prediction is proposed with the calculation process established. The thermal tests on a 250 MVA, 220 kV auto-transformer prototype is performed to validate the calculation techniques. The structural parameters of the external components (tank, yoke clamp, tie plate, embedded magnetic shielding, etc.) and the internal components (heel block, oil duct stay, etc.) are optimized based on Taguchi method. The temperature rise of the transformer with optimal parameters is calculated by multi-physics coupled analysis and the effectiveness of the optimization processes are validated.
Using the Power Delay Profile to Accelerate the Training of Neural Network-Based Classifiers for the Identification of LOS and NLOS UWB Propagation Conditions

ultra-wideband (UWB) technology enables centimeter-level localization systems based on the accurate estimation of the actual distance between transmitter and receiver, by means of the precise estimation of the signal time-of-flight. However, this is only possible when correctly detecting the first path of the incoming signal instead of a bounce or a reflection, which becomes challenging in non line-of-sight (NLOS) situations. There are many different approaches in the literature to alleviate the wrong detection of the first incoming UWB signal path. One of them considers machine learning techniques to design classifiers capable of distinguishing between line-of-sight (LOS) and NLOS propagation from available signal features. However, the performance and complexity of the obtained classifiers depend largely on the size of the input data associated to such features. Thus, features such as the channel impulse response (CIR) produce large amounts of data, yielding very complex classifiers. In this paper, we propose using a downsampled power delay profile (PDP) as an alternative feature consisting of input data much smaller than the CIR, although sufficiently representative, hence resulting in a lower computational cost while exhibiting a similar classification performance. Furthermore, another of the tasks addressed in this work is the study of the impact on the classification results of using a dataset for training where the samples of each class are not balanced from the point of view of energy. Finally, this work also studies how the classifiers based on the CIR or the PDP improve their performance when considering additional signal features such as the estimated range value or its energy level.
Research on Plant Disease Recognition Based on Deep Complementary Feature Classification Network

Traditional convolutional neural network classification models often only focus on the most distinguishing feature regions of the image and ignore the weaker feature regions. However, the image position distribution of plant diseases is very uneven. If we use convolutional neural network for plant disease recognition, there will be insufficient feature response, which will cause recognition errors. Aiming at such problems, we have designed a deep complementary feature classification network. First, the network uses DeepLabv3+ and Conditional Random Field (CRF) to generate disease part detection frames in a weakly supervised manner and combines semantic segmentation to extract disease object instances. Then we designed Complementary Feature Part Generation Models. Finally, it uses a bidirectional Gated Recurrent Unit (Bi-GRU) to perform the classification and recognition of the complementary features described above. We performed experiments on the PlantVillage dataset. The experimental results show that the proposed network recognition accuracy is 99.21%, which is 4.2% higher than the baseline model xception-65 used. We also performed experiments on the grape disease data set that we created. The accuracy of the proposed network recognition is 93.46%, which is 7.2% higher than the baseline model xception-65. In addition, compared with the better algorithms for plant disease identification in recent years, the accuracy performance has also been improved.
Implementation of machine learning algorithm in embedded devices

This paper describes the usage of neural networks in microcontrollers for deployment in embedded devices. The issue is focused on the design of a suitable neural network, its optimization and deployment in a 32-bit microcontroller with regards to the limiting factors of the chosen microcontroller. The introductory part of the article is a description of the used technology and hardware on which the solution will be implemented. Accelerometer motion recognition was chosen as a practical application. The proposed solution recognizes 6 basic movements, respectively movement in three axes. Tensorflow and Keras frameworks were used to design and implement a neural network. The created neural network model was after optimization implemented in the firmware of the STM32L4x microcontroller. The proposed solution implements automatic motion detection and its subsequent classification. The proposed principle can be applied to a group of sensors connected to the available interfaces of the microcontroller. Application with an accelerometer can be used to detect specific vibrations, application with MEMS microphones can be used to detect specific sound patterns that indicate a possible fault condition of the monitored device in industry.
A Transformer Protection Scheme Based on The Deep Forest Algorithm

A transformer protection scheme based on the deep forest is proposed to improve the performance of transformer protection. Considering that the magnetization curve can reflect the essential cause of the inrush current, but it is difficult to measure, the voltage and differential current sequences are selected as alternative input of the deep forest. The classification deviation of each layer is defined to automatically determine the depth of the deep forest. The protection acts based on the algorithm output. The deep forest is trained by the simulation sampling data from PSCAD/EMTDC simulation. The performance of the deep forest algorithm is verified through simulation data and dynamic model experimental data. The test results show that the deep forest algorithm can identify the operating states of transformer quickly and reliably. It has good generalization and no requirement of high sampling frequency, and has certain adaptability to CT saturation and over excitation.
Does visual search by neck motion improve hemispatial neglect?: An experimental study using an immersive virtual reality system

Unilateral spatial neglect (USN) is a higher cognitive dysfunction that can occur after a stroke. It is defined as an impairment in finding, reporting, reacting to, and directing stimuli opposite the damaged side of the brain. We have proposed a system to identify neglected regions in USN patients in three dimensions using three-dimensional virtual reality. The objectives of this study are twofold: first, to propose a system for numerically identifying the neglected regions using an object detection task in a virtual space, and second, to compare the neglected regions during object detection when the patient's neck is immobilized (‘fixed-neck’ condition) versus when the neck can be freely moved to search (‘free-neck’ condition). We performed the test using an immersive virtual reality system, once with the patient's neck fixed and once with the patient's neck free to move. Comparing the results of the study in two patients, we found that the neglected areas were similar in the fixed-neck condition. However, in the free-neck condition, one patient's neglect improved while the other patient’s neglect worsened. These results suggest that exploratory ability affects the symptoms of USN and is crucial for clinical evaluation of USN patients.
Artificial Bee Colony Optimized Self-tuning PI Speed Controller for FCS-MPCC of Permanent Magnet Synchronous Machines

In this paper, an artificial bee colony optimized self-tuning proportional integrator (PI) regulator is proposed for the finite control set model predictive current control (FCS-MPCC) of the permanent magnet synchronous machine (PMSM). In view of the MIT rule hypothesis, the self-tuning laws were defined. The optimized coefficients for the PI regulator were calculated using a custom defined objective function for artificial bee colony algorithm to manage external (load) and internal disturbances. The proposed self-tuning PI controller with optimized coefficients result in smaller steady state torque bias error with improved dynamic speed response especially during the speed reversal. Furthermore, the self-tuning laws are extremely basic and can be easily implemented. Numerical simulations were carried out to show the effectiveness of the proposed speed controller and comparison was drawn with the classical PI under the same test in the MATLAB/Simulink environment.
An IoT based Three-Dimensional Dynamic Drone Delivery (3D4) System

The next decades will witness a huge growth of cities whether from a population or an infrastructure standpoint. Consequently, services like last-mile delivery will be harder to manage and operate due to the complex city ecosystem (people, infrastructure, and services). Due to the increase in population, more high-rise buildings will be seen in cities. In addition, the rapid growth in information and communication technology will require smart ways to meet the delivery needs of people. This paper proposes a “Three-Dimensional Dynamic Drone Delivery (3D4)” system that aims to enable vertical deliveries by adding an extra dimension (Z-axis) to the conventional two-dimensional delivery systems. The proposed system enables the user to receive shipment anywhere as requested. This system also addresses common delivery problems such as conventional fixed-point deliveries, package security, and enhance customer satisfaction.
Influence of surface trap parameters on AC / DC flashover of epoxy resin

The surface flashover of electrical epoxy resin has been regarded as a key issue in high voltage insulation system. It is of great significance to study the influence factors on flashover properties of epoxy resin. Surface charge accumulation and decay is one of the key factors in flashover properties. In this paper, three types of epoxy resin samples, including CYD-128, DER-311 and 0164 were prepared, the influence of trap parameters of the epoxy resin samples on the surface flashover performance was investigated. Samples with a diameter of 50 mm and the thickness of 1 mm and 0.2 mm were prepared by the same curing procedure. Surface potential decay, DC surface flashover under SF6atmosphere, AC flashover in air under a standard atmospheric pressure were measured. The result shows that the sample DER-311 presented the highest deep trap energy level of 1.08eV. Its DC flashover voltage (33.56kV) and AC flashover voltage (6.37kV) had the lowest values. DC flashover voltage (34.56kV) of sample 0164 and its AC flashover voltage (7.46kV) are higher than that of sample DER-311. Sample CYD-128 has a proper deep trap energy level of 0.9geV and shallow trap density of 7.91×1022eV-1m-3and it had the highest flashover voltage. It is found that the flashover voltage decreased when the shallow trap density and the deep trap energy level increased. The deep trap captured a large number of electrons, leading to the surface charge accumulation. Meanwhile, the surface charges on the discharge path caused the electron avalanche and decrease in the flashover voltage. When the shallow trap density increased large enough, the migration of electrons on the sample surface becomes more easier, resulting in the decrease of the flashover voltage.
Fault Diagnosis of Wearable Temperature Sensors Based on Multi-scale Feature Extraction

Intelligent fault diagnosis is often used to improve the reliability, safety and stability of wearable sensors. In order to avoid the interference of expert experience to the fault diagnosis system, a multi-scale convolutional neural network model (MSCNN) is proposed. First, a novel MSCNN model is established to extract the deep features of the input data through convolution kernels of different sizes. Then, to improve the diagnostic efficiency of this method, a global average pooling layer is introduced to sub-sample the input features. Finally, the extracted sparse feature maps are input into the softmax classifier for fault classification. This method is applied to the diagnosis of wearable temperature sensor signals. The results show that this method has higher classification accuracy and faster efficiency than existing fault diagnosis methods such as artificial neural networks (ANN), traditional CNN, stackable automatic encoder (SAE) and support vector machines (SVM).
A metastable polymorph of metformin hydrochloride: Isolation and characterization using capillary crystallization and thermal microscopy techniques

Capillary crystallization techniques and thermal microscopy have been used to identify and characterize a metastable polymorph of metformin hydrochloride. The single crystal structure of the metastable polymorph (Form B) is reported and compared to the known thermodynamically stable form (Form A). There is a 5.8% density difference between Form A and Form B. N−H···N hydrogen bonds form a 1D rod motif in Form A as opposed to a 0D dimer in Form B, while the charge-assisted N−H···Cl hydrogen bonds maintain a layered motif in both Forms A and B. The preferential generation of metastable solid phases is discussed in the context of classical nucleation theory.
Identification of Critical Parameters Affecting Voltage and Angular Stability Considering Load-Renewable Generation Correlations

The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviors of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index have been chosen as the relevant stability indices for voltage stability, small-disturbance stability, and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and, hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behavior due to its correlation with other influential parameters.
Distributed Control Strategy of DC Link based on Q-Learning for Distribution Network Operation

With the integration of renewable generations and the increase of electrical demands, distribution networks are facing challenges including excessive power losses and voltage violations. This paper investigates the utilization a DC link to increase the operational flexibility of a distribution network. Considering the strong dependence on communication infrastructures and the complex optimization process of centralized control schemes, a distributed control strategy of DC link is proposed to provide continuous power flow control and voltage regulation with limited network information. In this strategy, Q-learning algorithm, which belongs to the category of machine learning methods, is used to learn interactions between a DC link and distribution network, and to adjust the active power outputs of the DC link online. The reactive power outputs at each terminal of the DC link are tuned optimally using local voltage information. Effectiveness of the proposed control strategy of DC link is verified on IEEE 69-bus distribution system. Results show that the proposed control strategy is able to reduce power losses and improve voltage profiles simultaneously with limited measurement from a distribution network. Compared to centralized controls, the proposed control strategy achieves near-optimal performances with a moderate computational burden.
Videogame design as a elicit tool for emotion recognition experiments

Videogames are powerful tools to elicit emotions in players because they use different resources (visual, audio, game mechanics, story-telling, etc.) to engage the players’ attention and enhance emotional experiences according to each videogame gender. Some videogames have been used as tools to elicit emotional responses from players in emotion recognition experiments, mostly related to game development scenarios. However, so far those videogames focus on elicit a particular set of emotions related to boredom, stress, fear or engagement, limiting the possibilities on using videogames to study emotion recognition, using a wider emotional spectrum. This paper presents the design and evaluation of a videogame as a tool to elicit different arousal – valence levels, and discrete emotions, for emotion recognition experiments under human-computer interaction scenarios. We performed a statistical analysis through self-assessed emotional responses from a group of 21 participants, using emotional scales and discrete emotions selection to identify the player experience in a 2D platform game. We found that with the structure proposed it is possible to elicit different emotions with the same tool, however, when we evaluated the performance across time, we found that the engagement is lost and the participants reported more answers related to boredom.
Electromagnetic Performance of the Novel Hybrid-Pole Permanent Magnet Machines for High Peak Torque Density

This paper proposes two novel hybrid rotors permanent magnet (PM) machines for the high torque density in short duration condition operation. In order to enhance the torque performance, the flux concentrated structure of spoke-type PM is employed to increase the air-gap flux density. Meanwhile, the non-magnetic connector of the rotor is employed to eliminate the magnetic flux leakage. The rotors of the conventional machines and the proposed machines are optimized by the finite element analysis (FEA). Furthermore, based on the comparisons of electromagnetic performances for the optimized machines, including the open-circuit flux density, torque, PM eddy current loss, overload capability, the characteristics of the proposed machines are analyzed. The results indicate that the proposed machine can improve the torque at rated and overload operation with growth rate 14.3% and 13.1%, respectively. Finally, a 12-slots/10-pole PM machine is prototyped and FEA is to be validated.
Stochastic Dispatch for Power and Gas systems under Uncertainties in Gas Network

The existing coordinated dispatch studies for an integrated electricity-gas system (IEGS) mainly focus on how to manage the uncertainties in nodal injected power, especially for wind power and solar power. However, uncertainties originating from pipeline parameters in a practical gas system have not been discussed by the literature. This paper proposes a two-stage stochastic dispatch model considering uncertainties in pipeline parameters within an IEGS. In the proposed model, the first-stage dispatch is for the base case while the second-stage dispatch is the regulation for each scenario. A second-order cone relaxation is introduced to address the non-convex gas flow model, and a bound-tightening algorithm based on convex envelope is proposed to ensure the tightness of the relaxation. The resulting stochastic dispatch is a tractable second-order cone programing. Numerical results demonstrate the effectiveness of the proposed stochastic dispatch method and show the effects of uncertain pipeline parameters on dispatch solutions.