Miracle-dz/UniFAR-FTUs huggingface dataset:
Method for Quantitatively Comparing Synergy of Stroke Patients using Non-Negative Matrix Reconstruction with Time Sequence Synchronization (ICCAS 2019)

Muscle synergy is the association of several muscle groups contracting simultaneously as a single functional unit. Recently, the synergy has been emphasized in neurophysiological studies to utilize it as an evaluation index for stroke patients. In this paper, we proposed novel method for quantitatively comparing muscle synergy of normal people and the stroke patient for evaluating severity of the stroke. The proposed method uses NNR (Non-Negative Matrix Reconstruction) algorithm for reconstructing muscle weight matrix W. The proposed method synchronizes the time sequence of EMG data of the stroke patient with the normal people based on working phases. The method improves the distortion problem of NNR algorithm through the synchronization of time sequence.
Co-ordinated frequency support of Solar-field/Wind based Independent Hybrid Micro-grid System using WOA optimized single Controller

Due to the rapid enhancement of renewable energy resources (RSs) as well as to control the environmental pollution, integration of RESs in the modern power system has been attracting a lot of attention. This article highlights an attempt to contain the deviation of system frequency under Wind turbine generator, Solar field, Diesel engine generator, Aqua-electrolyzer and Fuel cells based independent hybrid microgrid by an optimal single controller load frequency control (LFC) strategy with the help of a more recent Whale optimization algorithm. Integration of two RESs (WTG, SF) and load variation gives microgrid frequency fluctuation with different adverse effect of the power system. For controlling the frequency fluctuation an intelligent control strategy is used in proposed microgrid system to monitor and adjust the system frequency as well as manage the active power. The performance comparison of different controllers (I, PI, PID) as well as two algorithms (PSO & WOA) taken into consideration which reveals in terms of Peak deviations, settling time and objective function values. All the case studies have been simulated under MATLAB-Simulink environment and the optimized responses shows that WOA optimized PID controller is better than PSO optimized PID controller.
Automatic Knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 Knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.
Extrinsic parameter calibration of 2D radar-camera using point matching and generative optimization

The fusion of sensors is a task to improve the recognition performance of obstacles in an autonomous vehicle system. For the fusion of sensors, extrinsic parameter calibration with 6-dof is needed to integrate the data between the sensors. This paper proposes a method of calibration between 2D radar and camera using point matching. We use corner reflectors calibration target, which focuses the radar signal at the center of the target. Using this target, we set the center point as Ground truth, which improves the accuracy of the estimation of the calibration parameter. We simultaneously measure the target with radar and camera. After estimating the intrinsic parameters of the camera, the coordinate axes of the camera and the radar are matched based on the camera view. This paper estimates the extrinsic parameters using point matching and top-down method. The proposed method is verified through experiments and results.
Enhancement in classification accuracy of motor imagery signals with visual aid: An fNIRS-BCI Study

One of the most promising brain activity utilized in brain-computer interface (BCI) is motor imagery (MI). Due to weak hemodynamic response (HR) signal, the achieved classification accuracies using MI are not sufficiently high. In this study, the enhancement in HR was investigated during motor imagery tasks of ball squeezing with the right hand. Brain signals in the form of concentration changes in oxy-hemoglobin (ΔHbO) and deoxy-hemoglobin (ΔHbR) from the left sensorimotor cortex were obtained using functional near-infrared spectroscopy (fNIRS). The experiment was separated in two sessions: In the first session the MI task was performed without a visual aid, and in the second session of the same task, the visual aid was provided: A video was played on a screen that showed a person continuously squeezing the ball, which can help in enhancing the imagination, thus improvement in HR. Later the features of averaged ΔHbO were used for classification. The active channels were selected on the basis of t-values and trials of those channels were mean to obtain averaged ΔHbO. Consistent with literature, imagery task with visual aid, showed increased activation in ΔHbO. Moreover, linear discriminant analysis was used to classify signals by taking the mean and peak of the averaged ΔHbO resulting in average classification accuracies of approximately 66% and 77% for MI task, with and without visual aid, respectively. These results are convincing that showed improvement in MI ability which will be useful for fNIRS-based BCI applications.
Robust Quantile Regression Based Operation Energy Efficiency Analysis of Split Air Conditioner in Smart Buildings

Split air-conditioner is an important part in smart building, and its operation energy efficiency analysis plays a critical role in energy management and real-time operation of a smart building. In recent years, the energy efficiency of split air conditioners has usually been evaluated using a linear regression method. However, linear regression only studies the relationship between the independent variable and the conditional expectation of the dependent variable, it is impossible to comprehensively analyze the energy efficiency of real split air-conditioner. In this paper, a new method based on robust quantile regression is proposed to evaluate the energy efficiency of split air-conditioner. This method analyzes how the independent variables affect the median and multiple quantiles of the dependent variable. In addition, the proposed method does not require that the resultant statistical errors follow any Gaussian distribution. The simulation results show that the proposed method has stability and robustness in the energy efficiency assessment of split air-conditioners, so it has high application potential in smart buildings.
Formalization of the Analysis of Intelligent Monitoring Agents Influence on the Quality of Complex Information Protection Systems Functioning

The defining guidelines for the digital economy are presented. The importance of information security in the digital economy is shown. The demand for intelligent complex information security systems is justified. The reasons for using intelligent agent technologies to ensure information security are considered. Current trends in the development of research on agent technologies in the field of information security are described. Formalizations of the analysis of the influence of agents on the quality of functioning of complex information protection systems are proposed. The mathematical apparatus of the proposed formalizations is determined.
Coherent Radar Detection Framework With Non-Uniform Pulse Repetition Intervals

This paper deals with the coherent radar detection with non-uniform pulse repetition intervals (PRI), which has strong electronic counter-countermeasures capabilities to enemy jammer thanks to its low interception and difficult to sort. A novel coherent processing framework for the non-uniform PRI returns is proposed to detect moving targets by exploiting the PRI relationship of the transmitting time sequences. It can achieve the range and velocity measurement without ambiguity. In addition, the optimized weighted window in the Doppler domain is designed to suppress the sidelobe levels which is introduced by the non-uniform PRI sequences. Finally, several simulation results are provided and discussed.
Data Augmentation on Plant Leaf Disease Image Dataset Using Image Manipulation and Deep Learning Techniques

A large volume of data will increase the performance of machine learning algorithms and avoid overfitting problems. Collecting a large amount of training data in the agricultural field for designing plant leaf disease detection and diagnosis model is a highly challenging task which takes more time and resources. Data augmentation increases the diversity of training data for machine learning algorithms without collecting new data. In this article, augmented plant leaf disease datasets was developed using basic image manipulation and deep learning based image augmentation techniques such as Image flipping, cropping, rotation, color transformation, PCA color augmentation, noise injection, Generative Adversarial Networks (GANs) and Neural Style Transfer (NST) techniques. Performance of the data augmentation techniques was studied using state-of-the-art transfer learning techniques, for instance, VGG16, ResNet, and InceptionV3. An extensive simulation shows that the augmented dataset using GAN and NST techniques achieves better accuracy than the original dataset using a basic image manipulation based augmented dataset. Furthermore, a combination of Deep learning, Color, and Position augmentation dataset gives the maximum classification performance than all other datasets.
Developing Computational Intelligence for Smart Qualification Testing of Electronic Products

In electronics manufacturing, the necessary quality of electronic components and parts is ensured through qualification testing using standards and user requirements. The challenge is that product qualification testing is time-consuming and comes at a substantial cost. The work contributes to develop a novel prognostics framework for predicting qualification test outcomes of electronic components enabling the reduction of qualification test time and cost. The research focuses on the development of a new, prognostics-based approach to qualification of electronics parts that can enable “smart testing” using data-driven modelling techniques in order to ensure product robustness and reliability in operation. This work is both novel and original because at present such approach to qualification testing and the associated capability for test time reduction (respectively cost reduction) it offers are non-existent in the electronics industry. An effective way of using three different methods for development of prognostics models are identified and applied. Predictive models are constructed from historical qualification test data in the form of electrical parameter measurements using Machine Learning (ML) techniques. ML models can be imbedded within the sequential electrical tests qualification procedure and enable the forecasting of the pass/fail qualification outcome using only partial information from already completed electrical tests. Data-driven prognostics models are developed using the following machine learning techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN) and (3) K-Nearest Neighbor (KNN). The results show that with just over half of the individual tests completed, the models are capable of forecasting the final qualification outcome, pass or fail, with accuracy as high as 92.5%. The predictive power and overall performance of the researched models in predicting qualification test binary outcomes with varying ratios of Pass and Fail data in the processed datasets are analysed.
A Method of Flight Off-Block Time Prediction Based on LSI-CNN Model

In order to improve the operational efficiency and collaborative decision-making efficiency of airports, airlines and air traffic control departments, a LSI-CNN model based on latent semantic index(LSI) and convolutional neural network(CNN) structure is proposed in this paper. This model identifies the time nodes related to off-block time in flight support process by using latent semantic index technology. The input data after feature reconstruction is input into a specific convolutional neural network, and the final prediction is to get the time of off-block. The experimental results show that the average prediction accuracy of LSI-CNN model is higher than that of the model based on full-connected neural network in the error range of (±) 5 min, (±) 10 min and (±) 15 min. The model has good robustness and can accurately identify the important time nodes related to flights off-block time. It has higher prediction accuracy.
Nonlinear Adaptive Backstepping Controller Design for Permanent Magnet Synchronous Generator (PMSG)-Based Wind Farms to Enhance Fault Ride Through Capabilities

This paper employes a nonlinear adaptive backstepping control scheme to design controllers for the machine-side converter (MSC) and grid-side converter (GSC) in permanent magnet synchronous generator (PMSG)-based wind farms. The controllers for the MSC and GSC are designed to ensure fault ride through (FRT) operations of wind farms. The proposed controllers are designed by considering full electrical dynamics of PMSG-based wind farms while considering all parameters associated with these dynamics as completely unknown. Parameter adaptation laws are used to dynamically estimate these unknown parameters and adaptation laws are also used to estimate the stator flux which eliminates the necessity of an additional observer. The control and parameters adaptation laws are designed in such a way the stability of the system is ensured under any circumstance. The FRT capability of the proposed scheme is tested through simulation studies on a grid-connected PMSG-based wind farm by applying a large three-phase short-circuit fault at the point of common coupling (PCC). Simulation results clearly shows that the proposed scheme provides adequate reactive power supports to ensure FRT operations as compared to a partial feedback linearizing controller (PFBLC).
BSIM Compact Model of Quantum Confinement in Advanced Nanosheet FETs

We propose a compact model for nanosheet FETs that take the effects of quantum confinement into account. The model captures the nanosheet width and thickness dependence of the electrostatic dimension, density of states, effective mass, subband energies, and threshold voltages and includes them in the charge calculation, resulting in an accurate terminal charge and current characteristics. The model has been implemented using Verilog-A in the BSIM-CMG framework for all simulations. It has been validated with band-structure calculation-based TCAD simulations as well as measured data. We have also highlighted the significance of quantum mechanical effects on analog and RF performance of the device.
A New Approach for Developing a 3-D Stress Sensing Rosette Featuring Strain Engineering

Stress transducers play essential roles in diagnostic testing, monitoring, and nondestructive evaluation of many infrastructures. Particularly, 3-D piezoresistive (PR)-based stress sensors would tool up these applications with a high gauge sensing platform. In this article, strain engineering is utilized to develop a 3-D stress sensor where the microfabrication complexity and cost are reduced. Three types of prestrain are exploited for generating three different groups of PR coefficients; thus, a set of linear independent equations is obtained. The independency of these generated equations is verified both analytically and experimentally. A four-point testing is conducted on both chip wafer and flip chip on printed circuit board (PCB) structure to assess experimentally the capability of the strain approach to build a 3-D single polarity rosette. Both the analytical and experimental evaluations show the ability of the developed chip to extract the six stress components in a fully temperature compensation manner.
Asymptotic Cost Estimation for Scheduling Data Aggregation Trees in Sensor Networks

In Wireless Sensor Networks (WSNs), Data Aggregation Trees (DATs) are employed for energy efficient data gathering. Energy efficient data collection is a primary requirement in the smart world of Internet of Things (IoT) as it facilitates to extend the survivability of the network. DATs gather data efficiently by employing data aggregation functions at the aggregator nodes. The employed aggregation function influences the cost of communication and cost of computation at a node. The study in this paper presents the techniques to estimate the communication and computation costs incurred for DAT construction, asymptotically. The strength of the proposed techniques is its ability to enable the estimation of best, average and worst case cost of DAT construction and rescheduling scenarios. Based on the asymptotic analysis, the study in this paper demonstrates the utilization of the proposed techniques to estimate the best and worst cases for communication and computation cost to meet the design objective of adhoc WSN deployments.
An Adaptive Inertia Control to Improve Stability of Virtual Synchronous Machines Under Various Power Grid Strength

Grid-connected power converters can be operated as virtual synchronous machines (VSMs) to mimic the dynamics of conventional synchronous machines. VSMs can provide inertial responses to the power grid, and present desired dynamics when connected to weak grids that featured by high grid impedances. However, when the VSM is connected to a strong grid with low grid impedance, its stability margin will be reduced, which is particularly analyzed in this paper. Moreover, we demonstrate that the VSM's stability can be improved by properly reducing the virtual inertia. Based on the stability analysis, an adaptive inertia control (AIC) is proposed, which can regulate the virtual inertia according to the identified grid impedance to ensure that the VSM has a desired stability margin under various grid strength. The grid impedance is identified in real time along the system trajectory, using the recursive least square algorithm to enable the implementation in the controller. Simulations verify the validity of the analysis and the effectiveness of the AIC on improving the VSM's stability when considering various power grid strength.
State Estimation of Nonlinear Electromechanical System using Extended Kalman Filter

This paper presents an approach to estimate mechanical non-measurable coordinates of the state vector in servo tracking driver of the large radio telescope control system. The estimation algorithm of discrete extended Kalman filter based on the sequential recursive solution is developed for nonlinear systems. The proposed optimal state observer is investigated in the close-loop state feedback control system of large radio telescope. The results of computer simulation are carried out in MATLAB / Simulink environment.
A New Semantic Descriptor for Data Association in Semantic SLAM

To successfully implement SLAM based on semantic information, object recognition is essential. In our semantic SLAM approach, the robot should localize itself knowing only the pose of the surrounding objects. The implicit information in TOSM contains conceptual knowledge, in which we stored data that cannot be perceived by sensors only. However, it needs to distinguish not only an object class, but also which specific object instance a given detected object is related to. In many approaches, objects are recognized by detecting feature points and representing them as descriptors. There are several types of descriptors for feature points, such as SIFT and SURF. There are also descriptors describing a whole object instead of just feature points, like GOOD or HOOD. We suggest a new semantic descriptor, which includes more high-level information, and propose a process to recognize accurately through semantic analysis by semantic descriptors. In this paper, we introduce about our semantic descriptor model while giving some example cases, and then describe the process of data association by using the aforementioned descriptor.
Ensemble of Receptive Fields for Training Central-Focused Convolutional Neural Networks

Translation is a data augmentation method widely used in the image classification tasks. We analyze the mechanism of translation and discover that the central area of the images is more likely to be selected as convolutional neural network’s input. Inspired by the structure of human retina, we propose the hypothesis that the central area of the image contains more significant information than the marginal one. Comprehensive experiments are presented to prove that hypothesis and reach the conclusion that the receptive field is nonuniform. The central part of the image that is always selected by translation is called the focused area. Motivated by the demand to take use of different focused area and thus take use of different receptive fields, we propose a novel training mechanism that integrate different focused areas in one training process. Our method consists of several stages, each has its own focused area and learning rate, and achieves considerable result in the experiments. We call the integration of focused areas the ensemble of receptive fields.
Ambient Parametric Monitoring of Farms Using Embedded IoT & LoRa

This paper presents the development of a cost effective, self-sustainable and solar rechargeable smart farming product based on embedded IoT and providing end to end solution using the cutting edge LoRa technology. It portrays the innovative methods developed to monitor ambient parameters using the LoRa LPWAN. The product leverages the capabilities of the Arduino Mini MCU capitalizing the low power and ultra-low power optimization techniques to enhance self-sustainability of the device. It also provides an insight of the alternative, creative approaches developed for some soil sensors (NPK and pH) to make the product cost-effective. These real time ambient data & parametric trends can be monitored over the cloud at user’s convenience on an android application.
Intelligent Device Disambiguation for Smart Home Control

User interaction with smart devices is challenging when there are multiple devices that can perform the required task. Disambiguating between similar devices is a common problem that user faces when controlling devices from an app or from voice enabled smart assistants, because of the time required to interact. Moreover user command might be incomplete in case of smart assistants, leading to further challenges in identifying the user intended device. To predict the user intended device, we propose a machine learning based device disambiguation service using XGBoost algorithm. The predictions are based out of historical usage pattern of smart home users and is personalized for them. The machine learning model is optimized using random search over hyper-parameters in a completely automated fashion, which ensures optimum user experience. The solution addresses the important problem of identifying the device intended by the user and is a suitable platform for further improvements in voice assistant enabled smart home experience.