Miracle-dz/UniFAR-FTUs huggingface dataset:
Dry EEG in Sports Sciences: A Fast and Reliable Tool to Assess Individual Alpha Peak Frequency Changes Induced by Physical Effort

Novel state-of-the-art amplifier and cap systems enable Electroencephalography (EEG) recording outside of stationary lab systems during physical exercise and body motion. However, extensive preparation time, cleaning, and limited long-term stability of conventional gel-based electrode systems pose significant limitations in out-of-the-lab conditions. Dry electrode systems may contribute to rapid and repetitive mobile EEG acquisition with significantly reduced preparation time, reduced cleaning requirements, and possible self-application by the volunteer but are known for higher channel failure probability and increased sensitivity to movement artifacts. We performed a counterbalanced repeated measure endurance cycling study to objectively validate the performance and applicability of a novel commercially available 64-channel dry electrode cap for sport science. A total of 17 healthy volunteers participated in the study, performing an endurance cycling paradigm comprising five phases: (I) baseline EEG, (II) pre-cycling EEG, (III) endurance cycling, (IV) active recovery, and (V) passive recovery. We compared the performance of the 64-channel dry electrode cap with a commercial gel-based cap system in terms of usability metrics, reliability, and signal characteristics. Furthermore, we validated the performance of the dry cap during a realistic sport science investigation, verifying the hypothesis of a systematic, reproducible shift of the individual alpha peak frequency (iAPF) induced by physical effort. The average preparation time of the dry cap was one-third of the gel-based electrode caps. The average channel reliability of the dry cap varied between 80 ± 15% (Phase I), 66 ± 19% (Phase III), and 91 ± 10% (Phase V). In comparison, the channel reliability of the gel-based cap varied between 95 ± 3, 85 ± 9, and 82 ± 9%, respectively. No considerable differences were evident for the comfort evaluations nor the signal characteristics of both caps. A within-volunteers repeated measure analysis of variance (RM-ANOVA) did not show significant effects of the electrode type on the iAPF [F(1,12) = 1.670, p = 0.221, ηp2 = 0.122, Power = 0.222]. However, a significant increase of the iAPF exists from Phase II to Phases IV and V due to exhaustive physical task. In conclusion, we demonstrated that dry electrode cap is equivalent to the gel-based electrode cap based on signal characteristics, comfort, and signal information content, thereby confirming the usefulness of dry electrodes in sports science and other mobile applications involving ample movement.
A Space Vector Pulse Width Modulation with Capacitor Voltage Balance Control for Five-level Active Neutral-Point-Clamped Inverter Based on g-h Coordinate System

Active neutral-point clamped five-level(ANPC-5L) inverter is an attractive topology for high-power medium-voltage filed. Aiming at the capacitor voltage balance of ANPC-5L, this paper proposes a hybrid SVM modulation algorithm based on g-h coordinate system without judging sector nor lots of trigonometric function calculation. To realize flying capacitor voltage and dc-link capacitor voltage balance, the appropriate redundancy vector sequences are chosen by judging the current working mode. Simulation results are presented to verify the validity of this method in MATLAB.
A ZVS Buck Converter with fixed Frequency Control

A zero-voltage switched(ZVS) buck converter is proposed in this paper. With a clamp switch both power switches are operated in ZVS. To adapt to the high frequency application of this topology and further reduce the size and cost, this paper proposed a fixed frequency control method with simple structure. Higher efficiency and power density can be achieved. Experimental results based on a 30W, 500kHz prototype are provided to verity the effectiveness and feasibility of the proposed converter.
Ultra Reliable Distributed Control for Cooperative Vehicular Cyber Physical Systems

Cooperative vehicular cyber physical systems (vCPSs) can shape a group of mobile robots that can accomplish a cooperative task using wireless communications and distributed control. To this end, such a cooperative task can be coordinated and managed by a distributed control plane that will be able to encapsulate the required functionality in a layered architecture providing the required interoperability. In this paper, we propose a distributed control plane that consists of the cooperative awareness layer, the communication layer and the distributed control layer. We deal with the distributed control for vCPS employing distributed model predictive control for leader-follower formation control. Next, the design of ultra reliable and low latency wireless communications is specified, where a distributed solution is devised to provide ultra reliable distributed control for cooperative vCPS. Simulation results are obtained in order to highlight the impact of reliability constraint to join/leave manoeuvre for the considered use case.
A Global Optimization Method for Specular Highlight Removal From a Single Image

The presence of specular highlight is a critical issue for both natural and medical images such as those produced by laparoscopes, which can lead to erroneous visual tracking, stereo reconstruction, and image segmentation. Specular highlight removal from a single image is necessary for image analysis and applications. Due to the differences between natural and medical image scenes, existing literature to address this issue has only been effective on natural images or medical images with textureless regions. To overcome this limitation, we propose a global optimization method for specular highlight removal from a single image based on a dichromatic reflection model. In addition to introducing modified illumination chromaticity, the proposed method consists of two novel steps: one for estimating diffuse chromaticity by correcting hue and saturation on highlighted regions, and the other for estimating diffuse and specular reflection coefficients using convex optimization with double regularization. The estimated diffuse chromaticity is proven to approximate the true diffuse chromaticity and the proposed optimization algorithm is guaranteed to find the optimal diffuse coefficients. Experimental results show that the proposed method can effectively remove specular highlights from both natural images and endoscopic images with texture detail preservation. To further demonstrate the efficacy of our proposed method, an application of stereo reconstruction using a public dataset illustrates that our highlight removal method can enhance surface reconstruction accuracy from 1.10mm RMSD to 0.69mm RMSD.
Real-Time Detection for Camera Sensing System: Adaptive Cascade Single-Shot Detector

The real-time object detection has recently achieved a series of exciting effect on several dataset like Pascal VOC and MS COCO. These highly-developed algorithms enable real-time image processing from a camera sensing system to be implemented. This paper proposes an object detection model, which is named Adaptive Cascade Single-Shot detector (ACSD) based on earlier Single-Shot Multi box Detector (SSD). The model makes use of adaptive anchor boxes via training dataset statistic experience and add multi-scale cascade layer to reduce over-fitting due to exponentially vanishing positive samples, besides, we proposed a way using spectral clustering to make detection dataset. This model aims to improve the detection accuracy and the processing speed in real-time detection tasks. Experiments include several detection tasks on different datasets while the results demonstrate the improvement of ACSD in terms of effectiveness and efficiency. Working performance of the ACSD is verified by comparing it to several state-of-the-art approaches to guarantee it a promising method for the development of the camera sensing system.
Optimal Sensor Placement for Obstacle Detection of Manipulator Based on Relative Entropy

An optimal sensor placement method based on relative entropy is presented with the aim of maximizing obstacle information with limited number of sensors for manipulator. Two distribution - sensor detection distribution (SDD) and obstacle appearance distribution (OAD) - are defined, and the relative entropy of SDD from OAD is taken as optimization objective. Then, SDD is calculated by Monte Carlo simulation with the assist of collision detection between sensors and environment. A numerical computation method for the optimal placement is proposed in order to dealing with the problem of non-analytic expression of SDD. Performances of best and worst placement are compared though numerical example. The result shows that the best placement leads to a mission success rate about 29% higher than the worst.
Selective Harmonic Elimination PWM Switching Principle in Multi-Mode Modulation for Five-Phase Permanent Magnet Synchronous Motors

Multi-phase permanent magnet synchronous motors (PMSM) are potentially used in the field of rail transit and due to theirs high power density and high reliability. For high power applications, Multi-mode modulation strategy based on selective harmonic elimination pulse width modulation (SHEPWM) are often used to achieve excellent current performance at very low switching frequency. However, the current impact caused by transition process of SHEPWM with different carrier ratios is a terrible issue in engineering application. To address this problem, this paper investigates the generation mechanism of current impact in the transition process according to the equivalent circuit model of five-phase PMSM firstly. Furthermore, theoretical analysis results proved that each phase independent switching scheme can achieve different carrier ratio SHEPWM smooth transition. Finally, the simulation results have verified the correctness and effectiveness of the theoretical analysis.
A Cross Model Telco Industry Financial Distress Prediction in Indonesia: Multiple Discriminant Analysis, Logit and Artificial Neural Network

The competition between telco industries is getting stronger and expects telco companies to continually strengthen the fundamentals of it’s management, so that they will have the ability to survive in the competition with other companies. Incapability to anticipate the global changing trends would lead to a decrease in company business value, which in turn led to business losses. Bankruptcy is a very essential issue which must be aware by the company, the bankruptcy of a company can be measured and evaluated from financial statement data, by analyzing financial statements, the company management can take immediate action to restructure debt due to the effects of the liquidation of bankruptcy could be detrimental to creditors and investors. Academic research may provide a model to prevent bankruptcy indispensable in Indonesia, some of the bankruptcy prediction model uses financial data prior to predict financial difficulties. The study compared three models in this research, there are: Altman model, Ohlson model and Artificial Neural Network Backpropagation. This research aims to compare financial distress prediction model with the most appropriate application in Indonesia’s Telecommunication sector. Comparisons were made by analyzing the accuracy level of each model. The samples used were three telecommunication company that listed on Indonesia Stock Exchange in the period 2013-2017. In summary, the prediction models used in this research can be used to help investors and company management to predict business failure probability.
What Topics Do Developers Concern? An Analysis of Java Related Posts on Stack Overflow

As a popular on-line question and answer site [1], Stack Overflow has attracted millions of developers to communicate, collaborate, and share knowledge. Aiming to better understand the trend of Java, one of the most popular programming languages in the world, this study investigates millions of Java-related posts on Stack Overflow. In this study, we first employ Latent Dirichlet Allocation (LDA) to detect topics among Java-related posts. Then, we conduct further analysis over these detected topics, e.g., the most popular topic and the most difficult topic to answer. As a result, we detect 40 topics and top 10 key terms. The top four popular topics include "System Platform", "Java Archive", "Import Sources", and "Maven Build". Meanwhile, "List", "Web Development", "JavaFX", and "User Security" are the top four difficult topics. Our study provides new insights into Java-related topics on Stack Overflow.
Radar Waveform Design Using Lagrangian Dynamics for Co-Channel Interference Mitigation

In this paper, a novel radar waveform phase function design is presented that constructs a radar waveform that spectrally manuevers around in-band co-channel interference (CCI) from active communications users that are co-channel with the radar system. The design approach treats the CCI problem as the physical interpretable problem of optical refraction where the time-frequency (TF) signature of the radar waveform is made to be analogous to a light ray propagating from a start time and position (frequency) to an end time and position (frequency) and will naturally accelerate through the CCI as if the equivalent light ray accelerated in through different mediums (with an index of refraction related to CCI power spectral density (PSD)). Using the Euler-Lagrange equations, we design the radar phase function that is used to modulate a constant modulus (CM) waveform. The radar waveform design will generally result in a nonlinear-frequency modulated (NLFM) waveform that provides improved signal-to-interference-plus-noise-ratio (SINR) on a potential target in the presence of CCI leading to improved probability of detection performance. We provide theoretical analysis and simulation results for the proposed waveform design.
Short-Term Load Forecasting Using Ensemble Empirical Mode Decomposition and Harmony Search Optimized Support Vector Regression

Short-term load forecasting (STLF) is very significant for the planning and reliable operation of power grids. This paper proposes an STLF model by hybridizing ensemble empirical mode decomposition (EEMD) and harmony search (HS) optimized support vector regression (SVR), namely EEMD-HS-SVR model. Firstly, the original load time series are decomposed into a set of intrinsic mode functions (IMFs) and a residue by EEMD. Secondly, both IMF components and residue are forecasted by HS optimized SVR. Finally, the prediction values of the original load time series are calculated by the sum of the forecasting values of every sub-series. The proposed model is verified using two data sets, and results are compared against different alternative models. The forecasting results indicate that the proposed EEMD-HS-SVR model effectively generates more accurate predictive results.
Miniaturized Reconfigurable Antenna Based on Half-Mode Substrate Integrated Waveguide

In this paper, we introduce a novel reconfigurable antenna for implementation in 5G wireless systems. The proposed antenna is a leaky-wave antenna based on the half-mode substrate integrated waveguide. By introducing sets of switches over the top conductor plane, the antenna scans the space electronically. In the proposed antenna, beam steering is due to disturbances in the surface current under the influence of changing the bias voltage of the switches. This antenna design is to be implemented in 5G wireless communication systems. Hence, the center frequency is chosen as 28.5 GHz. The length, width, and height of the antenna are 68mm, 25mm, and 0.3mm, respectively. The compactness and configurability of the antenna makes it a suitable candidate for 5G wireless systems.
Transmitting Frequency Selection for HF Radar based on Convolutional Deep Belief Network

This paper proposes a transmitting frequency selection method for HF radar based on convolutional deep belief network (CDBN), which is applied to select relatively quiet frequency band for radar in order to improve the detection ability and survivability. This method employs CDBN to extract the features from spectrum data and classify the availability of frequency band to select the quiet band. The nodes of hidden layers are also visualized, and the physical explanation of visualizing result is given. Compared with convolutional neural network (CNN), deep belief network (DBN) and support vector machine (SVM), the proposed method has a better performance in the classification results.
Analyzing Tourism Mobile Applications Perceived Quality using Sentiment Analysis and Topic Modeling

Mobile application is one of the most important information platforms for international tourists. Millions of tourists use mobile applications to find information and make transactions. Two popular Online Travel Agent (OTA) mobile applications for travel-related activities providers are Traveloka and Tiket.com. These applications certainly must meet travelers’ needs to achieve satisfaction. Such satisfaction related to application Mobile Application Service Quality (MappSql) dimensions can be traced from thousands of their comments on the Google Play Store. From a set of reviews, information about the perception of mobile application quality can be obtained. Knowledge on user perceptions is very useful for company’s consideration in creating effective business and app features to increase users’ satisfaction. We propose Text Mining models to bring up hidden information regarding users’ verdict. The selected text analysis methods for this research are Sentiment Analysis and Topic Modeling. We find that positive or negative sentiments towards MappSql dimensions of online travel agent applications qualities can be revealed using sentiment analysis method. Topic Modeling method is used to bring up groups of important words of topics related to each mobile application service quality dimensions.
Passenger Vehicle Avoidance Time Model For Connected and Autonomous Vehicles

VANET-based Connected and Autonomous Vehicle technology has the potential to improve safety hence reduce road fatalities. Many simulations in VANET develop only message dissemination protocol without considering the safe time needed between moving vehicles and these are rather inaccurate for safety preventions because communications without an effective time gap between vehicles could still lead to collisions. Passenger vehicles avoidance time can be associated with L0 – L2 and L3 – L5 levels of autonomy, dependent on either human-induced deceleration braking or full automated system braking. Two passenger vehicle avoidance time models have been proposed. The results obtained shows marked differences in the needed avoidance time for safe vehicle braking to avoid a crash. A delay of 4s is suggested to maintain a suitable avoidance time between leading and following vehicles for CAVs in urban and highway roads.
A Novel Feature Fusion and Reprocessing Technique of Brain-Computer Interface for Motion Imagination

Brain-computer interface (BCI) refers to the direct communication and control channels established between human brain and computer or other electronic devices. As a novel way of man-machine interface, BCI enables paralyzed patients to see the new hope of autonomous interaction with the outside world. The background of this paper is BCI based on motion imagination. Different features such as discrete wavelet transform (DWT), power spectral density (PSD) and autoregressive (AR), can describe useful information from different angles, feature selection can reflect the classification results of BCI. In this paper, four BCI feature fusion techniques are proposed, and the results are compared. To further improve the classification, we employ different feature reprocessing techniques (PCA, ICA and KPCA) to deal with the fusion matrix. The classification result is the best when the feature fusion method is weighted addition of DWT, PSD and AR, while KPCA is the reprocessing technique.
Research on the Early-warning Model of Foaming Phenomenon in Desulfurization Solution System of Natural Gas Purification Plant Based on Artificial Intelligence Technology

In order to ensure the safety of industrial production process, identification and early-warning of abnormal working conditions are very important. In this paper, the abnormal "foaming phenomenon" of natural gas purification desulfurization solution system is taken as the research object. The identification of the current abnormal "foaming phenomenon" mainly relies on the traditional method of long-term manual judgment of field technicians, which consumes a lot of resources and is easy to cause negligence. The model is based on the real-time online operation data of the 300W/d desulfurization system of the purification plant. The artificial intelligence technology is used to model the abnormal "foaming events", and the automatic identification and early warning of such events is achieved. The experimental results prove that the accuracy of the early-warning model can reach 97%, and the early warning results have been affirmed by professionals. At the same time, on the basis of the successful early-warning model, it is integrated into the "safety and environmental protection early warning visual management system of the oil and gas production". The real-time trend of key data dimensions and the probability of abnormal foaming have been well performed. Real-time warning function of abnormal "foaming phenomenon" of the 300W/d equipment of the purification plant is realized.
A Torque Observer for IPMSM Drives Based on Deep Neural Network

This paper proposed a novel torque observer for interior permanent magnet synchronous machine (IPMSM) drives based on a deep neural network. Different from the conventional mathematical model based motor torque observers, the proposed torque observer could be built without the knowledge of motor parameters and could achieve high accuracy in observation of electromagnetic torque which is crucial for high-performance electric machine drives. The proposed torque observer is built based on end-to-end data so that the implication of the torque observer is significantly simplified compared with the conventional mathematical model based on torque observers. An IPMSM control scheme adopts the proposed torque observer is also proposed, and simulations were performed to verify the torque observer and the control scheme. Finally, the data obtained from experiments was used to train the proposed torque observer, the training results prove feasibility of the proposed model and its good torque estimation ability.
Robust Tracking Control of A Quadcopter Via Terminal Sliding Mode Control Based on Finite-time Disturbance Observer

Quadcopters are playing more and more important roles in different applications. However, the design of robust tracking control for quadcopters remains challenging due to its complex dynamics. Considering the underactuated nature and cascaded structure of the quadcopter system, the control framework is divided into the outer loop control and the inner loop control for simplicity of implementation. We proposes a robust control scheme consisting of a finite-time disturbance observer and a terminal sliding mode controller and (FTDO-TSMC) for both loops. The finite time convergence of the observer and closed-loop stability are validated theoretically. Finally, the effectiveness and of the proposed method are illustrated by simulation results.