Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Transformerless Asymmetrical Fifteen Level Inverter Topology for Renewable Energy Applications

In the field of medium and high power applications Multilevel Inverter (MLI) topology is an alternative concept. It has the capability to generate the high voltage staircase pseudo-sinusoidal waveform with less distortion and high quality. But it requires more number of Switching Components (SC) with complex PWM (Pulse Width Modulation) strategies hence the cost and size of inverter becomes high. So, in view of this authors investigated a novel asymmetrical transformerless MLI topology of fifteen level inverter is presented in this paper, with an attempt of reduction in overall device count (switches, diodes, capacitors, dc voltage sources, etc.,) compared to all existing multilevel inverters. The basic structure and operating modes of proposed MLI is explained clearly. It requires seven power switches (IGBT), three diodes and three DC-bus capacitors are required to generate fifteen level 1-֠voltage. Furthermore, an efficient PWM technique is implemented with seven reference signals whose magnitude is equal to carrier signal. The performance of proposed MLI is accomplished in terms of Total Harmonic Distortion (THD) at modulation index M=0.9. The evaluation of MLI is carried out through MATLAB/SIMULINK environment.
A Deep Multiple View Sentence Representation Model for Question Answering

Question answering (QA) between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in natural language processing. Existing deep models rely on a single sentence representation or multiple granularity representations for question answering matching, which cannot capture the semantic information well in the question answering matching process. To solve this problem, we propose a new deep multiple view sentence representation model (DMVSR) to match two question answering semantic sentences. After pre-processed by word embedding, each QA semantic sentence representation is generated by a bidirectional long short term memory (Bi-LSTM) and Convolution neural network (CNN). Through k-Max pooling and a multi-layer perceptron, the final QA matching score is produced by aggregating interactions. Our model has several advantages: (1) Using Bi-LSTM to capture the semantic information; (2) Using CNN to implement feature extraction and feature selection in semantic space; (3) Matching QA sentence representation by aggregate interactions with semantic information. In the experiments, we investigate the effectiveness of the proposed deep neural network structures of all different evidence. We demonstrate significant performance improvement against a series of standard and state-of-art baselines in terms of MAP, nDCG@3 and nDCG@5.
Nano-Ampere Low-Dropout Regulator Designs for IoT Devices

This paper presents two output-capacitor-free low-dropout regulators (LDOs) with nA quiescent current for Internet-of-Things (IoT) applications. The proposed LDO1 combines the dynamic current biasing and the adaptive current biasing techniques for drastically reducing the quiescent current to the nA level while achieving fast transient response. Based on LDO1, the proposed LDO2 adds an inverter-based dynamic loop to further improve the transient response. The prototypes are fabricated in a 65-nm low-leakage CMOS process, with active areas of 0.0042 and 0.0048 m2, respectively. Measurement results show that LDO1 and LDO2 consume 30 and 100 nA quiescent current, respectively, with 1 V input, 0.8 V output, and 100 nA load current. Stability analysis shows that both LDOs achieve good stability with load current ranging from 100 nA to 10 mA. With the load current steps from 100 nA to 10 mA in 1- $\mu \text{s}$ transition time, the measured voltage undershoots are 336.8 mV for LDO1 and 196 mV for LDO2. Therefore, we reach a figure-of-merit of 0.00159 ps.
Active Disturbance Rejection Control Approach to Tight Formation Flight of Unmanned Aerial Vehicles

This paper addresses the synchronized control problem of relative position and attitude for unmanned aerial vehicles(UAVs) to achieve the task of formation keeping and transformation. First, using second-order autopilot model and aerodynamic coupling model, a three-dimensional(3-D) nonlinear kinematic and dynamic model of tight formation is established. Second, based on the leader-follower structure, a double-layer active disturbance rejection control scheme of tight formation is proposed, of which the superstructure is to control the whole formation guiding, while the substructure is to adjust the geometry of tight formation. A detailed design of active disturbance rejection controllers applicable to velocity, heading and altitude three channels is included. Finally, simulation results are presented to illustrate the validity and effectiveness of the proposed controllers, which have the following properties: (1) effectively accounting for formation keeping and transformation with smaller overshoot and quicker response can be obtained. (2) good adaptive ability is verified through the comparison with PID flight controller.
Low Profile Circularly Polarized Monopole Antenna for Wireless Applications

The paper presents a planar low profile monopole antenna with an integrated circularly polarized band. The proposed monopole antenna defected ground surface is composed of a C-shaped slot and U-shaped slot for achieving dual band notching. The radiating patch is consisting of a square radiator loaded with U-shaped unsymmetrical slot for the generation of two current modes of almost equal magnitude and 90 degree phase dissimilarity between them. The antenna is simple as well as compact in size and is easily integrable with other microwave circuits.
Analysis of Antenna Performance Degradation due to VCO Source Using Active S-Parameters

This paper proposes a method to analyze antenna performance degradation due to electromagnetic interference (EMI) between an antenna and a noise source using active scattering $(S)$ parameters. The victim is a printed planar inverted-F antenna (PIFA) and the noise source over the victim is high frequency signals coming from a voltage controlled oscillator (VCO) via a loop shaped transmission line. Operating the VCO distorts the return loss of nearby antenna, which results in the degradation of the antenna performance. The excitation power of noise source was measured by probing the power of veo circuit, and were used for the input in the active $S$ -parameters calculations. The validity of the proposed method was confirmed by the good agreement between the simulated and measured results.
Stability Analysis of Discrete T-S Fuzzy Systems with Time-Varying Delay Using an Extended Reciprocally Convex Matrix Inequality

This paper concerns the stability analysis of discrete Takagi-Sugeno fuzzy systems with time-varying delays. A suitable Lyapunov- Krasovskii functional is constructed, and an extended reciprocally convex matrix inequality is used to estimate the forward difference of the functional. As a result, a less conservative stability criterion is obtained. Finally, a numerical example is given to demonstrate the advantage of the proposed method.
Sea Surface Temperature (SST) Variability of the Eastern Coastal Zone of the Gulf of California

The coastal zones are areas with a high flow of energy and materials where diverse ecosystems are developed. The study of coastal oceanography is important to understand the variability of these ecosystems and determine their role in biogeochemical cycles and climate change. Sea surface temperature (SST) analysis is indispensable for the characterization of physical and biological processes, and it is affected by processes at diverse timescales. The purpose of this work is to analyze the oceanographic variability of the Eastern Coastal Zone of the Gulf of California through the study of the SST from time series analysis of monthly data obtained from remote sensors (AVHRR-Pathfinder Version 5.1 and Version 5 resolution of 4 km, MODIS-Aqua, resolution of 4 km) for the period 1981 to 2016. The descriptive analysis of SST series showed that the values decrease from south to north, as well as the amplitude of the warm period decrease from south to north (cold period increase from south to north). The minimum values occurred during January and February, and ranged between 18 and 20 °C; and maximum values, of about 32 °C, arose in August and September. Cluster analysis allowed to group the data in four regions (south, center, midriff islands and north), the spectral analysis in each region showed frequencies of variation in scales: Annual (the main), seasonal, semiannual, and interannual. The latter is associated with the El Niño and La Niña climatological phenomena.
Infant Cry Classification Using Compression-Based Similarity Metric

Infant cry represents an interesting research topic not only in the healthcare domain, but also for researchers in the artificial intelligence domain. Understanding infant cries with specialized help (human or machine-based) proves to be a difficult task to accomplish in most of the cases. Measuring the similarity between two infant cry signals stands at the basis of providing an accurate mapping of these signals to the corresponding physiological and emotional state of a newborn. This paper proposes a solution for the classification of infant cry based on a modified K-Nearest Neighbors algorithm using the Normalized Compression Distance as a similarity measure. With a maximum accuracy rate of 80.39%, the experimental results confirm the effectiveness of the proposed methodology.
A Novel Contribution to Control a Wind Turbine System for Power Quality Improvement in Electrical Networks

The typical control strategy for wind energy conversion systems (WECS) is the maximum power coefficient tracking method. However, this method limits the participation of wind turbines in power quality improvement in the network. To overcome this limitation, this paper presents, in the first step, a new methodology that derives the power coefficient that permits to expand the role of WECS based on doubly-fed induction generators, to load balancing and reactive power support to the grid. The proposed method allows the WECS to provide active power to the grid under better conditions compared to the typical control method. However, in case multilevel converters are used for grid connection of wind turbines, the application of the proposed method may imply the accentuation of the inherent problem of dc capacitor–voltage imbalance of these converters, leading to jeopardize their operation. For this reason, in the second step, a predictive control algorithm capable of simultaneously improving the power quality in power grids and ensuring reliable operation of the associated converter is proposed. The simulation and experimental results are presented.
Hierarchical Model Predictive Control for the Fuel Cell Hybrid Electric Vehicles

F0636. In the proposed strategy, an upper-level MPC controller is designed to optimize the power splitting ratio (PSR) between the PEMFC and battery pack, and a predictable power profile is considered further to smooth the power requirement from the PEMFC. For the lower-level MPC controller, it is used to realize the tracking of maximum PEMFC net output power, by optimizing oxygen excessive ratio (OER). A portion of the US06 driving cycle is adopted to evaluate the performance of the proposed method, four combinations of H-MPC controller are compared in view of with/without the terminal weight and predictable power profile. Simulation results demonstrate that up to 9.57% equivalent fuel consumption is expected.
The influence of microstructure characteristic on the electromigration behaviour of line-type Sn58Bi solder joints

The electromigration (EM) behaviour in the joint mainly depends on the current density; however, for the limitation of experimental methods, the influence of microstructure characteristic of solder matrix on the magnitude and distribution of current density has long been neglected. In this study, the microstructure evolution of eutectic Sn-Bi alloy was modeled numerically using Monte Carlo Potts approach first, and simulation results are consistent well with experimental observation. Then, finite element models based on microstructural simulation results under current stressing were established and analyzed. Simulation results show that the characteristic of eutectic Sn58Bi alloy has obvious influence on the distribution and magnitude of current densities in Sn58Bi solder joints. Moreover, refining the eutectic microstructure has very limited influence on the average current densities in the solder joints, while it may reduce the maximum current density and improve the electromigration reliability. It is speculated that the coarsening process of eutectic structure under current stressing may may affect the EM behaviour of solder joints.
Software Architecture Design of the Real- Time Processes Monitoring Platform

Understanding of how business processes are executed in real-life is vitally important for a company. Any process leaves a digital footprint that can be transformed into so-called event logs and analyzed with process mining techniques. A software platform with the purpose of near realtime processes monitoring is implemented. Design of the represented platform is based on the lambda architecture combining online and offline process mining algorithms with advanced analytics based on machine learning.
Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All

Hidden Markov models (HMMs) underpin the solution to many problems in computational neuroscience. However, it is still unclear how to implement inference of HMMs with a network of neurons in the brain. The existing methods suffer from the problem of being nonspiking and inaccurate. Here, we build a precise equivalence between the inference equation of HMMs with time-invariant hidden variables and the dynamics of spiking winner-take-all (WTA) neural networks. We show that the membrane potential of each spiking neuron in the WTA circuit encodes the logarithm of the posterior probability of the hidden variable in each state, and the firing rate of each neuron is proportional to the posterior probability of the HMMs. We prove that the time course of the neural firing rate can implement posterior inference of HMMs. Theoretical analysis and experimental results show that the proposed WTA circuit can get accurate inference results of HMMs.
Research on Motion Detection Algorithm Based on Omnidirectional Vision

Motion detection is to detect and calculate the outline of the moving target in a video sequence, which is the key technique of intelligent vision surveillance system. Current motion detection algorithms are susceptible to interference from panoramic imaging illumination and resolution, sampling noise, shading and occlusion. In this paper, we improve the threshold method of subtraction image detection in motion detection, and put forward a partition adaptive double-threshold subtraction image threshold algorithm. Experimental results show that the proposed method can effectively suppress the above mentioned influences and make the edge of the detected target area smoother.
HV Pulse Transformer Generalized Equivalent Circuit Identification Based on Detailed Mechanical Structure

A modeling methodology of high-voltage pulse transformers based on high-order generalized equivalent circuits (HOGECs) identified from 3-D finite-element analysis is applied to a prototype, and experimental validation tests are performed. The identified HOGEC of the prototype is used to investigate the influence of the detailed mechanical structure and the local winding configuration on the pulse transformer operation performance.
Integrated Analyses of PALSAR and Landsat Imagery Reveal More Agroforests in a Typical Agricultural Production Region, North China Plain

As the largest among terrestrial ecosystems, forests are vital to maintaining ecosystem services and regulating regional climate. The area and spatial distribution of trees in densely forested areas have been focused on in the past few decades, while sparse forests in agricultural zones, so-called agroforests or trees outside forests (TOF), have usually been ignored or missed in existing forest mapping efforts, despite their important role in regulating agricultural ecosystems. We combined Landsat and PALSAR data to map forests in a typical agricultural zone in the North China Plain. The resultant map, based on PALSAR and Landsat (PL) data, was also compared with five existing medium resolution (30–100 m) forest maps from PALSAR (JAXA forest map) and Landsat: NLCD-China, GlobeLand30, ChinaCover, and FROM-GLC. The results show that the PL-based forest map has the highest accuracy (overall accuracy of 95 ± 1% with a 95% confidence interval, and Kappa coefficient of 0.86) compared to those forest maps based on single Landsat or PALSAR data in the North China Plain (overall accuracy ranging from 85 ± 2% to 92 ± 1%). All forest maps revealed higher accuracy in densely forested mountainous areas, while the PL-based and JAXA forest maps showed higher accuracy in the plain, as the higher omission errors existed in only the Landsat-based forest maps. Moreover, we found that the PL-based forest map can capture more patched forest information in low forest density areas. This means that the radar data have advantages in capturing forests in the typical agricultural zones, which tend to be missing in published Landsat-based only forest maps. Given the significance of agroforests in regulating ecosystem services of the agricultural ecosystem and improving carbon stock estimation, this study implies that the integration of PALSAR and Landsat data can provide promising agroforest estimates in future forest inventory efforts, targeting a comprehensive understanding of ecosystem services of agroforests and a more accurate carbon budget inventory.
Ameliorated Deep Learning based on Improved Denoising Autoencoder and GACNN

Improving the performance of deep learning and making it more in line with real life have always been the research direction of artificial intelligence. In this paper, a denoising autoencoder genetic algorithm convolution neural network (DGCNN) model based on deep learning is proposed. Two parts of the research work are combined to improve its performance. Firstly, the traditional autoencoder is replaced by the denoising autoencoder and improved the way of adding noise. Secondly, genetic algorithm is utilized to combine CNN at the stage of image classification. This allows DGCNN to cope with complex and volatile situations while enhancing image processing capabilities. Simulation results show that the method can enhance the ability of image processing than traditional methods. The performance of the proposed model is better than traditional method when the images of different loss levels are processed by this method. The results are verifying the feasibility and effectiveness of the model and algorithm. DGCNN shows better capacity in improving the performance of image processing and dealing with complex situations effectively.
Where Am I? Comparing CNN and LSTM for Location Classification in Egocentric Videos

Egocentric vision is a technology that exists in a variety of fields such as life-logging, sports recording and robot navigation. Plenty of research work focuses on location detection and activity recognition, with applications in the area of Ambient Assisted Living. The basis of this work is the idea that locations can be characterized by the presence of specific objects. Our objective is the recognition of locations in egocentric videos that mainly consist of indoor house scenes. We perform an extensive comparison between Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based classification methods that aim at finding the in-house location by classifying the detected objects which are extracted with a state-of-the-art object detector. We show that location classification is affected by the quality of the detected objects, i.e., the false detections among the correct ones in a series of frames, but this effect can be greatly limited by taking into account the temporal structure of the information by using LSTM. Finally, we argue about the potential for useful real-world applications.
Revitalizing the Yamuna River: Social Entrepreneurship Approaches

New Delhi, India's capital city, with a population of almost twenty-two million faces a daunting challenge: Its sacred river, the Yamuna, is one of the most polluted in the world. In fact, within the city limits, the Yamuna is primarily constituted by treated and untreated sewage and other toxic effluents. The water is rendered "dead" with zero oxygen, thus posing serious health hazards to the citizens of New Delhi. Might there be a way to cleanup and revitalize the river plain using social entrepreneurial approaches? In this paper, we propose to study the key ingredients required for creating and nurturing a social entreprenurship and innovation based ecosystem in the region. The methodology employed is qualitative in nature and draws on evidence based on interpretative interviews as well as direct and indirect observations. Using case examples of five socially driven ventures in the region, we examine how they they impact the local community in a manner that opens up new frontiers for positive social change. In doing so, these social entrepreneurs create value by introducing technology innovations that solve problems, while creating new opportunities for organizations and communities involved with the Yamuna river cleanup efforts.
Data Encoding Visualization based Cognitive Emotion Recognition with AC-GAN Applied for Denoising

Emotion is a subjective, conscious experience when people facing internal or external stimuli. This paper addresses the problem that affective computing is difficult to be put into real-world practical fields intuitively, such as emotion disease diagnosis and so on, due to the non-intuitive data representation. In view of the fact that people’s ability to understand twodimensional images is much higher than that of one-dimensional data, we use Markov Transition Fields to visualize time series signals. MTF images represent the first order Markov transition probability along one dimension and temporal dependency along the other. Besides, with the limitation of experimental equipment and individual differences among volunteers, noise is inevitable. We apply AC-GAN to remove noisy pixels within high dimension and acquire high resolution images before making classification. Then we use Tiled Convolutional Neural Networks on 2 real world datasets to learn high-level features from MTF images. The classification results of our approach are competitive with the state-of-the-art approach. This method makes the visualization based emotion recognition become possible, which is beneficial for the application of cognitive robots or in the medical fields, such as depression and other psychological problems diagnosis, and can help doctors and patients understand the condition more intuitively. Keywords—Emotion Recognition, EEG, Markov Transition Fields, AC-GAN, Tiled CNN