Miracle-dz/UniFAR-FTUs huggingface dataset:
Truth Inference With a Deep Clustering-Based Aggregation Model

Traditional truth inference algorithms take multiple source labels as input and infer true labels for objects. Besides source labels, object features have been introduced in inference algorithms to achieve superior performance. A typical algorithm such as learning from crowds learns a classification model with the guide of inferred true labels where true labels are inferred from source labels. However, the main shortcoming exists in current algorithms and limits their inference performance: label noise. Since source labels from real-world data are noisy, a classifier is likely to be misguided to learn an imprecise decision boundary. In this paper, we propose a deep clustering-based aggregation model (DCAM) to overcome the shortcoming. DCAM introduces clustering for object features to form fine-grained clusters, where objects in the same cluster are supposed to have similar labels. DCAM exploits a cluster label distribution to represent the labeling information of all objects in the corresponding cluster to overcome the problem of label noise. To implement the idea of clustering-based truth inference, DCAM integrates source label generation and deep clustering in a unified framework by utilizing maximum a posteriori (MAP) estimation. Therefore, the proposed model is a novel approach for truth inference with object features. Experimental results on eight real-world inference tasks show that DCAM has a significant improvement of inference accuracy over the state-of-the-art truth inference algorithms. We further discuss the effect of cluster numbers, the quality of clustering, and illustrate the learned embeddings to support the effectiveness of DCAM.
Improving the Energy Efficiency in a Single-Cell Cellular System by Adapting User Equipment LNA Stages and Base Station Transmit Power

In a receiver front-end, the noise Figure (NF) decreases and the third order intermodulation interference increases with increasing the number of active low noise amplifier (LNA) stages. Thus, the number of active LNA stages of a receiver front-end should be adapted based on the interferer power on it such that the overall interference is less than a predefined threshold. In this paper, we propose orthogonal multiple access (OMA) and power-domain non-orthogonal multiple access (NOMA) based downlink cellular systems in which receiver frontends of the user equipment (UEs) consist of cascaded LNAs with adaptive stages. To improve energy efficiency (EE), we provide a methodology to adapt the number of active LNA stages of the UEs and control the BS transmit power levels for downlink transmissions. The EE of the proposed systems under the provided methodology is evaluated and compared with the EE of OMA and NOMA cellular systems with single stage LNA. We find that the EE of OMA and NOMA cellular systems can be improved significantly by adapting the number of active LNA stages of the receiver front-ends and the impact of adapting the active LNA stages in improving EE is more significant in a NOMA system than an OMA system.
A Novel Algorithm for Financial Transmission Right Evaluation in Electricity Market

A novel Contingency Analysis algorithm has been proposed for Security Analysis (SA) to incorporate Congestion Revenue Right (CRR) options. By replacing fast forward and full backward operations with basic linear algebra operations – Multiplications and Additions, the performance can be improved. The new algorithm is easy to perform in parallel through compiler support of simultaneous multithreading (SMT) and OpenMP. The new algorithm has been implemented in ISO Day Ahead Market (DAM) and performance is improved by more than three times. The same technique can be applied to CRR auction markets, which typically contain abundant number of CRR options.
Transcapacitances in EPFL HEMT Model

In this article, we present a closed-form solution for transcapacitances in GaN HEMTs derived from a design-oriented charge-based model of the AlGaAs/GaAs- and AlGaN/GaN-based HEMTs that were recently proposed. The analytical expressions are based on the concept of normalized current and are validated with the technology computer-aided design simulations. This represents an essential step toward the ac analysis of the circuits based on the HEMT devices.
Integration of Blockchain in WBAN

In today modern digital world, there is huge need of security in medical field also. Among all modern data transfer techniques, Blockchain is a fast growing technology which provide a cryptodata through which we can transfer data between peer-to –peer node. Blockchain provide security which depends on highly cryptographic schemes. We can integrate blockchain with wireless body area network. Because WBAN is also a very emerging field of medical department. All the data of patient is maintained in a electronic health record (EHR). So Maintaining a EHR is very challenging face of medical field. In WBAN we transfer the patients data among different entities like healthcare server, medical staff, health insurer over the network. So we can provide security in this field through blockchain. In this paper we propose an architectural model of block chain with WBAN. We also mention some challenges which are faced in the field of medical health care applications.
Droop-based Control Scheme for MMC-HVDC Connected to Weak / Very Weak Grids

This paper proposes a droop-based control scheme for Modular multilevel converter (MMC) based HVDC systems connected to weak / very weak grids. The control scheme mainly includes P-f and Q-V droops, d-q voltage closed-loop, d-q current closed-loop, circulating current suppression, as well as submodule (SM) voltage balancing controls. Using droop control, frequency is obtained by controlling active power flow and Phase Locked Loop (PLL) which is usually used to obtain the frequency is avoided. Thus, MMC-HVDC systems with the proposed control scheme overcome the obstacles caused by PLL in weak grids applications. A 100 kW grid-connected MMC-HVDC system is built and simulated in Matlab/Simulink, and two control schemes, traditional vector control and droop-based control, are used to control the system. Performance of the MMC-HVDC system with two different control schemes are analyzed and compared. Comparison results show that vector control fails under weak / very weak ac grid condition, while the proposed droop-based control works well under both strong and weak grid conditions. Simulation results demonstrate the effectiveness of the proposed control scheme applied for MMC-HVDC connected to weak / very weak grids.
Bridging Learning Gap for Autism Spectrum Disorder

Technology is the most revolutionary and confidence inspiring step taken by mankind. The evolution of technology with the combination of health care has proven to be beneficial in every possible field spanning across all branches of medicine from progressive to in-born abnormalities affecting humans. In order to cater to the sector of people with in-born diseases specifically Autism, a certain kind of assistive technology is accessible that is, Augmentative and Alternative Communication (AAC). This paper introduces an android application AutistiCare targeting children of age 4 to 12 years old. It has features like information around us and caters to the health of the child. There are fun learning games which help the child build skills and coordination. AutistiCare aims at creating friendly yet informative interface for children who have been diagnosed with Autism. On the usage of the designed system, this study advocates the usability of android based application for bridging the learning gap for autistic children and brings forth the research direction in the field.
Automated Gastrointestinal Disease Recognition for Endoscopic Images

The human Gastrointestinal (GI) tract can be affected by different diseases and endoscopy has been seen to perform well for diagnosing GI tract problems. Accurate identification of underlying problems in GI tract endoscopic images is important as it affects decision-making on treatment and follow-up. In developing countries trained endoscopic experts are small in number and expensive. Even though medical recognition is a promising field of application for Artificial Intelligence (AI) publicly available datasets for such tasks are small in number. Kvasir dataset is one of the publicly available medical datasets. It consists of gastrointestinal endoscopic images that belong to eight different classes. We have automated recognition of GI tract landmarks and diseases, for classes that are available in Kvasir, with the use of Convolutional Neural Networks (CNNs). CNNs are widely used for visual recognition due to their ability to capture local features and their computational efficiency compared to fully connected networks. We have fine-tuned a residual model based on ResNet50 and a dense model based on DenseNet121 on Kvasir dataset. The models’ performance on a test set that consists of 75 images from each class is 86.9% for dense model and 87.8% for residual model. We have also built a user interface for users to select images and get recognition results. The interface built can serve as a decision support system for classifying GI tract endoscopic images. It can also further be extended for recognition in videos by feeding the video input as a sequence of images.
Combined-LSTM based User Electricity Consumption Prediction in a Smart Grid System

The prediction of user electricity consumption is important for smart grid systems. Conventional methods usually relay on the prior data distribution and estimate parameters by time series models. It is still a challenge on how to directly learn electricity consumption patterns from users’ history records, especially for the connection between peak and valley consumptions. Therefore, in this paper, we propose a novel deep neural network based model, named as Combined-LSTM, to predict the power consumption based on users’ records. It could directly learn hidden relationships among peak and valley volume throng combined recurrent networks. Experiments in real-world scenario demonstrates the effectiveness of our proposed method. It outperforms several state-of-art methods in both prediction accuracy and computational complexity.
Foretasting-Aided State Estimator for an Electrical Distribution System

Motivated by the increasing need for robust and accurate assessment of the electrical distribution system, a developed foretaste aided state estimator is proposed. Moreover, due to the lack of measurements in the electrical distribution system, the pseudo-measurements are needed to insure the observability of the state estimator. Therefore, the very short term load forecasting algorithm that insures the observability and provides reliable backup data in case of sensor malfunction or communication failure is proposed. The proposed very short term load forecasting is based on the wavelet recurrent neural network (WRNN). The historical data used to train the RNN are decomposed into low-frequency, low-high frequency and high frequency components. The neural networks are trained using an extended Kalman filter (EKF) for the low frequency component and using a square root cubature Kalman filter (SCKF) for both low-high frequency and high frequency components. To estimate the system states, state estimation algorithm based SCKF is used. The results demonstrate the theoretical and practical advantages of the proposed methodology.
New Data Mining Method based on Probabilistic-Possibilistic-Mean (Discrete Data Mining Algorithm)

The possibilistic mean is reviewed in this paper for prediction of academic data. The mean values of the probabilistic study of the possibilistic mean is classified by fuzzy numbers is the main result of this paper. This result is applied on the prediction of the academic performance over the academic data. Basically, this paper presents an analysis of academic data by fuzzy numbers. The variance of fuzzy numbers classes the big data into dynamic and compact data. This system performs efficiently over the various characteristic of fuzzy numbers. The illustration is also presented in this paper.
A Fast and Accurate Numerical Method for Optimal Torque Control of Interior Permanent Magnet Synchronous Motors

In this study, a fast and accurate optimal torque control scheme for interior permanent magnet synchronous motors (IPMSMs) is presented, which enables computing the optimal current references for maximum torque per ampere (MTPA) and flux weakening (FW) operations. Instead of analytically solving the optimal torque control problem that involves computationally expensive quartic equations, a numerical optimization technique is introduced to simplify the problem and solve it in iterations with reduced computation burden. Under the reasonable assumption of a slowly varying torque reference, linearizing the IPMSM torque equation helps to derive the solutions for the MTPA and FW operations in simpler forms. Unlike conventional numerical methods, convergence to the exact solution is ensured. A simulation using a 7.5-kW IPMSM model in MATLAB verifies the effectiveness of the proposed method by comparing it with the conventional methods in terms of solution accuracy and computational efficiency.
Distributed Adaptive Backstepping Secondary Control for Voltage Restoration in Islanded AC Microgrids

In this paper, an adaptive backstepping control design method is presented to solve the secondary voltage restoration control problem of AC microgrid system in the presence of parametric uncertainties. It is theoretically shown that the designed controller can ensure stability of the overall AC microgrid system and achieve voltage restoration. In order to illustrate the proposed scheme, simulation studies are carried out based on an islanded AC microgrid system with 4 distributed generators (DGs) built in the MATLAB Simulink environment under the circumstance that all the resistances of transmission lines are unknown. The simulation results do show its effectiveness and also verify the established theoretical results.
Bilateral Control of Elbow and Shoulder Joints Using Functional Electrical Stimulation Between Humans and Robots

Robotic remote-control technologies have a wide field of applications. Bilateral control is a type of remote-control technique. Most of the existing bilateral control techniques require complicated force transmission mechanisms in the master systems. Therefore, operators feel discomfort due to a sense of restraint by the exoskeleton-type robot arms. We attempted to solve this problem by incorporating functional electrical stimulation into the master system. In this study, bilateral control was proposed between a human and a three-joint robot with three degrees of freedom using functional electrical stimulation for shoulder and elbow joints. The experiment consisted of extracting a block of Jenga using the slave robot. The proposed method was compared to unilateral control in which the master moves freely without feedback to the master.
Application of Random Forest in Limited Size Human Long Non-coding RNAs Identification with Secondary Structure Features

In this work, preliminary experiments of using diverse machine learning algorithms and testing of multiple relevant features to discriminate between human lncRNAs and coding/partial coding sequences was performed. This research limited the size of human lncRNAs such that they are shorter than 1000 nucleotides. Various significant features in describing RNA sequence including sequenced based features, secondary structure features, base-pair features and structural robustness features were used in this study. Then, the top 20 significant features were selected using Wilcoxon rank-sum test and discovered that the secondary structure features are the unique characteristics for identifying the human lncRNAs which are quite difference with those in the groups of shorter and longer types of ncRNAs. Such features are suitable with the rule-based classifiers like Random Forest. According to 10-folding cross validation, the random forest model has shown the highest accuracy, sensitivity and specificity as well as the lowest false positive rate among all competitors. Furthermore, the model was compared with other state-of -the-art approaches such as CPC, CPAT, RNAcon and achieved the highest accuracy of 84.5% among all the participants.
Realization of S-band Amplifier using Gain Equalizer for Telemetry Applications

This paper presents the practical realization and measurement of an S-band amplifier which uses gain equalization technique to achieve very high gain flatness. Equalizer is designed, fabricated and the device is measured using VNA. Paper details the fabrication and tuning of the proposed circuit with improved gain flatness and compactness when compared with existing techniques for achieving the same. A return loss of less than 5 dB, Gain flatness of 0.5 dB, 1 dB compression point of greater than 15 dBm and gain of around 24 dB are specifications aimed in the fabricated device.
Low-Complexity Hybrid Precoding Analysis in 5G Massive Multiuser MIMO Systems

Linear precoding methods (i.e., Zero-Forcing precoding) has the potential to achieve optimal capacity performance, but gives rise to high complexity of the system in typical massive multiuser multiple input multiple output (MU-MIMO) systems. Hybrid precoding makes use of massive MU-MIMO systems to utilize large array gains of antenna and permits reduced cost and low power consuming system as well. In this paper, we consider a massive MU-MIMO network and investigate the performance of phased-Zero-Forcing (PZF) precoding, to exploit the capacity performance of the conventional baseband Zero-Forcing scheme in downlink communication. This low-complexity PZF scheme is examined in different wireless channels. A performance analysis is further conducted for large number of base station (BS) antennas. Simulation results verify that PZF scheme achieves a capacity performance that is close to (sometimes even equal to) that of the optimal ZF precoding in different fading environments.
Resistive-Aware Linear Approximations for Solving the Optimal Power Flow Problem for Distribution Networks

Although notable progress has been made to solve the Alternate Current (AC) Optimal Power Flow (OPF), the Direct Current (DC) and Decoupled OPF approximations are still preferable in broad scenarios of practical interest. These approximations typically assume that the resistive nature of the lines in the power system is negligible. This work discusses modifications of these assumptions and presents new approximations of the DC and Decoupled OPF in light of distribution systems, in which line resistances may not be negligible. The Enhanced DC (EDC) OPF and Coupled OPF (COPF) approximations are introduced and assessed, presenting favorable results compared to their classic counterparts.
IASelect: Finding Best-fit Agent Practices in Industrial CPS Using Graph Databases

The ongoing fourth Industrial Revolution depends mainly on robust Industrial Cyber-Physical Systems (ICPS). ICPS includes computing (software and hardware) abilities to control complex physical processes in distributed industrial environments. Industrial agents, originating from the well-established multi-agent systems field, provide complex and cooperative control mechanisms at the software level, allowing us to develop larger and more feature-rich ICPS. The IEEE P2660.1 standardisation project, "Recommended Practices on Industrial Agents: Integration of Software Agents and Low Level Automation Functions" focuses on identifying Industrial Agent practices that can benefit ICPS systems of the future. A key problem within this project is identifying the best-fit industrial agent practices for a given ICPS. This paper reports on the design and development of a tool to address this challenge. This tool, called IASelect, is built using graph databases and provides the ability to flexibly and visually query a growing repository of industrial agent practices relevant to ICPS. IASelect includes a front-end that allows industry practitioners to interactively identify best-fit practices without having to write manual queries.
Enhancement of security and energy efficiency in WSNs: Machine Learning to the rescue

With the new emerging technologies of this era, demands are increasing, concerning the increase in the population, Consumer desires everything to be high tech and automatized, Which leads to more sophisticated systems, WSNs or the wireless sensor networks can overcome these complications, Regardless sensors should be able to deliver diverse services, To make that possible Machine Learning techniques have been introduced which can help in efficient working of WSNs at a very low cost by stabilizing the energy and thus increasing the life span of the Wireless Sensor Networks, machine learning helps in various WSN applications some of those applications have been discussed further in this paper, there are various types of machine learning methods used in this paper and their comparisons have been given in tabular form.
Design Equations for DC-DC Quasi-ZSC

DC-DC quasi impedance source converters (q-ZSCs) with buck-boost converter gain have been recently proposed. Operation of these converters have been verified by simulation and experimental prototypes. However, the components selection for these converters were done arbitrarily due to lack of design equations. Arbitrary selection of components hinders optimal performance due to either selecting overrated or underrated components. This paper derives the design equations of a DC-DC q-ZSC and verify their validity by using them to design three converters with different ripple percentage for a specific application. Operations of the three converters were simulated and their response confirmed the validity of the equations. The equations showed that the requirement for each capacitor and inductor is different for its pair. Thus, use of asymmetric components will be more efficient.
Bias Propagation and Estimation in Homogeneous Differentiators for a Class of Mechanical Systems

Motivated by non-anthropomorphic dynamic stabilization of a walking robot, we consider the bias propagation problem for a homogeneous nonlinear model-based differentiator applied to a reaction wheel pendulum with a biased position sensor. We show that the bias propagates through the velocity observer and compromises the vertical stabilization. To cancel the impact of the bias, we propose to augment the differentiator with a reduced-order bias observer. Local asymptotic stability of the augmented nonlinear observer is shown, where the observer gain can be tuned using matrix inequalities. Experimental results illustrate the applicability of the proposed solution.
Directional Graph-Based Energy Model for IoT Wireless Relay Systems

The Internet of Things (IoT) consists of integrating sensors into ordinary objects that are connected to the Internet across wired and wireless networks. Due to its greater coverage and low upfront investment, wireless networks have the potential to allow the massive and fast deployment of IoT. In recent years, relay wireless systems equipped with directional antennas have been identified as a robust technology that can boost the performance of IoT networks in terms of coverage, connectivity, and capacity. Contrary to omnidirectional antennas, which can radiate energy in all directions, directional antennas can focus the energy in a specific direction, extending the coverage range for a given power level. In this paper, we propose a method to model the energy consumed by the relaying nodes when directional antennas are used in the IoT wireless network. This study has demonstrated that by adapting the beamwidth of the antennas, nodes can reach furthest nodes and consequently, reduce the number of hops between source and destination. This fact not only reduces the end-to-end delay and improves the network throughput but also reduces the average energy consumed by the entire network.