Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronous Line Loss Rate Analysis of Transformer District Based on K-Modes Clustering Algorithm and Deep Belief Network

This paper addresses an important problem in the management of daily synchronous line loss rate (SLLR): how to quantitatively assess the effects of each influential factor and identify the most critical ones? An SLLR calculation method based on the K-modes clustering algorithm and the deep belief network (DBN) is proposed. First, the influential factors of the SLLR in the transformer district are determined. Then, the transformer districts are classified into different clusters by using K-modes algorithm to balance the accuracy and computation burden. Next, the DBN based SLLR calculation model is built and trained for each cluster of transformer districts. In the end, the sensitivity analysis is conducted to quantify the effect of each factor and identify the most influential ones. Historical data from the power supply company is used as test case to verify the effectiveness and correctness of the proposed methodology.
The Effect of Robot Gaze Direction While Idle on User Comfort and Subjective Impressions

The current study investigated the effect of a robot’s gaze direction while not engaged in any tasks (i.e., while "idle") on users’ comfort and subjective impressions of the robot. We predicted that users would feel differently when the robot looked at the same objects as the user (i.e., during joint attention between robot and user) or not, even when the robot’s gaze direction was the same. We conducted an experiment in which participants watched a video clip five times in five different robot gaze direction conditions. To examine the effects of joint attention, participants were divided into two groups by the distance from the monitor on which the video was presented. Joint attention between the robot and participant occurred in different robot gaze directions between the two groups. We measured participants’ comfort, subjective impressions of the robot, and related variables. The results revealed that the robot’s gaze direction affected participants’ comfort and subjective impressions among participants 75 years old and above. However, we found no effect of joint attention. Additionally, participants felt most comfortable and had the most positive impressions of the robot when it looked at a monitor on which participants did not watch the video clip.
Foiling Sybils with HAPS in Permissionless Systems: An Address-based Peer Sampling Service

Blockchains and distributed ledgers have brought renewed interest in Byzantine fault-tolerant protocols and decentralized systems, two domains studied for several decades. Recent promising works have in particular proposed to use epidemic protocols to overcome the limitations of popular Blockchain mechanisms, such as proof-of-stake or proof-of-work. These works unfortunately assume a perfect peer-sampling service, immune to malicious attacks, a property that is di cult and costly to achieve. We revisit this fundamental problem in this paper, and propose a novel Byzantine-tolerant peer-sampling service that is resilient to Sybil attacks in open systems by exploiting the underlying structure of wide-area networks.
Research on Electric Vehicle Charge and Discharge Dispatching Strategy Considering the Demand on Both Sides of Supply and Demand

With the increasing penetration of electric vehicles, an orderly EV charging and discharging strategy is of great significance to ensure the economic and safe operation of the power grid. To this end, this paper proposes an EV intraday dispatching strategy that takes into account the needs of users and both sides of the grid. In the first-stage user-side optimization, the user-side requirements are improved by defining the average discharge rate index and dynamic discharge loss cost; in the second-stage optimization, the optimization needs of grid-side peak and valley filling are considered. In this paper, the simulation results under different strategy dispatching, such as charge and discharge with cost orientation as the target, show that the proposed strategy can effectively reduce the user’s cost and battery loss, reduce the load peak-valley difference, smooth the load fluctuation, and achieve a win-win situation for both grid and EV users.
An Effective Electronic waste management solution based on Blockchain Smart Contract in 5G Communities

we have become too dependent on technology owing to which the quantity of e-waste produced at the end of their life cycle has increased at a rapid pace. In later centuries, there would be an equivalent amount of carbon footprint by the smartphone sector as to that of transportation industry. The approximate estimation of the total carbon footprint while taking the other existing abundant electronic gadgets is enormous. Considering the growing volume of e-waste, the possibilities of these non-biodegradable elements contaminating the atmosphere are towering. The e-waste generated in all these recent years is being taken seriously by various nations and effective steps are being taken to overcome this challenge. To overcome this challenge, an effective e-waste management technique by means of blockchain in the 5G scenario is proposed. The proposed solution tracks the e-waste produced and motivates people by providing incentives to them for channelizing the e-waste via agencies managed by the government that effectively dispose the waste in an environmentfriendly way. Henceforth, a partnership model is proposed for the implementation of this method which leads to increase in jobs as well as proper organization of unplanned setup that is with a large amount of prospective potential.
Enhanced LoRa Data Rate through PATCH

LoRaWAN, as one of the promising low-power wide-area network technologies, enables the widespread applicability of the Internet of Things. It uses Chirp Spread Spectrum based LoRa physical layer to provide high receiver sensitivity and hence a long communication range. Due to inherent low data rates of LoRa, the packet duration increases resulting in more collisions, re-transmissions, and degradation in network throughput. In this paper, we propose a LoRa data rate enhancement scheme by utilizing the phase of the chirp signal as an additional channel. We show that the proposed scheme PATCH - Phase based AddiTional CHannel does not affect the demodulation of main LoRa channel. The proposed scheme supports an additional bit rate, improving the LoRa physical layer bit rate and transmission efficiency. The proposed scheme can provide 28.57% and 16.67% increase in physical layer bit rate for spreading factors 7 and 12, respectively.
Semantic Web Technologies for Ontologies Description: Case study in Geometry Education

In this paper we present the use of ontologies and technology tools to connect basic concepts of geometry stereometry. We connect the geometric object of circle with the stereometric objects of cylinder and we expand the meaning of the area with the meaning of volume. We visualized our ontology which moves forward to Steroids. For this purpose, an experiment was set up in a junior high school classroom. The ontology, via abstract and combined thinking, helped the students to have a better understanding of the geometrical meanings and their dynamic interconnections.
Improving Quality of Life with a Narrative Robot Companion: II – Creating Group Cohesion via Shared Narrative Experience*

One of the most difficult things for social robots is to enter the realm of human social relations. Here, we exploit recent advances in natural language processing (NLP) that provide robots access to human experience, which can allow them to enter into human social relations. When people are put together in arbitrary conditions, as in a home for the elderly, it can be difficult for them to share experience. To address this, we use recent advances in NLP to allow the robot to discover shared narratives between such group members. Our narrative companion extends the human capability to make social narrative links for building group coherence through sharing experience.Requirements are identified for a narrative companion to allow individuals within a group to focus their interactions on shared experiences and interests, to improve group coherence. The system should collect and organize members’ experiences, and should discover semantic similarity between different members’ experiences in order to create a group narrative. It should then accompany the group in their cohesion-enhancing experience of this narrative. Based on these requirements, and extending our previous work, we implement the V2.0 narrative companion on the Pepper robot.The system is validated in a case study where participants provide 5 favorite photographs and short answers to questions. The Narrative Semantic Similarity Analysis System (NarSim) generates a meaningful trajectory of narrative linking people and events depicted in the photographs. With this, Pepper then accompanies the group and prompts group members to enrich the shared narrative, to further enhance the group pleasure and cohesion. Results are presented, and future applications for improved quality of life are discussed.
Constrained H∞ Control Application to Inverted Pendulum with Control Moment Gyroscope

This paper presents the dynamics and control of a gyroscopic inverted pendulum where the stabilization of the inverted pendulum is achieved by control torque generated by gyroscopic precession momentum. Two flywheels rotating in opposite directions at the same speed are considered. The control strategy is proposed to stabilize the inverted pendulum at the upwarp unstable equilibrium position and to maintain the gimbal angle as small as possible. Such a problem is formulated as a constrained H∞ disturbance attenuation problem and then transformed into a solving of Linear Matrix Inequalities (LMIs). The effectiveness of the H∞ state-feedback controller is illustrated through simulation. It is shown that the state-feedback control strategy effectively stabilizes the inverted pendulum.
Broadband and High-Gain 400-GHz InGaAs mHEMT Medium-Power Amplifier S-MMIC

In this paper, the design and performance of a medium-power amplifier (MPA) submillimeter-wave monolithic integrated circuit (S-MMIC) is presented. The MPA demonstrates a flat small-signal gain of approximately 20 dB, measured over a frequency span from 290 to 410 GHz, and a respective 3-dB bandwidth from 280 to 430 GHz. The measured output power ranges from 1.5 to 3.3 dBm, over a large-signal drive from 355 to 395 GHz-limited by the measurement set-up-at a peak input power of -13 dBm. The power-added efficiency varies from 1 to 1.5 %. The amplifier utilizes four cascode stages, each realized via two 8-µm finger-width InAIAs/InGaAs metamorphic high-electron-mobility transistors in parallel for the common-source part and respectively two devices for the common-gate part of the cascode. Each transistor consists of two gate fingers. The total gate width (TGW) of the output stage is 32 µm. The total chip size, without RF- and DC-pads, is 200 × 800 µm2. The respective matching networks are implemented via miniaturized thin-film microstrip lines in tandem with metal-insulator-metal capacitors. A maximum output power per TGW of 66.2mW/mm is achieved with a transducer gain of 16 dB, representing a state-of-the-art performance for MMIC-based amplifiers between 355 and 395 GHz.
ADual-Mode Bias Circuit Enabled GaN Doherty Amplifier Operating in 0.85-2.05GHz and 2.4-4.2GHz

We report an ultra-wideband and high efficiency sub-6GHz power amplifier, which is based on multi-band Doherty load modulation. Key novelty is that a dual-mode bias circuit which operate as frequency-dependent compensating circuit at a half of center frequency 0.5fo mode, and DC-block and RF-choke at a center frequency fo mode. The amplifier using 0.15-μm GaN HEMT FETs achieved drain efficiency of 41.4-58.2% over 0.85-2.05GHz with 0.5fo mode, and 43.5-62.1% over 2.4-4.2GHz with fo mode at 6dB power back-off. This, to the best of authors' knowledge, is the widest coverage of frequency reported so far for a multi-band Doherty amplifier at these carrier frequency range. It is a very promising PA technology for sub-6GHz base station, enabling reduction of the total cost of ownership (TCO) for operators.
Power-Management Strategies in sEMG Wireless Body Sensor Networks Based on Computation Allocations: A Case Study for Fatigue Assessments

Surface ElectroMyoGraphy (sEMG) is widely applied to a variety of applications. Managing the power consumption of battery-constrained sEMG Wireless Body Sensor Networks (WBSN) is an important topic. In this paper, we use fatigue assessments as a case study. We apply the concept of distributed computing to explore the impact of computation allocations on the client power consumption and the requirement of architecture specifications. Regarding the CPU clock rate, we propose a power-saving method based on the ping-pong buffer mechanism and evaluate all the crucial factors which affect the power consumption such as sEMG sample rates, algorithmic computational costs, wireless throughputs, and selection of wireless technologies. To sum up, we conduct a comprehensive analysis of all possible distributed computing architectures of WBSN to determine the lowest-power WBSN architecture. The results show that the implementation based on the lowest-power WBSN architecture has lower power consumption compared with other hardwares. The average current of the proposed architecture can be reduced by 81.7% compared with the previous work. Besides, the battery life is 4.48 times that of the previous work under the continuous wireless connection equipped with the same 300mAh lithium battery. Compared with the commercial device, the battery life is 1.6 times that of the commercial one.
Detecting Malicious Ethereum Entities via Application of Machine Learning Classification

Malicious activities such as scams and frauds have imposed high costs for financial systems. The advent of blockchain-based cryptocurrencies such as Ethereum provides unprecedented characteristics. On one hand, the pseudonymity of the blockchain allows criminals to hide their actual identities, which is an appealing feature for conducting malicious activities. On the other hand, the public data of blockchain sets forth the opportunity for comprehensive forensic analysis. In this paper, we present a novel framework to identify malicious entities in the Ethereum blockchain network. The proposed framework composes of an efficient method for extracting a set of features from the Ethereum blockchain data to represent transactional behavior of entities. Our proposed solutions for detecting malicious entities employ variations of Logistic Regression, Support Vector Machine, Random Forest, and other ensemble methods such as Stacking and AdaBoost Classifier. The ensemble methods show high performance with F1 score of 0.996 in average. The results also imply that the proposed method of feature extraction is fairly efficient in presenting the network characteristics.
Detecting Human Motion Intention during pHRI Using Artificial Neural Networks Trained by EMG Signals

With the recent advances in cobot (collaborative robot) technology, we can now work with a robot side by side in manufacturing environments. The collaboration between human and cobot can be enhanced by detecting the intentions of human to make the production more flexible and effective in future factories. In this regard, interpreting human intention and then adjusting the controller of cobot accordingly to assist human is a core challenge in physical human-robot interaction (pHRI). In this study, we propose a classifier based on Artificial Neural Networks (ANN) that predicts intended direction of human movement by utilizing electromyography (EMG) signals acquired from human arm muscles. We employ this classifier in an admittance control architecture to constrain human arm motion to the intended direction and prevent undesired movements along other directions. The proposed classifier and the control architecture have been validated through a path following task by utilizing a KUKA LBR iiwa 7 R800 cobot. The results of our experimental study with 6 participants show that the proposed architecture provides an effective assistance to human during the execution of task and reduces undesired motion errors, while not sacrificing from the task completion time.
PCI-MF: Partial Canonical Identity and Matrix Factorization Framework for Channel Estimation in mmWave Massive MIMO Systems

Beamforming using massive number of antennas in millimeter wave (mmWave) communication is a promising solution for providing gigabits-per-second data rates in cellular networks. However, perfect channel state information (CSI) estimation is a key requirement, which is not practically feasible in massive multiple-input-multiple-output (MIMO) systems. Hence, compressive sensing (CS) and matrix completion methods have been proposed in the literature to reduce the channel estimation overhead. In this paper, a novel method utilizing partial canonical identity (PCI) based CS and matrix factorization (MF) framework, henceforth termed as PCI-MF, has been proposed to recover complete mmWave CSI by estimating only a few channel coefficients. Specifically, a few estimated noisy channel coefficients are represented as a combination of PCI and discrete Fourier transform (DFT) matrix in a CS framework to recover the sparsest solution of the channel matrix. This framework exploits the fact that both PCI and DFT matrices are highly incoherent. The sparse matrix determined above has been used to recover the rank of the channel matrix. The knowledge of the rank, along with the sparse coefficients recovered above, have been used jointly in a matrix factorization framework to recover the actual channel matrix. PCI-MF has been compared with the conventional and the state-of-the-art methods for two different datasets by varying parameters such as the number of transmitting and receiving antennas, antenna configuration, signal-to-noise ratio and measurement ratio. In order to validate the proposed method for realistic applications, one dataset is generated in a real-world setting in the New York City.
Location Evaluation of Multi-station Integration Based on AHP-Fuzzy Comprehensive Evaluation Method

Multi-station integration is an important support for achieving green environmental protection. At present, there are few studies on the evaluation system of multi-station location. Therefore, in this paper the influencing factors of location evaluation of single-station was analyzed in depth to sort out common factors and characteristic factors of multi-stations. Finally, a new evaluation indicator system for multi-station location was proposed, and analysis of candidate sites was conducted based on analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method.
High Efficiency, High Bandwidth Switch-Mode Envelope Tracking Supply Modulator

This paper presents a novel amplitude modulator concept for envelope tracking power amplifiers, which provides maximum efficiency in the lower output voltage ranges, thus nicely matching the probability density function of modern high-peak-to-average-power baseband signals. It can easily handle modulation bandwidths in excess of 300MHz. Furthermore, it operates purely in switch mode, unleashing great potential for overall efficiency. A first hardware implementation reaches around 60 % conversion efficiency and over 84 % when the output voltage is at its minimum value.
A Hybrid Meta-Heuristic Feature Selection Method for Identification of Indian Spoken Languages From Audio Signals

With the recent advancements in the fields of machine learning and artificial intelligence, spoken language identification-based applications have been increasing in terms of the impact they have on the day-to-day lives of common people. Western countries have been enjoying the privilege of spoken language recognition-based applications for a while now, however, they have not gained much popularity in multi-lingual countries like India owing to various complexities. In this paper, we have addressed this issue by attempting to identify different Indian languages based on various well-known features like Mel-Frequency Cepstral Coefficient (MFCC), Linear Prediction Coefficient (LPC), Discrete Wavelet Transform (DWT), Gammatone Frequency Cepstral Coefficient (GFCC) as well as a few deep learning architecture based features like i-vector and x-vector extracted from the audio signals. After comparing the initial results, it is observed that the combination of MFCC and LPC produces the best results. Then we have developed a new nature-inspired feature selection (FS) algorithm by hybridizing Binary Bat Algorithm (BBA) with Late Acceptance Hill-Climbing (LAHC) to select the optimal subset from the said feature vectors in order to reduce the model complexity and help it train faster. Using Random Forest (RF) classifier, we have achieved an accuracy of 92.35% on Indic TTS database developed by IIT-Madras, and an accuracy of 100% on the Indic Speech database developed by the Speech and Vision Laboratory (SVL) IIIT-Hyderabad. The proposed algorithm is also found to outperform many standard meta-heuristic FS algorithms. The source code of this work is available at: https://github.com/CodeChef97dotcom/Feature-Selection
Different Controlling Techniques of A PV-Based Microgrid Systems with Reduced Harmonics

This paper proposes the four different control techniques for a solar power based microgrid system. Four control techniques have been studied in this report such as Grid forming, Grid feeding, Grid supporting based on grid forming, and finally Grid supporting based on grid feeding. A prominent feature of these control schemes is that they do not require any external communication to control them rather automatically compensate according to the need. In this paper, a simple three-phase cycloconverter has been adopted to reduce the harmonics at the load bus. All simulations and performances of these control techniques are calculated through time-domain simulations by using MATLAB/SIMULINK.
A Spice-Compatible Macromodel for Field Coupling to Underground Transmission Lines Based on the Analog Behavioral Modeling

This article presents a time-domain macromodeling algorithm for buried transmission lines subject to coupling of an incident field and terminated with linear and nonlinear loads. Different from overhead lines, the determination of the transient ground admittance is of great importance due to the complexity of the analytical expressions and the necessity of assuring a good approximation over a wide frequency range. The proposed macromodel adopts the analog behavioral modeling (ABM) library of Spice solvers to model frequency-dependent transmission line parameters directly, and calculate convolution in time domain, making it simple and convenient to treat time-domain field-to-line coupling equations without the need of time and spatial discretization and curve fitting used in other time-domain numerical algorithms. Besides, the model complexity is not limited by the line length. Hence, the proposed algorithm can significantly simplify the modeling process and improve simulation efficiency, especially for long multiconductor transmission lines. Furthermore, an effective and simplified expression for self and mutual ground impedance and admittance of underground lines is proposed in this article. With the proposed expression, the time-consuming numerical integration can be avoided, leading to a saving of 98% of the computational time. Finally, several validation examples of field coupling to buried power cables terminated with linear and nonlinear devices are investigated. The comparison of calculated waveshapes and CPU times shows the accuracy and efficiency of the proposed method.