Miracle-dz/UniFAR-FTUs huggingface dataset:
How children interpret robots’ contextual behaviors in live theatre: Gaining insights for multi-robot theatre design

Robot theatre has been gradually used in children’s storytelling activities. The robotic character collaboratively exchange stories with children, react to their actions, and keep them engaged. However, the only robotic protagonist limits the richness of content and cannot fully support children’s creativity. To explore a tool with multiple robotic agents to better enhance story creation activities, we designed a simulated multi-robot theatre with three similarly shaped robots. We invited 28 kids (6-12 years old) to watch the video and retell the story. We encode children’s story recall and outline that robots’ contextual behaviors with emotion expression are reasonably enough for children over six-years-old to understand their performance. We also found no noticeable difference in story understanding among participants. We discuss the insights for designing robots’ contextual behaviors for story presentation and the considerations for implementing an accessible and feasible robot theatre creation platform.
Matrix Pencil Method applied to the compression of audio data in naval operations

In this paper, a technique for compressing data from a time-domain audio digital signal, using the Matrix Pencil Method (MPM) is presented. The results show that the selection of critical parameters like time window and sampling frequency can effectively work alongside MPM, to reduce an audio signal data to a set of residues and poles, and have an accurate reconstruction of the vector related to the original audio. The compression process is focused on acoustic signals coming from boat engines and the reconstruction made for these poles and residues provides an R2 correlation coefficient near to 100%. Besides, is possible to obtain a rate of compression between 80 and 93%. The audio signals are collected by hydrophones deployed in a coastal area, close to the shore. The signal under process is expected to present frequency components up to 1KHz. The data reduction enhances the transfer time and the bandwidth of the wireless communication channel under maritime conditions. Also, the proposed system considers the case of multiple sensor locations, acquiring a signal from multiple points in the area of influence allowing higher-level processes as the identification and tracking of the observed boat in naval operations.
A Vibratory Conveying System for Automatic Sorting of Lima Beans through Image Processing

In order to comply with lima beans peruvian export standards, this study develops an automatic sorter machine. This system uses vibratory conveying to linearly move the grains and to allow their faces to rotate. In addition, image processing algorithms were used for the detection and rejection of defective lima beans. After performing 2200 sorting tests, an industrial performance with an efficiency of 96.81% in acceptance and 95.26% in product rejection was obtained
Analytical model for the prediction of the Magnetic Field Distribution due to a finite length Permanent Magnet array for a Radially Magnetized Linear Oscillatory Machine

This paper present a two dimensional (2D) analytical model for longitudinal flux air-cored linear oscillatory generator. The proposed model considers a model in which the magnetic field is only generated by the permanent magnets. The analytical model is developed by taking the permanent magnet array as having a finite length which is repeated infinitely at a separation distance while the iron yokes are considered to be infinitely long; infinitely permeable. The solutions are obtained by employing the sub-domain method and the separation of variables. The magnetic field distribution is predicted by the analytical model developed and correlates well with the distribution generated by a finite element package
Hybrid Solar PV-Wind Generation System Coordination Control and Optimization of Battery Energy Storage System for Rural Electrification

The scarcity of electric power grid network in rural areas has made hybrid power generation from renewable energy sources (RESs) such as solar photovoltaic (PV) and wind inevitable. Reliable power supply from these RESs necessitates deployment of the large size energy storage system (ESS) to meet load demands that entails high investment cost. To reduce the ESS requirements, the optimal hybrid system design is vital. Effective reduction of system's cost is accomplished by optimizing the battery ESS. This paper proposes modeling and coordination control of hybrid solar PV-wind power generation system together with optimal BESS using Mixed Integer Linear Programming (MILP). The proposed scheme is designed with the objective of maintaining power flow between DC-AC buses by guaranteeing constant voltage and system frequency at the Point of Common Coupling (PCC). Simulation results obtained on MATLAB/Simulink confirms excellent performance of the proposed scheme.
Geo-Spatial Analysis of Renewable Energy Potential in the European Union Under Consideration of Social, Environmental, and Technical Limitations

The continuous deployment of renewable energy sources (RES) has significant impacts on the environment and, therefore, the expansion is limited by techno-economic, environmental, and social factors. These factors are comprehensively analyzed, with the German energy transition as a reference, and are applied as part of a GIS based geo-spatial analysis of the EU-28. In result, it was found that a total RES generation of approximately 10,600 TWh/a could be achieved. However, each country possesses an individual characteristic regarding future potential generation and RES portfolios.
Electromechanical Equivalent Circuit Model of a Piezoelectric Disk Considering Three Internal Losses

Heat generation by internal loss factors of piezoelectrics is one of the critical issues for high power density piezoelectric applications, such as ultrasonic motors, piezoelectric actuators and transducers. There are three types of internal losses in piezoelectric materials, namely dielectric, elastic and piezoelectric losses. In this paper, a decoupled equivalent circuit is proposed to emulate a piezoelectric disk in radial vibration mode considering all three types of internal losses. First, the decoupled equivalent circuit is derived according to the conventional electromechanical equivalent circuit model. Then, a piezoelectric disk configuration in radial vibration mode is explored and simulated. The resonance and antiresonance frequencies and their corresponding mechanical quality factors are achieved by the proposed circuit. In order to verify the accuracy of the simulation results, the piezoelectric disk is fabricated and tested. Simulation results with the new circuit exhibit a good agreement with experimental results. Finally, the equivalent circuit with only dielectric and elastic losses are simulated and compared which further validates the accuracy improvement of the new equivalent circuit considering all three losses.
TIRL: Enriching Actor-Critic RL with non-expert human teachers and a Trust Model

Reinforcement learning (RL) algorithms have been demonstrated to be very attractive tools to train agents to achieve sequential tasks. However, these algorithms require too many training data to converge to be efficiently applied to physical robots. By using a human teacher, the learning process can be made faster and more robust, but the overall performance heavily depends on the quality and availability of teacher demonstrations or instructions. In particular, when these teaching signals are inadequate, the agent may fail to learn an optimal policy. In this paper, we introduce a trust-based interactive task learning approach. We propose an RL architecture able to learn both from environment rewards and from various sparse teaching signals provided by non-expert teachers, using an actor-critic agent, a human model and a trust model. We evaluate the performance of this architecture on 4 different setups using a maze environment with different simulated teachers and show that the benefits of the trust model.
Understanding the Perception of Incremental Robot Response in Human-Robot Interaction*

Incremental feedback, i.e. the timely response to human behavior while it is happening, has previously been found to potentially speed up human-robot interactions, but it is unclear how people evaluate incremental robots. In this study, we show that the evaluation of incremental robot response depends on the actual success of the incremental feedback; that is, if the feedback leads to increased efficiency, people evaluate the robot as more competent and more credible. If the robot does not use incremental feedback, no interaction between evaluation and efficiency can be found. Thus, incremental feedback draws people’s attention to interaction success.
Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

Aided by scalable video coding, multirate multicast has become a promising technique of providing differentiated quality of experience (QoE) for massive numbers of video subscribers operating in heterogeneous channel conditions. Nevertheless, due to the time-varying nature of wireless channels and the subscribers’ diverse requirements, it is challenging to dynamically control the video rate in the light of the available radio resource to achieve the best QoE. To elaborate a little further, the time scale of resource scheduling is of short-term nature, which determines the short-term video quality variation, but from a service provider's perspective the design objective is to optimize the long-term QoE for all subscribers. Despite its importance, this problem has not been considered before. Explicitly, we formulated this problem as a time-averaged stochastic optimization problem which avoids the impact of both the short-term channel quality fluctuation and that of the video bitrates, whilst maintaining both inter- and intra- group fairness. The stratified structure of the problem inspires us to decompose it into a two-phase optimization: coarse grained assignment for each user group and fine grained assignment for each subgroup. We propose an adaptive multicast algorithm based on Lyapunov's optimization theory for solving this problem, by striking a compelling trade-off between the system's utility and its queue stability. We quantify the achievable performance of our proposed solution based on realistic video traces.
Conceptual Design and Demonstration of an Automatic System for Extracting Switching Loss and Creating Data Library of Power Semiconductors

A switching characterization (SC) test of power semiconductor devices (PSDs) gives us significant insight into the dynamic switching behavior of the device under various operating conditions. A double pulse test (DPT) is a widely used method for evaluating switching performance parameters of a PSD such as its switching losses, switching speed (di/dt, dv/dt), turn-on and turn-off times etc. The scientific information obtained from analysis of DPT results of a PSD helps in predicting its thermo-electric performance in a target power electronic converter. With conventional DPT setups, it is a time-consuming and error-prone process to manually conduct these tests under several permutations of test parameters and thereafter analyze the experimental data manually. This work presents a newly developed automated SC test system, which can run tests one after another, once the desired test parameters are entered in a graphic user interface. The test-control system also enables recording and systematic processing of the experimental switching data to deliver usable characterization results. The automatic, compact and modular design allows the proposed SC test platform to stand out from the conventional DPT setups. The design principles are experimentally verified by implementing a hardware prototype capable of testing PSDs up to 1000 V, 60 A, 250 °C.
Zur topologischen Algebra. III. Brouwersche und Cantorsche Gruppen

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Accuracy in Object Detection based on Image Processing at the Implementation of Motorbike Parking on The Street

The development of technology in object detection provides the opportunity for land transportation to be able to utilize the technology. One of the problems in the transportation sector is parking vehicles on the street. This problem is quite complex, for example motorbikes parking on the street, where motorbikes are parked have high density levels and irregular parking configurations, this becomes a separate difficulty in detecting motorbikes parked. The purpose of this research is to test the accuracy of the implementation of motorbike parking in the parking area on the street and the calculation of the number of parked motorbikes. The data used is collected through cameras installed in the parking area on the street from October 2018 to May 2019. The tests are measured using the Yolo (You Only Look Once) v3 method with the test results showing an Average Precision (AP) rate above 90% in motorbike detection and the calculation of the number of parked motorbikes having a deviation below 10%.
A Vehicle-Centric Probabilistic Approach to Virtual Cell Management in Ultra-Dense 5G Networks

The fifth generation (5G) cellular networks tend to be ultra-dense networks (UDN). Among the 5G UDN challenges, it is the creation and the management of virtual cells centered on the user. By the literature was not found any work that has directly associated a vehicle-centric probabilistic approach to virtual cell management in ultra-dense 5G networks as proposed in this paper as a contribution to the technological development on 5G networks use, what indicates its originality. Considering scenarios involving high mobility, such as the Internet of Vehicles (IoV) or vehicle-to-network (V2N) communications, this challenge becomes even greater. For this purpose, this article presents a virtual cell selection probabilistic approach focused on V2N communications. All the processing based on speed criteria and complex network metrics is performed inside a controller that is required to manage the virtual cells. The motivation to use different metrics of radio masts is that one has physical applications as an algebras operator in purely mathematical contexts, while others find wide application in network theory. Simulations were performed through the Network Simulator ns3. The results show that the proposed approach allows more assertive virtual cell selection, improving the services offered by IoV through the 5G networks.
Resilient Range-Based d-Dimensional Localization for Mobile Sensor Networks

Knowledge of node locations is essential to Wireless Sensor Networks (WSNs) in a wide range of potential applications and their function-dependent network protocols. A number of localization approaches have already been proposed to fulfill this requirement, but few of them can be applicable to mobile sensor networks, due to their low-dimensional embeddings, Euclidean distance representation limitations, frequent node mobility and additional measurement overhead in the network. In this paper, a resilient range-based <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional localization (RRDL) approach is proposed for mobile WSNs to resolve the issues. RRDL distinguishes itself from previous work with three remarkable characteristics: (1) it works for mobile networks embedded in <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional Non-Euclidean space; (2) it allows static ordinary nodes with pre-known locations to act as the alternative anchor nodes, thus tolerating the motion of the original anchor nodes to ensure that other ordinary nodes can obtain their locations in an efficient manner; and (3) it introduces an efficient path-learning approach, with the knowledge of the existing paths, to represent the real network distances as far as possible, thereby eliminating additional measurement overhead and tolerating node mobility in localization. With these characteristics, RRDL exploits the iterative factorization of the random distance matrix, formed by the distances to and from a set of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-hop static neighbors, to assign each current node <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional Non-Euclidean coordinate in a distributed manner. Simulation results demonstrate that RRDL achieves higher localization accuracy with a moderate communication cost in mobile sensor networks.
Fully Automated Control and Monitoring For Liquid Level System

The rapid growth of the Malaysian population has made managing and controlling water usage efficiently a significant priority. Malaysian's main source of clean water for daily use originates from water treatment plants (WTP). Generally, the plants are controlled and monitored manually, thus ensuring sufficient water supply for distribution-a difficult task. To overcome this, a better and improved technique of controlling and monitoring the level in the tanks in WTP is required. Therefore, in this research, an automated liquid level system, which mimics a real WTP, is proposed. In this project, a fully automated control for a prototype of a liquid level system is designed using OMRON Programmable Logic Controller (PLC) and monitored using CX-supervisor. Human-Machine Interface (HMI) is used for system integration-the hardware part-and CX-supervisor, a software where the HMI can monitor and control the process of the system. The output of the research work is a fully functional supervisory, control, and data acquisition (SCADA) system for the liquid level prototype. The proposed SCADA system is equipped with data logging capabilities for the purpose of failure detection which can be analyzed from the obtained data trending. The research output showcased a successful control system design of a liquid level process system, ready to be implemented within the water industry.
Machinery Health Monitoring Based on Unsupervised Feature Learning via Generative Adversarial Networks

It confronts great difficulty to apply traditional artificial intelligence (AI) techniques to machinery prognostics and health management in manufacturing systems due to the lack of abnormal samples corresponding to different fault conditions. This article explores an unsupervised feature learning method for machinery health monitoring by proposing a generative adversarial networks (GAN) model that exploits the merits of the autoencoder and the traditional GAN. The major contribution is that the data distribution of the normal samples is accurately learned by the GAN model within both the signal spectrum and latent representation spaces. Specifically, the discriminative feature for machinery health monitoring is learned in an unsupervised manner by the proposed method in three steps. First, the proposed GAN model is trained by the normal samples of the inspected machine with the aim to correctly reconstruct the signal spectrum and its latent representation. Then, the trained model is applied to test the online samples of the same machine with unknown health conditions. Finally, the dissimilarity between the tested samples and their reconstructed ones in the latent representation space is taken as the discriminative feature. The feature value will increase significantly if a fault occurs in the inspected machine because the abnormal samples are never trained in the proposed GAN model. Experimental studies on three different machines are conducted to validate the proposed method and its superiority over the traditional methods in detecting abnormal points and characterizing fault propagation.
Survivability Index and Evaluation Framework for Cyber Physical Power Systems

With the development of the CPPS system, assessing its survivability has become more and more important. This paper first analyzes the attack forms and the attack propagation process of a cyber-attack from a chronological perspective based on the interrelationship between the grid side and the information side. Then, survivability indexes are proposed based on the chronological characteristics of CPPS attack events. Finally, a framework for evaluating the survivability of CPPS systems under cyber-attack is proposed based on the three phases of cyber-attack and reliability indexes.
Evaluation of Immersive Teleoperation Systems using Standardized Tasks and Measurements

Despite advances regarding autonomous functionality for robots, teleoperation remains a means for performing delicate tasks in safety critical contexts like explosive ordnance disposal (EOD) and ambiguous environments. Immersive stereoscopic displays have been proposed and developed in this regard, but bring about their own specific problems, e.g., simulator sickness. This work builds upon standardized test environments to yield reproducible comparisons between different robotic platforms. The focus was placed on testing three optronic systems of differing degrees of immersion: (1) A laptop display showing multiple monoscopic camera views, (2) an off-the-shelf virtual reality headset coupled with a pantilt-based stereoscopic camera, and (3) a so-called Telepresence Unit, providing fast pan, tilt, yaw rotation, stereoscopic view, and spatial audio. Stereoscopic systems yielded significant faster task completion only for the maneuvering task. As expected, they also induced Simulator Sickness among other results. However, the amount of Simulator Sickness varied between both stereoscopic systems. Collected data suggests that a higher degree of immersion combined with careful system design can reduce the to-be-expected increase of Simulator Sickness compared to the monoscopic camera baseline while making the interface subjectively more effective for certain tasks.
On securing IoT from Deep Learning perspective

The extensive growth of the Internet of Things (IoT) has impacted diverse applications, including smart homes and cities, Intelligent Transport Systems (ITS) and smart factories. IoT integrates billions of smart devices -predicted to increase from 27 billion in 2017 to 125 billion by 2030- and manages communication between them. This degree of expanded connectivity requires extensive further analysis with respect to security, and the involvement of millions of factors and users increases vulnerability in IoT environments. However, Deep Learning (DL) approaches, which originated from machine learning (ML), have been efficient in many research fields, and current studies show the effectiveness of DL for IoT security applications. In this paper, we present detailed analyses of IoT security requirements and challenges, discuss the specific role of DL and review state-of-art research work in IoT environments using DL approaches. We also performed comparative analysis of DL algorithms such as RNN, LSTM, CNN, DBN and AE. And finally, we identified research issues in the current investigations, and outlined the future directions of DL algorithms in IoT security domains.