Miracle-dz/UniFAR-FTUs huggingface dataset:
Differences Between Continuous Single-Phase and Online Three-Phase Power-Decoupled Converters

Second-order oscillating power is always generated by a single-phase converter. To decouple this oscillating power from its dc link requires a power decoupling circuit to operate continuously. In contrast, ac grid voltages supplying a three-phase converter are usually balanced. There will, hence, be no oscillating power, if grid currents are balanced too. However, grid voltages may become unbalanced during fault or other disturbances, which, when multiplied with balanced currents, will result in oscillating power. Power decoupling for a three-phase converter is, thus, only necessary during fault, rather than continuously. Its target should also be to improve power quality even during fault, rather than to prolong lifetimes of capacitors claimed with single-phase decoupling. Those differences cause criteria applicable to selecting a power decoupling circuit for single- and three-phase converters to be different, especially when there are seven different types of faulted voltage conditions to consider. These considerations are now studied, before presenting experimental results for verifying the effectiveness of all studied three-phase power-decoupled converters.
Tutorial: Passive radar tutorial

Passive Radar signifying the localisation of a target by radar measurements without using own controlled emissions has been discussed, tried, reinvented, and matured within the last 80 years. Its advantages, like covert operation and saving the costs of a transmitter, are obvious. Military as well as civilian interests combined with the advances in technological developments have recently boosted research on passive radar and passive radar systems are currently approaching the market. This tutorial shall give an overview of the history, development, and processing in passive radar and enable the interested reader to further investigate the subject exploiting the presented material together with the cited references.
A Survey on the Efficiency Improve of Electrical Machines

Currently the worldwide energy utilization is extremely increasing. There is a real risk of depleting in a relatively short time period all the natural resources of the Earth, meanwhile destroying the environment. Basically, there are two ways to slow down these irreversible tendencies: by more and more intensively exploiting the renewable energy resources, and by increasing the efficiency of using the energy. It is well-known, that electrical motors are consuming the greatest part of the worldwide electric power produced. Therefore, without any doubt, their efficiency enhancements can have a very strong positive global effect. The paper is a review on the most relevant electrical machine efficiency increasing technical solutions.
Positive Emotion Elicitation in Chat-Based Dialogue Systems

We aim to draw on an important overlooked potential of affective dialogue systems—their application to promote positive emotional states, similar to that of emotional support between humans. This can be achieved by eliciting a more positive emotional valence throughout a dialogue system interaction, i.e., positive emotion elicitation. Existing works on emotion elicitation have not yet paid attention to the emotional benefit for the users. Moreover, a positive emotion elicitation corpus does not yet exist despite the growing number of emotion-rich corpora. Towards this goal, first, we propose a response retrieval approach for positive emotion elicitation by utilizing examples of emotion appraisal from a dialogue corpus. Second, we efficiently construct a corpus using the proposed retrieval method, by replacing responses in a dialogue with those that elicit a more positive emotion. We validate the corpus through crowdsourcing to ensure its quality. Finally, we propose a novel neural network architecture for an emotion-sensitive neural chat-based dialogue system, optimized on the constructed corpus to elicit positive emotion. Objective and subjective evaluations show that the proposed methods result in dialogue responses that are more natural and elicit a more positive emotional response. Further analyses of the results are discussed in this paper.
Dialogue Act Recognition for Open-Domain Based on Word-Level Sequence Annotation with CRF

In dialogue system, natural language understanding has a huge impact on human-machine interaction. Moreover, natural language contains a huge amount of noise within its content because the short length and free format. From this aspect, the demand of this paper is to design the useful feature representations and verify these feature sets by searching suitable machine learnings. Besides, the dialogue acts as labels associate with utterance can be viewed as sequence labelling problem. Considering this situation, this paper provides a word-level sequence annotation method, which annotates dialogue structural information and semantic information to each word of utterance. Meanwhile, Linear-CRF is employed in this paper because it is able to natively capture constrains of hidden state sequence and obtain optimal probability, which is perfectly suit for sequence labeling task. Compared with proposed method, this paper uses the Bag-of-word model to present utterance as baseline. According to result, all the designed features outperform the feature in baseline, which indicates that the proposed method is useful for DA recognition task.
Traffic Management Through Image Processing and Fuzzy Logic

With the fast-evolving technologies humans have become accustomed to getting everything at the ease of touch and hence it becomes necessary to revise and revamp the existing technologies. In this paper, an idea of traffic management system using image processing and fuzzy logic is proposed. Traffic surveillance is become a necessity and for this very reason there are closed circuit television (CCTV) cameras installed at various sites on the road sides as an effective method for traffic monitoring. This is from where the basic idea of linking the cameras to the traffic signal comes. Thereby not just making it simpler for effective traffic management but also aiding to its utility of surveillance.
Performance Improvement of Permanent-Magnet Synchronous Motor Using a New Deadbeat-Direct Current Controller

In this paper, a new deadbeat-direct current control (DB-DCC) method is proposed in which two adjacent active-voltage-vectors (AVVs) with one zero-voltage-vector (ZVV) are applied to an interior permanent-magnet synchronous motor in each control cycle. In this method, by considering the sign of the error between stator-current components and their corresponding reference values, two AVVs are selected. Then, the changes of stator-current components due to applying these AVVs are formulated. At the next step, duty-cycles of these AVVs are calculated such that deadbeat control of the stator-current components is achieved. To improve the efficiency of the proposed DB-DCC method, reference stator-current components are adjusted based on the principle of maximum-torque-per-ampere, while the ZVV is selected such that the minimum switching transition occurs in each control cycle. Performance of the proposed method is assessed in MATLAB software and in practice where the results indicate that the proposed method reduces the stator-current total harmonic distortion in the steady-state and improves the dynamic response of the motor in the transient-state. The performance of the proposed DB-DCC method is compared with recent model predictive-current controllers and deadbeat controllers where it is observed that by adopting the proposed method superior performance is achieved.
The Periodic Overlapping Group Sparsity: A Promising Technique for Extracting Sparse Fault Features in Rotating Machines

This paper introduces the periodic overlapping group sparsity (POGS) method, which is an effective tool for detecting faults in rotating machines. The POGS can estimate periodic-group-sparse features from measured noisy signals. Firstly, this paper presents the formulation of the optimization problem in the POGS method. Then, the derivation of the convexity conditions is introduced. Thus, the total cost function of the optimization problem formulated in the POGS method is guaranteed to be convex, while sparsity are maximally promoted using the non-convex regularization. Finally, a fast iterative algorithm is provided for its optimal solution. The effectiveness of the POGS is validated by analyzing numerical signals and experimental data.
Diachronic Reconstruction and Visualization of Lost Cultural Heritage Sites

Cultural heritage (CH) documentation is essential for the study and promotion of CH assets/sites, and provides a way of transmitting knowledge about heritage to future generations. The integration of the fourth dimension into geospatial datasets enables generating a diachronic model of CH elements, namely, a set of three-dimensional (3D) models to represent their evolution in various historical phases. The enhanced four-dimensional (4D) modeling (3D plus time) pursues a better understanding of the CH scenario, enriching historical hypotheses as well as contributing to the conservation and decision-making process. Although new geomatic techniques have reduced the amount of fieldwork, when put together, the geometric and temporal dimensions imply the interpretation of heterogeneous historical information sources and their integration. However, this situation could reach a critical point when the study elements are no longer present. The main challenge is to harmonize the different historical and archaeological data sources that are available with the current remains in order to graphically rebuild and model the lost CH assets with a high degree of reliability. Moreover, 4D web visualization is a great way to disclose the CH information and cultural identity. Additionally, it will serve as a basis to perform simulations of possible future risks or changes that can happen during planned or hypothetical restoration processes. This paper aims to examine the study case of a diachronic reconstruction by means of a mobile laser system (MLS) and reverse modeling techniques for a lost urban CH element: the citadel or Alcázar gate of Ávila. Within this aim, the final model is evaluated in terms of the consistency of the historical sources to assess its suitability considering the constructive interpretations that are required to integrate heterogenous data sources. Moreover, geometric modeling is evaluated regarding the current remains and its surroundings. Finally, a web 4D viewer is presented for its dissemination and publicity. This paper is an extended and improved version of our paper that was published in the 2018 ISPRS Technical Commission II Symposium, Riva del Garda, Italy, 3–7 June 2018.
Motor Nerve Conduction Tests in Carpal Tunnel Syndrome

Background: For the preoperatively often required confirmation of clinically defined carpal tunnel syndrome (CTS), sensory as well as motor nerve conduction studies can be applied. The aim of this study was to test the sensitivity of specific motor nerve conduction tests in comparison with, as well as in addition to, sensory nerve conduction tests. Methods: In 162 patients with clinically defined CTS, sensory and motor nerve conduction tests were performed prospectively. Sensitivity and specificity of all tests were computed. Also, Receiver Operating Characteristic (ROC) analyses were conducted. Results: Sensitivity for all sensory tests was at least 79.4% (DIG1). All tests had a specificity of at least 95.7%. The motor conduction test with the highest sensitivity was the TLI-APB (81.3%); its specificity was 97.9%. Conclusion: In the electrophysiological confirmation of CTS, sensory nerve conduction tests and terminal latency index have a high sensitivity. If, however, sensory nerve action potentials cannot be recorded, all motor nerve conduction tests have a high sensitivity.
Dynamic Analysis of Elastic Projecting Robot inspired by Chameleon Tongue

Chameleons can project their tongues as far as 1.5 body lengths with accelerations of up to 500 m/s2. Inspired by the rapid projection mechanism of the chameleon tongue, this paper carries out research on the biological structure and projection principle of the chameleon tongue, as well as a bionic mechanical device. Firstly, the biological phenomenon of rapid projection and extension is described and an elastic-energy storage/release mechanism is introduced to explain the projection principle. Based on the inspiration of the elastic-energy storage/release mechanism, an elastic projecting robot with multi-layer elastic nested structure is designed. Then the mechanical kinematic and dynamic analysis is proposed. Reasonable parameters which are based on the mechanical structure parameters are set in the Solidworks motion software for the 3-D model simulation of the robot. Finally, the simulation results are analyzed to verify the feasibility and availability of the structural design as well as provide a theoretical basis for the structure optimization of the elastic projecting robot.
Face Attributes Prediction Based on Deep Learning

Predicting face attributes is of great value in business user management and video surveillance as well as other fields. The face attributes mentioned in the topic include the factors such as gender, age, glasses, ethnic and expression. This paper proposes a kind of ‘end-to-end’ machine learning method to predict these five attributes. The convolution neural network MobileNet is adopted, and different loss functions are designed according to the characteristics of each attribute as well. At the same time, during the process of training, the five attributes are trained by sharing parameters. At last, in terms of the tests of 10,000 samples, the attribute prediction has achieved high performance which means that the accuracy rate of gender reached 97.8 % the average age error was 3.2, the accuracy rate of glasses was 99.3 % and the accuracy rate of nationality was 96.3 % as well as the accuracy rate of expression was 68.9 %.
ContinuumEA: a soft continuum electroadhesive manipulator

Soft robotic gripping technologies are highly important for pick-and-place of difficult-to-access, difficult-to-handle, and delicate materials. Electroadhesion (EA) is an electrically controllable, energy-saving, highly adaptable, and lightweight material handling technology. Current EA grippers are not able to handle objects in difficult-to-access places such as confined pipelines. We present ContinuumEA, a combination of a soft continuum arm and an EA end effector, for pick-and-place of objects in complicated environments. We embed both a camera and an EA gripper into the end effector of a multi-segment soft continuum arm to demonstrate capabilities of soft-and-safe movement, object search by visual feedback, and 3D movement and placement of objects using EA gripping. ContinuumEA has the potential to extend the gripping performance and capability of current EA grippers and increase their use in material handling applications in challenging environments.
Critical-Mode-Based Soft-Switching Modulation for High-Frequency Three-Phase Bidirectional AC–DC Converters

In this paper, a novel critical-conduction-mode-based modulation is proposed for three-phase bidirectional ac–dc converters. With this modulation, the switching frequency variation range shrinks, zero-voltage-switch soft switching is achieved, and the switching-related loss is reduced, which is especially beneficial for systems operating above hundreds of kHz high switching frequency with wide-band-gap power semiconductor devices to achieve both high power density and high efficiency. A 25 kW silicon carbide based high-frequency three-phase bidirectional ac–dc converter prototype is designed to achieve a power density of 127 ${\text{W/in}}^{3}$, which is at least five times higher than commercial products. All the control functions are digitally implemented with one low-cost microcontroller, and the aforementioned benefits are experimentally verified on this prototype under both inverter mode and rectifier mode operations. With the proposed soft-switching modulation, the tested peak efficiency is close to 99.0% for this prototype even at above 300 kHz high switching frequency operation.
Localization in Wireless Sensor Networks with Optimization and Security Schemes-A Review

Wireless sensors networks(WSNs) are systems of self-sufficient nodes used for monitoring an environment. Developers for WSNs face difficulties that emerge from correspondence join failures, memory, computational constraints, and also sets vitality. In this paper we are addressing techniques to overcome numerous multidimensional optimization issues and also some security issues in WSNs. The optimization problems are approached through bioinspired strategies like Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Grey wolf Optimization (GWO) technique and the security issues are addressed using Secure Localization (SCLoc) platform, Intelligent Arrival Estimation (IAE) and Gradient Descent approach.
MPPT with incremental Conductance and Control Analysis

Operational efficiency, performance optimization and system reliability for a smart grid system are a key criterion. Here we discuss incremental conductance method for tracking Maximum Power in PV system module [1]. This method is proposed because it is accurate, robust and easy to model the PV array. The model based controller is designed such that reference signal for PWM generation of the converter can be adjusted to get maximum generation power by PV module. The Boost DC is used into AC by using three phase Inverter, then the AC power is transmit to the Smart Grid. The MPPT Controller is suitable for studying Power and voltage as well the current and voltage curve for PV system. The proposed System is design on MATLAB/SIMULINK software. The below described model as well its control strategy gives us an exemplary tool for performance optimization of smart grid.
Floor Based Sensors Walk Identification System Using Dynamic Time Warping with Cloudlet Support

Current advances in sensor design technologies and computing power (computational and artificial intelligence) have made it possible to build smart assistive living systems that can improve the lives of people. Because older adults want to age in place at home, there is a need to monitor their health status, detect emergency situations, and notify health care providers. We have improved a floor based monitoring system, which we call the smart carpet, originally to detect falls, but we can take advantage of the continual 24/7 monitoring capability to get important information on gait, fall detection, counting the number of people traversing the carpet and studied the waveform for useful information. Recently, we studied the characteristics of the waveform of the scavenged signal from the sensors and used computational intelligence and feature extractions and classifications to separate people. In this paper, we used Dynamic Time Warping (DTW) to help improve on walk identification, compared with the MFCC feature extraction methods. Results showed that our system identifies walks using a dynamic time warping algorithm and KNN classifier with 86% precision, 76% recall, 81% accuracy. We also present a cooperative cloudlet mobile computing model for eldercare and medical applications where the decisions are very time sensitive. The sensors data will be sent to the nearest cloudlet for analysis and extracting real-time decisions in minimal delay. Users can obtain these results and make decisions by accessing the cloud through their mobile devices and in a real time manner.
An Approach in Testing the Absolute Maximum RF Input Power of an RF & Microwave Power Amplifier

an approach in testing the absolute maximum radio frequency (RF) input power rating of an RF and microwave power amplifier is presented. A developed test method applying pretest, posttest, and destructive RF input power stress sweeps were used to verify the safe points and survivability limit of a gallium nitride microwave monolithic integrated circuit. The test method created incorporates actual DC and RF measuring equipment for collecting and logging current and power levels from the device under test. The developed test method and actual implementation proved to accurately measure both the absolute maximum rating safe datasheet specification and the actual RF input power where the part reaches actual breakdown. At these conditions, it is assured that the actual characterization and results are robust and is aligned to simulated absolute maximum rating specified in the datasheet. The approach proved to be a milestone in evaluating and characterizing RF active devices.
Exploring Security and Authentication Issues in Internet of Things

The Internet of Things (IoT) is a current trend that extends the frontier of the Internet to comprise a wide diversity of computing devices. IoT is a network of a connected embedded devices, such as sensors, which are able to produce, communicates and share data with one another. Several applications of IoT including smart grid, smart transportation, and smart cities, and smart home. Connecting numerous IoT devices through the Internet presents many complex security challenges. Unfortunately, conventional security techniques cannot be suitable to secure IoT systems against IoT Based attacks and exploits. Therefore, this paper explores fundamentals of IoT, and security and privacy issues for IoT systems as well as a study of current IoT based authentication techniques and future work in this innovative area. Also, this paper present an IoT based Authentication Framework for Smart IoT Systems such as Home Automation System.
Towards Intelligent Personalization of IoT Platforms

Trigger-action programming is an emerging paradigm for personalizing the behaviour of Internet of Things applications and services. In this area some recent research efforts have been dedicated to designing environments able to support people without programming experience in specifying the desired personalization rules. Little attention has been paid to how to provide intelligent support in identifying relevant rules. In this position paper we discuss the possible approaches to making such personalization platforms able to exploit the data collected on the behaviour of the users as well as the surrounding devices and things.
Multi-Agent Reinforcement Learning for Efficient Content Caching in Mobile D2D Networks

To address the increase of multimedia traffic dominated by streaming videos, user equipment (UE) can collaboratively cache and share contents to alleviate the burden of base stations. Prior work on device-to-device (D2D) caching policies assumes perfect knowledge of the content popularity distribution. Since the content popularity distribution is usually unavailable in advance, a machine learning-based caching strategy that exploits the knowledge of content demand history would be highly promising. Thus, we design D2D caching strategies using multi-agent reinforcement learning in this paper. Specifically, we model the D2D caching problem as a multi-agent multi-armed bandit problem and use Q-learning to learn how to coordinate the caching decisions. The UEs can be independent learners (ILs) if they learn the Q-values of their own actions, and joint action learners (JALs) if they learn the Q-values of their own actions in conjunction with those of the other UEs. As the action space is very vast leading to high computational complexity, a modified combinatorial upper confidence bound algorithm is proposed to reduce the action space for both IL and JAL. The simulation results show that the proposed JAL-based caching scheme outperforms the IL-based caching scheme and other popular caching schemes in terms of average downloading latency and cache hit rate.