Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Model Predictive Current Control for Permanent Magnet Linear Generator of Direct-Drive Wave Energy Converters

Typically, direct-drive wave energy converter (DDWEC) is coupled with a permanent magnet linear generator (PMLG) to transfer energy from mechanical energy to the grid. In order to reduce the current ripple of PMLG, an improved model predictive current control is proposed in this paper. The extended voltage vectors are used to reduce the d-axis and q-axis current tracking errors of the PMLG. In order to reduce the calculation time caused by expanding the voltage vectors, a cascaded optimization algorithm is proposed in this paper, and only 11 voltage vectors calculations are required for each calculation cycle. Simulation results show that the control method proposed in this paper can reduce the current ripple, which is beneficial to improve the stability of the whole wave power generation system.
A compact simple circular disk printed monopole Antenna for multiple applications

This work presents simple circular patch printed monopole Antenna presented for ultra-wideband (UWB) applications, the proposed antenna has a dimension of 23mm*34mm*1.5mm fabricated on FR4 dielectric substrate material with dielectric constant 4.3. The magnitude of S11 < - 10 dB of the structure, which explains the fractional bandwidth of 2.76-17.15 GHz. The gain and antenna efficiency of the structure are 5.4dBi and 88%, respectively. The simulated results with commercial microwave studio simulator CST and compared with the commercial HFSS simulator are in relatively good agreement. All these properties suggest that the proposed structure has applications in WiMAX, WLAN, C-band, X-band and Ku-band, as well as various other applications in wireless communications.
All-digital Plug and Play Passive RFID Sensors for Indoor Temperature and Humidity Monitoring

This paper presents a plug & play, battery-free, and wireless sensor technology to enable low-cost indoor temperature and humidity measurements for energy-efficient building controls and operations. The proposed technology is based on the method of all-digital sensing, which powers the sensor to operate without on-node signal processing and battery. By integrating the sensor nodes with the radio frequency identification (RFID) technology, the sensor nodes can be wirelessly interrogated in a “plug & play” fashion. The sensing system was experimentally verified in a BACnet based building management system (BMS).
Robust Sliding Mode Based Finite-time Bilateral Shared Teleoperation System with Unsymmetrical Time-Varying Delay*

This paper presents sliding mode-based finite-time synchronization algorithms for bilateral shared teleoperation systems under unsymmetrical time-varying delay and uncertainty. The synchronization algorithms are designed by using Lyapunov and sliding mode control theory. First, the model-based finite-time synchronization algorithms are designed by using sliding mode control theory with the presence of time-varying delays and uncertainty. Then, robust adaptive finite-time synchronization algorithms are designed for bilateral shared teleoperation systems with the presence of time-varying delays. Adaptive learning algorithms learn and adapt with uncertainty associated with the unmodeled dynamics and external disturbances. Lyapunov analysis shows that the tracking errors between master and slave manipulators converge to the sliding surface in finite time. It also shows that the tracking errors asymptotically converge to zero on the sliding surface. The design provides finite-time convergence of the states to reach the sliding surface as opposed to asymptotic convergence-based bilateral shared teleoperation systems. The design and analysis do not use the exact bound of the uncertainty. In contrast with asymptotic design, finite-time convergence can ensure faster and more robust tracking in the presence of uncertainty and time-varying delays. Unlike reported designs, the proposed algorithms can ensure convergence with the presence of unsymmetrical time-varying delays.
Light harvesting self-powered strain sensor using 3C-SiC/Si heterostructure

Herein we report a light harvesting self-powered strain sensor using a monolithic heterojunction structure. Firstly, a strain sensor in a geometry of a cantilever with a 3C-SiC/Si heterojunction sensing element is fabricated. Secondly, light energy harvesting ability of the strain sensor is demonstrated under illumination of a commercial LED. Finally, the performance of the sensor under the self-powered mode is evaluated by inducing strain in the sensing element and simultaneously monitoring the change of converted electrical energy. Under illumination of LED with light power of 428 μW, the generated photovoltage is as high as approximately 16.4 mV. The sensor can harvest light energy convert it into electrical energy to power itself and sense the strain ranging from 200 ppm to 700 ppm with a sensitivity of approximately 0.06 μV /ppm.
Electromagnetic energy conservation with complex octonions

Octonions are the eight dimensional hypercomplex numbers that form a noncommutative and nonassociative division algebra. In this study, a general framework for the real, complex octonions and their algebra are provided by using the Cayley-Dickson multiplication rule between the octonionic basis elements. Maxwell’s equations without sources are shown in Gauss units in dimensionless form. The local energy conservation equation, which has been previously defined in a complexified quaternionic form, is similarly rearranged for isotropic media by using the complex octonions. As a result, the terms of density and flow of electromagnetic energy are attained.
Real-Time EEG Acquisition System for FPGA-based BCI

Multichannel electroencephalograph (EEG) signals data acquisition is non-invasive and easy to implement, thus being the preferred brain-related information carriers for Brain Computer Interfaces (BCIs). Since BCIs must be capable of processing information in real-time, their implementation in edge devices is currently being boosted, particularly on Field-Programmable Gate Arrays (FPGA). In this work, a real-time EEG acquisition system based on the XADC of the Xilinx Zynq FPGA is presented, meeting the requirements of a well-known BCI processing algorithm, the EEGNet. Performed tests show minimal error in the acquisition, whose effects in the EEGNet accuracy are negligible.
The super-twisting sliding mode control for half-bridge three-level LLC resonant converter

The zero-voltage switching capability of the half-bridge three-level LLC resonant converter(HBTHLLC) is possible, and the switch's maximum withstand voltage is just half the input voltage. High efficiency, minimal electromagnetic interferencehigh power density, and high power density are benefits of the converter. However, the HBTHLLC is a complex high-order system with dynamic characteristics drift problem, and its nonlinearity also brings great challenges to the design of the closed-loop controller. In this paper, a new super-twisting sliding mode(STSMC) control strategy is proposed for the HBTHLLC. This control strategy does not need to consider the internal parameters of the system, but only needs to solve the first derivative of the input, which is easy to calculate. The control strategy is simulated and verified in Simulink, and this method has good dynamic response ability and robustness.
A distributed adaptive data-driven predictive control for heterogeneous platoons with V2V communication trouble

A distributed adaptive data-driven predictive control method is proposed to comprehensively address the control issue of heterogeneous platoons under communication loss in this paper. In this algorithm, the introduction of Givens rotations makes it possible to achieve online parameter update without offline identification. The design of the adaptive criterion enables adaptive switching capability for multi-model systems. The Matlab/Carsim co-simulation under different scenarios verifies the robustness of our method and guarantees the string stability of the heterogeneous platoon.
Design and Implementation of a Laser Scanner Featuring Flexible Printed Circuit Boards

This paper presents a power-efficient design of a laser scanner featuring flexible printed circuit boards. We chose a flexible printed circuit board as the material because of its foldable and precision-manufactured properties. In addition to these properties, we can use copper pours to make high rigidity linkages, and drilling to make low rigidity hinges. Taking the advantage above, a power-efficient laser scanner with an integrated coil on the mechanism is designed. It overcomes the shortcomings of paper-based laser scanners, MEMS laser scanners, and previous flexible printed circuit board-based laser scanners. The prototype design reaches a laser scanning range of 44.96° by applying a 0.4 V amplitude voltage at a resonant frequency of 82 Hz.
Set-Valued Graphs: A Survey

Abstract The problem of set valuation of a graph requires both the vertices and edges of an undirected simple graph G to be labeled with subsets of a nonempty set. The label of an edge uv of G is obtained as the symmetric difference of the subsets assigned to the vertices u and v of G. A graph G is said to be set-valued if there exists an assignment of subsets of a nonempty set on the vertices of G such that the following two conditions holds:(i) all the subsets on the vertices are distinct and, (ii) all the subsets on the edges are distinct. The objective of this article is to organize and summarize much of the work done on set-valued graphs since its inception in 1983. Many open problems and conjectures are included. We explore new directions with regards to the enumeration of set-valued graphs.
A Continuous Emotional Music Generation System Based on Facial Expressions

Facial expressions are one of the most intuitive ways of expressing the human heart, which most naturally expresses a person's present emotions. Music is good at expressing emotions, which is most likely to resonate among all art forms. Therefore, expression and music have a common emotional expression. The framework presented in this paper includes the facial continuous expression recognition model and the continuous emotion music generation model. To achieve continuous emotion recognition, we introduce the Russell 2-dimensional valence-arousal emotion space. The novelty of our approach is the joint prediction of discrete and continuous emotions through facial expression recognition networks, and then the music generation model is driven by the continuous emotion vector to generate music corresponding to the predicted facial emotion. Finally, we used a pre-trained emotion classification network and an emotion average opinion score (MOS) to evaluate the consistency of the generated music emotion and the facial emotion.
Mechanical Properties of FeNiCoAlTaB Novel Materials

Fe<inf>40.95</inf> Ni<inf>28</inf> Co<inf>17</inf> Al<inf>11.5</inf> Ta<inf>2.5</inf> B<inf>0.05</inf> (at.%) shape memory alloys are studied by microscope and tension test. The FeNiCoAlTaB is heat treated at 1250°C for 24 hours to obtain the homogenous sample. From the mechanical results, the fracture stress of 6 hours sample is around 758 MPa and elastic modulus is 139 GPa.
A transformation framework for semantic interoperability in Industry 4.0

One of the concepts of Industry 4.0 is a decentralized, highly flexible and self-organizing factory. A key enabler for this vision is semantic interoperability, i.e., a shared and unambiguous understanding across all components of a production system about the meaning of data exchanged and services offered by components. We review current approaches towards semantic interoperability and propose a transformation framework that allows factory operators to assess their current maturity level and to determine measures for gradually transforming their production systems towards full semantic interoperability. For each level in this transformation framework, we propose suitable technology options.
Malay Sarcasm Detection on Social Media: A Review, Taxonomy, and Future Directions

Sarcasm is one of the biggest obstacles in sentiment analysis because sarcastic patterns typically describe the inverse meaning of a written sentence. It becomes a major impediment, particularly in social media analytics, because sarcasm reduces the accuracy of a sentence’s connotation. Research on sarcasm detection in Malay text social media is still in its early stages. This paper reviews Malay sarcasm detection on social media and some related work in the past. We present key elements of sarcasm detection along with a proposed taxonomy mapped from the literature review. A detailed taxonomy overview of sarcasm types, approaches, dataset, model-based, and features is presented. A click-based feature is suggested to be categorized as a new feature type because it is independent and is a unique feature embedded with social media itself. In addition, the use of social media datasets in detecting sarcasm has also been discussed in detail, including some examples of datasets used by previous researchers, especially in languages other than English. Finally, several challenging issues in detecting sarcasm are identified and reviewed. Future research direction in this area is suggested at the end of this paper.
Demand-for graph evaluation of traffic congestion due to Altruistic Lane Changes of CAVs

The development of automated driving technology is expected to reduce traffic congestion. It is necessary to consider the impact that vehicle control of connected automated vehicles (CAVs) has on the overall traffic flow in situations where automated and manually driven vehicles are mixed. In this paper, we represent the traffic flow as an asymmetric simple exclusion process (ASEP) network exploration of multi-agent systems to evaluate the traffic congestion due to the altruistic lane changing of CAVs. To evaluate the congestion, we propose a new method of representing traffic congestion, the Demand-for graph, which represents the action demands among agents. By expressing the demand for altruistic lane changes, deceleration, and acceleration in a demand-for graph, we can evaluate the scale of traffic congestion due to lane changes and the reduction speed of the congestion.
Active Self-weight Compensation for Direct-drive Robot Arm

In recent years, multi degree-of-freedom(DOF) robot arms for delicate movements are required in various fields such as industry, medical care, and service industry. For the delicate movements, high-precision force control is required. To achieve this, it is necessary to use direct-drive without gears that reduce back driverbility. However, conventional robot arms use gears to gain the large torque that supports the weight of the arm and hinder the realization of direct-drive. Therefore, in this study, we proposed a direct-drive robot arm with active self-weight compensation by motors. Then, the effect of the proposed method is shown by simulating the torque required to support the arm when the self-weight compensation is performed by the self-weight compensation motor in a 7-DOF manipulator. Furthermore, we actually manufactured a three DOF direct-drive robot arm using the proposed method. All the motors were placed at the base of the arm to prevent the increase in inertia of the robot arm, and the power was transmitted by wires and tubes.
Frequency Control of a Bidirectional Interlinking Converter in an Islanded Hybrid AC/DC Microgrid

This paper proposes a bidirectional virtual inertial control (BVIC) strategy for an islanded hybrid AC/DC microgrid (MG) including two renewable-energy power-generation systems and an energy-storage system (ESS). The studied hybrid AC/DC MG consists of a wind farm, a PV farm, a bidirectional DC-DC converter, and a bidirectional interlinking converter (BIC). The vanadium redox flow battery (VRFB)-based ESS is connected to the DC MG through a bidirectional DC-DC converter, while the DC MG is connected to the AC MG through the BIC. On the basis of the virtual synchronous generator, this paper uses the BVIC strategy based on BIC to achieve power distribution between the DC and AC MGs, suppress the deviations of both DC-link voltage and frequency of the AC side, and compare the differences for the studied system with and without the BVIC. The steady-state frequency-domain analysis of the studied system under different operating conditions is performed. Both dynamic and transient time-domain simulations of the studied system under various disturbance conditions are also carried out.
DDR Debug Methodology for Board Design Quality and System Robustness

System performance is a major indicator of a workstation or server. Besides the CPU’s (Central Processing Unit) computing power, the DRAM (Dynamic random-access memory) used in the memory system has a direct impact on the performance. To provide high bandwidth and large memory space, the memory system must operate in a high frequency and the system must have as many DRAM slots as possible, respectively.DDR4 (Double Data Rate 4th generation) has been introduced since 2014 and the current Intel® Xeon® D SoC (System-on-Chip) platform supports it to be operated in 2933 MT/s (Mega Transfers per Second) with two DRAM modules populated in a channel (2 DPC). Through the capability of supporting multiple channels (e.g., 4 channels), a system can support up to several Tera bytes of memory (e.g., 4x2x128 Giga Byte = 1 Tera Bytes). From system designer’s point of view, it is vital to test the robustness of the memory system in the EV (Electric Validation) phase of the design cycle. To achieve the goal, Intel not only asks designers to conduct “system test” by running test tools but also provides the “Intel Rank Margining Tool (RMT)” to help designer to understand how much eye height and eye width “margin” exist before the memory system run into issue. Since a severe low margin usually leads to a respin of the mainboard, both test and debug activities are critical to ensure the quality of the system design.In this paper, a systematic and scientific debug methodology is proposed and demonstrated to debug the low margin scenario.
Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms

Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model’s selected parameters.
Regression dilution effects in wind power prediction from wind speed forecasts

When there exists a cause-effect relationship de-scribed by an input-output structural equation, for instance the power curve of a wind turbine, the effect (generated power) is obviously predicted by feeding the cause (wind speed) into the structural model (the power curve). But what is going to happen if the wind speed is not directly observed and is surrogated by a guess, as happens with meteorological forecasts? Such a kind of errors-in-variables framework is well understood in the linear Gaussian case: the straightforward application of the structural equation does not give optimal predictions, a phenomenon known as regression dilution. In the present work, the practical significance of regression dilution effects in the nonlinear regression of wind power from forecasts of wind speed is assessed through data taken from the Global Energy Forecasting Competition 2012 (GEFCOM2012). It is found that the effect is relevant and some lessons are learned, in particular about the benefit of using regression models tailored to the specific prediction horizon.
Evaluation of Order Preserving Triclustering (OPTricluster) in 3 Dimensional Gene Expression Data Analysis Using Gene Ontology

Breast cancer is the most common cancer in woman and very dangerous that caused 30% of all new female cancers for each year. Nowadays, bioinformatics can be usefull to analyze some disease, especially breast cancer. Triclustering is an improvement in bioinformatics and can be used for 3-dimensional (3D) data. Gene-sample-time is commonly used for 3-dimensional gene expression data. One of the Triclustering methods is Order Preserving Triclustering (OPTricluster). OPTricluster forms a tricluster by identifying genes that have a similar pattern of expression change across time points under several experimental conditions. In this study, OPtricluster was used to finding tricluster of breast cancer gene expression data, by observing changes in gene expression levels in several time conditions. After clustering, the genes which contained in each cluster were evaluated by Gene Ontology to find out which genes were affected by estrogen stimulation at each time point. The result is there were 77 triclusters with 3 triclusters of constant patterns, 52 triclusters of conserved patterns, and 18 triclusters of divergent patterns. For the constant, conserved, and divergent patterns, we found there are several genes that are most affected by estrogen stimulation i.e., NOP16, PTBP1, and ITGB4, respectively.
Distributed Finite-time Coverage Control of Multi-quadrotor Systems

This paper investigates the distributed coverage control algorithm of robotic sensor networks consisting of multiple quadcopters which guarantees its finite-time convergence. The proposed technique alters the objective function originated from the locational optimisation problem to accommodate the consensus constraint, and solves the problem within a given time limit. The coverage problem is solved by sending angular-rate and thrust commands to the quadcopters. By exploiting the finite-time stability theory, we ensure that the rotational and translational controllers of the quadcopters are finite-time stable, able to be implemented distributedly, and able to collaboratively drive the quadcopters towards the desired position and velocity of the Voronoi centroid independent of their initial states.
FORMATION AND MOVEMENT OF WEST INDIAN HURRICANES

Abstract The physical features of the hurricane are fairly well understood. The explanation of the process of formation of the hurricane remains in more or less dispute, there being two hypotheses that attempt to satisfactorily explain its origin. There are reasons for believing that countercurrents, having their origin in differences in temperature over large geographic areas, initiate the conditions that give rise to the system of gyrating winds; that the condensation of water vapor supplies the energy necessary to maintain them through considerable periods of time. The movement of the hurricane is generally attributed to the general drift of the air in the region of the hurricane. The daily synoptic weather charts and the observations of the free-air directions and speeds of the winds in regions contiguous to hurricanes appear to indicate that hurricanes are carried forward on the border of the major wind system (the northeast trade) of the tropics, and that as this wind system changes its direction th.