Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Management for Standalone DC Microgrid Using Artificial Bee Colony

This paper presents an optimized energy management system for small-scale standalone DC microgrid that consists of solar photovoltaic, wind, and fuel cell coupled with a battery energy storage system. The energy management system has been designed based on low complexity fuzzy logic controller of only 25-rules to manage the power flow that supplies a house load during 24 hours and to maintain state of charge of the battery energy storage system. The fuzzy logic controller is optimized using one of the efficient optimization techniques, artificial bee colony to enhance the system efficiency. As a case study, real data for solar irradiation, ambient temperature, wind speed and house load profile were collected in a hot summer day from Dhahran city in Saudi Arabia and applied to the proposed system. Energy analysis of the microgrid operation during day hours and comparison of the system before and after optimization are presented.
Temperature-Aware Task Scheduling for Dark Silicon Many-Core System-on-Chip

Most of the cores in future many-core system-on-chip (MCSoC) will be off or ‘dark’ to manage the high power density and chip temperature of future chips. This problem is called dark silicon problem. This paper presents a task scheduling technique for optimizing the MCSoC performance under temperature constraint for dark silicon. The proposed technique uses both task migration and dynamic voltage frequency scaling (DVFS) to optimize many-core system performance, while the system temperature is kept in a safe operating range. Task migration improves system performance and balances heat distribution among cores by moving tasks from active cores to dark cores, while DVFS reduces core speed in case of thermal limit violation. Simulation results show that using both task migration and DVFS techniques reduces peak temperature by 12° C and keeps the average system temperature under the thermal limit, while the execution time increased by 16% compared with 50% when only DVFS technique is used.
Prototype Design For Energy Consumption In Smart Home Using IOT

Implementation of IOT is continued to be a challenging task in various industries. Power consumption for domestic purpose remains to a significant issue in modern era. The quickly growing conception of good home with good meters has its sizable amount of benefits. Our project is to monitor and control the usage of each and individual home appliances effectively. To consume the facility in good home terribly effectively automatic measuring threshold value (AMTV) formula is employed. By using this threshold value in AMTV algorithm, the user will notified about the consumption of electrical power. User can able to view the consumption of power in monthly, weekly and daily basis. Also, we can control each and every electrical appliance of our home from remote area. So the user can limit his usage of the appliances to avoid his EB bill amount without reaching the next slab in tariff table.
Frequency Regulation Strategy for DFIG Combining Over-speed Control and Adaptive Virtual Inertia

The doubly-fed synchronous generator (DFIG) with traditional visual inertia control has fast dynamic response speed but is lacked of refinement control under load fluctuation. In order to improve the dynamic stability of system frequency, a control strategy for DFIG based on over-speed control and adaptive virtual inertia is proposed in this letter. This method simulates the behavior of synchronous generator, constructs the relationship between frequency change rate and virtual inertia, and adaptively changes the inertia of virtual synchronous generator (VSG), thus improving the anti-interference capability of system. Compared with the traditional virtual inertia method, the algorithm has strong robustness. Simulation and experimental results show that the proposed method improves the dynamic stability of system frequency, verifying the feasibility and effectiveness of the proposed control strategy.
A Dual Band Dual Sense Circularly Polarised Single Feed Microstrip Patch Antenna

A novel and compact dual band dual sense circularly polarized microstrip patch antenna with single coaxial feed has been reported in the present work. The key idea of generating dual band circular polarisation (CP) is the integration of a square patch with corner truncation and a smaller concentric circular patch with double slits. The first resonance is provided by the larger patch whose corner truncation generates two orthogonal modes. The inner patch controls the higher-order resonance with the CP contributed by two narrow slits. The higher order resonating frequency can be monitored by controlling the dimensions of the circle and the slits. The antenna provides the CP in two orthogonal planes with two different sense of polarisation. The lower order CP is of left-handed orientation, whereas the higher order shows right-handed polarization. The cross-polarization level is also found to be very low.
Comparison Analysis of the Artificial Neural Network Algorithm and K-Means Clustering in Gorontalo Herbal Plant Image Identification System

The objective of this study was to analyze the comparison between artificial neural network algorithm and k-means clustering to see the extent of the effectiveness of this algorithm on the identification of Gorontalo herbal plant image. This study uses a digital imaging processing method with segmentation and extraction techniques. Segmentation proses used thresholding method. The next process was extraction process of the characteristics of the image of the herbal plant using the shape and color characteristics to obtain the metric, eccentricity, hue, saturation, and value of the plant was carried out. These five parameters were used as parameters to identify the herbal plant image. This study used 91 images which consisted of 80 imagery training and 11 test images. The study revealed that k-means clustering accuracy was 27.27% whereas the artificial neural network algorithm accuracy was 54.54%. In this case artificial neural networks had better accuracy than K-means.
Properties of Planar Waveguide With Gain (Absorption), Applied on a Glass Prism

Structure that consists of glass prism, with successively applied two dielectric layers with small and high refractive indices has been analyzed using matrix method. Moreover, the second waveguide layer has complex refraction index. In the case of absorbing layer, for optical waves with certain wavelengths and angles of incidence, the reflection coefficient is equal to zero. In the other case, where the dielectric layer with complex refractive index has amplifying properties, the reflection coefficient at certain wavelengths and angles of incidence becomes significantly high.
Optimization of beam scanning Reflectarray using $M_{Q}C_{10}$-BBO and SNO Algorithms

In this paper, some numerical results about the optimization of a reflectarray with scanning capabilities are presented. Two different, innovative algorithms are compared, the $M_{Q}C_{10}$-Biogeography Based Optimization ($M_{Q}C_{10}$-BBO) and the Social Network Optimization (SNO): both are applied to the design of a planar Reflectarray with scanning capabilities, and the obtained results, validated by a full-wave analysis, prove that the two methods allow to have good features over a scanning angle of ±40°.
Diffusion MRI Consistent Wire Models for Efficient Solutions of the Anisotropic Forward Problem in Electroencephalography

The surface Boundary Element Method (BEM) is one of the most commonly employed formulations to solve the forward problem in electroencephalography, but the applicability of its classical incarnations is lamentably limited to piecewise homogeneous media. Several head tissues, however, are strongly anisotropic due to their complex underlying microstructure. This implies that standard boundary integral formulations oversimplify the electrical properties of the head and produce unrealistic solutions, something that drastically limits the suitability and impact of BEM technologies to brain imaging. This contribution addresses this issue by observing that the brain anisotropy in the white matter is due to the presence of neuronal wire-like structures. We then extend the well known wire integral equations used for high frequency problems to the imperfectly conducting quasi-static case and we propose a new hybrid wire/surface/volume integral equation. When applied on multimodal magnetic resonance images combined with tractography, this new approach can flexibly and realistically handle the conductivity anisotropy in any head compartment providing high level of accuracy and efficiency. The beneficial properties of the new formulation together with its impact on brain imaging is demonstrated via numerical results on both canonical and realistic case scenarios.
Merged Processing Element for Polar Code Decoder

Polar codes are a class of forward error correction codes that can provably achieve the capacity of discrete memoryless channels. In some polar decoding schemes, successive cancellation decoders carry out estimations of the original messages, using update rules from two functions that involve arithmetic operations. Processing elements are basic blocks of the decoder that implement these functions. The interconnection of a great number of these elements forms a network which operates on the log-likelihood ratios from the channel to finally achieve the best possible estimation of the original message bits, hence the importance of a thorough design of them. Any simplification or resource saving in the design of these elements directly impacts the hardware complexity of the decoder. Also, any improvement in their throughput should have a similar effect in the performance of the decoder. In this work we present two generic, fully combinational designs for processing elements. They take into consideration the possibility of overflow of the quantized values, and use pre-computation to reduce latency. These designs can be used in different hardware decoder architectures to improve their throughput and area requirement.
Additive Manufacturing of 300 GHz Corrugated Horn Antennas

A metallic 3D-printed J-band corrugated horn antenna is presented. The horn antenna is designed in a simulative approach to meet specific requirements regarding symmetrical radiation, high gain, strong side lobe attenuation, good matching and a focused main lobe in the range of 8° - 10°. Due to material limitations in the manufacturing process, the antenna material’s conductivity is improved by electroplating a thin layer of gold. Finally, measurement results of the power transmission and the near and far field are provided to validate the specifications of the manufactured horn antennas, demonstrating that the design goals are met.
A Non-Iterative Switching Status Combination Judgement Algorithm for Half-Bridge Sub-Circuit Based Voltage-Source Converters in EMTP-type Simulation Program

The large number of switching devices in high-frequency power conversion systems have lead to a computational challenge for electromagnetic transients (EMT) simulation programs, as it can significantly increase the simulation time. Based on the half-bridge sub-circuit, this paper analyses the possible parasitic switching events of adjacent switches on the same bridge arm, and a fast switching status combination judgement algorithm is proposed. This algorithm does not have the process of iterative detection when searching for stable switch combination, thus it can effectively reduce the simulation time. Different testing cases are simulated in both the proposed-algorithm-embedded cloud power system simulation platform (CloudPSS) and the PSCAD, which shows a desirable accuracy of the proposed algorithm.
Design of a C-band Reflectarray Antenna for Near-Field Applications

The design of a C-band (at 5.8 GHz) reflectarray antenna for nearfield focusing is presented. This work is motivated by the increasing interest in using reflectarray antennas for near-field applications such as wireless power transfer and RFID sensing. Simulation and measurement results are presented to show that the goal of achieving a focal point of about 10 to $12\ \boldsymbol{\lambda}$ away from the reflectarray antenna is met.
A Quad-Band Dual-Sense Circularly-Polarized Square-Ring Antenna for Multi-Functional Wireless Applications

A quad-band dual-sense circularly-polarized (CP) antenna is presented for multi-functional wireless applications. Initially, the corner-fed square-ring radiator is designed with one end open-circuited. The surface currents flow along the circumference of the square-ring, which excite dual-band dual-sense CP radiation with a right-hand circular polarization (RHCP) at one frequency band and a left-hand circular polarization (LHCP) at the other band. Later, a rhombic-shaped slot and an inverted V-shaped parasitic element, used to balance the surface currents in horizontal and vertical directions at the middle and low frequency regions, excite two additional LHCP bands. The proposed antenna is systematically presented through a CP generation principle, a surface current distribution, and parametric analysis. The measured results yield an impedance bandwidth (IBW) of 83.9% (2.33–5.70 GHz) and the 3-dB axial ratio bandwidths (ARBWs) of 7.3% (2.38–2.56 GHz), 9.0% (2.75–3.03 GHz), 3.7% (3.42–3.53 GHz), and 7.1% (5.16–5.54 GHz). The maximum broadside gains achieved within the axial ratio bandwidths are 2.5, 2.9, 3.4, and 2.7 dBi, respectively. Compared to other reported works considering quad-band CP radiation, the proposed antenna achieves a wider ARBW and smaller size. To the best of the author’s knowledge, the proposed work is the first paper presenting a planar quad-band dual-sense CP antenna for WLAN (2.5/5.3 GHz), WiMAX (3.5 GHz), and Marine Service (3.0 GHz) wireless applications.
A Two-Step Environment-Learning-Based Method for Optimal UAV Deployment

Unmanned aerial vehicles (UAVs) can be used as low-altitude flight base stations to satisfy the coverage requirements of wireless users in various scenarios. In practical applications, since the transmitted power and energy resources of the UAVs are limited and the propagation environments are complicated and time-variant, it is challenging to control a group of UAVs to ensure coverage performance while preserving the connectivity and safety of the UAV networks. To this end, a two-step environment-learning-based method is proposed for the intelligent deployment of the UAVs. First, a machine learning algorithm is used to establish an accurate prediction model of the link qualities from the UAVs to the users under a specific scenario for the next step. Then, a modified deep deterministic policy gradient (DDPG) algorithm is employed to control the movements of the UAVs according to the predicted link qualities and to maximize the proportion of covered users. The prioritized experience replay mechanism is introduced to the standard DDPG algorithm to accelerate the deployment procedure. The coverage performance is analyzed in both the interference-free situation and the situation with co-channel interference. Simulation results have shown that the proposed method has a higher convergence speed than the standard DDPG method. Additionally, the proposed deployment method can achieve higher coverage performance and better adaptability to the dynamic environment than three commonly used methods, the random method, the K-means-based method, and the statistical-channel-model-based method.
Neural-Network-Based Distributed Formation Tracking Control of Marine Vessels With Heterogeneous Hydrodynamics

In this paper, a robust scheme is presented for distributed formation tracking control of marine vessels with heterogeneous hydrodynamics. Provided that multiple ships sail in proximity at sea, the hydrodynamic forces and moments would be changed greatly due to the ship-ship interactions, thereby causing the heterogeneous hydrodynamics; nevertheless, it is usually ignored in formation control of marine vessels. The heterogeneous hydrodynamics is very difficult to model and induces the uncertainty; therefore, it is treated as unknown dynamics in this paper. The neural network (NN), which is well-known for approximation-based control, is employed to handle the unknown dynamics. Note that the NN is employed to approximate the unknown dynamics collectively for reducing the learning parameters. Furthermore, unlike traditional formation control of coordination of positions, the consensus mechanism of velocities is also included into the control design to ensure the performance of formation maintenance. The presented distributed controller for each ship only utilizes the information from itself and its neighbors, aiming to prevent single-point failure in harsh marine environment. All the closed-loop signals are proved to be stable based on Lyapunov theory. Various simulations are conducted to validate the effectiveness of proposed control design.
Interaction Characteristics of Ports of PET in AC/DC Hybrid System under Large Disturbances

Power electronic transformer (PET) will plays a key role in the development of smart grids in the future for its flexible control and operation modes. Based on PET topology and control strategy, this paper analyzes the interaction characteristics between the PET ports and discusses the stability of a typical AC/DC hybrid system with the PET in the case of short-circuit faults at different ports. The simulation results show that the short-circuit fault of 750V DC port has the greatest impact on the stability of the system. Based on the result, the solution to install the fault current limiter (FCL) on the 750V port of PET is presented. The simulation results show that the installation of the fault current limiter can effectively improve the stability of the system.
Research on Minimum Schedulable Power Optimization of CCHP System

In this paper, a dynamic optimal setting method of schedulable minimum power for combined cooling, heating and power (CCHP) system is proposed, which aims at maximizing the energy utilization of gas engine in CCHP microgrid. Firstly, according to the short-term energy supply demand of cold/heat/electricity load under isolated grid, a hierarchical scheduling strategy is developed for the CCHP microgrid. Based on the dispatching strategy, the method of setting the minimum dispatching power of the CCHP microgrid is improved. The minimum dispatching power of the CCHP microgrid is dynamically adjusted according to the energy supply situation in different periods to ensure that the gas engine always operates in the most efficient state, making full use of the energy of the gas engine to supply electric load, while maximizing the output of cold/heat energy. Through the example analysis, the method proposed in this paper realizes the maximum utilization of electric/cold/heat of gas engine, and verifies the effectiveness of the method.
A Fuzzy Multi-objective Operation Strategy of Energy Storage System based on Gravity Search Algorithm

As the key element of active distribution systems (ADSs), energy storage systems (ESSs) play multiple important roles including enhancing reliability and improving economic efficiency. In this study, a novel day-ahead operation strategy is proposed for ESSs, and it is developed as a fuzzy multi-objective model without violating the operating constraints. In this model, three objectives are taken into account adequately and equally, including the enhancement of reliability, the improvement of economic efficiency, and the promotion of operation friendliness. In order to harmonize the relation among these objectives, the approach based on the fuzzy mathematical theory is adopted. To solve this proposed fuzzy multi-objective model, the gravity search algorithm (GSA) is adopted. Simulation results obtained based on the modified IEEE-33 bus distribution system are provided and discussed, where the availability and the feasibility of the proposed model and adopted algorithm are verified.
A Multi-Band Dielectric-Loaded Horn Antenna with Two Feed Systems

For obtaining a novel horn with low cross-polarization components over multiple frequency bands, an axially symmetric dielectric rod with a tapered profile is loaded in the horn up to the aperture, and coaxial grooves are also loaded around the circular aperture. Thus, this paper presents a designed horn with two different types of waveguide-ports for feeding signals for 12/14 GHz (Ku band) and 20/30 GHz (Ka band), respectively, and evaluates the frequency characteristics at each band.
Spectral Features of EEG Signals for the Automated Recognition of Negative Emotional States

In the presented paper we investigate the properties of spectral EEG features for the detection of negative emotional states. In particular, the proposed features represent the dynamics of energy distribution in the frequency range of 20–35 Hz, based on a time-frequency analysis of multichannel EEG signal. The experimental evaluation is based on data from the DEAP database. We report results with J48- and SMO-based classifiers, in terms of average classification accuracy, 94.3% and 96.8%, respectively.
Efficient Abnormal Load Identification in Large-Scale Power System employing Rayleigh Quotient Method and Parallel Computing Techniques

This paper proposes an efficient abnormal load identification method in large-scale power system employing Rayleigh quotient method and parallel computing techniques. It firstly constructs a group of data source matrices with random load fluctuation and noise interference for each zone, respectively. Then their standard matrices and the sample covariance matrices of each zone can be formed. Secondly, the maximum eigenvalue of sample covariance matrix (MESCM) for each zone could be acquired rapidly with the help of the Rayleigh quotient method and the parallel computing techniques. In this way, the rapid identification of abnormal load may be achieved by the violation checking of MESCM. Utilizing PST and Matlab® software, the case studies have been carried on an IEEE 54-machine and 118-bus system and a Polish 420-machine and 2736-bus system. The results in comparison with the traditional mean spectral radius(MSR) based method show that the proposed methodology is valid and efficient.
Optimal Coupling of Multiple Microgrid Clusters

Large remote areas may consist of multiple microgrids (MG), each owned and operated by a different owner (operator) in standalone mode. However, they can be coupled provisionally to support each other during overloading or excessive generation by renewables. This paper proposes a technique to maintain the voltage and frequency (VF) of each MG within the desired range in such situations. To this end, a multilevel optimization approach is utilized that determines the most suitable actions to recover the troubled MG. At first, the proposed technique utilizes the local resources of an MG, such as adjusting the generation levels of the dispatchable sources, charging or discharging of existing battery energy storages, and determining the best configuration for the microgrid’s network. It then proceeds to determine the required support from neighboring healthy MGs if the local supports are not adequate. In that case, the proposed technique optimally selects the MGs (and level of support) to be coupled to support the troubled MG(s). In this regard, it considers the trading cost, reliability, emission and VF of the prospective coupled MG’s network while forming an MG cluster. The performance of the developed technique is evaluated through numerical analyses in MATLAB.
Integrated Flexible Resources and Energy Markets in the Danish Multi-energy System

The core concept of the multi-energy system (MES) brings new insights for flexible resources. Integrated flexible resources (IFR) and the ancillary integrated energy market (IEM) become critical and effective measures to ensure reliable and flexible operation of the future MES. This paper firstly introduces the basic concept and the benefits of IFR. The existing status and future energy markets integration in Denmark are then summarized. Finally, the research opportunities of IFR and the IEM in the MES are proposed.