Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Bidding Strategy of DER Aggregator Considering Dual Uncertainty via Information Gap Decision Theory

Distributed energy resources especially wind and photovoltaic power, and demand response are highly valued in recent years for their advantages in environmental protection, sustainable development, and so on. However, their volatility poses double risks to the DER aggregator when formulating a profitable bidding strategy and schedule scheme. To this end, first, this article proposes an information gap decision theory-based optimal bidding strategy to model the dual uncertainties confronted by the DER aggregator without knowing the specific distribution pattern of uncertainties. Second, the DER aggregator is assumed to be risk-averse or opportunity-seeking, and the corresponding strategies could be obtained. The former comes up with a robust strategy under severe uncertain circumstances, and the latter presents a profit-maximization scheme while enduring more risks. The validity of the proposed method is examined using the dataset from the Thames valley vision project; the obtained results demonstrate that proper adjustment on aggregator's bidding strategy could be achieved based on its preference for high-profit or stability, which is also applicable for other market entities.
A 10 Bit Phase-Interpolator-Based Digital-to-Phase Converter for Accurate Time Synchronization in Ethernet Applications

This work presents a 10 Bit digital-to-phase converter (DPC), for usage within an accurate time synchronization via Ethernet with a nominal frequency of 125 MHz. It is based on a 16-phase ring oscillator, stabilized by a phase-locked loop (PLL), followed by a 6 Bit phase-interpolator (PI). The DPC generates 1024 clock phases with a nominal resolution of 0.35°, covering a full 360° phase span. A prototype of the DPC is fabricated in a 180 nm technology from TSMC and achieves a differential and integral nonlinearity (DNL and INL) of 0.47° and 2. 33º, respectively, while the power consumption is only 4.0 mW. As the DPC is based on a built-in PLL, it requires only a low frequency reference clock, which makes it very well suited for easy integration in complex systems (e.g. time synchronization via Ethernet transceivers).
Bandwidth-Enhancement of Dielectric Resonant Antenna with Loading of Rectangular Patches

In this paper, a wideband patch-loaded dielectric resonator antenna (PLDRA) is proposed under multi-resonant modes. Initially, a rectangular radiating patch is placed on the defective dielectric resonator. Its main target is to reallocate the Mode2 and Mode3 of the radiating patch closely to Model of the traditional DRA. After that, a square patch is mainly applied to improve the impedance matching during the operating band. Finally, the proposed antenna is simulated and verified. The results show that the impedance bandwidth of the PLDRA is significantly increased to about 24.9%. Meanwhile, it holds the low-profile property of about 0.08 free-space wavelength.
Face Recognition Using Tensors and Gradient Descriptors

Face recognition algorithms are key issues in many applications, still challenging in terms of complexity and reliability. In this paper we propose a different approach for a solution based on tensors and multilinear singular value decomposition (MLSVD). We define a new tensorization of face image descriptors built into gradient space and rely on such feature vectors to identify a subject from the database. The tensor is decomposed using MLSVD model to form a linear set of equations with a Kronecker product solution. We show that our method lowers the computation complexity while keeping high accuracy in the recognition process.
A Study on Patterns of Neural Activity Generation from A Bio-realistic Cerebellum Neural Network

The biological neural network signal flow is a complex process and difficult to interpret. The purpose of this study is to build a model of a simplified cerebellum neural network and investigate its working behavior in generating neuronal patterns. The model is constructed by referring to the neural anatomic structure of the mammal cerebellum and then simulated using Matlab Simulink software. The artificial neural network dimension is much smaller than the size of the real cerebellar neural network in mammal due to hardware simulation resource limitation. Thus, the utilization of extensivescale spiking neural networks is limited due to the grown of computational costs associated with propagating bio-realistic neural models. The parameters correlated with the neuron function also affect the biological plausibility of the spiking neural network. At the conclusion, we present the pattern generation of a cerebellum- like neural network and briefly explained why such patterns are generated.
Comparative Studies of Different Methods for Short-term Locational Marginal Price Forecasting

This paper presents studies on short-term locational marginal price (LMP) forecasting using six common methods, namely, Persistence, Autoregression Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN-LSTM), and CNN-Fully-Connected Network (CNN-FCN). These six methods were used to study one location in the 2008~2018 datasets of PJM (Pennsylvania, New Jersey, Maryland) power market. Simulation results show that performance of each method is hyperparameter-dependent. Also, the deep learning-based LSTM, CNN-LSTM and CNNFCN outperform the supervised learning-based RNN. The traditional ARIMA and persistence usually attained the worst accuracy in terms of coefficient of determination and normalized mean squared error.
Resource Allocation for Multi-UAV Assisted IoT Networks: A Deep Reinforcement Learning Approach

The wireless communication system for the massively heterogeneous Internet of Things (IoT) network hinders the allocation of resources. For this study, an unmanned aerial vehicle (UAV) is considered as a base station (BS) for IoT system communications to maximize the utilization of future wireless network capacity. Two UAVs are used as the power source and an information transmitter (Tx). To be easily managed, IoT devices are categorized into two clusters of urban and semi-urban features based on their signal distribution and fluctuations. Next, the deep reinforcement learning (DRL) approach is proposed as a resource allocation (RA) scheme. The allocation of resources for bandwidth, throughput, and power consumption issues are considered to be our resources. TensorFlow (Python) programming tool is executed to evaluate and estimate the overall capability of the system. Finally, we analyzed the proposed approach based on different scenarios. Based on the experimental results, our method shows promising outcomes with rapid convergence, suitable for heterogeneous networks, and low complexity based on the evaluation tasks of classification and regression.
Site Requirements for the Oxidative Coupling of Methane on SiO2-Supported Mn Catalysts

The oxidative coupling of methane (OCM) to C2 hydrocarbons (C2H4 and C2H6) was examined on Mn/SiO2 and sodium salt-modified Mn/SiO2 catalysts containing different oxo anions, i.e., WO42-, MoO42-, SO42-, PO43-, P2O74-, CO32-, and SiO32-. The catalysts were characterized using X-ray diffraction, X-ray photoelectron spectroscopy, laser Raman spectroscopy, and temperature-programmed reduction with H2. The structural and catalytic properties of the catalysts largely depend on the presence of the Na+ ions and the identity of the oxo anions of the salts. Mn/SiO2 consisted of Mn3O4 and amorphous SiO2 phases. The addition of the sodium salts to Mn/SiO2 led to the transformation of amorphous SiO2 exclusively to α-cristobalite, and the concurrent oxidation of Mn3O4 to different Mn species. Mn2O3 was the predominant species as the salts contained oxo anions of WO42-, MoO42-, SO42-, PO43-, and P2O74-, whereas the basic sodium salts of CO32- and SiO32- led to the preferential formation of Mn4+ species. These effects on.
Real-Time Robotic Grasp Detection with Multi-Scale Feature Fusion

Grasping is an essential problem that is a serious challenge in area of robotics. In recent years, computer vision based methods have been proved to be effective to solve this problem. With the introduction of deep learning, superior strategies that provide advantages over traditional approaches have emerged. Robotic grasp detection needs to consider both coarse-grained and fine-grained information, while previous works did not take full advantage of the latter, leading to loss of accuracy. In this paper, we propose a novel grasp detection model to predict a five-dimensional representation for grasps with RGB-D images. A fully convolutional network is employed with multi-scale feature fusion structure, which can combine feature information on different scales. Experiments show that our multi-scale model has made significant progress in accuracy compared to a single-scale, while maintaining the performance of real-time computation.
Minimization of Cogging Torque in Axial Field Flux Switching Machine Using Arc Shaped Triangular Magnets

Axial flux permanent magnet machine (AFPMM) provides high torque characteristics at low speeds without any mechanical gears. AFPMMs have numerous applications in wind energy, electric cars, and direct drive elevator applications. These machines have low cost and improved power to weight ratio. However, single sided AFPM suffers from torque ripples because of its non-sinusoidal back emf, cogging torque, and rotor eccentricity. There are two major components of pulsating torque, namely torque ripples and cogging torque. In PM machine design, the cogging torque is a serious concern because it adds unwanted harmonics to the pulsating torque. Whereas the torque ripples cause noise and vibrations. In order to gain high efficiency, torque ripples should be minimum. The aim of this research is to design “Slotted axial field flux switching permanent magnet machine”. Mathematical models are used to design the machine and Finite element method (FEM) has been used to analyze the machine. In addition, Latin hypercube sampling (LHS) has been used to create the samples. Finally, Kriging Method is used for approximating the model and genetic algorithm has been applied to get the optimum machine. The results showed 61.8 % reduction of the cogging torque in the proposed machine model as compared to the conventional one. Moreover, the optimized model further provided 6.15 % reduction in the cogging torque as compared to the proposed one.
Improved Negative Differential Resistance Characteristics in Surface Tunnel Transistors

We propose and demonstrate a new type of Surface Tunnel Transistor (STT) which displays improved negative differential resistance (NDR) characteristics. In this device, called channel-doped STT (CD-STT), the channel region is direclly doped with donors in order to increase the channel carrier density. A peak current is obtained that is 103 times larger than that of a conventional STT. To reduce the valley current further, a blocking layer consisting of an i-GaAs is inserted at the gate/drain overlap region. The resulting device, called blocking layer inserted STT (BLI-STT), exhibits excellent NDR characteristics with a peak-to-valley ratio (PVR) of 4.8 at room temperature, a value which is more than 4 times larger than that of a conventional STT.
Design of Closed-loop Inverter Using High Gain Converter

The Recent technological advancement of Power Electronics field have utmost importance for renewable energy applications like solar, wind etc. Voltage generally obtained from PV cells are converted to high voltage with better efficiency using Power electronics converters. In this paper, the proposed non- isolated high gain converter is utilized to serve that purpose in place of conventional boost converter. The source voltage is kept varying 9 (v) to 25 (v) and feedback mechanism is implemented to obtain desired voltage as output. The converter is connected with the Single-phase Voltage Source Inverter (VSI) which is operated with SPWM switching technique. The voltage at output end is attained constant of the inverter with implementation of closed-loop mechanism. It provides sustainable operation with better stability of the system. The simulation is performed in MATLAB for R and R-L types of loads. Reduction of harmonic contents are performed using suitable design of filter and smoothen output signal of Inverter and keep it within IEEE standard limit.
Secrecy Rate and Energy Harvesting Trade-off in Cognitive Wireless Energy Transfer Systems

This paper considers a novel secure cognitive wireless energy transfer system, where a wireless energy transfer (WET) system coexists with a wireless information transmission (WIT) system in the presence of an illegal eavesdropper. For the considered coexisting scenario, the energy transmitter of WET helps the WIT system to achieve secure communication in reward for being allowed to used the WIT’s spectrum band. According to the condition of whether the energy transmitter is truthful or not, we propose two types of cooperative jamming scheme, namely noise jamming and information jamming, for the energy transmitter. For each proposed scheme, we characterize the achievable secrecy rate-harvested energy region by optimizing the energy transmitter’s beamforming vectors. We also provide the semi-definite relaxation-based approach to solve the resulting non-convex optimization problems optimally. The numerical results are finally provided to show the effectiveness of the proposed scheme.
Self-Supervised Anomaly Detection from Distributed Traces

Artificial Intelligence for IT Operations (AIOps) combines big data and machine learning to replace a broad range of IT Operations tasks including reliability and performance monitoring of services. By exploiting observability data, AIOps enable detection of faults and issues of services. The focus of this work is on detecting anomalies based on distributed tracing records that contain detailed information of the services of the distributed system. Timely and accurately detecting trace anomalies is very challenging due to the large number of underlying microservices and the complex call relationships between them. We addresses the problem anomaly detection from distributed traces with a novel self-supervised method and a new learning task formulation. The method is able to have high performance even in large traces and capture complex interactions between the services. The evaluation shows that the approach achieves high accuracy and solid performance in the experimental testbed.
User Location and Collaborative based Recommender System using Naive Bayes Classifier and UIR Matrix

The world is filled with information and getting the right information is a challenging task for internet users and online buyers. Recommender system helps internet users to get their information in a short span of time. It acts as an information extraction system that works behind users to perform their search easier. The recommender system comes under user’s content or item based search, similar users browsing behavior called collaborative and combination of both known as a hybrid. Here collaborative-based approach is adopted which recommends items to their users based on their past browsing behavior. In this article, the User-Item-Rating matrix is formulated concerning user personal profile, rating of the product, and reviews given by the users during their previous browsing history. In this research, user location is considered as an important attribute to group similar users. It also attempts to suppress the scalability and sparsity problems of the traditional collaborative filtering approach. So, the User-Item-Rating (UIR) matrix has considered the location, ratings and reviews for future recommendation. The Navie Bayes classifier algorithm is used to provide accurate topmost recommendations to internet users. The data set is taken from the MovieLens and IMDb database. The accuracy of the recommender system is measured based on the main metric f-measure. The experimental result has proven the improvement of the recommender system with the mentioned added attributes.
Review of NILM applications in smart grids: power quality assessment and assisted independent living

Smart grids are a concept that is becoming a concrete reality in developed countries, and is only just emerging in developing countries. In both cases, they are transforming the customers' relationship with electricity in unexpected ways. In this work, it is shown an overview of some functionalities that will be developed within the smart grids, which have a common denominator: the use of non-intrusive load monitoring techniques. Some of these new functionalities have special relevance in the electrical networks of developing countries which present complex challenges and need to be updated fast. There are also some of the features that will be shown to present great challenges in developed countries at present, but which will be of interest in the future in the rest of the world.
Time-multiplexed 10Gbps Ethernet-based Integrated Logic Analyzer for FPGAs

We designed a time-multiplexed high-speed integrated logic analyzer (ILA) probing system for FPGA device technology, which is currently under implementation. The high-speed interface is a 10 Gbps Ethernet on fiber optics that connects the proposed ILA to the host computer. For the proposed design, we deduce the mathematical model for the performance. We report our preliminary test results, future work and conclusions.
Performance of Cooperative Relayed NOMA with Energy Harvesting Nodes in Underlay Networks

In this paper, we consider a novel underlay relayed non-orthogonal multiple access (NOMA) network with cooperative relaying by an energy harvesting (EH) near-user ($NU$) so to improve the performance of a far-user ($FU$) without requiring the $NU$ to expend its own battery energy. The $NU$ is assumed to harvest energy from the relay using the power-splitting protocol. Expressions are derived for end-to-end outage probability and throughput of both $NU$ and $FU$. Although the source, relay and $NU$ limit their transmit powers to ensure that the peak interference caused to the primary is below the interference temperature limit (ITL), the network still performs better than orthogonal multiple access (OMA) based network. We show that combining of signals from relay and $NU$ ensures good $FU$ performance in this power constrained scenario. We demonstrate that both NOMA power allocation parameter as well as the energy harvesting parameter need to be chosen carefully to maximize the sum throughput. Simulation results corroborate the derived analytical expressions.
An Extensible Framework of Monocular SLAM with Depth Recovery for an Unmanned Aerial Vehicle*

With only a monocular sensor, an extensible framework of simultaneous localization and mapping (SLAM) is designed with depth recovery for a DJI-autonomous drone, which is integrated in an onboard computer by utilizing the Robot Operating System (ROS). Specifically, on the basis of the repository ORB-SLAM2, the scaled pose of the onboard monocular vision is obtained, and the AprilTag2 is utilizied to make fusion operation to recover scene depth information. In addition, based on the monocular sensor, a pose conversion module and a pose release module are designed to allow the drone to directly use the corresponding pose information. With the provided pose information, an outer-loop geometric tracking controller for the unmaned aerial vehicle (UAV) is integrated to complete flight tasks generated from the trajectory planning module, by calculating the required thrust and attitude. Since communication interfaces are defined clearly in ROS, the proposed framework can be easily implemented, making a UAV executes the trajectory tracking and landing tasks stably and accurately with merely monocular vision. Practical experiments are performed to validate the effectiveness of the proposed framework.
Transfer Learning based Convolutional Neural Network Model for Classification of Mango Leaves Infected by Anthracnose

Anthracnose is a plant disease caused by fungi that develop saucer shaped acervulus spore on leaves sunken, it lesions leaves and sooty mold mango fruit leaves. The Anthracnose disease affect the quality of mango fruit and productivity. Hence, it is essential to develop espousing model to detect lesion areas on Mango leaves, identify level of infection, diagnosis of the Anthracnose disease. The deep learning techniques are known for their performance and Convolutional Neural Networks(CNNs) are widely accepted model for pattern identification and image categorization. Therefore, this paper propose a modified version of VGGNet model called V2IncepNet that integrate best feature of VGGNet and Inception module. The VGGNet module extract basic feature while inception module perform extraction of high-dimensional features and classification of images. This paper use various features such as leaf color, venation, petiole condition, tip shape, tip conditions, leaf shape, leaf margin, dark spots on leaf blade and on midrib, margin of burns on leaf, leaf blade, midrib, and petiole. In our data set there are 2268 color images of Mango leaves, which include 1198 on-field self-captured real-time color images and 1070 color Mango leaves images downloaded from Plantvillage. The experiment result envisage that the proposed model can classify Anthracnose disease infection level on Mango leaves with accuracy not less than 92%. The proposed model is simple yet efficient.
Smart Illumination for Large Area Buildings Using RF Communication

In the present work we propose the development of an intelligent lighting system aimed to be used in large area buildings and taking as example some buildings of the Technical University of Cluj-Napoca (Romania). The system incorporates several modules, each module replacing a lighting switch in the building. The purpose of these modules is to control the lighting fixtures in the hallways of the buildings according to the movement in the covered area and the light intensity. The modules communicate with each other by radio communication, through a tree structure. All modules transmit information to the central node consisting of an Arduino Uno development board and the NRF24L01 radio communication module. The central node transmits the serial information via USB connection to the PC, where the information is processed by a custom program developed to display information related to the status of the modules and to control the modules remotely.
A novel control scheme for unbalanced power sharing and PCC voltage improvement based on the equivalent impedance in islanded microgrid

This paper introduces an equivalent negative sequence impedance control method and a negative sequence power-sharing scheme based on adopting virtual negative sequence resistance for the typical islanded microgrids with multiple distributed generation units and unbalanced loads. Firstly, the principle of the method that controls negative sequence components through negative sequence impedance control is discussed. Then a droop relationship between the controlled negative sequence impedance of distributed generations and their remaining power capacity is introduced, which achieves reasonable negative sequence current allocation among different distributed generation units. Moreover, to reduce the fluctuations of system frequency with unbalanced loads, a frequency feedback compensation loop is introduced. Finally, the simulation results verify the effectiveness of the proposed method.