Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Electric Vehicles on the Expansion Planning of Distribution Systems considering Renewable Energy, Storage and Charging Stations

Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EVs) and renewable energy sources (RES). EVs introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS and EV charging stations (EVCS) are jointly considered. The charging demand necessary for EVs transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the k-means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system.
Evaluating Extended Pruning on Object Detection Neural Networks

CNNs are the state-of-the-art for many computer vision problems, including object detection. However, reducing the computational complexity of a CNN is a key prerequisite to deploying state-of-the-art deep learning networks in many low power embedded real-time robotic applications. Pruning has been shown to be an effective method to reduce the computational complexity of a Convolutional Neural Network (CNN) while maintaining accuracy. In the literature, accuracy lost through pruning is recovered with extended fine-tuning of the pruned network at the end of the pruning procedure, but further pruning is not conducted after extended fine-tuning. In this work we modify the pruning procedure to incorporate extended fine-tuning at intervals during the procedure to maintain network accuracy while pruning further than would otherwise be possible. We evaluate this procedure on a small scale custom object detection dataset and the more challenging standard PASCAL VOC dataset. On the former the new procedure achieves a 19.6× reduction in FLOPS for a drop of only 0.4% mean Average Precision (mAP) while the latter achieves only a 1.8× reduction in FLOPS for a drop of 0.8% mAP. The results indicate differing levels of parameter redundancy in the initial networks.
Linked Open Data for New Item Problem Solving in Collaborative Recommender Systems

Data on the web has grown insanely large to the point that humans cannot deal with using traditional tools. Hence the need for adequate tools to filter such huge amount of information and extract only the useful part has risen. Collaborative Recommender Systems based on Collaborative Filtering are one of the de facto tools for such purpose. Their primary goal is to suggest the suitable items for the appropriate users. However, due to the lack of information about the new entities, these systems may suffer from what is known as the cold start problem. In this work, we propose a solution to overcome the main case of cold start problem, namely new item problem. For that, we propose to use the Linked Open Data in the hope to find enough information about new items, thus bridge the gap between the recommender system and available data. We report on some promising experiments of the proposed solution performed on MovieLens data sets.
Operations of DC Microgrids in Coordination with AC Grids Based on Nonlinear Backstepping Controllers

In this paper, an operational scheme is developed for DC microgrids in coordination with existing AC grids using nonlinear backstepping controllers. The proposed operational scheme is designed to maintain the voltage stability of DC microgrids as well as to provide ancillary services to the AC grid by supplying excess power to AC loads. At the same time, the proposed scheme also ensures the power balance within DC microgrids by consuming power from AC grids when there are power shortages. The microgrid structure in this paper comprises a solar photovoltaic (PV) unit, permanent magnet synchronous generator (PMSG)-based wind generation system, and batter energy storage system (BESS) with AC and DC loads. A bi-directional DC-DC converter is used with the BESS in order to maintain the supply-demand balance within the DC microgrid through charging and discharging. Similarly, a bi-directional voltage source converter (VSC) with an LC filter on the AC-side is used to manage the power flow between the DC microgrid and AC grid. The nonlinear backstepping controllers are used to control all converters within the proposed microgrid structure where these controllers are designed based on the dynamical models of different components. Control Lyapunov functions (CLFs) are used to analyze the overall stability of the microgrid and simulation studies are carried out to validate the performance of the proposed operational scheme under different operating conditions. Simulation results are also compared with an existing nonlinear sliding mode controller (SMC)-based operational scheme in order to demonstrate the superiority of the proposed scheme.
A Demand Response Product of Air-conditioning Service for Load Aggregator Business

As a typical flexible load, the air-conditioning loads can benefit the potential and commodity value as flexible resources in demand responses. This paper treats the air-conditioning service as a demand response product, and proposes an operation mode for this product considering the perspective of load aggregator to provide comfortable air-conditioning service. The operation mode includes the service control strategy and benefit-sharing mechanism. The service control strategy is based on considering the different selected grades of user comfort. Also, Service Status I (select grade) and Status 13 (change grade) are set up in this control strategy to take advantage of the adjustable capacity of air-conditioning loads fully. Meanwhile, benefit-sharing mechanism is divided into pricing and compensation. The index that quantifies the service value is developed considering both the commodity-attributes of service and the comfort differences of grades. Finally, the operation mode of air-conditioning service is verified by a case study.
eDoS Mitigation for Autonomic Management on Multi-Tier IoT

In this age of the Internet of Things and ubiquitous computing, autonomic management has become a critical component in cloud platforms. Autonomic management helps systems adapt seamlessly and efficiently to rapidly fluctuating workloads. However, economic Denial of Sustainability (eDoS) attacks can directly target the autonomic management to waste resources. In this paper, we propose an eDoS mitigation framework that incorporates online anomaly detection with our Elascale autonomic management system to thwart eDoS attacks in real-time. This allows the detection system to be application-agnostic as this framework utilizes only resource statistics of the monitoring applications. We present the design and implementation of our anomaly detection framework with Elascale. We evaluate Hierarchical Temporal Memory (HTM) and Tukey with Relative Entropy against spatial and temporal anomalies. Our results prove that the HTM-based anomaly detection method outperforms with significant accuracy.
Transmission Network Expansion Planning Towards a Low-Carbon Economy with Fuzzy Modeling of Wind Generation

Transmission network will play a key role in developing a low-carbon power system, especially in the contribution to renewable energy sources utilization, such as wind and hydro power energy utilization. Conventional methods for transmission network expansion planning (TNEP) are mainly concerned with the economic and security factors. In this paper, a specific method for TNEP towards LCE with fuzzified wind generation is proposed, with carbon emission arising from wind spillage taken into account. Wind power output is expressed by a trapezoidal fuzzy number, and then a method for evaluating wind energy spillage caused by transmission capacity deficiency and a low-carbon adjustment strategy of generators for coping with the transmission overloading problem are developed. The proposed method was applied to an analytic study of the transmission planning of the modified New England (NE) 39-bus system to reveal its rationality and feasibility.
Resistive RAM With Multiple Bits Per Cell: Array-Level Demonstration of 3 Bits Per Cell

We demonstrate, for the first time, resistive RAM (RRAM) arrays where each cell can store 3 bits. Such full array-level demonstration is possible through special techniques (e.g., that exploit RRAM-specific characteristics of variations in cell resistances), presented in this paper, which efficiently allocate resistance range corresponding to each bit combination (required for proper write operation) while maintaining appropriate sensing margin (required for proper read operation). Our techniques are not restricted to 3 bits-per-cell only (and, in this paper, we demonstrate 2 bits-per-cell at the array level as well). Our measured results are based on multiple 4 Kbit arrays of 1T1R HfOx-based RRAM integrated in the back end of the line of 130-nm silicon CMOS technology. We achieve 3 bits-per-cell (2 bits-per-cell) RRAM with 11 (3) programing iterations on average.
A Feedback Perspective on Forced Oscillation/Small-Signal Stability Discrimination

Recent deployment of wide-area measurement systems with increased reporting rates viz-à-viz SCADA protocol have revealed the presence of forced oscillatory behavior in electric power grids. Real-time discrimination between forced oscillations and a system at the onset of rotor instability poses a difficult problem to researchers wishing to develop algorithms intended to inform operators of the true stability status. However, wide-area damping controllers (WADCs) designed to prevent small-signal instability events from occurring are not confused by the potential failure to discriminate the events correctly and are capable of maintaining stability, regardless of the excitation source. Thus, it is suggested that a feedback controller is the appropriate architecture for ensuring stability of the smart grid. This notion is supported by analysis of a linear system representative of the western North American Power System which is destabilized, followed by the stable system subject to disturbances at several critical frequencies. A full nonlinear model replicating the major components of the wNAPS is utilized to demonstrate the efficacy of the WADC under similar influence of forced oscillations and decaying plant stability.
A New Approach for Power-Saving Analysis in Consumer Side Based on Big Data Mining

With the development of smart grid, power companies have accumulated massive amounts of data including running data of electricity distribution and multi-dimensional data about meteorology, users’ file and social economy. In this article, we propose a power-saving analysis method based on big data mining. Firstly, all users belonging to the same industry should be extracted, then high-dimension data would be clustered based on the indexes describing the characteristic of electricity consumption. Secondly, the benchmark user will be selected based on the comprehensive assessment. So each user’s power-saving potential can be calculated compared with the benchmark user, then the power-saving sensitivity coefficients can be calculated by using partial least squares regression, which leads to effective energy-saving strategies. Finally, the electricity saving prediction model is established by the XGBoost algorithm. The analysis process is performed with field data and a power-saving analysis platform is applied in Shanghai demonstrating the method’s effectiveness.
Semantic Networks Based Approach for SaaS Management in Cloud Computing

Nowadays, Cloud Computing has emerged as a new model for hosting, managing and delivering services (IaaS, PaaS, SaaS) over the Internet. Enterprises and vendors are continuously migrating their services to the Cloud, this resulted an exponential amount of data, services and resources stored in data centers. Since there is no standard about Cloud service description or publication, therefore a key requirement that every Cloud provider needs to take into consideration is efficient management of resources and services by providing automated solutions, for a better service publication and discovery. The proposed approach aims to manage Cloud SaaS services for an efficient publication by classifying them into sets according to their domain to reduce the search space then interconnecte the SaaS services of the same domain in a Semantic Network using the similarity measure (Input/Output similarity) between concepts. The proposed solution takes advantages from multidimensional index framework, WordNet Domain Ontology and semantic Web. A guided publication process and an implemented prototype are introduced validating the system using a real data set corpus.
Predictive trajectory estimation during rehabilitative tasks in augmented reality using inertial sensors

This paper presents a wireless kinematic tracking framework used for biomechanical analysis during rehabilitative tasks in augmented and virtual reality. The framework uses low-cost inertial measurement units and exploits the rigid connections of the human skeletal system to provide egocentric position estimates of joints to centimeter accuracy. On-board sensor fusion combines information from three-axis accelerometers, gyroscopes, and magnetometers to provide robust estimates in real-time. Sensor precision and accuracy were validated using the root mean square error of estimated joint angles against ground truth goniometer measurements. The sensor network produced a mean estimate accuracy of 2.81° with 1.06° precision, resulting in a maximum hand tracking error of 7.06 cm. As an application, the network is used to collect kinematic information from an unconstrained object manipulation task in augmented reality, from which dynamic movement primitives are extracted to characterize natural task completion in N = 3 able-bodied human subjects. These primitives are then leveraged for trajectory estimation in both a generalized and a subject-specific scheme resulting in 0.187 cm and 0.161 cm regression accuracy, respectively. Our proposed kinematic tracking network is wireless, accurate, and especially useful for predicting voluntary actuation in virtual and augmented reality applications.
Wavelet-Based Detection of Transients Induced by DC Faults Using Boundary Protection Principle

This paper presents a high-speed method for direct current (DC) line fault detection based on the boundary protection principle. It uses only the DC current signal by means of the first level wavelet coefficients resulted from stationary wavelet transform (SWT). There is no need for communication links, and an objective and generic strategy to determine the occurrence of these internal faults is proposed. Simulation results are presented in order to evaluate the suggested method performance addressing the critical cases of DC faults, alternating current (AC) faults at rectifier and inverter sides, and commutation failures.
A Comparison of Methods of Facial Expression Recognition

Emotional recognition based on facial expressions is a very active research topic in the field of human-computer interaction. There has been a great body of work with in-depth of study in this area. In this paper, we analyze and compare the state-of-the-art facial expression recognition methods, propose some evaluation dimensions and discuss possible directions for future research.
Lossless and Lossy Direct Compression Design With Multi-Signal Symptom Detection for Low-Temperature Wearable Devices

In this paper, the lossless and lossy direct compression design (LLDC) is proposed for the wearable devices. The electrocardiogram (ECG), blood pressure (BP), and respiration (RESP) can be applied to the compression design for cardiovascular diseases. The proposed LLDC can detect the abnormal symptoms and dynamically change the compression modes with different sample intervals and data precisions. In our experiments, the symptoms are detected, and the transmitted power and temperature for the wearable devices are reduced. The compression ratios for ECG, BP, and RESP are up to 7.94, 6.06, and 6.51 with percentage root-mean-square difference less than 6.5%. For ECG, BP, and RESP, the energies of the wearable devices are reduced by 50%–87%, 38%–89%, and 41%–89%; the temperatures are reduced by 2.6 °C–4.2 °C, 1.3 °C–3 °C, and 1.7 °C–3.6 °C, respectively.
Influence of Fundamental Model Uncertainties on Silicon Solar Cell Efficiency Simulations

We determine the uncertainties on simulated efficiencies of silicon solar cells due to uncertainties of the fundamental physical models. To determine these, we refit their parameters to the underlying measurement data. Using a metamodeling and Monte Carlo simulation approach, we then deduce how these propagate to the simulated solar cell efficiency. This is evaluated for 150-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick 1 <inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>cm p-type standard and advanced silicon passivated emitter and rear cells (PERC), as well as for an ideal silicon solar cell. When sticking to the best-known set of physical models, we determine resulting efficiency uncertainties as low as 0.06%<sub>abs</sub> for usual PERC cells and 0.1%<sub>abs</sub> for an ideal cell. In a variance-based sensitivity analysis, we find the uncertainties of the bandgap and the hole density-of-states effective mass as well as the Auger recombination in the limiting case to dominate the efficiency uncertainty. In addition to these relatively low uncertainties, larger discrepancies may arise when applying different physical models for otherwise fixed device properties. We determine comparably large efficiency discrepancies of up to 0.6%<sub>abs</sub> for the two most prominent bandgap narrowing (BGN) models, and up to 0.15%<sub>abs</sub> for the two most recent parameterizations of free carrier absorption. We also show evidence that for the specific case of a strong space charge region being present at a recombining surface, Schenk’s BGN model fails to replicate experiments. Those discrepancies highlight the necessity of further research on those models. Finally, an error of up to 0.08%<sub>abs</sub> is observed by simplifying diffused regions on textured surfaces to a planar geometry.
A Dual-Band Antenna With Dual-Circular Polarization for Nanosatellite Payload Application

A Compact dual-band circular polarized antenna is proposed for a small satellite payload communication system. The antenna consists of a pentagonal radiator and a stacked rectangular parasitic element to achieve lower and higher axial ratio, respectively. The antenna exhibits two −10 dB impedance bandwidth of 380 MHz (2.44–2.82 GHz) and 770 MHz (3.05–3.83 GHz) with a compact size of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda \times 0.33\lambda \times 0.03\lambda $ </tex-math></inline-formula> (with respect to lower end frequency). Average antenna peak gain of nearly 4.4 dB at lower band and 6.5 dB at higher band has been achieved with directional radiation patterns. The antenna performance has been investigated using 1U nanosatellite structure to realize the operation in a real-time environment.
Analysis of the Long-Term Performance Degradation of Crystalline Silicon Photovoltaic Modules in Tropical Climates

The long-term performance degradation of five different types of commercial crystalline silicon (c-Si) photovoltaic (PV) modules (one sample for each type) in the tropics, or more specifically the tropical rainforest climate “Af” by the Köppen climate classification (Singapore), is investigated over a period of seven years, based on outdoor <italic>I–V</italic> measurements at 10-s intervals. The degradation rates of these c-Si PV modules, based on the temperature-corrected performance ratio, range from –0.03 to –0.47%/year, which satisfy the products’ performance warranty. Further analysis of the short-circuit current (<italic>I</italic><sub>sc</sub>), open-circuit voltage, and fill factor for these modules shows that the performance degradation is mainly attributed to the <italic>I</italic><sub>sc</sub> loss. The possible causes of the module performance deterioration are also discussed.
Hardware-in-the-Loop Validation of the Frequency Divider Formula

This paper validates a theoretical approach, namely, the frequency divider formula, recently proposed by the first and fourth authors to estimate local frequency variations based on the synchronous machine rotor speeds and on signals from phasor measurement units (PMUs). The validation is based on simulations performed in a Real-Time Digital Simulator with physical PMUs connected in the loop. The case study considers the well-known WSCC 3-machine, 9-bus test system. Simulation results show the high accuracy of the frequency divider formula to estimate the frequency at every bus of the network. Results also show that the frequency divider prevents the numerical issues due to fast variations of the voltage when measured by the PMU and indicate that such a formula can be utilized to test the fidelity of PMU implementations.
Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System

The Neural Engineering Framework (NEF) provides a methodology for implementing algorithms and models using spiking neurons. Although it is possible to run simulations based on the NEF on Von Neumann hardware, neuromorphic hardware holds the promise of increased computational efficiency and lower power implementation. This work describes an implementation of the NEF on IBM's TrueNorth Neurosynaptic system. Using one TrueNorth chip, a NEF neural population of 629 neurons representing five dimensions is demonstrated on hardware. However, the crossbar array architecture itself, utilized in the TrueNorth hardware, can be used to compute the basic NEF calculations for any sized neural population, representing any dimensionality. The computation time is a function of the maximum values used in the computations.
A High Performance Approach for Classification of Motor Imagery EEG

Use of Motor Imagery in EEG signals is gaining importance to develop Brain Computer Interface (BCI) applications in various fields ranging from bio-medical to entertainment and its classification at lower complexity in EEG signals is gaining importance. Filter Bank Common Spatial Pattern (FBCSP) has been a promising feature extraction approach to deal with subject-specific behavior in Motor Imagery classification. Though its computational complexity has been on the lower side, the approach has not yet matched the classification accuracy of non-FBCSP approaches. By introducing a combined feature set of Band Power (BP) features and Time Domain (TDP) features in FBCSP approach we have succeeded in proposing an approach of high classification accuracy while out competing the computational complexity of the best reported non-FBCSP approaches. We have also analyzed the impact of parameter variations on classification accuracy and achieved 0.59 mean kappa value for Dataset 2a BCI competition IV on four class problem. This is the highest reported for FBCSP approaches along with the lowest inter-subject variation.