Miracle-dz/UniFAR-FTUs huggingface dataset:
Operation and control of a microgrid in isolated mode with multiple distributed generation systems

In this paper performance study of the microgrid in isolated mode of operation is presented using the real time hardware in loop tool. The droop control and virtual complex impedance concept has been implemented for accurate load sharing among the inverter based DG system in the microgrid. The control strategy for operation of inverters in autonomous mode of operation is given. The implementation detail of the microgrid with voltage source inverter based DG systems is given in this paper. The real time performance verification of the implemented microgrid is presented. The performance results of the microgrid reported in this paper indicate that the DG systems are able to share the load in proportion to their ratings. Also, the results show that the voltage and frequency of the microgrid are maintained without any transients.
Modeling in Petri Nets for micro smart grid operation based on IEC 61850 architecture

Smart Grid (SG) systems are considered a sustainable alternative to power supply problems all over the world. SG systems are open, distributed and heterogeneous, what raises a huge demand over its design process. At the present, SG projects are usually done by applying deductive methods, where requirement-specifications do not reach a complete model. This article presents a proposal for application of Systems Design methods in the development of a micro smart grid (MSG), through a case study, applied to the communities of the Amazon region. The methodology consists of using formal mechanisms already consolidated in system design, whose main objective is to analyze implementation problems and to have feedback. For the specification of requirements, was used to the IEC 61850 architecture, the behavior dynamics of the system was analyzed and verified using the Petri Nets (RdP), a tool that allowed to verify and identify possible contradictory states and undesired conditions. The results showed it was possible to develop a Petri Nets model based on the IEC 61850 architecture that allowed to model the automation of the MSG system operation, and to design a control solution for MSG, considering the particularity and constraints of the Amazon region.
Ice velocity of Jakobshavn Isbræ, Petermann Glacier, Nioghalvfjerdsfjorden, and Zachariæ Isstrøm, 2015–2017, from Sentinel 1-a/b SAR imagery

Abstract. Systematically monitoring Greenland's outlet glaciers is central to understanding the timescales over which their flow and sea level contributions evolve. In this study we use data from the new Sentinel-1a/b satellite constellation to generate 187 velocity maps, covering four key outlet glaciers in Greenland: Jakobshavn Isbræ, Petermann Glacier, Nioghalvfjerdsfjorden, and Zachariæ Isstrøm. These data provide a new high temporal resolution record (6-day averaged solutions) of each glacier's evolution since 2014, and resolve recent seasonal speedup periods and inter-annual changes in Greenland outlet glacier speed with an estimated certainty of 10 %. We find that since 2012, Jakobshavn Isbræ has been decelerating, and now flows approximately 1250 m yr−1 (10 %), slower than 5 years previously, thus reversing an increasing trend in ice velocity that has persisted during the last decade. Despite this, we show that seasonal variability in ice velocity remains significant: up to 750 m yr−1 (14 %) at a distance of 12 km inland of the terminus. We also use our new dataset to estimate the duration of speedup periods (80–95 days) and to demonstrate a strong relationship between ice front position and ice flow at Jakobshavn Isbræ, with increases in speed of ∼ 1800 m yr−1 in response to 1 km of retreat. Elsewhere, we record significant seasonal changes in flow of up to 25 % (2015) and 18 % (2016) at Petermann Glacier and Zachariæ Isstrøm, respectively. This study provides a first demonstration of the capacity of a new era of operational radar satellites to provide frequent and timely monitoring of ice sheet flow, and to better resolve the timescales over which glacier dynamics evolve.
The small molecule STF-62247 induces apoptotic and autophagic cell death in leukemic cells

Adult T cell leukemia/lymphoma (ATL) is an aggressive malignant T cell disease caused by human T cell leukemia virus-I (HTLV-1). Treatment outcomes for aggressive subtypes of ATL remain poor, with little improvement in overall survival since HTLV-1 was discovered. Therefore, new therapeutic strategies for ATL are required. STF-62247 induces autophagy and selectively kills renal cell carcinoma without apoptotic cell death. Here, we demonstrate that STF-62247 reduced cell viability and resulted in autophagosome accumulation and autophagy in leukemic cell lines (S1T, MT-2, and Jurkat). Interestingly, STF-62247 induced apoptosis in HTLV-1-infected cell lines (S1T and MT-2), as indicated by DNA fragmentation and caspase activation, but not in non-HTLV-1-infected Jurkat cells; a caspase inhibitor did not prevent this caspase-associated cell death. STF-62247 also increased nuclear endonuclease G levels. Furthermore, STF-62247 reduced cell viability and increased the number of apoptotic cells in peripheral blood mononuclear cells collected from patients with acute ATL, which has a poor prognosis. Therefore, STF-62247 may have novel therapeutic potential for ATL. This is the first evidence to demonstrate the cell growth-inhibitory effect of an autophagy inducer by caspase-dependent apoptosis and caspase-independent cell death via autophagy and endonuclease G in leukemic cells.
Forecasting Stock Market with Neural Networks

Prediction of stock market returns is an important issue in finance. The aim of this paper is to investigate the profitability of using artificial neural networks (ANNs). In this study, the ANNs predictions are transformed into a simple trading strategy, whose profitability is evaluated against a simple buy-hold strategy. We adopt the neural network approach to analyze the Taiwan Weighted Index and the S&P 500 in the States. Consequently, we find that the trading rule based on ANNs generates higher returns than the buy-hold strategy.
Application of hierarchical temporal memory theory for document categorization

The current work intends to study the performance of the Hierarchical Temporal Memory(HTM) theory for automated classification of text as well as documents. HTM is a biologically inspired theory based on the working principles of the human neocortex. The current study intends to provide an alternative framework for document categorization using the Spatial Pooler learning algorithm in the HTM Theory. As HTM accepts only a stream of binary data as input, Latent Semantic Indexing(LSI) technique is used for extracting the top features from the input and converting them into binary format. The Spatial Pooler algorithm converts the binary input into sparse patterns with similar input text having overlapping spatial patterns making it easy for classifying the patterns into categories. The results obtained prove that HTM theory, although is in its nascent stages, performs at par with most of the popular machine learning based classifiers.
Design of an all optical encoder using 2D photonic crystals

A new all optical 4×2 encoder is designed using hexagonal photonic crystal ring resonators (PCRR). The encoder which has 4 inputs and 2 outputs is implemented using photonic crystals consisting of silicon rods in air substrate. The basic operation is governed by the hexagonal shaped ring resonator which resonates at a particular wavelength. The encoder uses two such ring resonators having same resonant wavelength and 4 linear waveguides to guide the flow of light. The hexagonal shaped resonators proposed have a unique core structure in the shape of the human eye. The characteristic wavelength of the structure is obtained as 1550nm and this is verified using FDTD and PWE methods. The structure possesses several advantages over its predecessors such as simple and compact design, low input power requirement and small size. The footprint for the proposed structure is 130.6 μm2.
Deadline-Aware Peer-to-Peer Task Offloading in Stochastic Mobile Cloud Computing Systems

By taking advantage of pervasive mobile devices and their pairwise encounters, Mobile Cloud Computing (MCC) offers an efficient solution for mobile devices to execute complex applications in a collaborative manner. In this paper, we consider the problem of distributed task offloading in MCC systems with deadline constraints. We propose an online distributed task offloading (DTO) algorithm for practical MCC systems where each mobile user can dynamically make offloading decisions to nearby mobile devices in order to process computation tasks in a collaborative manner. The DTO scheme is lightweight and fully distributed. Through rigorous theoretical analysis, we prove that the proposed DTO algorithm can meet the deadline constraints of the computation tasks and achieve a near-optimal system-wide utility. Furthermore, through real testbed experiments and trace-driven simulations, we compare the DTO scheme with several baseline methods and demonstrate its effectiveness.
A comparative study of effective way to modify different object in image and video using different inpainting methods

The inpainting method is popular for various applications like images, videos, multimedia etc. the inpainting can be helpful for the hiding the useful information from the source it can be used in security purpose, intelligence bureau. inpainting can be used for coding of images as well as hiding useful information from mischievous, now days CCTV footage include mischievous contents that can be modified before it getting viral in society to evade unwanted consequences in society, this techniques can be useful to extract useful information from old photos and videos without any extra effort this extracted information can be process for further accountability, inpainting techniques modify old images that's are like to be contemporary so using this techniques lot of information could be modified and perceive.
A developed feature selection method for classification based on united information gain

In big data times, effective feature selection methods become more and more important because of high dimensional character. In this paper, we propose a developed feature selection method UIG, what effectively improves the accuracy of classifiers. Firstly, all the attributes are systemized pairwise to calculate their united information gain to form a United Information Gain Matrix. Secondly, the United Information Gain Matrix is discretized by a predefined parameter. Finally, UIG searches the biggest full-1 sub-matrix to find the maximum valid subset of attributes. Then classifier can be built by the selected attributes. Experiments on several public data sets show that the proposed method is effective and robust for different situations.
Consensus-based anomaly detection for efficient heating management

The analysis of data to monitor human-related activities plays a crucial role in the development of smart policies to improve well being and sustainability of our cities. For several applications in this context anomalies in time series can be associated to smaller timeframes such as days or weeks. In this work we propose a consensus-based anomaly detection approach that exploits the power of the Symbolic Aggregate approXimation (SAX) and the specificity of such time series. In our approach, the normalization of the signal becomes a proper element of the modeling. In fact, we conjecture that different normalization horizons allow to include in the shape of the timeseries patterns an additional, variable, component from a longer period trend. To support the analysis phase, a calendar can be used as an additional source of information to discriminate between really unwanted anomalies and expected anomalies (e.g. weekends), or even to signal a possible anomaly whenever a “normal” behavior is not expected. Preliminary experiments on temperature analysis in an indoor environment, with the scope of thermal energy saving, showed that our approch effectively identifies all known anomalies, and also pointed out some unexpected, but clear, anomalies.
Statistical Machine Learning vs Deep Learning in Information Fusion: Competition or Collaboration?

Information fusion is the process of coherently and intelligently combining knowledge extracted from different sensors/ modalities, in order to obtain more useful or discriminant information for the purpose of multimedia processing and biometrics, among others. The key to successful information fusion is to intelligently exploit the intrinsic relations between the data of different modalities. Statistical machine learning (SML) has played a major role in developing new information fusion methods, by incorporating prior knowledge and entropy metric, correlation analysis, inherent statistical structures of input data, and nonlinear relations. On the other hand, the recent development of deep learning (DL) draws enormous attention from the machine learning community. DL algorithms possess deep structures, requiring a large amount of data to train the huge number of parameters, an ultra-expensive process. However, the payoff is enormous; unprecedented success in many applications. This paper will first review recent development of both SML and DL in the context of information fusion, then analyze their pros and cons, and compare their performance in a number of application domains. Based on preliminary results, some thoughts will be presented on how SML and DL can work together to bring the study in machine learning to the next level, better serving human needs.
A Simplified Model Predictive Control for a Dual Three-Phase PMSM With Reduced Harmonic Currents

The conventional model predictive control for dual three-phase motors only evaluates the largest voltage vectors to alleviate the computation burden, but at the costs of large current harmonics and torque ripple. This paper presents a simplified model predictive torque control (MPTC) for an asymmetrical dual three-phase permanent magnet synchronous motor (PMSM), which can significantly improve the machine performances. First, the 36 active voltage vectors in the outer three layers are considered with the proposed control method. In order to suppress the stator harmonic currents, the voltage vectors are preselected based on a switching table according to the flux position and torque deviation in the $\alpha \hbox{-} \beta, \text{x--}y$ subspace. Simultaneously, the number of prediction voltage vectors can also be reduced, thus effectively decreasing the computation time. In particular, with the proposed MPTC method, the harmonic currents can be effectively suppressed. Furthermore, the computational burden can be considerably alleviated. In the meantime, the merits of the conventional MPTC such as fast dynamic response and intuitive implementation are preserved. Finally, both simulation and experimental results are performed to verify the validation of the proposed MPTC methodology when comparing with the conventional MPTC for the dual three-phase PMSMs.
A new adaptive controller for robot manipulators considering actuator dynamics

Traditional controllers for robot manipulators give poor performance in high speed operations due to neglected actuator dynamics. This paper proposes a new adaptive controller for robot manipulators in high speed trajectory following considering of actuator dynamics. The new structure not only estimate dynamics of the links and motors of robot manipulators, but also keep the calculation requirement to a minimum to facilitate its real-time implementation. The design is based on the regressor-free approach supported by the function approximation technique under rigorous mathematic justification. A 2-DOF rigid robot manipulator is used in the computer simulation to verify the efficacy of the proposed design.
An adaptive droop control and virtual harmonic resistance method in islanding microgrid

In islanding microgrid, the fundamental power and harmonic current cannot be shared well with the traditional droop control because the system impedance mismatches its capacity for the parallel inverters. This paper proposes an improved droop control method with variable droop coefficient and variable no-load voltage which can reduce the active circulation in parallel inverters. Meanwhile, the adaptive virtual harmonic impedance is used to realize the harmonic current sharing based on the harmonic equivalent model of parallel system, and the harmonic current circulation of the parallel system is restrained effectively. Simulation results validate the proposed control strategy.
Evaluation of SAR in human body model comprising of implanted pacemaker

The proposed paper deals with simulation and analysis of the mobile phone near-field exposure by evaluation of the specific absorption rate within an anatomical realistic human model comprising of implanted pacemaker model. The research was carried out using electromagnetic modeling based on the Finite Integration method. The main objective was to compare levels of absorbed electromagnetic energy as function of antenna distance and mobile phone carrier frequency. The results have shown that averaged SAR induced by the antenna model operating on GSM standard were approx. three times higher than in the case of UMTS standard. However, dosimetric limits for public exposure were not exceed in all considered scenarios.
Data Center Demand Response in Deregulated Electricity Markets

With the development of deregulated electricity markets, a customer can enter a contract with one of several competing utility companies. Meanwhile, a utility company is motivated to increase its market share by helping its customers manage their energy usage and save money through demand response programs. In this paper, we study the demand response program in deregulated electricity markets for data centers that often have significant flexibility in workload scheduling. We consider the real-time pricing and model the data centers’ coupled decisions of utility company choices and workload scheduling as a many-to-one matching game with externalities. To solve such a game, we show that it admits an exact potential function, whose local minima correspond to the stable outcomes of the game. We further develop a distributed algorithm that guarantees to converge to a stable outcome. Compared with the scenario without data centers’ demand response, we show through simulation that the proposed algorithm can reduce the average contract payment of data centers by 18.7% and increase the revenue of the utility companies that offer lower electricity tariffs up to 80% by attracting more data centers as customers.
Differentially private histogram publishing through Fractal dimension for dynamic datasets

Differential privacy is a prevalent research area that has been widely explored in the data release and analysis in recent decades. So far, we have focused on statistics that are derived from a static dataset. However, numerous applications require continuous publication of statistics, such as real-time disease outbreak discovery. With this consideration, a differentially private histogram publishing method with fractal dimension mining technology was proposed. The work aimed at hiding sensitive information while publishing, improving data utility and processing efficiency for multi-dimensional data. We used the fractal dimension to cluster datasets and counted values of each class. Through adding another algorithm to release the final histogram with Laplace noise, differential privacy is achieved. Extensive experiments with several real datasets confirm that our proposal achieves better privacy protection for dynamic datasets.
3D body reconstruction using graph optimization

Three dimensional (3D) body reconstruction can be used in many applications. In this paper, we present a system that uses eight Kinect sensors to reconstruct the whole body. For each depth image captured by Kinect sensor, boundary points and the ones that are too near or too far away from the sensor are deleted. Then we propose an improved iterative closest point (ICP) based registration algorithm to align different point clouds. In order to minimize the accumulate errors in registration procedure, a graph optimization method is utilized in this paper. Experiments have been done to verify our algorithm. The results demonstrate that the proposed algorithm can produce accurate 3D models of human bodies.
Analysis of Platelet-Rich Plasma Extraction Variations in Platelet and Blood Components Between 4 Common Commercial Kits

Methods: Three normal adults each donated 181 mL of whole blood, some of which served as a control and the remainder of which was processed through 4 PRP separation kits: GPS III (Biomet Biologics), Smart-Prep2 (Harvest Terumo), Magellan (Arteriocyte Medical Systems), and ACP (Device Technologies). The resultant PRP was tested for platelet count, red blood cell count, and white blood cell count, including differential in a commercial pathology laboratory. Glucose and pH measurements were obtained from a blood gas autoanalyzer machine.
Azores FREEsubNET Workshop : Habitat Mapping of Espalamaca Vents and Monte da Guia

This document presents the objectives, the work done and the results for the Habitat Mapping Scenario of the Azores FREEsubNET workshop, held in Horta from 19 to 23 July 2010. Three algorithms were tested, two of which have been developed in the FREEsubNET network, for the purpose of image mosaicking of two chosen sites: the Espalamaca hydrothermal vents and a small zone inside the Monte da Guia marine reserve. The former was very recently discovered and is very important from the biological point of view. The test was challenging but the results validated the developed algorithms. In addition, data from a mission with a multibeam sonar in the Espalamaca site are presented in order to be fused with the optical maps.
Gear and bearings fault detection using motor current signature analysis

The importance of monitoring machine health increases with the aim of reducing maintenance costs and improving availability and quality of service. In this context, the main challenge for researchers is to find a convenient and effective method of monitoring gearboxes in a non-invasive manner. Because of busy industrial environments, background noise reduces the sensitivity of vibration signals and acoustic emissions. Consequently, the Motor Current Signal Analysis (MCSA) is a promising non-intrusive alternative to vibration measurement for gearbox monitoring. On the other hand, the monitoring of bearing faults is very important in maintenance programs since these problems account for over 40% of the total amount of failures in the induction motor. Hence, this article presents the use of the Discrete Wavelet Transform (DWT) as well as the K-means clustering technique applied to bearing and gear faults indicators. The Discrete Wavelet Transform is used as a filtering method in order to extract the various frequency bands containing the defect profile. The indicators used in this work are descriptors of geometric shapes that are obtained from Clarke transformation. These indicators provide a particular shape in the presence of a mechanical fault. The results of the carried out experiments demonstrate the efficiency of the Discrete Wavelet Transform combined with the Clarke's shape indicators for bearings and gears fault classification.