Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Power Control of a Wind-Hydro-Grid based Interconnected System for Rural Electrification

This paper deals with active and reactive power control of a wind, hydro and grid based interconnected system for rural electrification. A fixed feed of power supply is taken from the grid and the rest of the consumer load is supplied by the renewable energy sources in order to provide continuous and reliable power supply to a cluster of villages. Dynamic modeling of squirrel case induction generator (SCIG) based wind energy conversion system (WECS) and static synchronous compensator (STATCOM) for reactive power compensation has also been carried out in the study. Comparative analysis of different types of perturbations given to the proposed system has been conducted in the study. The PI controllers used in the system have been tuned using the Particle Swarm Optimization (PSO) algorithm technique.
Algorithms for Extraction of Nanowire Lengths and Positions From Optical Section Microscopy Image Sequence

This paper presents algorithms for estimating length, location, and orientation of nanowires in a fluidic workspace using images obtained by optical section microscopy. Images containing multiple nanowires are first segmented to locate general areas of interest, which are then analyzed to determine discrete nanowire parameters. We use a set of image processing techniques to extract features of nanowire image patterns, e.g., boundary of nanowire, linear edges, and the intensity profile of nanowire's diffraction fringes. The parameters of the features are then used to estimate length, 3D position, and 3D orientation of nanowires. A scene representing the workspace is reconstructed using the estimated attributes of nanowires, and it is constantly updated upon the capture of every image frame. We believe that the work described in this paper will be useful for assembly of nanowires using optical tweezers.
Database Replication Techniques by Adriano Caloiaro Advisor

Caloiaro 0.1 Acknowledgments I would just like to thank my advisor Dr. Dan Plante for always being available for help and an encouraging word whenever it was needed. Dr. Plante has always been able to explain problems in the clearest way possible to make complex problems as easy to understand as possible. I believe Dr. Plante will always be an asset to the Mathematics
Smartphone-Captured Ear and Voice Database in Degraded Conditions

The identification of persons through earprint and voice print is one of the basic biometric matters. The context of use is situated in smart homes. The main issue is the lack of databases containing voice records and ear images from the same persons collected with a smart phone. In this paper, we propose a new database for the persons identification through ear and voice print in degraded conditions. The database contains 111 subjects with more than 12 samples for each one. The new database is collected in degraded conditions, the ear images have different occlusion as scarf, hair, glasses , earrings while the voice records are collected with different types of noise in the background as music, cars, street, conversation. Interested researchers could contact the author to acquire the database.
An Assistance System For Building Intelligent Spaces Based On Mapsharing Among A Mobile Robot And Distributed Sensors

Abstract An intelligent space is a space constructed with many networked sensors. Humans and robots in the space are extracted and tracked cooperatively by the networked sensors. The intelligent space can achieve position-based supports to humans and robots according to integration of networked sensors. Generally, the networked sensors are distributed and fixed on the structures in the space such as walls, ceilings and etc. In order to track moving objects such as humans and robots in the intelligent space, all networked sensors have to obtain objects positions in the unified world coordinate. In that case, positions and orientations of the networked sensors must be also known in the unified world coordinate system. However, it is time-consuming to measure positions of many sensors in the world coordinate accurately and manually. This study aims to develop a system for supporting estimation of positions and orientations of the networked sensors in the intelligent space. In this paper, a configuration of the proposed system is introduced. The proposed system consists of map building systems of the mobile robot and the distributed sensors. A global map from the robot and local maps from the distributed sensors are compared. Then, the local maps of the distributed sensors are associated with the global map and the positions of the distributed sensors are estimated in the global map. For improvement of map matching, angle differences between maps are evaluated. Some experimental results in an actual environment show that the proposed system achieve sensor position estimation easily.
Combining Clone Detection and Latent Semantic Indexing to Detect Re-implementations

Semantic redundancies are frequently reported in practice and cause increased efforts for development and maintenance. However, instances are hard to find with existing approaches that tend to deliver a daunting number of imprecise findings for this specific problem. Can these issues be mitigated by combining different detection techniques? In this paper, we investigate whether a combination of clone detection and latent semantic indexing improves the detection of candidate re-implementations. We evaluate the combination of both techniques on an industrial system, assess the results of both techniques, characterize the different findings, and present a practitioner judgement of their relevance. Our findings suggest that (1) latent semantic indexing and clone detection complement each other, (2) aggregated clone detection can be a better indicator for re-implementations than LSI, and (3) the combination of the techniques provides high quality result sets which were considered relevant and actionable by practitioners.
Control of SCIG Based Distributed Power Generation Scheme Supplying Nonlinear and Unbalanced Load

The proposed work has considered, developed, and implemented a combined control to handle squirrel cage induction generator (SCIG) fed to stand-alone nonlinear and unbalance load. The novelty of the proposed work is implemented successfully with transition from linear load to nonlinear and unbalance load. The main objective is to eliminate the harmonic component during nonlinear loading and to remove the negative sequence component of the unbalance load current. To improve the voltage quality, a control scheme, including filter parameter, has been implemented in this work for the load side converter. Further, the three-stage control technique is used for machine side converter to keep the dc link voltage fixed throughout the operating conditions. The harmonic mitigation and maintenance of output load voltage and frequency are achieved with the control of load side converter. The simulation results have been presented to validate the effectiveness of the proposed controllers.
CLASSIC: a structural data model for objects

CLASSIC is a data model that encourages the description of objects not only in terms of their relations to other known objects, but in terms of a level of intensional structure as well. The CLASSIC language of structured descriptions permits i) partial descriptions of individuals, under an 'open world' assumption, ii) answers to queries either as extensional lists of values or as descriptions that necessarily hold of all possible answers, and iii) an easily extensible schema, which can be accessed uniformly with the data. One of the strengths of the approach is that the same language plays multiple roles in the processes of defining and populating the DB, as well as querying and answering. CLASSIC (for which we have a prototype main-memory implementation) can actively discover new information about objects from several sources: it can recognize new classes under which an object falls based on a description of the object, it can propagate some deductive consequences of DB updates, it has simple procedural recognizers, and it supports a limited form of forward-chaining rules to derive new conclusions about known objects. The kind of language of descriptions and queries presented here provides a new arena for the search for languages that are more expressive than conventional DBMS languages, but for which query processing is still tractable. This space of languages differs from the subsets of predicate calculus hitherto explored by deductive databases.
SleepScale: Runtime joint speed scaling and sleep states management for power efficient data centers

Power consumption in data centers has been growing significantly in recent years. To reduce power, servers are being equipped with increasingly sophisticated power management mechanisms. Different mechanisms offer dramatically different trade-offs between power savings and performance penalties. Considering the complexity, variety, and temporally-varying nature of the applications hosted in a typical data center, intelligently determining which power management policy to use and when is a complicated task. In this paper we analyze a system model featuring both performance scaling and low-power states. We reveal the interplay between performance scaling and low-power states via intensive simulation and analytic verification. Based on the observations, we present SleepScale, a runtime power management tool designed to efficiently exploit existing power control mechanisms. At run time, SleepScale characterizes power consumption and quality-of-service (QoS) for each low-power state and frequency setting, and selects the best policy for a given QoS constraint. We evaluate SleepScale using workload traces from data centers and achieve significant power savings relative to conventional power management strategies.
Three-Phase Grid-Connected Photovoltaic Switched Boost Inverter with Low-Voltage Ride-Through Capability

The recent trends of the high level of penetration of photovoltaic (PV) systems with the grid, due to increasing load demands and continuous depletion of conventional energy sources, have attracted more extensive research in this area. Generally, PV systems utilize two-stage topologies which suffer from less efficiency, poor dynamic behavior etc. So, in this paper, the three-phase switched boost inverter (SBI) has been adopted, as a single-stage topology, to integrate PV system and the grid. Additionally, it has advantages over classical voltage source inverter (VSI) such as satisfying both dc and ac load requirements simultaneously, a wide range of output ac voltage, the capability to allow shoot-through, superior protection against electromagnetic interference (EMI) noise etc. With the help of a maximum power point tracking (MPPT) algorithm, Incremental Conductance method, the SBI extracts maximum available power from the PV system. In order to achieve accurate tracking of the grid current, the proportional resonant (PR) has been chosen as current controller which is coupled with harmonic compensator (HC) for superior harmonic performance. According to many updated grid standards, PV systems should incorporate low-voltage ride-through (LVRT) capability, i.e., to support the grid under fault conditions. Hence, the proposed system, called PVSBI in this paper, uses a control strategy to work with both MPPT and LVRT mode of operation. The three-phase PV-SBI has been verified in normal grid conditions, as well as balanced and unbalanced grid voltage sag conditions, using PSCAD/EMTDC.
Incomplete-data classification using logistic regression

A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data (features). Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional density function (conditioned on the non-missing data). Conditional density functions are estimated using a Gaussian mixture model (GMM), with parameter estimation performed using both expectation maximization (EM) and Variational Bayesian EM (VB-EM). Using widely available real data, we demonstrate the general advantage of the VB-EM GMM estimation for handling incomplete data, vis-à-vis the EM algorithm. Moreover, it is demonstrated that the approach proposed here is generally superior to standard imputation procedures.
Short (resp. Fast) CCA2-Fully-Anonymous Group Signatures using IND-CPA-Encrypted Escrows

In the newest and strongest security models for group signatures [7, 10, 41], attackers are given the capability to query an Open Oracle, OO, in order to obtain the signer identity of the queried signature. This oracle mirrors the Decryption Oracle in security experiments involving encryption schemes, and the security notion of CCA2-full-anonymity for group signatures mirrors the security notion of IND-CCA2-security for encryption schemes. Most group signatures escrows the signer identity to a TTP called the Open Authority (OA) by encrypting the signer identity to OA. Methods to efficiently instantiate O(1)-sized CCA2-fully-anonymous group signatures using IND-CCA2-secure encryptions, such as the Cramer-Shoup scheme or the twin encryption scheme, exist [7, 10, 41, 49]. However, it has long been suspected that IND-CCA2-secure encryption to OA is an overkill, and that CCA2-fullyanonymous group signature can be constructed using only IND-CPA-secure encryptions. Here, we settle this issue in the positive by constructing CCA2-fully-anonymous group signatures from IND-CPA-secure encryptions for the OA, without ever using IND-CCA2-secure encryptions. Our technique uses a single ElGamal or similar encryption plus Dodis and Yampolskiy [35]’s VRF (Verifiable Random Function). The VRF provides a sound signature with zero-knowledge in both the signer secret and the signer identity, while it simultaneously defends active OO-query attacks. The benefits of our theoretical advance is improved efficiency. Instantiations in pairings result in the shortest CCA2-fully-anonymous group signature at 11 rational points or ≈ 1870 bits for 170-bit curves. It is 27% shorter (and slightly faster) than the previous fastest [12, 41] at 15 rational points. Instantiations in the strong RSA framework result in the fastest CCA2-fully-anonymous group signature at 4 multi-base exponentiations for 1024-bit RSA. It is 25% faster than the previous fastest at 5 multi-base exponentiations [3, 20, 41].
Get as you Pay Model for IaaS Cloud Computing

The IaaS cloud computing model is the foundation over which all other cloud computing services can be built. Thus, the migration from the traditional datacenter model to the cloud-computing model is highly dependent on the guarantees that the IaaS Cloud Provider can offer Cloud User to sustain service quality. These guarantees are expressed using Service Level Agreement (SLA) whose need to be aligned and tightly coupled with the cloud service lifecycle so as to reflect the financial and technical dynamicity of the SLA. However, we argue that the SLAs used at present are deficient in expressing solid guarantees on the service quality and lack the auto-negotiation feature required by cloud services. In that respect, SLAs used in practice do not fully abide by the "Pay as you Go" model for cloud computing. In this paper, we propose the "Get as you Pay" model that extends the "Pay as You Go" model by augmenting it with a descriptive SLA that supports the auto-scaling of resources. For validating the "Get as you Pay" model, we walk through a scenario to show how applying the model provides an expressive and negotiable SLA.
Development of Multi-View Video Browsing Interface Specialized for Developmental Child Training

Efforts to support the skills of developmental child training/education workers are implemented using visual information captured by nursery teachers, therapists, and children’s intervention work. This paper proposes a highly operable multi-view video browsing interface utilizing a multi-touch input method, which is suitable for users who are unfamiliar with handling devices that require complicated operations. We conduct evolutional questionnaires and request surveys at an actual developmental child training site and propose an interface based on the results. The proposed interface improves the operability and visibility by two approaches. First, we differentiate between the operation methods to reduce the operation errors that occur during image browsing. Second, we introduce image processing to promote the understanding of the presented image. In experimental evaluations, we investigate the video presentation of scenes that users can easily understand and verify the operability improvement of the proposed interface by a comparative experiment using our pilot system.
A Novel Four Terminal Integrated Submodule Modular Multilevel Converter

Modular multilevel converters (MMC) are extensively used in high voltage direct current (HVDC), flexible AC transmission system (FACTS) and high voltage, high power applications such as motor drive systems. The conventional MMC suffers from low frequency fluctuation in submodule (SM) capacitor's voltage. As a result of this, employment of high value capacitance (typically in mF) becomes mandatory. In addition to this, all the capacitors in conventional MMC are not actively utilized by the circuit as some capacitors get bypassed or remain floating. The two aforementioned issues make the use of bulky capacitors mandatory and also increase the capacitor count in the topology. In this paper, a novel scheme of sharing the capacitors between sub-modules is introduced to mitigate these issues. A novel four terminal integrated SM (FTIS) is proposed by combining the upper and lower SM of the conventional MMC. This concept reduces the number of SM capacitors to half as compared to the conventional MMC topology. Three-level and five-level FTIS-MMC are simulated and validated in MATLAB/SIMULINK environment. Various waveforms are showcased under different conditions to show the efficacy of proposed converter.
A polynomial-time approximation algorithm for joint probabilistic data association

Joint probabilistic data association (JPDA) is a powerful tool for solving data association problems. However, the exact computation of association probabilities {/spl beta//sub jk/} in JPDA is NP-hard, where /spl beta//sub jk/ is the probability that j-th observation is from k-th track. Hence, we cannot expect to compute association probabilities in JPDA exactly in polynomial time unless P = NP. In this paper, we present a simple Markov chain Monte Carlo data association (MCMCDA) algorithm that finds an approximate solution to JPDA in polynomial time. For /spl epsiv/ > 0 and 0 < /spl eta/ < . 5, we prove that the algorithm finds good estimates of /spl beta//sub jk/ with probability at least 1 - /spl eta/ in time complexity O(/spl epsiv//sup -2/ log /spl eta//sup -1/N(N log N + log /spl epsiv//sup -1/))), where N is the number of observations.
Model-Based Fault Detection and Diagnosis of Slip-Ring Induction Motors: A Simulation Study

In this paper, a new non-invasive model-based fault detection and diagnosis of stator and rotor inter-turn faults in slip ring induction motors (SRIMs) are presented. Analytical modeling of SRIM with electrical faults is formulated. Innovation sequence generated from extended Kalman filter is used for accurate fault detection. Further, fault diagnosis is carried out using Brute-force search algorithm. Simulation results show that the presented method is an effective diagnosis procedure for major electrical faults in SRIMs. Robustness towards supply imbalance and speed varying conditions are the significant advantages of the proposed strategy.
Acoustic Stimulation Following Sleep Spindle Activity May Enhance Procedural Memory Consolidation During a Nap

Acoustic and electrical brain stimulations are techniques well known to enhance memory consolidation by driving slow oscillations (SO, < 1Hz) and sleep spindle activity. Recent studies have suggested that the temporal relationship between SO and sleep spindle activity may be an important key to understanding memory consolidation mechanisms. We hypothesized that evoking SO after sleep spindle activity may enhance memory consolidation. To derive these spindle-SO pairs, we delivered acoustic stimulation after sleep spindle detection and investigated its effects on memory consolidation with behavioral tests and analyses of neurophysiological features. Thirteen healthy male subjects (mean ± SD age: 26.3 ± 2.4 years) participated in this study. Subjects took a nap with acoustic stimulation after spindle activity detection and a sham nap without acoustic stimulation. All subjects performed word-pair memorization and finger tapping tasks before and after their nap. We found phase-locked SO and delta (1–4 Hz) activity during the stimulation nap in response to acoustic stimuli, and the subjects had a greater improvement in finger tapping tasks after the stimulation nap than after the sham nap (p = 0.014). We found strong motor-learning enhancement after the stimulation nap, but this effect was limited to the subjects who did not demonstrate evoked spindle activity after their acoustic stimulation. Evoked spindle activity occurred in the up-state following the negative peak in auditory evoked potential (AEP), and this activity was observed only in subjects who had a greater AEP amplitude than normal SO. Based on these results, we suggest that subject-specific stimulation parameters, such as acoustic amplitude and timing, improve motor learning, and are appropriate to drive SO without causing a spindle response.
Effect of Skewing in a Variable Flux Interior Permanent Magnet Synchronous Machine

The motivation of this work is to minimize cogging torque and torque ripple in a 6 pole, 27 slot variable flux interior permanent magnet synchronous machine (VF IPMSM) by skewing the permanent magnets (PMs) for few steps. Finite Element Analysis (FEA) shows that step skewed PMs significantly minimize cogging torque and torque ripple in the 6 pole VF IPMSM. But each step of skewed PMs in a pole for a variable flux permanent magnet machine has to be magnetized or demagnetized properly. A proper current pulse is required to demagnetize each step in a PM-pole. Finite Element Analysis (FEA) is used to select the proper current pulse to demagnetize each step of the step skewed PM-pole.
Solar based Air Conditioner with BLDC Motor and Battery Backup

This paper proposes the designing of an air conditioner utilizing solar energy obtained from photovoltaic (PV) panels. In this proposed system maximum power is obtained from PV panel by the help of perturb & Observe (P&O) tracking algorithm. Duty cycle of buck-boost converter, which maintains the voltage at the output of the inverter to control the brushless DC (BLDC) motor to drive the compressor and blower is controlled by perturb and observe technique. Battery is connected to the system through a charge controller to increase the trustworthiness of the system. The proposed air conditioner is designed to obtain satisfactory system performance even under dynamic conditions. The performance and effectiveness of the system is evaluated and verified using simulation.