Miracle-dz/UniFAR-FTUs huggingface dataset:
"Online Off-Site Condition Monitoring Of Three Phase Induction Motor by Using GSM Technology"

This paper describes online off-site condition monitoring of three phase induction motor by using GSM technology. In this approach, a microcontroller based hardware unit has been developed to continuously monitoring and measure the stator current of the motors. In a motor monitoring system the motor is connected with one microcontroller based hardware unit, which is also connected to Global System for Mobile Communication (GSM) modem. The preliminary level of fault or abnormality in operation of motor is diagnosed by the Fast Furrier Transform in MATLAB and the fault details are reported to the assigned operator through an SMS service. In extreme case, the provision of motor is shut down by a return SMS is also provided. A lab model is set up and is working satisfactorily. Condition monitoring of induction motor have a challenging task for engineers in industries. There are many conditions monitoring methods including vibration monitoring, thermal monitoring, chemical monitoring all these monitoring methods required expensive sensors or specialized tools whereas online off-site condition monitoring of induction motor using GSM does not required additional sensors. This also contains AVR controller which generates controlled signals which is transmitted to server also makes use of mobile frequency band provides complete automation. It monitored stator fault, rotor fault, bearing fault by using microcontroller which communicates with GSM and GSM send message to the user. Condition monitoring of induction motor is a process that may be used to great advantage in agricultural field as well as in the industrial application. Motor current acts as an excellent transducer for detecting fault in the motor. Spectrum analysis of the motor's current and voltage signals can hence detect various faults without disturbing its operation. Current signature analysis involves the measurement of electric current around any one phase either through clamp on meters or through CT's. This current is then transformed into its frequency spectra and analyzed for detection of fault. The proposed work for project aims is to detect the rotor fault by online off site condition monitoring of induction motor using GSM technology by developing working model of system. II. INDUCTION MOTOR FAULT Induction machine failure surveys have found the most common failure mechanisms in induction machines are stator related faults, rotor related faults, bearing related faults and other faults. Stator fault: - Almost 38% of induction machine failures fall stator fault. The three phase stator winding consists of coils of insulated copper wire placed in stator slots. Stator winding faults are caused by insulation failure between two
FARS: A page replacement algorithm for NAND flash memory based embedded systems

Page replacement algorithms in most operating systems are optimized for disk based systems. However, in recent years, embedded systems are usually equipped with NAND flash memory because it has many attractive features such as small size, fast access speed, shock resistance, and lightweight. Therefore, a new replacement algorithm is needed for NAND flash memory based embedded systems. In this paper, we propose a new replacement algorithm for embedded systems with NAND flash memory. The proposed algorithm focuses on reducing the replacement cost and I/O execution time in order to improve the performance of embedded systems. Trace-driven simulations show that the proposed algorithm performs better than existing algorithms in terms of the replacement cost and I/O execution time.
Requirements and implementation of a flexible kinetic modeling tool.

UNLABELLED Kinetic (or compartment) modeling is a highly versatile tool for the analysis of experiments within living systems. In PET, it is essential for developing tracers, for assessing tracer behavior and for extracting quantitative information about the target process. However, tools to support the modeling tasks involved are not easily available. METHODS This article presents a requirements analysis for kinetic modeling in PET. The interactive kinetic modeling tool KMZ implements many of these features. It facilitates model development by a set of predefined models and by the ease of introducing new models. Monte Carlo studies allow assessing parameter identifiability. The responses in the different compartments as well as the expected time-activity curve can be simulated for specific model configurations. For measured time-activity curves, model optimization can be performed by the Powell or the Marquardt algorithm. Both support weighted nonlinear least-squares fitting and allow optional constraints of parameter ranges. To further improve parameter estimation, the fitting of several regional time-activity curves can be coupled, resulting in lower standard errors for parameters common among regions. It is possible to highly automate the evaluation of study series and to forward the results into statistical analysis tools. RESULTS The KMZ tool has proven highly suitable in evaluating data from different types of studies, and the intuitive user interface enables medical doctors to successfully perform routine evaluations after a short training period. CONCLUSION A portable kinetic modeling tool with the described features would provide easy access to model development and may help consolidate kinetic modeling in clinical settings for well-defined applications.
Exploiting characteristics of machine learning applications for efficient parameter servers

Large scale machine learning has emerged as a primary computing activity in business, science, and services, attempting to extract insight from quantities of observation data. A machine learning task assumes a particular mathematical model will describe the observed training data and use an algorithm to identify model parameter values that make it fit the input data most closely. That is, the computation attempts to optimize the model by minimizing an objective function, which generally describes the error. Depending on the application, such models can expose relationships among data items (e.g., for grouping documents into topics), predict outcomes for new data items based on selected characteristics (e.g., for classification tasks and recommendation systems), correlate effects with causes (e.g., for genomic analyses of diseases), and so on. We focus on one major subset of these ML tasks that we refer to as iterative machine learning. In iterative ML, the algorithm starts with some initial parameter value guesses, and then performs a number of iterations to refine them. Each iteration evaluates each input datum, one by one, against current model parameters and adjusts parameters to better fit that datum. Various stopping conditions may be used, such as when the objective function improvement slows sufficiently or just a given amount of time has been spent refining. The expensive computation required often makes it desirable to run these ML tasks distributedly on a cluster of machines. We focus on a data-parallel approach of parallelizing such tasks, in which the training data is partitioned among workers, and the workers iteratively make changes to the shared model parameter data, based on their local training data. While other designs can be used, an increasingly popular design for maintaining the distributed shared parameter data is to use a so-called parameter server architecture [1, 25, 21, 7, 3, 6, 5, 4, 34]. The parameter server manages the shared parameter data for the application and takes care of all the distributed system details, such as propagating parameter updates and synchronizing parameter data among workers. The efficiency of the parameter server system is critical to the efficiency of such ML tasks, and a better parameter server design can sometimes improvement the efficiency by an order of magnitude or more.
Accurate Analytical Delay Models for VLSI Interconnects

Elmore delay has been widely used to estimate the interconnect delays in the performance-driven synthesis and layout of VLSI routing topologies. For typical RLC interconnections, Elmore delay can deviate signiicantly (by up to 33% or more) from SPICE-computed delay, since it is independent of inductance. We develop an analytical delay model to incorporate inductance eeects into the delay estimate. Our method achieves delay estimates that are within 10% of SPICE-computed delay across a wide range of interconnect parameter values. We also give two example applications of our new analyses: (i) a condition under which interconnect delays can be minimized by introducing controlled ringing, and (ii) an extension of the New Delay model for estimation of delay in interconnect trees. 1 Overview Accurate calculation of propagation delay in VLSI interconnects is critical to the design of high speed systems. With the evolution of VLSI technology, transmission line eeects now play an important role in determining interconnect delays and system performance. Various techniques have been proposed for the simulation of interconnects. These techniques are based on either simulation techniques or (closed-form) analytical formulas. Direct simulation tools such as SPICE give the most accurate insight into arbitrary interconnect structures, but are computationally expensive. Transient simulation of lossy interconnects based on convolution techniques is presented in RP91, LK92]. Faster techniques based on moment computations are proposed in RGP91, RBR92, SK93]. The above methods, being simulation-based, are all computationally too expensive to be
Reorganisation of Somatosensory Cortex after Tactile Training

Topographic maps in primary areas of mammalian cerebral cortex reorganise as a result of behavioural training. The nature of this reorganisation seems consistent with the behaviour of competitive neural networks, as has been demonstrated in the past by computer simulation. We model tactile training on the hand representation in primate somatosensory cortex, using the Neural Field Theory of Amari and his colleagues. Expressions for changes in both receptive field size and magnification factor are derived, which are consistent with owl monkey experiments and make a prediction which goes beyond them.
Effects of Advancing Age on the Processing of Semantic Anomalies in Adults: Evidence from Event-Related Brain Potentials

Age-related changes in the processing of sentence-embedded semantic anomalies were examined using auditory event-related potentials (ERPs). Semantically incongruous words elicited an N400 effect in middle-aged (50s: 55.6 years) and elderly (60s: 64.1 years) subjects, whereas in older elderly adults (70s: 74.9 years) this effect approached significance. N400 peak latencies were not delayed with advancing age; however, there was a reliable linear decrease in mean and peak amplitude, with slopes being similar to those previously reported for the visual N400 effect. A P600 effect was obtained in response to semantic anomalies and it was neither delayed in latency nor reduced in amplitude with advancing age. However, it was found to be larger over anterior sites in elderly and older elderly subjects.
Implementation and Evaluation of the JobTracker Initiative Task Scheduling on Hadoop

MapReduce is one of the major successful framework to process large-scale data efficiently. Distributed programs can be implemented easily by describing only two methods, Map and Reduce. In Hadoop which is an open source implementation of MapReduce, a JobTracker (master program in Hadoop) assigns Map Tasks and Reduce Tasks to TaskTrackers (slave programs which execute the tasks). In an environment that multiple Hadoops are running on a physical machine, its computational resources should be shared by every Hadoop (Multi-Hadoop environment). In this environment, available computational resources of each Hadoop fluctuate dynamically by behaviors of other Hadoops. Therefore, the JobTracker needs to decide assignment of tasks based on loads and available computation resources on the cluster (JobTracker Initiative Task Scheduler). In this paper, we propose a method which decides the number of task executions in order to use computational resources efficiently based on a load on each computer. And we evaluate its performance, and our results show that the proposal method has achieved a reduction of execution times of jobs by about 11.1% in Multi-Hadoop environment as compared to original Hadoop.
Birds of a feather get misidentified together: High entitativity decreases recognition accuracy for groups of other‐race faces

Purpose The cross-race effect can be exaggerated when faces are presented in groups, leading to less accurate eyewitness identifications (Pezdek, O'Brien, & Wasson, 2012, Law Hum. Behav., 36, 488). Our current study examined the effect of entitativity, the degree to which members of a group are perceived as a coherent unit (Campbell, 1958, Behav. Sci., 3, 14), on recognition accuracy for same- and cross-race faces presented in groups. Methods White participants viewed 16 slides of 3-face groups (eight White groups, eight Black groups). Prior to viewing the faces they were told that the entitativity of each 3-face group was high (‘friends who do things together’) or low (‘people in line at the bank’). They were then tested on 32 individually presented faces (16 old and 16 new). Results When cross-race faces were presented in high rather than low entitativity groups, less accurate face recognition memory resulted. Increasing group entitativity decreased recognition accuracy for cross-race faces but increased recognition accuracy for same-race faces. Conclusions The results suggest that the perception of a group negatively impacts eyewitness memory. Contextual factors such as entitativity need to be considered along with other estimator variables when assessing eyewitness identification accuracy.
Linear transformation of Hidden Markov Models based on linear regression

This paper treats a linear transformation of word templates in a w ord recognition system. The object of the transformation, called LMR-transform, is to adapt the recogniser to a new acoustical environment. The transform is derived by linear regression on pairs of word utterances from two different acoustical environments. The use of the transform has been evaluated for recognition accuracy, speaker-independence and training data requirements in three different fields of application. The main field is noise adaptation in a car-environment. Other fields involve microphone and speaker adaptation. A database recorded in a car incorporating six speakers using five different microphones has been utilised for evaluation. The transform increased recognition accuracy with all of the evaluated fields of application.
Capital Structure Determinants of Russian Public Companies

This paper investigates the determinants of capital structure choice of Russian public companies. The sample comprises 7 times more companies than any other known study of Russian firms. Significant influences on financial leverage are identified for the company’s profitability, tangibility of assets, growth opportunities, size, ownership structure, credit rating, accounting standard, life cycle stage, level of intellectual capital, as well as for time effects. These results, first, help to challenge the conclusions of the prior research. Second, they evidence the validity of the trade-off and pecking order theories, market-timing and managerial over-optimism hypotheses. Third, they prove the influence on capital structure of several institutional features of the Russian corporate environment: higher non-transparency of economic relationships and a monopolistic character of the economy.
Accurate positioning of mobile phones in a crowd using laser range scanners

In this paper, we propose a novel approach to positioning mobile phone users in a crowd of people. We assume such a situation that people in specific space (e.g., an exhibition hall) have mobile phones and they periodically probe proximity between the surrounding users via short-range wireless communication (e.g., Bluetooth). We also assume that laser range scanners (LRSs) are deployed in the space to track positions of those mobile phone users. By fusing the accurate, but anonymous trace information obtained by LRSs and the identified phone-to-phone proximity information, our method derives accurate identified traces of each mobile phone user, which are essential for pedestrian navigation and other emerging location/situation-aware applications. Through extensive simulations, we show that the tracking error quickly converges below 1m in most cases.
Design of the HP PA 7200 CPU

A new and distinct cultivar of miniature rose plant named 'POULrat', characterized by its extreme compactness which is suitable for year-round forcing in glasshouses, thereby reducing or eliminating the need for application of plant growth regulators; good bud count for a compact variety; stable, non-fading yellow color; excellent disease resistance; attractive dark green foliage with semi-glossy finish; and limited numbers of thorns.
POPL: G: Refinement Types for Incremental Computational Complexity

With recent advances, programs can be compiled to efficiently respond to incremental input changes. However, there is no language-level support for reasoning about the time complexity of incremental updates. Motivated by this gap, we present CostIt, a higher-order functional language with a lightweight refinement type system for proving asymptotic bounds on incremental computation time. Type refinements specify which parts of inputs and outputs may change, as well as incremental computational complexity, a measure of time required to propagate changes to a program’s execution trace, given modified inputs. We prove our type system sound using a new step-indexed cost semantics for change propagation and demonstrate the precision and generality of our technique through examples.
Acid mine drainage - effect on creeks or streams in a mining community in Ghana and treatment options

Heavy metals contamination and other physico-chemical changes in underground and surface water by acid mine drainage (AMD) were determined on the impact on creeks or streams. High levels of these metals have been reported in mining areas over the years; however, investigations indicates that less monitoring has been done on water discharged as effluent for effective maintenance mechanisms to be effected. Field and laboratory studies were carried out within the mining community on Latitude S 2° 35 W and Longitude N 5° 31W to investigate the levels of heavy metals (As, Fe, Cu, Zn, Mn and Cd) and physico-chemical changes (pH, temperature, E.C, D.O, alkalinity, S042−, free CN) in both underground and surface water for seven sampling sites. Results from atomic absorption spectrometry (AAS) determination indicated an average concentrations of iron (6.185 mg/l) and manganese (2.271 mg/l) in both surface and underground waters which are higher than threshold limits for mining effluent. The physico-chemical changes were determined using standard methods. Results also indicated higher levels of electric conductivity (1435.0µs/cm) and sulphate (344.0 mg/l). Differences in pH, temperature, E.C, alkalinity, S042-, Fe, Cu, Zn and Mn levels in the seven water samples were statistically significant (p ≤ 0.05). This may be attributed to the higher release of metals, relative to H+. Result also showed significant impact of AMD on water quality in the mining area. This effect could be attributed to the release of metals as a result of oxidation of metal-bearing sulphides in the mine dumps and pits at the selected sampling sites. The study showed that addition of lime as a treatment option has not been effective. Hence new conventional methods such as Aerobic wetland, Compost or Anaerobic Wetland, Open Limestone Channels, Diversion wells, Anoxic Limestone Drains (ALD) etc. should be considered in the treatment of acidic water (AMD) in the mining area.
Developing a FPGA-Based High Performance , Power-Aware Architecture for the Correction of Radial Lens Distortion in Video Stream

Images captured by wide-angle cameras show barrel type spatial (or radial lens) distortion due to wide-angle configuration of the camera lens where image regions farther from the center are compressed in a nonlinear fashion. The radial lens distortion correction technique based on least squares estimation corrects a distorted image by expanding it nonlinearly so that straight lines in the object space remain straight in the image space. An absolute pipelined architecture is designed to correct radial lens distortion in images for real-time applications in FPGA-based system. The architecture includes a CORDIC based rectangular to polar coordinate transformation module, a back mapping module for nonlinear transformation of corrected image space to distorted image space, a CORDIC based polar to rectangular coordinate transformation module, and a linear interpolation module to calculate the pixels’ intensities. The design utilizes quadrant symmetry property to eliminate redundant computation to conserve power. Furthermore, power-efficient design technique is utilized in the arithmetic unit to reduce dynamic power consumption by decreasing the switching activity. The architecture can sustain a maximum throughput rate of 93 1024×1024 frames per second. The pipelined architecture design will facilitate the use of dedicated hardware that can be mounted along with the camera unit.
Language-aided robotic teleoperation system (LARTS) for advanced teleoperation

The language-aided robotic teleoperation system incorporates two sets of teieoperational languages with a master-slave manipulator. One offers flexible means, called software jigs, to specify motion constraints superimposed on operational motion, simplifying task motion. The other offers an easy method of teaching elementary tasks which frequently appear in teleoperation. Re-execution of the thus-taught program is effectively utilized.
Peer-to-Peer Data Mining Classi ers for Decentralized Detection of Network Attacks

Data mining aims to extract from huge amount of data stochastic theories, called knowledge models, to explain or predict complex phenomenon. In this paper we propose new distributed data mining algorithms to recognize network attacks against a set of devices from statistic data generated locally by each device according to the standard Simple Network Management Protocol (SNMP) available in each modern operating systems. The idea is to place an autonomous mining resource in each network node that cooperates with its neighbors in a peer-to-peer fashion in order to reciprocally improve their detection capabilities. Differently from existing security solutions, which are based on centralized databases of attack signatures and transmissions of huge amount of raw traffic data, in this solution the network nodes exchange local knowledge models of few hundred bytes. The approach efficacy has been validated performing experiments with several types of attacks, with different network topologies and distributions of attacks so as to also test the node capability of detecting unknown attacks.