Tuning Time Research Articles

An automaton-based index scheme supporting twig queries for on-demand XML data broadcast

XML data broadcast is an efficient way to deliver semi-structured information in a wireless mobile environment. In the literature, many approaches have been proposed to disseminate XML data via wireless broadcast. However, because of the existence of “*” and “//” in queries, their performance deteriorates. In this paper, we propose a novel indexing method called Deterministic Finite Automaton-based Index (abbreviated as DFAI) on the XPath queries. Different from existing approaches which build index based on XML documents, DFAI is built based on the submitted queries. The new index treats the XPath queries as a DFA and it improves the efficiency of broadcast systems significantly. We further propose a compression strategy to reduce the index size of DFAI as well. Besides, as an extension, we extend the DFAI to support twig queries. Experiment results show that our method achieves a much better performance in terms of both access time and tuning time when compared with existing approaches.

Model‐based MPI‐IO tuning with Periscope tuning framework

SummaryFor many parallel applications, I/O performance is a major bottleneck. MPI‐IO, defined by the MPI forum, can help parallel applications overcome the performance and portability limitations of existing parallel I/O interfaces. Although autotuning has been used to improve the performance of computing kernels, MPI‐IO autotuning has rarely been studied. To automate MPI‐IO performance tuning, we designed and implemented an automatic tuner. The tuner relies on the Periscope tuning framework for transparently passing hints to the MPI‐IO library and for automatically collecting performance data. Unlike computational code, each MPI‐IO function takes a relatively long time to complete. Thus, exhaustively searching through the entire parameter space is impractical. So we developed a performance model that can direct us to shorten the tuning time. Copyright © 2015 John Wiley & Sons, Ltd.

An auto-tuning PID control system based on genetic algorithms to provide delay guarantees in Passive Optical Networks

Passive Optical Networks (PONs) are the most important access architectures since their deployment is massive all around the world. However, the QoS (Quality of Service) and the efficient management of the network resources have become the key point, especially with the new emerging services and applications. In particular, the delay and the bandwidth are becoming important limiting factors for the user experience. As a consequence, in a previous research we proposed the implementation of PID (Proportional-Integral-Derivative) control strategies to manage these networks parameters in PONs, demonstrating higher efficiency and more robustness than other previous existing algorithms. It is worth emphasizing that this is the first time to apply this control strategy in PONs access networks. However, in this paper we improve the PID control strategy by automating the tuning process with a genetic algorithm. Indeed, we have developed a novel automatic tuning technique based on genetic algorithms to tune a P controller that provides delay guarantees. Simulation results show that the control strategy reduces the tuning time up to 64% in comparison with the Ziegler–Nichols manual technique (ZN). On the other hand, it is demonstrated that our proposal is more accurate and robust that ZN since the genetic algorithm automatically evolves to the best solutions of the tuning parameters in contrast to the manual experiments required for the ZN method. Furthermore, we have complemented the use of the P controller with a new dynamic Admission Control (AC) module. This module implements a policy to selectively transmit or drop packets and leads a better delay control. The simulation analysis reveals that the real time evolution of the delay with the dynamic AC is more stable when compare to a conventional and simple fixed AC, reaching differences near one order of magnitude in the delay fluctuations.

Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling

Industrial demand for models and simulation tools that can predict dimensional errors in manufacturing processes is vigorous. One example of these processes is ball-end finishing of inclined surfaces, which is a very complex task, due to the high number of variables that may influence dimensional errors during a cutting process and their different nature. This work firstly analyses the potential of semiempirical models to address the ball-end milling finishing, to conclude that these models are unable to process and to replicate the full range of milling strategies and slope combinations. Secondly, it goes on to analyse the possibilities of artificial neural networks as a means of overcoming this limitation. Two types of neural networks, multilayer perceptron (MLP) and radial basis functions (RBF), are tested. The results show that RBFs predict better than MLPs in all cases, achieving a precision of 1.83 μm in root mean squared error (RMSE) and a correlation coefficient of 0.897 with a 10 × 10 cross-validation scheme. Their training and tuning times are also 2.5 times shorter in all cases. Finally, the use of 3D figures, generated from the best RBF model, yields interesting industrial results in the field of process engineering.

Influence of z/w relation in Chill Block Melt Spinning (CBMS) process and analysis of thickness in ribbons

Purpose – The purpose of this paper is to present an analysis over own and other authors data related to the process of Chill Block Melt Spinning (CBMS) and propose a model of analysis for interpreting. Design/methodology/approach – The methodology used in this work is to present the data analyzed by other authors, organize own data similarly to establish comparison, and established models and propose a possible physical processes interpretation. Findings – Similarity between own experimental data. with others data reported by other authors, both z/w ratio and the thicknesses of the films produced has been found. This allows us to establish an exponential decay of the parameters studied and possibly link it the Newtonian cooling to which the samples are subjected in its production. Research limitations/implications – This work is the first model set up to predict dimensions in design process by CBMS as a function of parameters of the ribbon production process. Practical implications – The prediction of the product dimensions, with adjusting the initial parameters, allows to improve the process of ribbon production, this saves tuning time of the machine and provides certainty in the molten material ejection. Social implications – The efficient production of magnetic materials lets save efforts in the raw material process preparing in magnetic cores for the energy sector. This, improves production besides benefit society by the final product and the energy savings. Originality/value – The value of this paper is to propose a model of analysis that allows standardize production parameters, and could even allow the use of these models in computer programs, process simulators in a more effective manner.

Further development of adaptable automated visual inspection—part I: concept and scheme

The issue of adaptability in the design of automated visual inspection (AVI) systems has been a major road block in the application of AVI technology to manufacturing industry. The objective of an adaptable AVI system is to reduce system tuning time while maintaining the quality of inspection. In the authors’ previous work (Int J Adv Manuf Technol 59:655–667, 2012), we have proposed a novel adaptable AVI scheme, with which an AVI system can be trained online to accommodate new patterns. As such, the system can be deployed to undertake new inspection tasks or adapt to new operation conditions without excessive offline training efforts. The research presented in this two-part article is our further development of the adaptable AVI scheme, namely A2VIS. In this research, we focus on elaborating the concept, designing the structure, implementing the functions, and evaluating the performance of A2VIS in a systemic perspective. In part I, the proposed A2VIS is presented as a generic scheme at the conceptual level, with respect to the system components and structure, operating procedure, and performance measures. The key element in the design of the A2VIS is an adaptable inspection model that integrates subject localization, feature extraction, and classification functions as trainable submodels. In part I of this paper, we developed a generic tool for an adaptable AVI system without specifying the use of particular machine vision and machine learning techniques. Interested readers may choose techniques for implementing the proposed scheme according to their own preferences. In part II of this article, we focus on the system implementation and evaluation of A2VIS using such techniques as template matching, principal component analysis (PCA), and support vector machine (SVM).

Trading Energy Savings and Network Performance in Reconfigurable TWDM-PONs

In time and wavelength division multiplexed (TWDM) passive optical networks (PONs), energy savings at the optical line termination (OLT) are achievable by dynamically allocating the number of active wavelengths in the network and by turning off unutilized OLT transceivers. However, optical network unit (ONU) and PON parameters might impact network performance. This paper evaluates the impact of ONU parameters (e.g., transceiver tuning time) and other PON parameters (e.g., reconfiguration period) on OLT energy savings and average frame delay when dynamic wavelength allocation is performed periodically. Results show that the average frame delay is mainly affected by tuning time and reconfiguration period with lower absolute values if only one ONU buffers traffic during tuning. Increased tuning time trades increased delay with increased energy savings while increased reconfiguration period trades decreased delay with decreased energy savings.

Heuristic Algorithm for Efficient Data Retrieval Scheduling in the Multichannel Wireless Broadcast Environments

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the server’s point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. The most important issues in mobile computing are energy efficiency and query response efficiency. However, in data broadcast the objectives of reducing access latency and energy cost can be contradictive to each other. Consequently, we define a new problem named Minimum Cost Data Retrieval Problem (MCDR) and Large Number Data Retrieval (LNDR) Problem. We also develop a heuristic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time.

An SVM-based solution for fault detection in wind turbines.

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.

A New Control Framework of Electric Power Steering System Based on Admittance Control

This brief presents a new control framework of electric power steering (EPS) systems for ground vehicles based on admittance control. An impedance filter is used to define the desired admittance of the system under the torsion bar (T-bar), and a position tracking control loop is used to enforce its apparent admittance. A road feedback path implemented with a road feedback filter provides the driver with awareness of road conditions with selected frequency components and magnitudes. In this new control framework, the base admittance of the system under the T-bar, which determines the base steering feel, can be configured independently from the closed position control loop; such separation of the closed-loop control design and steering feel configuration can potentially reduce the overall system tuning and calibration time. Common EPS characteristics are related to the design parameters of the new control framework. Numerical cosimulations, including an EPS system model and a vehicle model under different road conditions and maneuvers, show that the base steering feel matches the designed steering feel, and the system has good road disturbance rejection. With the road feedback filter, the base steering feel is augmented and the road condition awareness is improved.

Ultrafast tunable chirped phase-change metamaterial with a low power.

We numerically demonstrate an all-optical tunable dual-band double negative (DNG) index chirped metamaterial (MM) in the mid-infrared (M-IR) region. This MM possesses an ultrafast and significant tunability under low pump light power, realized by combining phase change material (PCM). It has a configuration of elliptical nanohole array (ENA) penetrating through metal/PCM/metal (Au-Ge(2)Sb(2)Te(5)-Au) films. Here, we consider the case when the chirp is introduced by displacing the positions of the ENA along the short axis of the elliptical apertures inside the primitive cell, which can achieve multiple internal surface-plasmon polariton (SPP) modes at the inner metal-dielectric interfaces of the structure and thus providing a dual-band negative index with simultaneous negative permittivity and permeability. The influence of amorphous and crystalline states of Ge(2)Sb(2)Te(5) on the effective optical parameters of the structure is analyzed. Switching between these states provides a large wavelength shift of the structure's effective optical parameters. A photothermal model is used to study the temporal variation of the temperature of the Ge(2)Sb(2)Te(5) layer to show a potential to switch the phase of Ge(2)Sb(2)Te(5) by optical heating. Generation of the tunable dual-band DNG index presents clear advantages as it possesses a fast tuning time of 0.4 ns, a low pump light intensity of 7.3μW/μm(2), and a large tunable wavelength range of 978 nm. We expect that our design may have potential applications in actively tunable multi-band nanodevices.

Signature Driven Hierarchical Post-Manufacture Tuning of RF Systems for Performance and Power

Integration of RF circuits in deeply scaled CMOS technologies and severe process variation in those technology nodes result in poor manufacturing yield. A post-manufacture tuning approach for yield improvement of RF systems is developed in this paper that uses hierarchical behavioral models of the RF systems. The proposed method first determines module-level performances from the system-level response (signature) to an applied test using top-down model diagnosis. Then, constrained optimization is performed to determine the best module-level tuning parameter values that satisfy system-level specifications (bottom-up analysis) in a power-conscious manner. Both top-down and bottom-up analysis techniques are supported by hierarchical RF behavioral models. The relationship between module-level tuning parameters and module-level performance metrics changes from instance to instance depending on amount of process variation and this factor is included in the proposed tuning procedure. A key benefit of the proposed approach is that only a single-test application is needed at a fixed number of tuning knob settings resulting in reduction in tuning time. The efficiency of the proposed technique is demonstrated using simulation results and hardware experiment.

Novel aspects of plasma control in ITER

ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily for ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

Facial neuromuscular signal has recently drawn the researchers’ attention to its outstanding potential as an efficient medium for Muscle Computer Interface (MuCI) applications. The proper analysis of such electromyogram (EMG) signals is essential in designing the interfaces. In this article, a multiclass least-square support vector machine (LS-SVM) is proposed for classification of different facial gestures EMG signals. EMG signals were captured through three bi-polar electrodes from ten participants while gesturing ten different facial states. EMGs were filtered and segmented into non-overlapped windows from which root mean square (RMS) features were extracted and then fed to the classifier. For the purpose of classification, different models of LS-SVM were constructed while tuning the kernel parameters automatically and manually. In the automatic mode, 48 models were formed while parameters of linear and radial basis function (RBF) kernels were tuned using different optimization techniques, cost functions and encoding schemes. In the manual mode, 8 models were shaped by means of the considered kernel functions and encoding schemes. In order to find the best model with a reliable performance, constructed models were evaluated and compared in terms of classification accuracy and computational cost. Results reported that the model including RBF kernel which was tuned manually and encoded by one-versus-all scheme provided the highest classification accuracy (93.10%) and consumed 0.98s for training. It was indicated that automatic models were outperformed since they required too much time for tuning the parameters without any meaningful improvement in the final classification accuracy. The robustness of the selected LS-SVM model was evaluated through comparison with Support Vector Machine, fuzzy C-Means and fuzzy Gath-Geva clustering techniques.

Comparison of Broadcast Scheduling Techniques in Mobile Computing Environment

In a mobile environment, data broadcast is considered as the most effective and efficient way to disseminate data for mobile clients. There are three primary issues to be resolved which are access time, tuning time and power consumption for the build of an optimal environment. The objective of a good broadcast strategy is to maintain short access and latency time while conserve battery power. This paper investigates various data broadcast scheduling techniques and delivery models such as Push based, Pull based and Hybrid. The comparison helps us explore areas of research benefiting our concerns in noting their advantages and disadvantages. In this paper, we propose a technique that combines the hybrid broadcast scheduling approach with allocation of data items over multiple broadcast channels. The proposed technique aims to reduce access time and correspondingly saves battery power of the mobile clients.

PID Tuning of Servo Motor Using Bat Algorithm

The Proportional-Integral-Derivative (PID) controller uses three parameters to produce the desired output of a system. The desired system performances are in terms of overshoot, rise time, settling time and steady state error. This has brought about various methods to tune the controller to the desired response. Therefore, the presence of the bat algorithm as part of the system will reduce the time and cost of tuning these parameters and improve the overall system performance.

Comparative Study of Indexing Techniques for Data Dissemination in Wireless Environment

In the past few years there has been a rapid growth of mobile devices in the commercial market due to the emphasis of wireless data applications along with various evolving technologies. Data broadcast is the powerful tool for data dissemination such that it allows several users on the same channel by providing high bandwidth utilization consequently reducing the tuning time. This can be achieved if the data is indexed in the database such that the most frequently accessed data is kept together to reduce the search time in the database which in turn reduces the access time and improves the power consumption of the mobile users. This paper provides the analysis of various indexing techniques along with their advantages and disadvantages.

ルールベース型欠陥分類器の判定パラメータ自動調整技術

SUMMARY In automatic defect classification of semiconductor wafers using scanning electron microscope images, we propose a technique of tuning decision parameters for rule-based defect classifiers. The proposed method adopts a coarse-to-fine search for reduction in processing time. However, due to search leakage, there is no guarantee that the same solutions as in a full parameter search can be obtained. In order to prevent leakage in the coarse search theoretically, the proposed method evaluates a candidate parameter set based on the estimated range of classification accuracy attained by not only the candidate but the surrounding solutions eliminated by the coarse search. Experiments on real image data demonstrate the effectiveness of the proposed method. The proposed method can extract the same solutions as the full parameter search within almost the same processing time as the conventional coarse-to-fine search. When the sampling step of the coarse search is three to six, while the tuning time of the conventional coarse-to-fine search is 1 to 21 s, that of the proposed method is 5 to 35 s.

고속 주파수 합성기용 광대역 DDS 모듈

Abstract In this paper, a wideband DDS module covering the frequency range from 0.5 to 1.1 GHz was designed and fabricated. The clock frequency of the DDS was selected 2.4 GHz in order for 600 MHz output bandwidth. Multiple spurious cancelling signals having same amplitude and 180 ° phase difference compared to the spurious were created at the additional path and added to the output signal within DDS for the spurious performance improvement. The fabricated DDS module showed better spurious performance than the commercial DDS one more than 10 dB and frequency tuning time was 340 ns below.Key words: Wideband Synthesizer, DDS, Spurious Cancellation UV WX'Y(Agency for Defense Development) *Z[W\]^W (Department of Radio Science & Engineering, Chungnam National University) Manuscript received August 29, 2014 ; Revised September 29, 2014 ; Accepted November 7, 2014. (ID No. 20140829-065)Corresponding Author: Dong-Chul Park (e-mail: dcpark@cnu.ac.kr) _. 서 론 6(A`Qabc,12de f ]ghiK 1jk <L:l m, nb 6712!Po+BCAp q I4 r0+#$. st! 1jknbuA%(instantaneous bandwidth)o+T $v#12w 12:@AxyMz+nbuA@{ :|f m, Q+T /01234Q} l # I4 :0'J$. ~!PDAC(Digital-to-Analog Converter)QK# €I4 B‚+DDSQK#!P/-ƒ I4Q:„ e , 12†e‡I4o+T#3x/012ˆ‰ ŠJ$

Rapid reversible phototuning of lasing frequency in dye-doped cholesteric liquid crystal.

Lasing in induced cholesteric liquid crystal (CLC) containing highly photosensitive azo chiral dopant (ChD) and pyrromethene laser dye was investigated. Due to the absence of excitation energy transfer from the dye molecules to the molecules of ChD in such a system, a low lasing threshold was achieved. When using violet and green light-emitting diodes, reversible frequency tuning was obtained in the range of about 30 nm with a tuning time 1-2 orders of magnitude smaller than in conventional CLC laser systems based on azo and azoxy photosensitive compounds. Further increase of the intensity of the excitation light by using a green laser pointer allowed us to achieve a record phototuning speed (∼21 nm in 148 ms).