Classification of Malaysian vowels using formant based features

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software, especially using English as the language of choice. Despite of all these advances, machines cannot match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. In this paper, a new feature based on formant is presented and evaluated on Malaysian spoken vowels. These features were classified and used to identify vowels recorded from 80 Malaysian speakers. A back propagation neural network (BPNN) model was developed to classify the vowels. Six formant features were evaluated, which were the first three formant frequencies and the distances between each of them. Results, showed that overall vowel classification rate of these three formant combinations are comparatively the same but differs in terms of individual vowel classification

A Framework of an Intelligent Maintenance Prognosis Tool

The technology of prognosis has become a significant approach but its implementation in maintenance has a major extension. The ability prognosis in the medical area has been established to estimate the future of human health. However, in maintenance, application of prognosis is not yet seen as a practical use for making better maintenance decision. To date, research in this area has been done in proposing prognosis techniques or model but leaving the implementation of prognosis as their future work. In this paper, an overview of prognosis in maintenance is presented. By using the data-driven approach, a framework for implementing of an intelligent maintenance prognosis tool is introduced. The framework utilizes the existing equipment operating performance data in the industry for prognosis process. Next, the framework combines the ability of prognosis in estimating remaining useful life (RUL) of equipment with the maintenance action knowledge to generate a well-received maintenance plan

N-(2,2-Dimethyl­propano­yl)-N′-(2-meth­oxy­phen­yl)thio­urea

In the title compound, C13H18N2O2S, the carbonyl­thio­urea fragment is nearly planar with an r.m.s. deviation of 0.0096 Å. The dihedral angle between carbonyl­thio­urea group and the benzene ring is 19.16 (16)°. There are two intra­molecular N—H⋯O hydrogen bonds, which lead to two pseudo-six-membered rings. Weak intra­molecular C—H⋯S hydrogen bonding also occurs

Mapping the daily rainfall over an ungauged tropical micro-watershed: a downscaling algorithm using gpm data

In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100-1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds

A training monitoring system for cyclist based on wireless sensor networks

This paper presents a training monitoring system for cyclist that is based on the technology of wireless sensor networks (WSNs). A stable and reliable wireless cyclist monitoring system is vital to establish a smart and efficient sports management program. A training monitoring system has been developed and tested in a real cyclist training environment in a velodrome. The system is designed is such a way that the packet loss rate is minimum. Using TelG mote as the basis, customized sensor nodes that function as a forwarder node and the relay nodes are developed to form the WSN. This WSN is linked to the cloud network on the Internet. The cloud network is then established and end users application for data accessing is designed. Several experiments have been conducted in a real scenario in a velodrome to measure the reliability of the system architecture. It is shown from the experiments that the proposed system is reliable even when the cyclist is moving at a high speed. The packet loss is less than 2% which does not give a huge impact to the data transmission

Database Performance Tuning Methods for Manufacturing Execution System

In manufacturing industry where data are produced and shared every day, data volumes could be large enough for the database performance to become an issue. Manufacturing Execution System (MES) is such a system that cannot tolerate with poor database performance as the system relies heavily on real-time reporting that requires instance query responses. Manufacturing products’ quality and production targets can be affected as the result of delayed queries. Therefore, the need to maintain the acceptable level of database performance in this domain is crucial. One task in maintaining database performance is identification and diagnosis of the root causes that may cause delayed queries. Poor query design has been identified as one major cause of delayed queries that affect real-time reporting. Nevertheless, as various methods available to deal with poor query design, it is important for a database administrator to decide the method or combination of methods that work best. In this paper, we present a case study on the methods used by a real manufacturing industry company called as Silterra and the methods proposed in the literature that deal with poor query design. For each method, we elicit its strength and weaknesses and analyse the practical implementation of it

Detection Of Defects On Weld Bead Through The Wavelet Analysis Of The Acquired Arc Sound Signal

Recently, the development of online quality monitoring system based on the arc sound signal has become one of the main interests due its ability to provide the non-contact measurement. Notwithstanding, numerous unrelated-to-defect sources which influence the sound generation are one of the aspects that increase the difficulties of applying this method to detect the defect during welding process. This work aims to reveal the hidden information that associates with the existence of irregularities and porosity on the weld bead from the acquired arc sound by applying the discrete wavelet transform. To achieve the aim, the arc sound signal was captured during the metal inert gas (MIG) welding process of three API 5L X70 steel specimens. Prior to the signal acquisition process, the frequency range was set from 20 Hz to 10 000 Hz which is in audible range. In the next stage, a discrete wavelet transform was applied to the acquired sound in order to reveal the hidden information associated with the occurrence of discontinuity and porosity. According to the results, it was clear that the acquired arc sound was not giving an obvious indication of the presence of defect as well as its location due to the high noise level. More interesting findings have been obtained when the discrete wavelet transform (DWT) analysis was applied. The analysis results indicate that the level 8 of the approximate and detail wavelet coefficient have given a significant sign associated with the presence of irregularities and porosity respectively. Moreover, despite giving the information on the surfaces pores, the detail wavelet coefficient was found to give a clear indication of the sub-surface porosity formation during welding process. Hence, it could be concluded that the hidden information with respect to the occurrence of discontinuity and porosity on the weld bead could be obtained by applying the discrete wavelet transfor

Role of membrane surface charge and complexation-ultrafiltration for heavy metals removal: a mini review

Membrane technology is a promising water purification unit operation from household use to industrial application owing to its simplicity of operation, efficient recovery and minimum need for chemical and space allocation. Due to that reason, study on the membrane applications have becoming more popular among scientific community nowadays and one of the applications is removal of heavy metal using ultrafiltration (UF). However, a stand-alone UF will be not able to carry out the removal of heavy metals effectively. Certain modification is required in order to enhance its rejection via unique facilitated mechanism. Thus in this review, role of surface charge interaction as well as the method of complexation-ultrafiltration were discussed

A study of frequency and pulses for stepper motor controller system by using programmable logic controller

The stepper motor movement process produced different frequency and pulses. This research explained about the frequency and pulses for the stepper motor movement by using Programmable Logic Controller (PLC) as research method. The study was done to find the suitable frequency and pulses for stepper motor movement by developing a prototype stepper motor controller system. The pulse frequency used did not affected the distance of moving load in the stepper motor operations. The increasing number of pulse frequency only will affect the time taken for the stepper motor to complete its operations. The result showed that number of pulse frequency at high operation was 5000 Hz. Pulse number reacted as a manipulated variable that affected both factor which is time taken of stepper motor operation and the distance of moving load

ARPES Study of the Metal-Insulator Transition in Bismuth Cobaltates

We present an angle-resolved photoemission spectroscopy (ARPES) study of a Mott-Hubbard-type bismuth cobaltate system across a metal-insulator transition. By varying the amount of Pb substitution, and by doping with Sr or Ba cation, a range of insulating to metallic properties is obtained. We observe a systematic change in the spectral weight of the coherent and incoherent parts, accompanied by an energy shift of the incoherent part. The band dispersion also shows the emergence of a weakly dispersing state at the Fermi energy with increasing conductivity. These changes correspond with the changes in the temperature-dependent resistivity behavior. We address the nature of the coherent-incoherent parts in relation to the peak-dip-hump feature seen in cuprates superconductors