IET Science, Measurement & Technology
Volume 11, Issue 2, March 2017
Volumes & issues:
Volume 11, Issue 2
March 2017
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- Author(s): Ki Wook Lee ; Jun Gi Jeong ; Young Joong Yoon ; Jung Ho Kim ; Chanho Kook
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 141 –148
- DOI: 10.1049/iet-smt.2015.0298
- Type: Article
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p.
141
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This study deduces numerical and simulation-aided approaches of quality factor affected by intense beam loading in the input cavity. Intense relativistic electron beam loading based on the model of electron density distribution is applied to analyse the particle-microwave interaction. The expression for gap voltage difference in the input cavity between without beam-loading and with it is derived to find out the beam-loaded quality factor. The simulated data by decaying time method of particle-in-cell code being in a good agreement with that attained by the numerical approach are presented. Those presented approaches will be particularly available for a relativistic klystron amplifier with relatively low quality factor and intense relativistic electron beam in the input cavity.
- Author(s): Madhan Nanchan Suresh ; Govindasamy Ananthi ; Sundarrajan Jayaraman Thiruvengadam ; Varadhan Abhaikumar
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 149 –154
- DOI: 10.1049/iet-smt.2015.0306
- Type: Article
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149
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Orthogonal frequency division multiplexing (OFDM) is a promising technique for wireless system due to its high spectral efficiency and its capability to counteract frequency selective fading channels. However, OFDM system is highly vulnerable to timing offset. This study proposes a novel timing estimation algorithm for downlink OFDM systems. A novel training sequence structure using Chu sequence is proposed. A new property of the inverse discrete Fourier transform of Chu sequence is derived. This property is used to obtain channel impulse response to estimate start of the OFDM symbol. The mean square error and timing detection performance of the proposed algorithm are shown to be better than the existing methods.
- Author(s): Chao Wang and Jungang Miao
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 155 –163
- DOI: 10.1049/iet-smt.2016.0072
- Type: Article
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155
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The design, implementation, measurements, and broadband calibration of a 16-channel vector modulator (VM) module covering 1.5–2.4 GHz band are presented in this study. Since the practical realisation of the VM suffers from the effects of non-ideal characteristics, a broadband calibration technique is adopted which greatly improves the transmission of phase and amplitude accuracy of the VM over a wide frequency range. The measurement results of the calibrated VM are given to show the high calibration accuracy and efficiency of the proposed technique.
- Author(s): Sudeshna Dasgupta ; Smita Sadhu ; Tapan Kumar Ghoshal
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 164 –170
- DOI: 10.1049/iet-smt.2016.0105
- Type: Article
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164
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Disturbance observers (DOBs) are increasingly being suggested as a necessary component in active anti-disturbance control. Although the structure and design pragmatics of DOBs for linear systems are well known, very few publications deal with DOB for non-linear systems. A new method for designing DOBs for non-linear systems has been proposed. Instead of the usual observer gain based approach, the proposed approach employs Lie derivative and Hirschorn inverse. The algorithm for the proposed DOB, its applicability, structure, along with its block diagram have been presented and discussed. Performance of the proposed DOB has been evaluated using highly non-linear chemical processes. Additionally, the performance is compared with that for a well-known DOB to demonstrate the superiority of the proposed DOB.
- Author(s): Kaarthika Ramalingam ; Thiyagarajan Devasena ; Bakthavatchalam Senthil ; Ramakrishnan Kalpana ; Ramasamy Jayavel
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 171 –178
- DOI: 10.1049/iet-smt.2016.0215
- Type: Article
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171
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Melamine (C3H6N6) is a commercially synthesised organic compound which is illegally added to milk during processing to boost the apparent protein content. Melamine cannot be metabolised by the body and forms insoluble crystals which causes tissue damage and renal dysfunction. Hence, there is a need to develop a rapid and reliable method for melamine detection. Attempted here is to develop a simple procedure to detect melamine in milk using silver nanoparticles (AgNPs). Borohydride-reduced AgNPs, citrate-capped AgNPs and polyvinyl pyrrolidone-capped AgNPs are synthesised and characterised for their size, composition, structure etc. and studied for the suitability in this application. The first two NPs are determined to be suitable for this detection and exhibit a colour change from pale yellow to red at different concentration of melamine. To measure the melamine concentration, quantification of colour change and the transmitted light intensity is done using a colour sensor when the mixture is exposed to standard light source. Compared with visual observation in colour change, the transmitted light intensity provides more information to quantify melamine, more specifically citrate-capped AgNPs are found to provide better results than borohydride-reduced AgNPs. The results of this study are much agreeable to that obtained through standard laboratory experiment. Hence, this proposed method would be useful in developing an electronic-based melamine testing instrument that can be used rapidly at home.
- Author(s): D. Kavitha ; T.K. Sindhu ; T.N.P. Nambiar
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 179 –185
- DOI: 10.1049/iet-smt.2016.0226
- Type: Article
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179
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Electrical properties of polymeric insulators can be improved by adding high-permittivity nanofillers. In this work, the aim is to analyse the effect of filler permittivity, its size, shape, concentration and the interparticle distance on the electrical properties of the nanocomposites. Nanocomposites are fabricated with four types of fillers and with various concentrations and the effect of various filler parameters on permittivity, breakdown strength and loss tangent of the nanocomposites are analysed. Simulation of electric field is used to demonstrate the increase in the volume of enhanced electric field region which affects the short-time breakdown strength. The effect of filler parameters on the electrical properties of the composite is demonstrated through experimental and theoretical analysis.
- Author(s): Dangdang Dai ; Xianpei Wang ; Jiachuan Long ; Meng Tian ; Guowei Zhu ; Jieming Zhang
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 186 –193
- DOI: 10.1049/iet-smt.2016.0255
- Type: Article
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186
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Partial discharge (PD) detection and recognition are of great significance to the condition monitoring of gas-insulated switchgear (GIS). In the current work, ultra-high-frequency (UHF) signals induced by PD current pulses are measured and used to represent PD source. For PD classification, feature parameters need to be extracted from UHF signals. Therefore, this study proposes a new feature extraction method that is based on S-transform (ST) and singular value decomposition (SVD). PD UHF signals generated by four kinds of artificial defects are collected and analysed. ST is used to acquire the joint time–frequency information of the PD UHF signal. SVD is used to acquire the time–frequency characteristics of the UHF signal. Based on the distribution difference of time–frequency characteristics of different kinds of PDs, a 24-demensional feature vector is finally extracted. Support vector machine optimised by particle swarm optimisation algorithm is employed as classifier to recognise the four kinds of PDs. Results show that the proposed feature extraction method can effectively identify the designed four kinds of PDs even with few samples and strong background noise.
- Author(s): Shailesh Kumar ; Tarikul Islam ; Kuldeep Kumar Raina
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 194 –203
- DOI: 10.1049/iet-smt.2016.0259
- Type: Article
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A mathematical model for estimating the moisture content in silica-gel inside a breather of a power transformer has been developed. The model equations are derived considering the possible real-time operating conditions of the transformer. These operating conditions are the fast rise or decrease of oil temperature due to a step change in the electrical loads and the cyclic heating and cooling of the oil due to the natural loading of the transformer. An experimental setup has been developed for monitoring the variations in moisture concentration in silica-gel inside an artificially constructed breather. Finally, the validation of the model has been done with the experimentally obtained data and the standard input signals. The model has been analysed using the equilibrium relations of the moisture concentration between the silica-gel and the environment of the breather. The response of the model indicates the dynamic behaviour of a moisture sensor placed in the breather. The model has been used in predicting the moisture level in the silica-gel, the moisture concentration inside the breather and the degradation in the performance of the silica-gel.
- Author(s): Soumya Chatterjee ; Arpan Kumar Pradhan ; Sovan Dalai ; Biswendu Chatterjee ; Sivaji Chakravorti
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 204 –212
- DOI: 10.1049/iet-smt.2016.0264
- Type: Article
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204
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In conventional frequency domain spectroscopy (FDS) measurement, sinusoidal excitation is applied to a composite oil-paper insulation under test, for a minimum of two cycles over a wide range of frequencies typically starting from 1 kHz to 1 mHz or even upto 0.1 mHz. The frequency sweep down to 1 mHz, is inevitably time consuming especially in lower frequency ranges. Considering the aforesaid fact, the main aim of the present study is to reduce overall FDS measurement time. The insulation under test is subjected to some fraction of one full cycle of excitation, and the corresponding dielectric response current is measured for each fraction. Using least square curve-fitting technique, response current over two cycles is predicted from which dielectric dissipation factor (tanδ) is calculated. For this contribution, sinusoidal and non-sinusoidal excitations in form of triangular waveform with five different initial slopes are used as excitation voltage. The technique is experimentally validated on three samples with different preset moisture contents prepared in the laboratory. The dielectric dissipation factor obtained by using the proposed technique is compared with the conventional method using two cycles, which show negligible differences. Most important of all is the overall test time, which can be brought down significantly when compared with conventional FDS measurement.
- Author(s): Saad Mohamed Darwish
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 213 –219
- DOI: 10.1049/iet-smt.2016.0265
- Type: Article
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p.
213
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Evaluation of student performance is one of the essential parts of educational systems that incorporated with uncertainty in human knowledge. Rating and predicting students’ educational achievement using arithmetical and statistical procedures may not explicitly advise the best route to estimate human acquisition of knowledge and experiences. Researchers in this field tended to adopt soft computing to defeat the challenges of analytical techniques in order to resolve this evaluation performance problem. Fuzzy logic is utilised to manage the intrinsic uncertainty associated with teachers’ subjective assessments and permits reproduction of student modelling in the linguistic form – the same form the human teachers do. The obstacle with existing fuzzy rule-based systems is that the size of the rule base (the number of rules) grows exponentially with the expansion of the number of fuzzy sets included in the rules. This exponential increment in the size of the rule base enlarges the search time and the memory space needed. In this study, a fuzzy rule base compaction (densification) using genetic algorithm is suggested for uncertain measurement of student's performance. The proposed approach consists of three stages: knowledge recovery, coding, and compaction or optimisation. The comparative outcomes demonstrated that the recommended evaluator has a magnificent dynamic response than that of the traditional one.
- Author(s): Amandeep Singh and Anand Parey
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 220 –225
- DOI: 10.1049/iet-smt.2016.0291
- Type: Article
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220
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Most research efforts in gearbox fault diagnosis thus far have focused on diagnosing gearbox faults under stationary conditions. Efforts in diagnosing gearbox faults under non-stationary conditions have mostly involved an analysis of gearbox vibration signals under the speed-up or run-down processes. This paper attempts to diagnose faults in a single stage spur gearbox under non stationary conditions arising from fluctuating loads at the output of gearbox. The vibration signal corresponding to each independent revolution is synchronized from the revolution point of view by converting into the angular domain. This is accomplished experimentally by a simple process referred to as the independent angular re-sampling (IAR) technique. The IAR technique is accomplished by employing a multiple pulse tachometer arrangement. Through the IAR process, non-stationary signals in the time domain are converted into quasi-stationary signals in the angular domain. The angular domain signals, each representing one revolution of the gearbox drive shaft, are then decomposed with continuous wavelet transform. Optimal scales are identified based on superior energy-Shannon's entropy ratio of continuous wavelet coefficients (CWCs). The classification accuracy of a multilayer perceptron neural network is compared when CWCs from all scales and when CWCs from the optimal scales are fed to the neural network.
- Author(s): Samik Chakraborty ; Madhuchhanda Mitra ; Saurabh Pal
- Source: IET Science, Measurement & Technology, Volume 11, Issue 2, p. 226 –233
- DOI: 10.1049/iet-smt.2015.0308
- Type: Article
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226
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Multimodal biometric authentication requires fusion of information extracted from different biometric modalities. Face recognition is the most common and versatile biometric parameter used for years. Recently, biosignals such as electrocardiogram (ECG), photoplathysmogram etc. are under study for probable use in authentication work. It is also established that multi-parameter approach in biometric analysis plays a vital role in increasing accuracy and robustness and preventing spoofing in spite of more computational demand. In the present study, information fusion based authentication system is proposed using face and ECG. Instead of conventional face image, a unique frontal face textural signal is proposed. This leads to simpler data processing similar to that of ECG signal. Finally, information from both the signals is fused at mother template generation level. A good accuracy is achieved using mean square deviation method as presented in the result section. A stability study is also made with five volunteers to check the long term variability of the features.
Analysis of the quality factor of input cavity with intense beam loading in a relativistic vacuum tube
Timing estimation algorithm for OFDM-based wireless systems
Implementation and broadband calibration of a multichannel vector modulator module
Designing disturbance observer for non-linear systems – a Hirschorn inverse approach
Silver nanoparticles for melamine detection in milk based on transmitted light intensity
Impact of permittivity and concentration of filler nanoparticles on dielectric properties of polymer nanocomposites
Feature extraction of GIS partial discharge signal based on S-transform and singular value decomposition
Modelling of breather for transformer health assessment
Reducing frequency domain spectroscopy measurement time for condition monitoring of transformer oil-paper insulation using non-sinusoidal excitations
Uncertain measurement for student performance evaluation based on selection of boosted fuzzy rules
Gearbox fault diagnosis under fluctuating load conditions with independent angular re-sampling technique, continuous wavelet transform and multilayer perceptron neural network
Biometric analysis using fused feature set from side face texture and electrocardiogram
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