An approach to optimum combustion control using parallel type and cross-limiting type technique

In the paper, a new approach to the combustion control by introducing the parallel type air-fuel combustion control and cross-limiting type (or lead-lag type) combustion control technique designed in one control system in a boiler is presented. This new approached combustion control system is designed in such way that controlled the air-fuel ratio in furnace in parallel way for small steam demand variation and in cross-limiting way for high steam demand variation. This approach does not require any extra field instrumentation or modified process parameter. All original instruments are used to measure the process parameters for combustion control and final control elements controlled the process. It is shown that air and fuel flow can be successfully optimized for the required steam demand by new designed combustion control. The accurate air-fuel ratio obtained in the proposed technique also minimizes some problems of the conventional system like incomplete combustion during sudden load change, boiler stuck-up problem and bump problem. The complete derivation of the required mathematical models is reported in the paper. Performance of three boilers incorporated with the proposed system is observed for a long period and the performance report is presented. A very efficient performance in three boilers has been observed.     

Multiple-model based predictive control of nonlinear hybrid systems based on global optimization using the Bernstein polynomial approach

In this paper, we propose a Bernstein polynomial based global optimization algorithm for the optimal feedback control of nonlinear hybrid systems using a multiple-model approach. Specifically, we solve at every sampling instant a polynomial mixed-integer nonlinear programming problem arising in the model predictive control strategy. The proposed algorithm uses the Bernstein polynomial form in a branch-and-bound framework, with new ingredients such as branching for integer decision variables and fathoming for each subproblem in the branch-and-bound tree. The performance of the proposed algorithm is tested and compared with existing algorithms on a benchmark three-spherical tank system. The test results show the superior performance of the proposed algorithm.     

Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion

This article uses powerful technique of artificial neural network (ANN) models to simulate and estimate structural response of two-storey shear building by training the model for a particular earthquake. The neural network is first trained for a real earthquake data and the numerically generated responses of different floors of two-storey buildings as the training patterns. Trained ANN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by ANN model are good for practical purposes. It is worth mentioning that although the simulation is done with numerically generated response data for particular earthquake, the idea may also be used for actual experimental (response) data.     

Advances in Social Media Research: Past,Present and Future

Social media comprises communication websites that facilitate relationship forming between users from diverse backgrounds, resulting in a rich social structure. User generated content encourages inquiry and decision-making. Given the relevance of social media to various stakeholders, it has received significant attention from researchers of various fields, including information systems. There exists no comprehensive review that integrates and synthesises the findings of literature on social media. This study discusses the findings of 132 papers (in selected IS journals) on social media and social networking published between 1997 and 2017. Most papers reviewed here examine the behavioural side of social media, investigate the aspect of reviews and recommendations, and study its integration for organizational purposes. Furthermore, many studies have investigated the viability of online communities/social media as a marketing medium, while others have explored various aspects of social media, including the risks associated with its use, the value that it creates, and the negative stigma attached to it within workplaces. The use of social media for information sharing during critical events as well as for seeking and/or rendering help has also been investigated in prior research. Other contexts include political and public administration, and the comparison between traditional and social media. Overall, our study identifies multiple emergent themes in the existing corpus, thereby furthering our understanding of advances in social media research. The integrated view of the extant literature that our study presents can help avoid duplication by future researchers, whilst offering fruitful lines of enquiry to help shape research for this emerging field.     

High Performance Super-twisting Control for State Delay Systems

This paper addresses the design of conventional sliding mode and super-twisting controls for the uncertain system with state delay to achieve the improved transient response. To ensure the stable sliding motion two approaches are proposed. In the first approach, LMI conditions using Lyapunov-Krasovskii functional are derived to guarantee the stability of the sliding motion. In the second approach, we propose the sliding mode control based on a nonlinear switching functional, with which the sliding motion is governed by delay-free dynamics. Effectiveness of the proposed design approaches are shown through numerical simulation of reheat power system.     

Extremes in total ozone content over northern India

Using daily station total ozone data from TOMS on Nimbus‐7 (1979–1993) and Earth Probe (1997–2005) satellites of National Aeronautics and Space Administration (NASA)/Goddard Space Flight Center (GSFC) during the period 1979–2005, the characteristic features of extremes in the total ozone content and the frequency of the low/high ozone days have been carried out over the northern parts of India in the winter season. Stations located in the north of 20° N latitude regions of India have been chosen for the study. To determine whether the day is a low, high or normal ozone day, the statistical percentile thresholds are computed based on the daily data during winter months (January and February). It has been observed that the trends in the frequency of low ozone days are increasing and for the high ozone days are decreasing during 1979–1993. Similarly the trends in the highest total ozone reaching during January and February are decreasing. The recent period (1997–2005) shows opposite trends that are not statistically significant or stable during the period. Even the mean total ozone during January and February for the period 1979–1993 show the decreasing trends. Overall the trends in the total ozone extremes and the frequency of low/high ozone days are found to be decreasing over the northern parts of India during the winter season.     

Fault diagnosis of ball bearings using machine learning methods

Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.     

Efficient staircase scheme with seamless channel transition mechanism

Video applications require large amount of bandwidth and also storage space. The Fast Broadcasting (FB) scheme is one of the simplest schemes to provide video services, but it requires considerably large buffer storage. This scheme assumes constant video popularity and thus has fixed amount of bandwidth requirement. The popularity of videos generally does not remain the same. It varies over a period of time and accordingly the bandwidth allocation should be adjusted. The variability in bandwidth requirement for videos based on popularity is overcome by incorporating seamless channel transition mechanism in a broadcasting scheme. After incorporating seamless channel transition mechanism, the scheme still delivers continuous video data to old and new users without jerks and hiccups. The FB scheme with seamless channel transition mechanism requires less buffer storage than the FB scheme. The staircase scheme also allocates a fixed amount of bandwidth to a video. The FB and staircase schemes divide a video into same number of segments; thus have same user’s waiting time. The basic difference between the FB and staircase schemes is that in the staircase scheme the segments are further divided into subsegments in order to reduce the buffer requirement. To consider variability of video popularity in the staircase scheme, seamless channel transition for staircase (SCTS) scheme has been discussed. The SCTS scheme performs better than the seamless Fast Broadcasting scheme as regard the buffer storage, while maintaining the same user’s waiting time. In this paper, we propose an efficient staircase scheme with seamless channel transition mechanism that performs better than the SCTS scheme. In the proposed scheme, the video (ESS scheme) data is downloaded at slower rate than that in the SCTS scheme without disrupting the user services and requiring less buffer storage.     

Hierarchical Structure of Silk Materials Versus Mechanical Performance and Mesoscopic Engineering Principles

A comprehensive review on the five levels of hierarchical structures of silk materials and the correlation with macroscopic properties/performance of the silk materials, that is, the toughness, strain‐stiffening, etc., is presented. It follows that the crystalline binding force turns out to be very important in the stabilization of silk materials, while the β‐crystallite networks or nanofibrils and the interactions among helical nanofibrils are two of the most essential structural elements, which to a large extent determine the macroscopic performance of various forms of silk materials. In this context, the characteristic structural factors such as the orientation, size, and density of β‐crystallites are very crucial. It is revealed that the formation of these structural elements is mainly controlled by the intermolecular nucleation of β‐crystallites. Consequently, the rational design and reconstruction of silk materials can be implemented by controlling the molecular nucleation via applying sheering force and seeding (i.e., with carbon nanotubes). In general, the knowledge of the correlation between hierarchical structures and performance provides an understanding of the structural reasons behind the fascinating behaviors of silk materials.