Description:
This paper investigates the effect of different types of nano-grating structures embossed on top of the substrate of solar photovoltaic (PV) cell for high conversion efficiency. The simulation results for light reflection are obtained by using Opti-wave finite difference time-domain (Opti-FDTD) software. These nano-grating structures have different shapes, such as triangular, trapezoidal, pillar and parabolic. These nano-grating profiles work as a multilayer anti-reflective coating for GaAs solar cells and reduce the light reflection from the surface of the panel and increase the light trapping capacity inside the solar cell. These structures allow the gradual change in refractive index and provide a high transmission and less reflection of light that confirms excellent anti-reflective coating and increased light trapping capacity inside the cell substrate. For this simulation, different periodic shaped arrangements were made to obtain the higher conversion efficiency, the factors considered while develop the design are the aspect ratio (AR), thickness of the nano-grating structure and duty cycles. The simulation result shows that the light reflection loss in pillar shaped nano-grating structures having 150 nm of height and a 50% period (i.e., duty cycle) is ~0.5% only, which is the lowest reflection loss obtained, when compared with the triangular and trapezoidal shaped nano-grating structures, it is approximately 38% more efficient in trapping the incident light.
Description:
This research is supported by the School of Engineering and Technology, Melbourne, Victoria; Centre for Intelligent Systems, Brisbane, QLD, Central Queensland University, Australia.
A constraint-based evolutionary learning approach to the expectation maximization for optimal estimation of the hidden Markov model for speech signal modeling
Description:
An L-2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of smoothing of the residuals the algorithm terminates. The rate of convergence of the algorithm is analyzed in case of an unbounded response variable. The method is used to fit a sum of maxima of minima of linear functions to a given data set, and is compared with other nonparametric regression estimates using simulated data.
Description:
An L-2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of smoothing of the residuals the algorithm terminates. The rate of convergence of the algorithm is analyzed in case of an unbounded response variable. The method is used to fit a sum of maxima of minima of linear functions to a given data set, and is compared with other nonparametric regression estimates using simulated data.
Description:
This paper addresses the problem of finding the optimal robot move cycle to minimise the cycle time of two-machine cells. The earlier robot's function was mainly moving parts between machines in a manufacturing process. We lift this assumption on robot tasks and assumed a special robot, namely multi-function, which performs a unique operation in transit. This robot starts performing this operation after unloading a part from input buffer and finishes it before loading the part to the output buffer. The processing mode on robot is “stop resume”. Thus, regardless of the gap interrupts during the operation, the robot continues processing on part when it is reloaded to the robot without any loss in time. The focus of this study is on one-unit cycles since they are very popular in industry. The cycle time of two possible one-unit cycles is obtained, and the optimality condition of them is determined.
Description:
This paper presents a technique that was used in the development of Mechatronics courses in Thailand and Australia. The technique was based on project-based learning and industry engagement. Mechatronics course development was supported by AusAID grant to the Royal Thai Government (RTG) to establish six different mechatronics programs in six Universities. Mechanisms were established to ensure industrypsilas input throughout the stages of mechatronics course development at each University. This paper discusses those mechanisms.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM.
Description:
In a transform domain distributed video coding scheme, the correlation between the current encoding unit, e.g. block and slice, and the corresponding side-information is modeled using a virtual channel. This correlation model is then used for rate allocation, quantization, and Wyner-Ziv coding. Since the encoder can only have an estimate of the correlation instead of the exact knowledge of the side-information, the decoder will fail to recover the quantized transformed coeffi- cients with a nonzero probability. In this paper, we propose to integrate a scheme at the decoder to recover the undecoded coefficients using the spatial smoothness property of individual video frames. Simulation results demonstrated that, at different decoding failure probabilities, a transformed coeffi- cient recovery scheme can significantly improve the quality of videos in terms of both PSNR and SSIM
Description:
Existing spatial shape-error concealment techniques are broadly based upon either parametric curves that exploit geometric information concerning a shapepsilas contour or object shape statistics using a combination of Markov random fields and maximum a posteriori estimation. Both categories are to some extent, able to mask errors caused by information loss, provided the shape is considered independently of the image/video. They palpably however, do not afford the best solution in applications where shape is used as metadata to describe image and video content. This paper presents a novel image-dependent spatial shape-error concealment (ISEC) algorithm that uses both image and shape information by employing the established rubber-band contour detecting function, with the novel enhancement of automatically determining the optimal width of the band to achieve superior error concealment. Experimental results qualitatively and numerically corroborate the enhanced performance of the new ISEC strategy compared with established shape-based concealment techniques.
Description:
This paper describes a new methodology for developing common maintenance strategy based on reliability-centered maintenance (RCM) principles. The development took place in a major industrial establishment in South America. The name of this industrial establishment is suppressed for commercial confidentiality. Performance-based maintenance contract has become a general trend in many industrial organizations. This allows the organizations to focus on their core business while others handle maintenance tasks. This paper demonstrates that the transition of a maintenance contract based on body shop to a one based on performance is feasible. As demonstrated in the paper, this can be accomplished by changing the maintenance strategy from being based on vendor recommendations to a strategy based on RCM principles. The new strategy can computer-simulated to calculate the maintenance indicators. The produced indicators are the basis to change the traditional maintenance contract to a performance based maintenance contract. The experience gained and the method of simulation is explained in this paper. The optimization process and its results are discussed.
Description:
We propose Video Driven Traffic Modelling (VDTM) for accurate simulation of real-world traffic behaviours with detailed information and low-cost model development and maintenance. Computer vision techniques are employed to estimate traffic parameters. These parameters are used to build and update a traffic system model. The model is simulated using the Paramics traffic simulation platform. Based on the simulation techniques, effects of traffic interventions can be evaluated in order to achieve better decision makings for traffic management authorities. In this paper, traffic parameters such as vehicle types, times of starting trips and corresponding origin-destinations are extracted from a video. A road network is manually defined according to the traffic composition in the video, and individual vehicles associated with extracted properties are modelled and simulated within the defined road network using Paramics. VDTM has widespread potential applications in supporting traffic decision-makings. To demonstrate the effectiveness, we apply it in optimizing a traffic signal control system, which adaptively adjusts green times of signals at an intersection to reduce traffic congestion.
Description:
Music emotion annotation is a task of attaching emotional terms to musical works. As volume of online musical contents expands rapidly in recent years, demands for retrieval by emotion are emerging. Currently, literature on music retrieval using emotional terms is rare. Emotion annotated data are scarce in existing music databases because annotation is still a manual task. Automating music emotion annotation is an essential prerequisite to research in music retrieval by emotion, for without which even sophisticated retrieval methods may not be very useful in a data deficient environment. This paper describes a machine learning approach to annotate music using a large number of emotional terms. We also estimate the training data size requirements for a workable annotation system. Our empirical result shows that 1) the task of music emotion annotation could be modelled using machine learning techniques to support a large number of emotional terms, 2) the combination of sampling method and data-driven detection threshold is highly effective in optimizing the use of existing annotated data in training machine learning models, 3) synonymous relationships enhance the annotation performance and 4) the training data size requirement is within reach for a workable annotation system. Essentially, automatic music emotion annotation enables music retrieval by emotion to be performed as a text retrieval task.
Description:
The study is focused on estimating the severity level of the bearing faults which helps in determining the residual life of the equipment and planned maintenance. A novel technique, adaptive severity estimation model (ASEM) is proposed based on adaptive selection of wavelet decomposition nodes and support vector machines. Vibration data from multiple severity levels are used to build the fault estimation model. An adaptive criterion for wavelet decomposition node selection is developed which helps ASEM to achieve robustness in estimating fault severity under varying signal to noise ratio (SNR), a key demand in industrial environment. The simulated data with known severity level is used to parameterize the estimation model. The fault severity estimation performance of ASEM is also validated for the real vibration data and its robustness is gauged under varying SNR conditions.
Description:
WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.
Description:
This paper describes research conducted on the design of a new automatic fish sorting system at Monash University, Australia. A concurrent mechatronics approach was implement to achieve the project's goals. Also, a technique based on digital imaging was developed for the task of identification of fish sizes. The system is being implemented in the local fish export industry, providing a low cost operation with improved productivity and reliability in the fish sorting process. A computer-based vision system is used for analysis of the fish. A new algorithm is developed to directly determine the length of fish with fast on-line real-time processing. The measurement technique is also applicable to various species. The design specifications of the vision system and the camera capture devices are also presented in this paper
Description:
Although transmission policy is crucial in extending lifetime of sensor networks, most existing policies make simplified assumptions which include: i) circular cluster with cluster head (CH) at the center, ii) uniform periodic data generation model and iii) unrestricted transmission range for nodes. But, in practice, these assumptions are too restrictive for real-world deployment of heterogeneous sensor networks where clusters are usually polygonal. Moreover, in multi hop transmission energy consumption by sensors varies greatly with their distance from CH and even among sensors in the critical ring due to non-uniform relay traffic caused by asymmetric polygonal structure of cluster. In this paper, we propose a new transmission policy where sensors transmit at optimally determined hop sizes that varies over lifetime and a distributed hop selection algorithm that regulates each packet's arrival to CH within a given hop limit. Our formulation considers generic polygonal cluster, stochastic data generation model where data generation rate by sensors vary with events and limited transmission range for sensors. Performance analysis shows significant improvement in lifetime and better uniformity in energy usage among sensors in the proposed policy irrespective of cluster size, hop limit and maximum allowable transmission range of nodes
Description:
This paper investigates the scheduling of a rotationally arranged robotic cell with the Multi-Function Robot (MFR). The earlier known robotic study in this area assumed that the robot only moves the part between machines. We lift this assumption on robot tasks and assumed a special class of robots which is also able to perform a special operation in transit. The aim is to find a minimum cycle time for identical part production. Considering additive and constant travel-time, the distance between any two machines is varying or constant based on the robot acceleration/deceleration for incompact and compact cells. The lower bound of the cycle time is deduced to evaluate the optimality of two practical permutations namely uphill and downhill. It also identifies the regions where using a Multi-Function Robotic Cell (MFRC) is more economical than a Single-Function Robotic Cell (SFRC).
Description:
Most existing data models and transmission policies for sensor network assume uniform periodic data generation and unconstrained transmission range for sensor nodes, both assumptions being too restrictive to capture and analyze real- world operation for practical deployment. In this paper, we consider these two practical aspects and present a new transmission policy formulated after (i) stochastic data model where a set of events occur with certain probabilities and rate of data generation by a sensor varies based on sensed event and (ii) limited transmission range of sensors. Assuming co-centric rings around the base station, located at a generic location (internal or external to the network area), ring thickness and hope sizes over lifetime is determined by formatting an optimization problem where nodes in each ring may transmit data at different hop sizes at a given instant and also vary hop sizes over lifetime. Performance analysis shows significant improvement in network lifetime and better uniformity in energy usage distribution in the proposed policy irrespective of network size and maximum allowable transmission range of nodes.
Description:
Abstract—Gene Regulatory Network (GRN) plays an important role in the understanding of complex biological systems. In most cases, high throughput microarray gene expression data is used for finding these regulatory relationships among genes. In this paper, we present a novel approach, based on decoupled SSystem model, for reverse engineering GRNs. In the proposed method, the genetic algorithm used for scoring the networks contains several useful features for accurate network inference, namely a Prediction Initialization (PI) algorithm to initialize the individuals, a Flip Operation (FO) for better mating of values and a restricted execution of Hill Climbing Local Search over few individuals. It also includes a novel refinement technique which utilizes the fit solutions of the genetic algorithm for optimizing sensitivity and specificity of the inferred network. Comparative studies and robustness analysis using standard benchmark data set show the superiority of the proposed method.
Description:
In this paper, we have extended the Rice-Golomb code so that it can operate at fractional precision to efficiently exploit the real-valued predictions. Coding at infinitesimal precision allows the residuals to be modeled with the Lap lace distribution. Unlike the Rice-Golomb code, which maps equally probable opposite-signed residuals to different integers, the proposed coding scheme is symmetric in the sense that, at infinitesimal precision, it assigns code words of equal length to equally probable residual intervals. The symmetry of both the Lap lace distribution and the coding scheme facilitates the analysis of the proposed coding scheme to determine the average code-length and the optimal value of the associated coding parameter.
Description:
This study examines the relationship between positive affectivity, negative affectivity, job satisfaction, and life satisfaction using a sample of 558 urban employees from Dalian. Positive and negative affectivity were measured with Watson's PANAS scale, job satisfaction was measured with Spector's JSS scale, and life satisfaction was measured with the International Wellbeing Group's PWI scale. All the scales are well established multi-item scales that have been validated both in English speaking populations and in China. The statistical analysis found that affectivity is a source of both job satisfaction and life satisfaction. Job satisfaction is positively related to life satisfaction, supporting the spillover theory. Job satisfaction partly mediates the relationship between affectivity and life satisfaction. The practical implications for managers is that because of the dispositional source of job and life satisfaction, managers need to put more emphasis on improving job satisfaction and subjective quality of life by improving the workplace environment.