658 results
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2. Impact of Sustained Supply Voltage Magnitude on Consumer Appliance Behaviour.
- Author
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Elphick, Sean, Robinson, Duane A., Perera, Sarath, Knott, Jonathan C., David, Jason, and Drury, Gerrard
- Subjects
CONSUMER behavior ,VOLTAGE ,DISTRIBUTED power generation ,HIGH voltages ,ENERGY consumption - Abstract
Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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3. Development of MEMS Sensor for Detection of Creatinine Using MIP Based Approach – A Tutorial Paper.
- Author
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Prabhu, Sumedha Nitin, Gooneratne, Chinthaka Pasan, and Mukhopadhyay, Subhas Chandra
- Abstract
Creatinine is a biochemical waste that is disseminated continually inside human blood. A synthetic polymer is developed in this study using Molecularly Imprinted Polymerization (MIP) technology with the precipitation polymerization method. The MIP polymer is used for finding the levels of creatinine from human serum samples with different creatinine concentrations. The MIP polymer is very selective to the specific adsorption of the molecules of creatinine. The produced MIP polymer is suitable for evaluating creatinine concentrations until 50 parts per million (ppm) that is thrice greater than the standard. The fabrication of chip-sized Micro-Electro-Mechanical-Systems (MEMS)-based interdigital (ID) sensors is described. The operation of the MEMS sensor is verified using the technique of Electrochemical Impedance Spectroscopy (EIS). The presented details of the development of MEMS ID sensor and creatinine specific MIP polymer are for detecting raised biotoxic waste levels, i.e. creatinine management is part of our research. The raised levels need monitoring by frequent pathological visits to those patients who have impaired kidney functioning. The complete system will be helpful for creatinine management at any time from home at a low cost. While early detection of an increase in creatinine and monitoring of kidney health to prevent further health-related complications are the goal of this research, results of up to 50 ppm are accessed. Until 50 ppm of MIP functionality is checked for confirming the MIP polymer adsorption of creatinine and the results are shown, which helps establish the sensing technology even if used for a patient with a high creatinine level. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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4. Peering at the peer review process for conference submissions.
- Author
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Gardner, Anne, Willey, Keith, Jolly, Lesley, and Tibbits, Gregory
- Abstract
For many scholars conference papers are a stepping stone to submitting a journal article. However with increasing time pressures for presentation at conferences, peer review may in practice be the only developmental opportunity from conference attendance. Hence it could be argued that the most important opportunity to acquire the standards and norms of the discipline and develop researchers' judgement is the peer review process — but this depends on the quality of the reviews. In this paper we report the findings of an ongoing study into the peer review process of the Australasian Association for Engineering Education (AAEE) annual conference. We began by examining the effectiveness of reviews of papers submitted to the 2010 conference in helping authors to improve and/or address issues in their research. Authors were also given the chance to rate their reviews and we subsequently analysed both the nature of the reviews and authors' responses. Findings suggest that the opportunity to use the peer review process to induct people into the field and improve research methods and practice was being missed with almost half of the reviews being rated as ‘ineffectual’. Authors at the 2011 AAEE conference confirmed the findings from the 2010 data. The results demonstrate the lack of a shared understanding in our community of what constitutes quality research. In this paper in addition to the results of the above-mentioned studies we report the framework being adopted by the AAEE community to develop criteria to be applied at future conferences and describe the reviewer activity aimed at increasing understanding of standards and developing judgement to improve research quality within our engineering education community. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
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5. A Survey on Deep Learning Techniques for Stereo-Based Depth Estimation.
- Author
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Laga, Hamid, Jospin, Laurent Valentin, Boussaid, Farid, and Bennamoun, Mohammed
- Subjects
DEEP learning ,COMPUTER vision ,MACHINE learning ,AUGMENTED reality ,LEARNING communities ,AUTONOMOUS vehicles - Abstract
Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large uniform regions, and occlusions. Motivated by their growing success in solving various 2D and 3D vision problems, deep learning for stereo-based depth estimation has attracted a growing interest from the community, with more than 150 papers published in this area between 2014 and 2019. This new generation of methods has demonstrated a significant leap in performance, enabling applications such as autonomous driving and augmented reality. In this paper, we provide a comprehensive survey of this new and continuously growing field of research, summarize the most commonly used pipelines, and discuss their benefits and limitations. In retrospect of what has been achieved so far, we also conjecture what the future may hold for deep learning-based stereo for depth estimation research. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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6. A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective.
- Author
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Matcha, Wannisa, Uzir, Noraayu Ahmad, Gasevic, Dragan, and Pardo, Abelardo
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This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics are grounded in the literature on self-regulated learning and how self-regulated learning is supported. To address this limitation, this review analyzed the existing empirical studies on LADs based on the well-known model of self-regulated learning proposed by Winne and Hadwin. The results show that existing LADs are rarely grounded in learning theory, cannot be suggested to support metacognition, do not offer any information about effective learning tactics and strategies, and have significant limitations in how their evaluation is conducted and reported. Based on the findings of the study and through the synthesis of the literature, the paper proposes that future research and development should not make any a priori design decisions about representation of data and analytic results in learning analytics systems such as LADs. To formalize this proposal, the paper defines the model for user-centered learning analytics systems (MULAS). The MULAS consists of the four dimensions that are cyclically and recursively interconnected including: theory, design, feedback, and evaluation. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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7. A Smoothing Method for Ramp Metering.
- Author
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Gu, Chuanye, Wu, Changzhi, Teo, Kok Lay, Wu, Yonghong, and Wang, Song
- Abstract
Ramp metering offers great potential to mitigate traffic congestion and improve freeway management efficiency under traffic congestion conditions. This paper proposes an optimization program for freeway dynamic ramp metering based on Cell Transmission Model (CTM). This problem has been formulated as a discrete time optimal control problem with smooth state equations and constraints to meter traffic inflow from on-ramps. In the proposed model, the ‘min’ operators in the primal CTM are non-differentiable and thus, the corresponding optimal control problem cannot be solved directly using conventional gradient based methods. In this paper, we introduce a smooth approximation to approximate the ‘min’ operators and then a unified computational approach is developed to solve the problem. Theoretical analysis is carried out, showing that the optimal solution obtained from the approximated problem converges to the optimal solution of the primal CTM. Compared to the classical inequality relaxation method, our method can resolve the flow holding-back problem and reduce under fundamental diagram phenomenon. Compared with the Big-M method, our method has better efficiency. To achieve the desired traffic response control in real application, a series of online optimal control problems are solved using Model Predictive Control (MPC). Simulation studies show that our method can significantly improve freeway traffic management efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. Woody vegetation landscape feature generation from multispectral and LiDAR data (A CRCSI 2.07 woody attribution paper).
- Author
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Suarez, Lola, Jones, Simon, Haywood, Andrew, Wilkes, Phillip, Woodgate, William, Soto-Berelov, Mariela, and Mellor, Andrew
- Abstract
There is a need for accurate estimation of Australian woody vegetation parameters. State and Commonwealth land management agencies are mandated to report about forest condition every five years. The CRCSI 2.07 “Australian woody vegetation landscape feature generation from multi-source airborne and space-borne imaging and ranging data” aims at producing ready-to-use methods to report forest condition based on remote sensing data. The first efforts have focus on field data techniques and canopy structure characterization using LiDAR data. Results demonstrate canopy profile can be accurately estimated using Weibull probability density functions at 30×30m pixel size. Moreover different field techniques to measure vegetation fractional cover has been tested and compare finding differences up to 15%. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
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9. Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization.
- Author
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Mao, Tuo, Mihaita, Adriana-Simona, Chen, Fang, and Vu, Hai L.
- Abstract
Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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10. Game-Theoretic Electric Vehicle Charging Management Resilient to Non-Ideal User Behavior.
- Author
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Mediwaththe, Chathurika P. and Smith, David B.
- Abstract
In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging start time and charging energy profiles to minimize overall EV charging energy cost by including consideration of the actions of the neighboring aggregators. The competitive interactions of the aggregators are modeled by developing a two-stage non-cooperative game among the aggregators. The game is then studied under prospect theory to examine the impacts of non-ideal actions of the aggregators in selecting EV charging start times according to subjectively evaluating their opponents’ actions. It is shown that the non-cooperative interactions among the aggregators lead to a subgame perfect $\epsilon $ -Nash equilibrium when the game is played with either ideal, or non-ideal, actions of the aggregators. A case study presented demonstrates that the benefits of the coordinated EV charging strategy, in terms of energy cost savings and peak-to-average ratio reductions, are significantly resilient to non-ideal actions of the aggregators. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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11. Network-Aware Coordination of Residential Distributed Energy Resources.
- Author
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Scott, Paul, Gordon, Dan, Franklin, Evan, Jones, Laura, and Thiebaux, Sylvie
- Abstract
Rooftop solar and batteries, along with other distributed energy resources (DERs), add a new demand-side flexibility, which, when harnessed, will enable distribution operators to more efficiently manage their constrained networks. This paper presents network-aware coordination (NAC), an approach for coordinating DER within unbalanced distribution network constraints, which utilizes the alternating direction method of multipliers (ADMMs) to solve a distributed receding-horizon OPF. As far as we are aware, this paper is the first to report on the practical implementation and performance of an ADMM-based technique solving a significant network problem in live operations. We present real-world trial results of NAC coordinating 31 residential batteries on a constrained feeder within Tasmania’s 11-kV distribution network. The batteries are coordinated to manage the network’s constraints during periods of high feeder demand, decreasing the need for expensive conventional network management, in this case a diesel generator. We achieve a 34% reduction in diesel over seven peaks with 31 batteries capable of meeting 10% of peak demand. Supplementary simulations indicate the potential for a 74% diesel reduction if battery numbers were increased to 100. We find that compared to uncoordinated battery response, the NAC achieves 13% lower total costs over the trial period. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
12. Various Interactive and Self-Learning Focused Tutorial Activities in the Power Electronic Course.
- Author
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Shahnia, Farhad and Yengejeh, Hadi Hosseinian
- Subjects
ELECTRICAL engineering ,POWER electronics ,COMPUTER assisted instruction ,ENGINEERING education ,SCHOOL year ,STUDENT projects ,PROBLEM-based learning - Abstract
Contribution: This paper introduces the real-world limitations and non-technical aspects of power electronics (PEs) projects to students through innovative tutorial activities. Background: Many electrical engineering curricula offer a PE courses (PECs) for third- or fourth-year undergraduate students. Prior research on PEs education mainly focused on improving students’ experimental skills through developing practical experiments, laboratory activities, and problem/project-based learning. An instructional approach that instead employs real-world knowledge and skills is worth evaluating. Intended Outcomes: Students should be able to consider real-world technical and non-technical limitations when applying theory to design PE circuits and converters, and be able to select and carry out appropriate tests to troubleshoot circuits. Application Design: Prior research on engineering education emphasized the importance of introducing real-world limitations to the students as part of their curriculum. This paper suggests that the tutorial activities presented in a PEC can help students acquire skills in designing and troubleshooting a circuit or system according to desired technical aspects, real-world limitations, and available data. Findings: Evidence of the validity of this approach in a PEC at two Australian universities, over four academic years, is provided. Students receiving the new tutorial activities had percentage scores some 10–15 points higher than those who had traditional tutorials. Another evaluation reveals the students’ vibrant participation in the activities during the new tutorial sessions. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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13. Low-Variance Memristor-Based Multi-Level Ternary Combinational Logic.
- Author
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Wang, Xiao-Yuan, Dong, Chuan-Tao, Zhou, Peng-Fei, Nandi, Sanjoy Kumar, Nath, Shimul Kanti, Elliman, Robert G., Iu, Herbert Ho-Ching, Kang, Sung-Mo, and Eshraghian, Jason K.
- Subjects
LOGIC circuits ,LOGIC ,DATA transmission systems ,MANY-valued logic - Abstract
This paper presents a series of multi-stage hybrid memristor-CMOS ternary combinational logic stages that are optimized for reducing silicon area occupation. Prior demonstrations of memristive logic are typically constrained to single-stage logic due to the variety of challenges that affect device performance. Noise accumulation across subsequent stages can be amortized by integrating ternary logic gates, thus enabling higher density data transmission, where more complex computation can take place within a smaller number of stages when compared to single-bit computation. We present the design of a ternary half adder, a ternary full adder, a ternary multiplier, and a ternary magnitude comparator. These designs are simulated in SPICE using the broadly accessible Knowm memristor model, and we perform experimental validation of individual stages using an in-house fabricated Si-doped HfOx memristor which exhibits low cycle-to-cycle variation, and thus contributes to robust long-term performance. We ultimately show an improvement in data density in each logic block of between $5.2\times - 17.3\times $ , which also accounts for intermediate voltage buffering to alleviate the memristive loading problem. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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14. Undergraduate Students’ Engagement With Systems Thinking: Results of a Survey Study.
- Author
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Camelia, Fanny and Ferris, Timothy L. J.
- Subjects
EDUCATION ,ENGINEERING students ,SYSTEMS theory education - Abstract
This paper describes the results obtained for the affective engagement of students with systems thinking (ST). In prior work, the authors have developed and validated a questionnaire instrument for measuring affective engagement of undergraduate engineering students with ST. This paper presents results obtained when the questionnaire was used with undergraduate students. Two surveys with different versions of the questionnaire, one using positive grammar questions only and the other using a mix of positive and negative constructs, were used to measure the students’ engagement with ST and its relationship with gender, age, and work experience. Each questionnaire version was applied to a different sample, the first, 186 participants, completed the positive grammar version, and, the second group of 163 completed the mixed version. The results show that participants in both studies valued ST in each of the three dimensions of the ST construct. Statistical tests confirmed no significant gender differences in either study. Student engagement with the practical dimension of ST was shown to vary, with statistical significance, with groups of age, years of work experience, and country of the university. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
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15. Optimization of Distributions Differences for Classification.
- Author
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Bonyadi, Mohammad Reza, Tieng, Quang M., and Reutens, David C.
- Subjects
MULTIDISCIPLINARY design optimization ,DISTRIBUTION (Probability theory) ,QUASI-Newton methods - Abstract
In this paper, we introduce a new classification algorithm called the optimization of distribution differences (ODD). The algorithm aims to find a transformation from the feature space to a new space where the instances in the same class are as close as possible to one another, whereas the gravity centers of these classes are as far as possible from one another. This aim is formulated as a multiobjective optimization problem that is solved by a hybrid of an evolutionary strategy and the quasi-Newton method. The choice of the transformation function is flexible and could be any continuous space function. We experiment with a linear and a nonlinear transformation in this paper. We show that the algorithm can outperform eight other classification methods, namely naive Bayes, support vector machines, linear discriminant analysis, multilayer perceptrons, decision trees, and $k$ -nearest neighbors, and two recently proposed classification methods, in 12 standard classification data sets. Our results show that the method is less sensitive to the imbalanced number of instances compared with these methods. We also show that ODD maintains its performance better than other classification methods in these data sets and hence offers a better generalization ability. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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16. Finite-Time Bipartite Tracking Control for Double-Integrator Networked Systems With Cooperative and Antagonistic Interactions.
- Author
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Ning, Boda, Yu, Xinghuo, Wen, Guanghui, and Cao, Zhenwei
- Subjects
SYSTEMS integrators ,FINITE, The ,TELECOMMUNICATION systems ,TIME perspective ,ARTIFICIAL satellite tracking - Abstract
This paper is concerned with bipartite tracking for double-integrator networked systems with signed communication graphs, where both cooperative and antagonistic interactions coexist. A finite-time bipartite tracking framework is established, where followers track either the state or the opposite state of a leader. Different from some conventional results with convergence over an infinite time horizon, the finite-time convergence in this paper is achieved in an accurate manner. Under structurally balanced signed graphs, an integral sliding mode based finite-time bipartite tracking controller is proposed. The construction of an integral sliding mode variable is to ensure that the system dynamics is driven onto a sliding surface in finite-time. On the sliding surface, neighbouring states are used together with the homogeneous technique to guarantee that bipartite tracking is achieved in finite-time. To further realize fixed-time bipartite tracking, a controller is designed by using the integral sliding mode and the bi-limit homogeneous concept. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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17. MAUP and LiDAR derived canopy structure (A CRCSI 2.07 woody attribution paper).
- Author
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Wilkes, Phillip, Jones, Simon, Suarez, Lola, Haywood, Andrew, Mellora, Andrew, Soto-Berelov, Mariela, and Woodgate, William
- Abstract
MAUP theory is applied to a LiDAR dataset acquired over a forested scene. The Weibull Probability Density Function (PDF) has been fit to LiDAR derived canopy height profiles for plots covering the complete 1 × 1 km scene. Ten plot sizes are tested from 10 – 300 m. Parameters describing the location and scale of the PDF are used as analogous of canopy height and canopy length respectively. Results suggest that, for a structurally homogenous forested scene, localised variance decreases for canopy height with increasing plot dimensions. The opposite is apparent for canopy length, it is suggested this is a result of a spatially heterogeneous understorey layer negatively skewing the distribution. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
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18. Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework.
- Author
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Kong, Xiangjie, Wang, Kailai, Hou, Mingliang, Xia, Feng, Karmakar, Gour, and Li, Jianxin
- Abstract
Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To address this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multi-pattern approach to predict the bus passenger flow by taking advantage of graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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19. Improving Voltage Regulation and Unbalance in Distribution Networks Using Peer-to-Peer Data Sharing Between Single-Phase PV Inverters.
- Author
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Gerdroodbari, Yasin Zabihinia, Razzaghi, Reza, and Shahnia, Farhad
- Subjects
INFORMATION sharing ,VOLTAGE ,PEER-to-peer architecture (Computer networks) ,REACTIVE power - Abstract
This paper proposes a novel reactive power-based control strategy for single-phase PV inverters (PVIs) to simultaneously improve voltage unbalance (VU) and voltage regulation (VR) in low-voltage distribution networks. The proposed strategy relies on communication links between neighboring PVIs to exchange limited data. In this strategy, each PVI finds communication paths between itself and the closest neighboring ones connected to other phases. Then, using the obtained paths and the maximum and the minimum voltage magnitude of the grid, PVIs improve both VU and VR at the same time. The performance of the proposed control strategy is evaluated by various simulation studies using the IEEE European low-voltage test feeder and considering different operational conditions. In addition, the impacts of moving clouds and a failure in the communication links have been assessed. The simulation results exhibit that using the proposed control strategy, the voltage magnitude of all the nodes will remain within the allowed limits and at the same time, the phase voltage unbalance factor will be also significantly improved. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
20. Scheduling and Power Control for Connectivity Enhancement in Multi-Hop I2V/V2V Networks.
- Author
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Nguyen, Bach Long, Ngo, Duy Trong, Dao, Minh N., Bao, Vo Nguyen Quoc, and Vu, Hai L.
- Abstract
Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs’ coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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21. Circular Formation Control of Multiple Unicycle-Type Agents With Nonidentical Constant Speeds.
- Author
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Sun, Zhiyong, de Marina, Hector Garcia, Seyboth, Georg S., Anderson, Brian D. O., and Yu, Changbin
- Subjects
UNICYCLES ,DRONE aircraft control systems ,CIRCULAR motion - Abstract
This paper discusses the problem of controlling formation shapes for a group of nonholonomic unicycle-type agents with constant speeds. The control input is designed to steer their orientations and the aim is to achieve a desired formation configuration for all the agents subject to constant-speed constraints. The circular motion center is adopted as a virtual position for each agent to define the desired formation shape. We consider several different formation design approaches based on different formation specifications under different interaction graphs. In particular, two different formation design approaches, namely, a displacement-based approach and a distance-based approach, are discussed in detail to coordinate constant-speed agents in achieving a desired formation shape with stable circular motions via limited interactions. The communication and measurement requirements for each approach are also discussed. Furthermore, we propose a combined controller to stabilize a formation shape and synchronize the heading of each agent simultaneously. The effectiveness of the proposed formation control schemes is validated by both numerical simulations and real experiments with actual unmanned fixed-wing aircraft. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
22. Characterizing Voltage Sags and Swells Using Three-Phase Voltage Ellipse Parameters.
- Author
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Alam, Mollah Rezaul, Muttaqi, K. M., and Bouzerdoum, Abdesselam
- Subjects
ELECTRIC potential ,VOLTAGE control ,WAVE amplification ,ELECTRIC networks - Abstract
This paper presents an algorithm for detection, classification, and characterization of voltage sags and swells in electricity networks using three-phase voltage ellipse parameters. The proposed method employs the instantaneous magnitude of three-phase voltage signals in three axes, which are separated from each other by 120 ^\circ . Thus, the resultant rotating vector, namely, the three-phase voltage vector, traces an ellipse. Then, the parameters of the ellipse, which include minor axis, major axis, and inclination angle, are used to develop the proposed algorithm for classification and characterization of voltage sags and swells. The proposed method is validated using real data of the IEEE working group and some measured real data of the Belgian transmission grid. The method is further tested for the sags and swells generated due to balanced and unbalanced faults at different buses, in test distribution networks (radial and mesh type) embedded with distributed generation and in a practical distribution network of Australia. This paper also demonstrates the proposed algorithm as a real-time sag/swell monitoring tool. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
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23. Uplink Performance Analysis of Dense Cellular Networks With LoS and NLoS Transmissions.
- Author
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Ding, Tian, Ding, Ming, Mao, Guoqiang, Lin, Zihuai, Lopez-Perez, David, and Zomaya, Albert Y.
- Abstract
In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs’ and UEs’ positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 10^1\sim 10^3\,\textrm BSs/km^2 . However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 10^2\sim 10^3\,\textrm BSs/km^2 , while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 10^1\sim 10^2\,\textrm BSs/km^2 . [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
24. Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition.
- Author
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Latif, Siddique, Rana, Rajib, Khalifa, Sara, Jurdak, Raja, and Schuller, Bjorn W.
- Abstract
Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generalisation is the scarcity of emotion datasets, which is a significant roadblock to designing robust machine learning (ML) models. Recent works in SER focus on utilising multitask learning (MTL) methods to improve generalisation by learning shared representations. However, most of these studies propose MTL solutions with the requirement of meta labels for auxiliary tasks, which limits the training of SER systems. This paper proposes an MTL framework (MTL-AUG) that learns generalised representations from augmented data. We utilise augmentation-type classification and unsupervised reconstruction as auxiliary tasks, which allow training SER systems on augmented data without requiring any meta labels for auxiliary tasks. The semi-supervised nature of MTL-AUG allows for the exploitation of the abundant unlabelled data to further boost the performance of SER. We comprehensively evaluate the proposed framework in the following settings: (1) within corpus, (2) cross-corpus and cross-language, (3) noisy speech, (4) and adversarial attacks. Our evaluations using the widely used IEMOCAP, MSP-IMPROV, and EMODB datasets show improved results compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
25. A Comparative Review of Recent Kinect-Based Action Recognition Algorithms.
- Author
-
Wang, Lei, Huynh, Du Q., and Koniusz, Piotr
- Subjects
HUMAN activity recognition ,HUMAN behavior ,COMPUTER vision ,DEEP learning ,ALGORITHMS - Abstract
Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare 10 recent Kinect-based algorithms for both cross-subject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that the skeleton-based features are more robust for cross-view recognition than the depth-based features, and that the deep learning features are suitable for large datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
26. A Recursive Decomposition Method for Large Scale Continuous Optimization.
- Author
-
Sun, Yuan, Kirley, Michael, and Halgamuge, Saman K.
- Subjects
COEVOLUTION ,RECURSIVE functions ,DECOMPOSITION method ,MATHEMATICAL optimization ,RESOURCE allocation - Abstract
Cooperative co-evolution (CC) is an evolutionary computation framework that can be used to solve high-dimensional optimization problems via a “divide-and-conquer” mechanism. However, the main challenge when using this framework lies in problem decomposition. That is, deciding how to allocate decision variables to a particular subproblem, especially interacting decision variables. Existing decomposition methods are typically computationally expensive. In this paper, we propose a new decomposition method, which we call recursive differential grouping (RDG), by considering the interaction between decision variables based on nonlinearity detection. RDG recursively examines the interaction between a selected decision variable and the remaining variables, placing all interacting decision variables into the same subproblem. We use analytical methods to show that RDG can be used to efficiently decompose a problem, without explicitly examining all pairwise variable interactions. We evaluated the efficacy of the RDG method using large scale benchmark optimization problems. Numerical simulation experiments showed that RDG greatly improved the efficiency of problem decomposition in terms of time complexity. Significantly, when RDG was embedded in a CC framework, the optimization results were better than results from seven other decomposition methods. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
27. A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning.
- Author
-
Keerthisinghe, Chanaka, Verbic, Gregor, and Chapman, Archie C.
- Abstract
This paper presents a computationally efficient smart home energy management system (SHEMS) using an approximate dynamic programming (ADP) approach with temporal difference learning for scheduling distributed energy resources. This approach improves the performance of an SHEMS by incorporating stochastic energy consumption and PV generation models over a horizon of several days, using only the computational power of existing smart meters. In this paper, we consider a PV-storage (thermal and battery) system, however, our method can extend to multiple controllable devices without the exponential growth in computation that other methods such as dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) suffer from. Specifically, probability distributions associated with the PV output and demand are kernel estimated from empirical data collected during the Smart Grid Smart City project in NSW, Australia. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 26.3% without a noticeable increase in the computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
28. Pushing the Limits of Deep CNNs for Pedestrian Detection.
- Author
-
Hu, Qichang, Wang, Peng, Shen, Chunhua, van den Hengel, Anton, and Porikli, Fatih
- Subjects
ARTIFICIAL neural networks ,ARTIFICIAL intelligence ,ALGORITHMS ,IMAGE processing ,BIG data - Abstract
Compared with other applications in computer vision, convolutional neural networks (CNNs) have underperformed on pedestrian detection. A breakthrough was made very recently using sophisticated deep CNN (DCNN) models, with a number of handcrafted features or explicit occlusion handling mechanism. In this paper, we show that by reusing the convolutional feature maps of a DCNN model as image features to train an ensemble of boosted decision models, we are able to achieve the best reported accuracy without using specially designed learning algorithms. We empirically identify and disclose important implementation details. We also show that pixel labeling may be simply combined with a detector to boost the detection performance. By adding complementary handcrafted features such as optical flow, the DCNN-based detector can be further improved. We advance the state-of-the-art results by lowering the log-average miss rate from 11.7% to 8.9% on the Caltech data set and from 11.2% to 8.6% on the Inria data set. We also achieve a comparable result to state-of-the-art approaches on the KITTI data set. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
29. Mining Markov Blankets Without Causal Sufficiency.
- Author
-
Yu, Kui, Liu, Lin, Li, Jiuyong, and Chen, Huanhuan
- Subjects
ARTIFICIAL neural networks ,MARKOV processes ,MACHINE learning - Abstract
Markov blankets (MBs) in Bayesian networks (BNs) play an important role in both local causal discovery and large-scale BN structure learning. Almost all existing MB discovery algorithms are designed under the assumption of causal sufficiency, which states that there are no latent common causes for two or more of the observed variables in data. However, latent common causes are ubiquitous in many applications, and hence, this assumption is often violated in practice. Thus, developing algorithms for discovering MBs without assuming causal sufficiency is of practical significance, and it is crucial for causal structure learning in real-world data. In this paper, we focus on addressing this problem. Specifically, we adopt a maximal ancestral graph (MAG) model to represent latent common causes and the concept of MBs without assuming causal sufficiency. Then, we propose an effective and efficient algorithm to discover the MB of a target variable in an MAG. Using benchmark and real-world data sets, the experiments validate the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
30. A Vision-Based Pipeline for Vehicle Counting, Speed Estimation, and Classification.
- Author
-
Liu, Chenghuan, Huynh, Du Q., Sun, Yuchao, Reynolds, Mark, and Atkinson, Steve
- Abstract
Cameras have been widely used in traffic operations. While many technologically smart camera solutions in the market can be integrated into Intelligent Transport Systems (ITS) for automated detection, monitoring and data generation, many Network Operations (a.k.a Traffic Control) Centres still use legacy camera systems as manual surveillance devices. In this paper, we demonstrate effective use of these older assets by applying computer vision techniques to extract traffic data from videos captured by legacy cameras. In our proposed vision-based pipeline, we adopt recent state-of-the-art object detectors and transfer-learning to detect vehicles, pedestrians, and cyclists from monocular videos. By weakly calibrating the camera, we demonstrate a novel application of the image-to-world homography which gives our monocular vision system the efficacy of counting vehicles by lane and estimating vehicle length and speed in real-world units. Our pipeline also includes a module which combines a convolutional neural network (CNN) classifier with projective geometry information to classify vehicles. We have tested it on videos captured at several sites with different traffic flow conditions and compared the results with the data collected by piezoelectric sensors. Our experimental results show that the proposed pipeline can process 60 frames per second for pre-recorded videos and yield high-quality metadata for further traffic analysis. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
31. A Novel Methodology to Assimilate Sub-Path Flows in Bi-Level OD Matrix Estimation Process.
- Author
-
Behara, Krishna N. S., Bhaskar, Ashish, and Chung, Edward
- Abstract
Traditional bi-level origin-destination (OD) matrix estimation process adjusts the matrix (at the upper level) based on the deviation between the observed and simulated traffic counts. The problem is mathematically under-determined, and the quality of the solution can be enhanced by restricting the upper level search space with information from other sources. This paper presents a methodology that assimilates sub-path flows in the upper level objective function. The contributions of the study are two-fold: first, it proposes the idea of “structural comparison of sub-path flows” to relax the requirement of “known” penetration rate of vehicles’ trajectories; second, it proposes an innovative upper level formulation where the structural difference between the observed and assigned sub-path flows is integrated with the traditional deviations between the observed and assigned link flows. The sub-path flows can be estimated from advanced data sources such as Bluetooth MAC scanner. The proposed methodology is tested using simulation on a realistic network from Brisbane, Australia and results indicate its practical relevance for situations when the penetration rate of Bluetooth trajectories is generally unknown. The proposed method has a better ability to maintain structural consistency and showed considerable improvements in the quality of OD estimates as compared to the traditional traffic counts-based approach. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
32. Passenger Segmentation Using Smart Card Data.
- Author
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Kieu, Le Minh, Bhaskar, Ashish, and Chung, Edward
- Abstract
Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An
a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services. [ABSTRACT FROM PUBLISHER]- Published
- 2015
- Full Text
- View/download PDF
33. Impacts of Coefficients on Movement Patterns in the Particle Swarm Optimization Algorithm.
- Author
-
Bonyadi, Mohammad Reza and Michalewicz, Zbigniew
- Subjects
COEFFICIENTS (Statistics) ,PARTICLE swarm optimization ,STATISTICAL correlation ,STOCHASTIC convergence ,VARIANCES - Abstract
In this paper, we investigate movement patterns of a particle in the particle swarm optimization (PSO) algorithm. We characterize movement patterns of the particle by two factors: 1) the correlation between its consecutive positions and 2) its range of movement. We introduce the base frequency of movement as a measure for the correlation between positions and the variance of movement as a measure for the range of movement. We determine the base frequency and the variance of movement theoretically and we show how they change with the values of coefficients. We extract a system of equations that enables practitioners to find coefficients’ values to guarantee achieving a given base frequency and variance of movement, i.e., control the movement pattern of particles. We also show that if the base frequency of movement for a particle is small, mid range, or large then the particle’s position at each iteration is positively correlated (smooth movement), uncorrelated (chaotic movement), or negatively correlated (jumping at each iteration) with its previous positions, respectively. We test the effects of the base frequency and variance of movement on the search ability of particles and we show that small base frequencies (i.e., smooth movement) are more effective when the maximum number of function evaluations is large. We found that the most frequently-used coefficient values in PSO literature impose mid-range base frequencies that correspond with a chaotic movement. We also provide new sets of coefficients that outperform existing ones on a set of benchmark functions. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
34. Load Balancing in Low-Voltage Distribution Network via Phase Reconfiguration: An Efficient Sensitivity-Based Approach.
- Author
-
Liu, Bin, Meng, Ke, Dong, Zhao Yang, Wong, Peter K. C., and Li, Xuejun
- Subjects
NONCONVEX programming ,SMART meters ,SMART power grids ,LOAD balancing (Computer networks) ,SENSITIVITY analysis ,VOLTAGE control ,VEHICLE routing problem - Abstract
Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. An Adaptive SOSM Controller Design by Using a Sliding-Mode-Based Filter and its Application to Buck Converter.
- Author
-
Liu, Lu, Zheng, Wei Xing, and Ding, Shihong
- Subjects
FILTERS & filtration ,SLIDING mode control - Abstract
In this paper, a novel adaptive second-order sliding mode (SOSM) control method is proposed by combining a new adaptive strategy with the backstepping-like technique. The new adaptive strategy is first constructed by means of the equivalent control for which a sliding-mode-based filter is employed rather than the widely-used low-pass filter such that the parameter restriction under the usage of low-pass filter can be relaxed. Then, by applying the proposed adaptive strategy and the idea of adding a power integrator, an adaptive SOSM method is established to finite-time stabilize the sliding variables. The feature of the proposed SOSM method lies in that the gain will vary with the size of the lumped uncertainty so as to avoid the overestimation of the gain. The stability analysis is given based on the finite-time Lyapunov theory. The theoretical results are finally applied to the voltage regulation problem of a Buck converter. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
36. GIS-Based Probabilistic Modeling of BEV Charging Load for Australia.
- Author
-
Li, Mengyu, Lenzen, Manfred, Keck, Felix, McBain, Bonnie, Rey-Lescure, Olivier, Li, Bing, and Jiang, Chaoyang
- Abstract
Due to the unknown spatio-temporal distribution of a battery electric vehicle’s (BEVs) charging load, introducing large quantities of BEVs into the transportation sector has drawn growing concerns about the negative impacts on the power grid system. Based on real-world vehicle driving survey data, this paper presents a deterministic and a probabilistic model to quantitatively investigate the spatio-temporal distribution of BEV charging load for Australia. Whilst the trip-chain-related travel parameters for the deterministic model are directly taken from travel survey data, those for the probabilistic model are generated by the ${k}$ -nearest-neighbor algorithm. The probabilistic model is validated and applied to simulate the spatio-temporal distribution of BEV load based on GIS-gridded data for Australia. We are able to distinguish different temporal BEV charging load distributions for weekdays vs. weekends, and with heavy spatial concentration in capital cities. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
37. A Policy-Based Security Architecture for Software-Defined Networks.
- Author
-
Varadharajan, Vijay, Karmakar, Kallol, Tupakula, Uday, and Hitchens, Michael
- Abstract
As networks expand in size and complexity, they pose greater administrative and management challenges. Software-defined networks (SDNs) offer a promising approach to meeting some of these challenges. In this paper, we propose a policy-driven security architecture for securing end-to-end services across multiple SDN domains. We develop a language-based approach to design security policies that are relevant for securing SDN services and communications. We describe the policy language and its use in specifying security policies to control the flow of information in a multi-domain SDN. We demonstrate the specification of fine-grained security policies based on a variety of attributes, such as parameters associated with users and devices/switches, context information, such as location and routing information, and services accessed in SDN as well as security attributes associated with the switches and controllers in different domains. An important feature of our architecture is its ability to specify path- and flow-based security policies that are significant for securing end-to-end services in SDNs. We describe the design and the implementation of our proposed policy-based security architecture and demonstrate its use in scenarios involving both intra- and inter-domain communications with multiple SDN controllers. We analyze the performance characteristics of our architecture as well as discuss how our architecture is able to counteract various security attacks. The dynamic security policy-based approach and the distribution of corresponding security capabilities intelligently as a service layer that enables flow-based security enforcement and protection of multitude of network devices against attacks are important contributions of this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
38. Spatial Optimization for the Planning of Sparse Power Distribution Networks.
- Author
-
Fletcher, James R. E., Fernando, Tyrone, Iu, Herbert Ho-Ching, Reynolds, Mark, and Fani, Shervin
- Subjects
GENETIC algorithms ,GEOGRAPHIC information systems ,ELECTRIC power distribution grids ,STEINER systems ,POWER distribution networks - Abstract
This paper presents a novel method to determine the optimal routing of medium voltage distribution networks in sparse rural areas. The objective is evaluated through minimizing the net present cost of the network over a selected time period. A problem specific genetic algorithm is proposed to address the optimal network routing problem and compute the optimized topology throughout a geographically constrained region. The proposed method simultaneously considers nonfixed candidate lines to overcome search space restrictions through a variable length encoding structure and the use of Steiner points, and a shortest path algorithm to traverse between point-to-point connections in the constrained region. Geographical restrictions on network routing are considered through the formation of a rasterized map. The network is modeled at the branch level and considers both greenfield and expansion planning to highlight the effects of accessibility restrictions. The optimization model is applied to a real rural distribution network in the South–West of Western Australia. Alternative network topologies are found to provide significant improvements over the existing network and traditional reconfiguration based methods for evaluating the minimum cost sparse rural distribution network. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
39. Distributed Consensus-Based Economic Dispatch in Power Grids Using the Paillier Cryptosystem.
- Author
-
Yan, Yamin, Chen, Zhiyong, Varadharajan, Vijay, Hossain, M. J., and Town, Graham E.
- Abstract
Economic dispatch is a critical problem in operation of power grids. A consensus-based algorithm was recently proposed to solve the economic dispatch problem in a distributed manner. In this paper, we propose a novel secure scheme for the consensus-based economic dispatch algorithm using the Paillier cryptosystem. This secure scheme ensures that not only the network transmitted information is protected from external malicious party but also the privacy information of each node remains intact. The proposed secure scheme has two features. First, it relies on the solution to the so-called structural consensus problem with time-varying network weights. Second, it contains a strategy for transmitting encrypted information and generating network weights with randomness, as well as treatment of the practical issues like quantization error and computation overflow/underflow. The performance in terms of cost optimization and privacy-preserving is verified by rigorous theoretical analysis and numerical simulation. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
40. Performance Analysis of Raptor Codes Under Maximum Likelihood Decoding.
- Author
-
Wang, Peng, Mao, Guoqiang, Lin, Zihuai, Ding, Ming, Liang, Weifa, Ge, Xiaohu, and Lin, Zhiyun
- Subjects
MAXIMUM likelihood decoding ,LINEAR codes ,MATRICES (Mathematics) ,MONTE Carlo method ,FORWARD error correction ,PARITY-check matrix - Abstract
In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
41. Performance Impact of LoS and NLoS Transmissions in Dense Cellular Networks.
- Author
-
Ding, Ming, Wang, Peng, Lopez-Perez, David, Mao, Guoqiang, and Lin, Zihuai
- Abstract
In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
42. Linear Combining of Nonlinear Preprocessors for OFDM-Based Power-Line Communications.
- Author
-
Juwono, Filbert H., Guo, Qinghua, Chen, Yifan, Xu, Lu, Huang, Defeng David, and Wong, Kit Po
- Abstract
Nonlinear preprocessors, including conventional clipping, blanking, joint blanking/clipping, and deep clipping, have been employed to mitigate the impulsive noise in orthogonal frequency division multiplexing-based power-line communications. Those nonlinear preprocessors are characterized by one or two thresholds, which are optimized to achieve an optimum output signal-to-noise ratio (SNR). In this paper, we aim to further improve the output SNR by linearly combining two nonlinear preprocessors. Both analytical and simulation results show that the proposed method yields better output SNR and symbol/bit error rate performance than the individual ones. [ABSTRACT FROM PUBLISHER]
- Published
- 2016
- Full Text
- View/download PDF
43. Voltage Restoration and Adjustable Current Sharing for DC Microgrid With Time Delay via Distributed Secondary Control.
- Author
-
Xing, Lantao, Guo, Fanghong, Liu, Xiaokang, Wen, Changyun, Mishra, Yateendra, and Tian, Yu-Chu
- Abstract
As a key part of modern power systems, DC microgrid is becoming increasingly important. Among different control methods for DC microgrid, secondary control has been widely investigated since it can guarantee both current sharing and DC bus voltage restoration. However, the existing secondary control results only consider fixed current sharing ratio among DC converters, and thus they cannot be applied to the case where an adjustable current sharing ratio is desired. Motivated by this observation, this paper presents a new distributed secondary control strategy. By imposing a time-varying droop gain and specifying the “virtual voltage drop,” this strategy is able to ensure adjustable current sharing ratio among DC converters. Moreover, the effects of time delay on the control performance is also analyzed. Three case studies and two hardware-in-the-loop (HIL) tests are provided to verify the efficacy of the presented strategy. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
44. Suboptimal Control and Targeted Constant Control for Semi-Random Epidemic Networks.
- Author
-
Li, Kezan, Zhang, Haifeng, Zhu, Guanghu, Small, Michael, and Fu, Xinchu
- Subjects
NEUROCYSTICERCOSIS ,PONTRYAGIN'S minimum principle ,EPIDEMICS - Abstract
Compared with traditional models, semi-random epidemic network models may be more reasonable to describe the real dynamics of many epidemics. In this paper, we first investigate the optimal control problem (OCP) of semi-random epidemic networks. By using the Pontryagin’s minimum principle, we obtain the optimal control strategy aimed to minimize the total epidemic incidence and control cost. We then define a centrality index which can measure average control strength of the optimal control. Based on this index, the OCP is converted into a static OCP (SOCP), whose solution is utilized to design a nonidentical constant control (NCC). NCC is suboptimal as it is optimal on a subset of the whole control set, and is determined by only the network’s clustering coefficient and initial condition. We finally propose an effective targeted constant quarantine control by using this centrality index. The results uncover the relationship between the optimal control and the network’s topological structure, provide a convenient method to determine suboptimal control, and present a strategy for targeted constant control. This paper can help to design effective control strategies for more general epidemic networks in the real world. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
45. Game Theoretic Suppression of Forged Messages in Online Social Networks.
- Author
-
Wang, Xu, Zha, Xuan, Ni, Wei, Liu, Ren Ping, Guo, Y. Jay, Niu, Xinxin, and Zheng, Kangfeng
- Subjects
ONLINE social networks ,FORGING ,BEHAVIORAL assessment ,ELECTRONIC books - Abstract
Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
46. A Joint Scheduling and Power Control Scheme for Hybrid I2V/V2V Networks.
- Author
-
Nguyen, Bach Long, Ngo, Duy Trong, Dao, Minh N., Duong, Quang-Thang, and Okada, Minoru
- Subjects
NONLINEAR programming ,VEHICULAR ad hoc networks ,SCHEDULING - Abstract
In automotive infotainment systems, vehicles using the applications are serviced via continuous infrastructure-to-vehicle (I2V) communications. Additionally, the I2V communications can be combined with vehicle-to-vehicle (V2V) connectivity owing to the small area covered by road side units (RSUs). However, dozens of vehicles have to compete for limited bandwidth when they request service simultaneously in the covered area. In this paper, we propose a joint scheduling and power control scheme for I2V and V2V links in the RSUs’ coverage range. Mapping the I2V and V2V links to tuple-links, we formulate a mixed-integer nonlinear programming (MINLP) problem where frequency scheduler and power controller for those tuple-links are jointly designed. Then, we employ the delayed column generation technique and the transmission pattern definition to decompose the MINLP problem into a transmission pattern scheduling problem, as well as a power control problem. Therein, the transmission pattern scheduling problem is solved by linear programming while a greedy power control algorithm is developed. Simulation results with practical parameter settings show that our proposed scheme outperforms several conventional schemes in terms of service disruption and achieved throughput while maintaining throughput fairness among the requesting vehicles. In particular, a high channel number, a small power level number, and a large buffer size at the requesting vehicles are shown to be helpful for our proposed scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
47. Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR.
- Author
-
Cortes, Carlos A. Tirado, Chen, Hsiang-Ting, Sturnieks, Daina L., Garcia, Jaime, Lord, Stephen R., and Lin, Chin-Teng
- Subjects
HEAD-mounted displays ,VIRTUAL reality ,CENTER of mass - Abstract
Room-scale 3D position tracking enables users to explore a virtual environment by physically walking, which improves comfort and the level of immersion. However, when users walk with their eyesight blocked by a head-mounted display, they may unexpectedly lose their balance and fall if they bump into real-world obstacles or unintentionally shift their center of mass outside the margin of stability. This paper evaluates balance recovery methods and intervention timing during the use of VR with the assumption that the onset of a fall is given. Our experiment followed the tether-release protocol during clinical research and induced a fall while a subject was engaged in a secondary 3D object selection task. The experiment employed a two-by-two design that evaluated two assistive techniques, i.e., video-see-through and auditory warning at two different timings, i.e., at fall onset and 500ms prior to fall onset. The data from 17 subjects showed that video-see-through triggered 500 ms before the onset of fall can effectively help users recover from falls. Surprisingly, video-see-through at fall onset has a significant negative impact on balance recovery and produces similar results to those of the baseline condition (no intervention). [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
48. Unbalance Mitigation via Phase-Switching Device and Static Var Compensator in Low-Voltage Distribution Network.
- Author
-
Liu, Bin, Meng, Ke, Dong, Zhao Yang, Wong, Peter K.C., and Ting, Tian
- Subjects
STATIC VAR compensators ,NONCONVEX programming ,PHASOR measurement ,HEURISTIC algorithms ,LOW voltage systems - Abstract
As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be adequately addressed. To mitigate unbalances in LVDN, phase-switching devices (PSDs) and static var compensator (SVC) are two equipment that is cost-effective and efficient. However, most existing research on operating PSDs is based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem after developing an SVC model for dispatch purpose. Compared with existing work, the proposed method aims at minimizing current unbalance based on their phasor values and takes the network's operational requirements into account. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on two modified IEEE systems and a practical Australian LVDN demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
49. Characterization of Voltage Dips and Swells in a DG-Embedded Distribution Network During and Subsequent to Islanding Process and Grid Reconnection.
- Author
-
Alam, Mollah Rezaul, Muttaqi, Kashem M., and Bouzerdoum, Abdesselam
- Subjects
ELECTRIC potential ,DISTRIBUTED power generation ,ELECTRIC circuits ,POTENTIAL energy ,DISTRIBUTED resources (Electric utilities) - Abstract
Stand-alone operation of distributed generations (DGs) under an islanded mode is achieved by appropriate switching of controllers from grid-parallel to stand-alone mode. Conversely, during grid restoration, reverse switching operation is employed. These operations cause voltage quality issues; among these issues, voltage dips and swells are two crucial events that are encountered during and subsequent to islanding. This paper characterizes the voltage dips and/or swells caused by the islanding of DG and its subsequent pre- and post-islanding events. Pre-islanding events encompass the fault-initiated islanding scenarios, whereas postislanding events are associated with transitional state, island stabilization, and grid-reconnection states. Considering pre- and post-islanding scenarios, this paper classifies and characterizes the voltage dips and swells using an algorithm incorporating three-phase voltage ellipse and three-dimensional (3-D) polarization ellipse parameters. Three-phase voltage ellipse parameters, namely, major axis, minor axis, and inclination angle of an ellipse, are exploited for characterization and classification of voltage dips/swells based on their affected phases, whereas 3-D polarization ellipse parameters are employed for classifying seven dip types, namely, A, B, D, F, E, C, and G. Islanding and its subsequent scenarios are simulated using a test distribution network of Australia embedded with DG, and the voltage dips and swells are characterized using the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
50. An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data.
- Author
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Kong, Weicong, Dong, Zhao Yang, Ma, Jin, Hill, David J., Zhao, Junhua, and Luo, Fengji
- Abstract
Appliance-level load models are expected to be crucial to future smart grid applications. Unlike direct appliance monitoring approaches, it is more flexible and convenient to mine smart meter data to generate load models at device level nonintrusively and generalise to all households with smart meter ownership. This paper proposes a comprehensive and extensible framework to solve the load disaggregation problem for residential households. Our approach examines both the modelling of home appliances as hidden Markov models and the solving of non-intrusive load monitoring based on segmented integer quadratic constraint programming to disaggregate a household power profile into the appliance level. Structure of our approach to be implemented with current smart meter infrastructure is given and simulations are performed based on public datasets. All data are down-sampled to the rate that is consistent with the Australia smart meter infrastructure minimum functionality. The results demonstrate that our approach is able to work with existing smart meters to generate device level load model for other smart grid research and applications. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
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