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Special Feature that lists the names of people involved in the review of technical papers for the IEEE Transactions on Industry Applications and Industry Applications Magazine. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Given a set of facilities and a set of users, a reverse nearest neighbors (RNN) query retrieves every user $u$ for which the query facility $q$ is its closest facility. Since $q$ is the closest facility to $u$ , the user $u$ is said to be influenced by $q$ . In this paper, we propose a relaxed definition of influence where a user $u$ is said to be influenced by not only its closest facility but also every other facility that is almost as close to $u$ as its closest facility is. Based on this definition of influence, we propose reverse approximate nearest neighbors (RANN) queries. Formally, given a value $x>1$ , an RANN query $q$ returns every user $u$ for which $dist(u,q) \leq x\times NNDist(u)$ where $NNDist(u)$ denotes the distance between a user $u$ and its nearest facility, i.e., $q$ is an approximate nearest neighbor of $u$ . In this paper, we study both snapshot and continuous versions of RANN queries. In a snapshot RANN query, the underlying data sets do not change and the results of a query are to be computed only once. In the continuous version, the users continuously change their locations and the results of RANN queries are to be continuously monitored. Based on effective pruning techniques and several non-trivial observations, we propose efficient RANN query processing algorithms for both the snapshot and continuous RANN queries. We conduct extensive experiments on both real and synthetic data sets and demonstrate that our algorithm for both snapshot and continuous queries are significantly better than the competitors. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the design and analysis of coded slotted ALOHA (CSA) schemes in the presence of channel erasure. We design the code probability distributions for CSA schemes with repetition codes and maximum distance separable codes to maximize the expected traffic load, under both packet erasure channels and slot erasure channels. We derive the extrinsic information transfer (EXIT) functions of CSA schemes over erasure channels. By optimizing the convergence behavior of the derived EXIT functions, the code probability distributions to achieve the maximum expected traffic load are obtained. Then, we derive the asymptotic throughput of CSA schemes over erasure channels. In addition, we validate that the asymptotic throughput can give a good approximation to the throughput of CSA schemes over erasure channels. [ABSTRACT FROM AUTHOR]

Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet—a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Synchronous generators are operating for less time than before or being decommissioned in the National Electricity Market (NEM) of Australia, due to the proliferation of asynchronous wind and solar generation. Sub-networks of the NEM will face inertia shortages in the near future. This paper develops a formulation of zonal inertia constrained generator dispatch for power systems with a diversified generator portfolio including synchronous generators, synchronous condensers, inverter-interfaced generators and energy storages. Zonal inertia constraints are formulated in unit commitment and optimal power flow to limit the rate of change of frequency (RoCoF) in the event of network separation. Load frequency relief is also considered to reduce the ramp rate requirement of primary reserve. The proposed formulation can reduce the average cost of primary reserve and maintain zonal inertia adequacy to constrain RoCoF in case of the trip of the interconnector(s). [ABSTRACT FROM AUTHOR]

With recent advances in information technologies such as global position system and mobile internet, a huge volume of spatio-textual objects have been generated from location-based services, which enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture the user's intention. It has been demonstrated to be fundamental and useful for many real applications. Despite its usefulness, the SPM problem is computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. We experimentally evaluate the performance of ESPM on real large datasets. Our results show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost. [ABSTRACT FROM AUTHOR]

Person re-identification (re-ID) is of great importance to video surveillance systems by estimating the similarity between a pair of cross-camera person shorts. Current methods for estimating such similarity require a large number of labeled samples for supervised training. In this paper, we present a pseudo-pair based self-similarity learning approach for unsupervised person re-ID without human annotations. Unlike conventional unsupervised re-ID methods that use pseudo labels based on global clustering, we construct patch surrogate classes as initial supervision, and propose to assign pseudo labels to images through the pairwise gradient-guided similarity separation. This can cluster images in pseudo pairs, and the pseudos can be updated during training. Based on pseudo pairs, we propose to improve the generalization of similarity function via a novel self-similarity learning:it learns local discriminative features from individual images via intra-similarity, and discovers the patch correspondence across images via inter-similarity. The intra-similarity learning is based on channel attention to detect diverse local features from an image. The inter-similarity learning employs a deformable convolution with a non-local block to align patches for cross-image similarity. Experimental results on several re-ID benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-arts. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality. [ABSTRACT FROM PUBLISHER]

In Australia and many other countries, distribution network service providers (DNSPs) have an obligation to their customers to provide electrical power that is reliable and of high quality. Failure to do so may have significant implications ranging from financial penalties theoretically through to the loss of a license to distribute electricity. In order to ensure the reliability and quality of supply are met, DNSPs engage in monitoring and reporting practice. This paper provides an overview of a large long-running power-quality monitoring project that has involved most of Australia's DNSPs at one time or another. This paper describes the challenges associated with conducting the project as well as some of the important outcomes and lessons learned. A number of novel reporting techniques that have been developed as part of the monitoring project are also presented. A discussion about large-volume data management, and issues related to reporting requirements in future distribution networks is included. [ABSTRACT FROM PUBLISHER]

This paper derives the optimal power allocation for a coordinated orthogonal frequency-division multiplexing (OFDM) transmission system in which $K$ coordinated transmission points (CTPs) coherently transmit and allocate power across $N$ subchannels under both total and individual power constraints. In maximizing the system capacity, previous works showed that, under a total power constraint, the optimal transmission strategy is a maximum-ratio transmission (MRT) for CTPs with a waterfilling type of power allocation solution for the subchannels. For CTPs with both total and individual power constraints, we derive a new optimal co-phasing power allocation with the following property: For any given subchannel, if the optimal power allocation of one CTP is zero, then the power allocation of all the other $K$ − 1 CTPs on that subchannel must also be zero; otherwise, the non-zero power allocation on all CTPs must follow a proportional principle which establishes the relationship between the optimal power allocation for all subchannels and all CTPs. This property highlights that the optimal power allocation for CTPs with individual power constraints is different from waterfilling and MRT, as more power is not necessarily allocated to the subchannels with better channel conditions. Numerical results are presented to verify our theoretical findings. [ABSTRACT FROM AUTHOR]

The presence of shadows in images significantly affects the performance of many computer vision tasks and visual processing applications, such as object tracking, object classification, and behavior recognition. Most methods have been designed to detect shadows in specific situations, but they often fail to distinguish shadow points from the foreground object in many problematic situations, such as chromatic shadows, non-textured and dark surfaces, and foreground–background camouflage. In this paper, we propose a new feature-based image patch approximation and multi-independent sparse representation technique to tackle these environmental problems. In this method, two illumination-invariant features—binary patterns of local color constancy and light-based gradient matching—are introduced, along with the intensity-reduction histogram. These features are extracted from image patches and are used to construct two over-complete dictionaries for objects and shadows, respectively. Given a new image patch, its best approximation for a number of iterations is found from each dictionary. For each iteration, an independent class assignment is performed by finding its distances from the reference dictionaries. The patch is then assigned to a class based on its probability of occurrence. The proposed framework is evaluated on common shadow detection data sets, and it shows improved performance in terms of the shadow detection rate and discrimination rate compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In practice, the load-curve and distributed generation (DG) penetration level determines the power imbalance level that a network can experience if islanding occurs. Therefore, with the prior knowledge of load-curve and DG penetration level, the setpoint of rate-of-change-of-frequency (ROCOF) relays can be adjusted so as to make them suitable for a real network. This paper first investigates the subcritical power imbalance region of ROCOF relays through analytical formulation followed by extensive simulation study in order to establish the maximum boundary limit of ROCOF's nondetection zone (NDZ) under all possible deficit/excess of active and/or reactive power imbalance scenarios. Second, ROCOF's reliability (assessed by detection rate and false alarm rate) is expressed analytically and then, validated numerically by simulating a test network of Australia in MATLAB and OPAL-RT real-time digital simulation platform. Finally, ROCOF's performance is assessed through receiver operating characteristics curves and a detailed reliability study under variable setpoints and detection time of the relays; the assessment considers the number of islanding events associated with the time-wise percentage of power imbalance level computed from the net load demand and variable DG penetration in a real network. All these test results demonstrate a clear operational guideline for ROCOF relay. [ABSTRACT FROM AUTHOR]

To fulfil the needs of the future, the Australian building sector seems to contemplate toward sustainable design. A Green Star Environmental Rating System is one of many green-building rating systems that has been employed throughout the world. For this rating system, the “Material” category occupies 14% of credit points, which could be achieved from eight major categories. To help engineers and designers have simple tools to process sustainable projects, this paper develops a computer-aided model to calculate life-cycle greenhouse gas (GHG) emissions for conventional and high-strength concrete to maximize Credit 19B.1: Life-cycle impacts—Concrete in the Green Star Design and As Built in Australia. The model has been built under Microsoft Excel and Visual Basic platforms; thus, it is flexible and appears to be one of the effective ways to provide concrete mixture design using its life-cycle GHG emissions. Options to maximize Credit 19B.1 have also been discussed for normal and high-strength concrete. The model demonstrates the relationship between the utilization of supplementary cementitious material, coarse and fine aggregates used in concrete, a water-to-cement ratio with concrete strength, as well as sustainable points to be achieved. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical approach for estimating residential PV and electrical demand models using historical data. In brief, the method involves first clustering historical data into different day types, and then estimating PV and demand models using kernel regression. Clustering is done to capture intraday variations in the PV and demand profiles, with the aim of capturing much of these intertemporal correlations in the day-type labels. This allows the draws from the kernel estimates within a day type to be done independently. This approach conforms with a Markov decision process construction of the smart home energy management system (SHEMS) problem, which is the ultimate target of the modeling procedure. Moreover, in practical applications, the SHEMS will need the type of a coming day in order to select a daily demand model, which can be done seamlessly using state identification methods. In comparison, forecasting a day's demand profile using time series forecasting methods produces a prediction method that does not provide a probability structure that is directly incorporated into a Markov decision process scheduling model. [ABSTRACT FROM AUTHOR]

The growing adoption of residential photovoltaic (PV) systems around the world is presenting distribution network operators (DNOs) with technical challenges, particularly on low voltage (LV) networks. The need to mitigate these issues with simple yet effective measures in countries with high PV penetrations is likely to drive the adoption of limits on the very exports that affect this infrastructure. Defining the most adequate limit, however, requires understanding the tradeoffs between the technical benefits and the effects on PV owners. This paper proposes two methodologies: an optimal power flow (OPF) based technique to define the export limit that solves technical problems with minimal curtailment, and a Monte Carlo based analysis to investigate the spectrum of such tradeoffs considering different PV penetrations and export limits. A real U.K. residential LV network with 180 customers is analyzed using realistic 1-min resolution daily load and PV generation profiles across seasons. Results demonstrate that, for DNOs, the OPF-based approach is effective in determining the most technically adequate export limit. However, for policy makers, the spectrum of tradeoffs provided by the Monte Carlo approach can help defining export limits that reduce curtailment at the expense of partially mitigating technical issues. [ABSTRACT FROM AUTHOR]

Anomalous GPS radio occultation (RO) events are characterised as those with L1 bending angle greater than their corresponding L2 bending angle. An investigation by EUMETSAT and the United Kingdom Meteorological Office revealed there are regions in the earth’s atmosphere where at times up to 60% of Global Navigation Satellite System Receiver for Atmospheric Sounding RO events, at the Tangent Point height of 50 km, exhibited anomalous bending angle results. The exact source of these anomalous RO events has been unclear to the RO data user community, i.e., data processing artifact or atmospheric phenomenon. In this paper, the regions of increased occurrence of anomalous RO have been identified to be the mid-latitude ionospheric trough, ionospheric polar hole, and poleward edges of the equatorial anomaly. They are more frequent at nighttime and in the southern hemisphere winter months. This is when the plasma density in these regions is depleted. However, within these regions, there are ionospheric features of increased electron density gradients such as at the walls of the mid-latitude ionospheric trough. 3-D numerical ray tracing simulations of GPS RO are presented, showing that these increased electron density features in a weakly ionized ionosphere can produce the anomalous bending angle results. [ABSTRACT FROM AUTHOR]

Real social networks contain many communities, where members within each community are densely connected with each other, while they are sparsely connected with the members outside of the community. Since each member can join multiple communities simultaneously, communities in social networks are usually overlapping with each other. How to efficiently and effectively identify overlapping communities in a large social network becomes a fundamental problem in the big data era. Most existing studies on community finding focused on non-overlapping communities based on several well-known community fitness metrics. However, recent investigations have shown that these fitness metrics may suffer free rider and separation effects where the overlapping region of two communities always belongs to the denser one, rather to both of them. In this paper, we study the overlapping community detection problem in social networks that not only takes the quality of the found overlapping communities but also incorporate both free rider and separation effects on the found communities into consideration. Specifically, in this paper, we first propose a novel community fitness metric - triangle based fitness metric, for overlapping community detection that can minimize the free rider and separation effects on found overlapping communities, and show that the problem is NP-hard. We then propose an efficient yet scalable algorithm for the problem that can deliver a feasible solution. We finally validate the effectiveness of the proposed fitness metric and evaluate the performance of the proposed algorithm, through conducting extensive experiments on real-world datasets with over 100 million vertices and edges. Experimental results demonstrate that the proposed algorithm is very promising. [ABSTRACT FROM AUTHOR]

This paper considers density evolution for low-density parity-check (LDPC) and multi-edge type LDPC (MET-LDPC) codes over the binary input additive white Gaussian noise channel. We first analyze three single-parameter Gaussian approximations for density evolution and discuss their accuracy under several conditions, namely, at low rates, with punctured and degree-one variable nodes. We observe that the assumption of symmetric Gaussian distribution for the density-evolution messages is not accurate in the early decoding iterations, particularly at low rates and with punctured variable nodes. Thus, single-parameter Gaussian approximation methods produce very poor results in these cases. Based on these observations, we then introduce a new density evolution approximation algorithm for LDPC and MET-LDPC codes. Our method is a combination of full density evolution and a single-parameter Gaussian approximation, where we assume a symmetric Gaussian distribution only after density-evolution messages closely follow a symmetric Gaussian distribution. Our method significantly improves the accuracy of the code threshold estimation. Additionally, the proposed method significantly reduces the computational time of evaluating the code threshold compared with full density evolution thereby making it more suitable for code design. [ABSTRACT FROM PUBLISHER]

Market and network integration of distributed energy resources can be facilitated by their coordination within a virtual power plant (VPP). However, VPP operation subject to network limits and different market and physical uncertainties is a challenging task. This paper introduces a framework that co-optimizes the VPP provision of multiple market (e.g., energy, reserve), system (e.g., fast frequency response, inertia, upstream reactive power), and local network (e.g., voltage support) services with the aim of maximizing its revenue. To ensure problem tractability, while accommodating the uncertain nature of market prices, local demand, and renewable output and while operating within local network constraints, the framework is broken down into three sequentially coordinated optimization problems. Specifically, a scenario-based robust optimization for day-ahead resource scheduling, with linearized power flows, and two receding horizon optimizations for close-to-real-time dispatch, with a more accurate second-order cone relaxation of the power flows. The results from a real Australian case study demonstrate how the framework enables effective deployment of VPP flexibility to maximize its multi-service value stack, within an uncertain operating environment, and within technical limits. [ABSTRACT FROM AUTHOR]

This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are complementary and indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP can facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks. [ABSTRACT FROM AUTHOR]

The laboratory plays an important role in teaching engineering skills. An Electrical Engineering department at an Australian University implemented a reform to monitor and improve student satisfaction with the teaching laboratories. A Laboratory Manager was employed to oversee the quality of 27 courses containing instructional laboratories. Student satisfaction surveys were carried out on all relevant laboratories every year, and the data were used for continuous improvement. This paper will investigate the reforms that were implemented and outline a number of the improvements made. It also examines the program's overall impact on: 1) overall satisfaction; 2) laboratory notes; 3) learning experiences; 4) computer facilities; 5) engineering equipment; and 6) condition of the laboratory. Student satisfaction with the laboratories increased by 32% between 2007 and 2013. The results show that the laboratory notes (activity and clarity) and the quality of the equipment used are among the most influential factors on student satisfaction. In particular, it is important to have notes or resources that explain in some detail how to use and troubleshoot equipment and software used in the laboratory. [ABSTRACT FROM AUTHOR]

Plug-in electric vehicles (PEVs) have a potential amount of battery energy storage capacity, which is not fully utilized in regular day-to-day travels. The utilization of spare PEV battery capacity for grid support applications using vehicle-to-grid concept is becoming popular. Depending on the stress on the grid during peak load periods, a small amount of peak-shaving support from the PEVs in a feeder can be useful in terms of grid support. However, as the PEV batteries have limited capacity and the capacity usage is also constrained by travel requirements, a strategy is proposed in this paper for an effective utilization of the available PEV battery capacity for peak shaving. A controllable discharging pattern is developed to most utilize the limited PEV battery capacity when peak shaving is most valuable based on the demand pattern. To ensure an effective use of the available PEV battery capacity for travel, which is the main usage of the PEVs, and for grid support application, dynamic adjustments in PEV discharging rates are made. The effectiveness of the proposed strategy is tested using a real distribution system in Australia and based on practical PEV data. [ABSTRACT FROM AUTHOR]

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it serves as the predecessor of the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. In this paper, we study the performance of the synchronous dynamic TDD from a media access control layer viewpoint, which has been widely adopted in the existing 4G systems. Furthermore, we analyze the coverage probability and the area spectral efficiency in the DL and UL of dense SCNs considering the synchronous dynamic TDD transmissions, and the performance impact of dynamic TDD transmissions on the ASE in the DL and UL of dense SCNs is discussed. Moreover, the performance impact of interference cancellation is also explored. Our analytical results shed new light on the performance of dynamic TDD in future synchronous 5G networks. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of the base station (BS) idle mode capacity (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs) with both line-of-sight (LoS) and non-line-of-sight transmissions. Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical set-up with a finite number of UEs in our analysis. Moreover, in our model, the small BSs (SBSs) apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the macro BSs (MBSs) apply enhanced inter-cell interference coordination, in the form of almost blank subframe (ABS) mechanism. For this model, we derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency, will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs. [ABSTRACT FROM AUTHOR]

Over the last decade, many power systems have significantly changed with the proliferation of renewable generation sources, such as wind and solar photovoltaic. Due to their variability and nonsynchronous nature, new challenges and complexities have emerged regarding operational security of modern power systems. The 2016 South Australia (SA) blackout was the first known blackout due to such a high renewable situation. An official report has recently been published to review the causes and provide the corresponding recommendations for improvement of network operation, control, and security. However, there are still a number of critical issues and debates which remain unsolved, such as network bottleneck identification, overvoltage explanation, pole slip concern, frequency dip mystery, and frequency/voltage instability debate. In this paper, based on the reconstruction of the event, these unsettled issues are prudently analyzed to unveil their root causes. In addition, an innovative scheme is proposed to prevent the blackout by identifying the network separation at an early stage. This research will not only further advance the understanding of the 2016 SA blackout, but also will provide valuable guidelines for the management of future renewable-rich networks. [ABSTRACT FROM AUTHOR]

As a response to the challenge of data traffic explosion in wireless networks, content caching in device-to-device (D2D) communication networks has emerged as a promising solution. However, in practical deployment, D2D content caching has its own problems. In particular, not all of the user devices are willing to share the content with others due to numerous concerns, such as security, battery life, and social relationship. In this paper, we consider the factor of social relationship in the deployment of D2D content caching. First, we apply stochastic geometry theory to derive an analytical expression of downloading performance for the D2D caching network. Specifically, a social relationship model with respect to the physical distance is adopted in our analysis to obtain the average download delay performance using random and deterministic caching strategies. Second, to achieve a better performance in more practical and specific scenarios, we develop a socially aware distributed caching strategy based on a decentralized learning automaton, to optimize the cache placement operation in D2D networks. Different from the existing caching schemes, the proposed algorithm not only considers the file request probability and the closeness of devices as measured by their physical distance but also takes into account the social relationship between D2D users. Our simulation results show that the proposed algorithm can converge quickly and outperforms the random and deterministic caching strategies. With these results, our work sheds insights on the design of D2D caching in the practical deployment of 5G networks. [ABSTRACT FROM PUBLISHER]

The purpose of this paper was to develop and validate a new theoretically based scale to measure students’ learning of systems thinking in relation to the affective domain in the context of systems engineering education. Two variant questionnaires are reported here, one using only questions constructed using positive grammar and the other using a mix of positive and negative constructs, each applied to a different sample. The first group of 186 participants completed the positive version of the questionnaire, and, the second group of 163 completed the mixed version. Construct validity was examined through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). EFA was conducted to find the factors underlying each questionnaire. CFA was conducted to confirm the better questionnaire version and to confirm the factors which underlie both versions. The results indicate that a three factor, 16 item, scale with a mix of positive and negative wording is the better instrument with which to measure students’ engagement with systems thinking. The results also indicate that the three factor, 16 item construct is a better representative of both versions of the questionnaire, whether the questionnaire has only positive questions or a mix of positive and negative questions. [ABSTRACT FROM PUBLISHER]

Despite the fact that great progress has been made on single image deraining tasks, it is still challenging for existing models to produce satisfactory results directly, and it often requires a single or multiple refinement stages to gradually improve the quality. However, in this paper, we demonstrate that existing image-level refinement with a stage-independent learning design is problematic with the side effect of over/under-deraining. To resolve this issue, we for the first time propose the mechanism of learning to carry out refinement on the unsatisfactory features, and propose a novel attentive feature refinement (AFR) module. Specifically, AFR is designed as a two-branched network for simultaneous rain-distribution-aware attention map learning and attention guided hierarchy-preserving feature refinement. Guided by task-specific attention, coarse features are progressively refined to better model the diversified rainy effects. By using a separable convolution as the basic component, our AFR module introduces little computation overhead and can be readily integrated into most rainy-to-clean image translation networks for achieving better deraining results. By incorporating a series of AFR modules into a general encoder-decoder network, AFR-Net is constructed for deraining and it achieves new state-of-the-art results on both synthetic and real images. Furthermore, by using AFR-Net as a teacher model, we explore the use of knowledge distillation to successfully learn a student model that is also able to achieve state-of-the-art results but with a much faster inference speed (i.e., it only takes 0.08 second to process a $512\times 512$ rainy image). Code and pre-trained models are available at $\langle $ https://github.com/RobinCSIRO/AFR-Net $\rangle $. [ABSTRACT FROM AUTHOR]