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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]

With the advancements of the battery energy storage systems (BESSs), reduction of their manufacturing costs and government subsidies, the BESS uptake is likely to increase rapidly in power distribution networks. This paper investigates the effects of residential BESSs on low-voltage (LV) networks using the actual household load profiles equipped with BESS and solar-photovoltaic (PV) systems. The electricity consumption data collected via smart meters (2200 households with PV/BESS, 1950 households with PV only and 1000 households without a PV or a BESS) at different solar-PV penetration levels and network types are used to simulate real network operating scenarios. A real LV distribution network in Australia is analysed in DIgSILENT PowerFactory under different scenarios, such as, customers with and without a solar-PV/BESS, with a solar-PV but without a BESS, and without a solar-PV, by using both the power flow and quasi-dynamic simulation studies under balanced and the unbalanced network loading conditions. According to the study, customers experience large voltage excursions from solar-PV power exports, which could be resolved by the household BESS, provided that the BESS charging is coordinated with the solar-PV production. Moreover, quasi-dynamic simulation shows that the BESS could reduce the violation of the over-voltage limit during the solar peak hours (midday) by lowering the worst-case feeder voltage by 3%. Finally, extreme-event (high solar PV generation scenario) simulation shows that the implementation of the BESS controller to facilitate charging BESS during afternoon solar-PV export may reduce the negative grid impact and will assist to avoid network upgrades. [ABSTRACT FROM AUTHOR]

Accurate solar photovoltaic (PV) generation forecast is critical to the reliable and economic operation of a modern power system. In practice, due to various faulty issues in the sensor, communication, or database system, the historical and online measurement data may not be always complete, and the missing data could dramatically degrade the forecasting model's accuracy. To solve this problem, this paper proposes an integrated missing-data tolerant model for probabilistic PV power generation forecasting. Taking historical PV generations as input, this model is based on a recursive long short-term memory network (Rec-LSTM), which can provide multi-step ahead forecasting of the probability distribution of PV generation. The unobserved input data will be imputed recursively based on the model output at the previous time step. During the training process, the imputations and forecasting values are iteratively updated by the negative log-likelihood loss function. As a salient advantage, this method can deal with data missing scenarios at both offline and online stages. Numerical experiments are conducted on two one-year datasets from Australia and Singapore, respectively. Probabilistic forecasting for both large-scale and small-scale building-level PV power generation is tested at the time resolution of 15 mins. Testing results show the proposed method can achieve superior probabilistic prediction accuracy as well as strong robustness under various data missing scenarios, compared to other state-of-the-art methods. [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]

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 proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [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]

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]

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]

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]

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]

Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, a novel quantum detector tomography method is proposed. First, a series of different probe states are used to generate measurement data. Then, using constrained linear regression estimation, a stage-1 estimation of the detector is obtained. Finally, the positive semidefinite requirement is added to guarantee a physical stage-2 estimation. This Two-stage Estimation (TSE) method has computational complexity $O(nd^{2}M)$ , where $n$ is the number of $d$ -dimensional detector matrices and $M$ is the number of different probe states. An error upper bound is established, and optimization on the coherent probe states is investigated. We perform simulation and a quantum optical experiment to testify the effectiveness of the TSE method. [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]

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]

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]

Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes. In this paper, we investigate skeleton-based action prediction, which aims to recognize an action from a partial skeleton sequence that contains incomplete action information. We propose a new Latent Global Network based on adversarial learning for action prediction. We demonstrate that the proposed network provides latent long-term global information that is complementary to the local action information of the partial sequences and helps improve action prediction. We show that action prediction can be improved by combining the latent global information with the local action information. We test the proposed method on three challenging skeleton datasets and report state-of-the-art performance. [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]

The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user–user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen–Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics. [ABSTRACT FROM AUTHOR]

Accurate and reliable low-voltage load forecasting is critical to optimal operation and control of distribution network and smart grid. However, compared to traditional regional load forecasting at high-voltage level, it faces tough challenges due to the inherent high uncertainty of the low-capacity load and distributed renewable energy integrated in the demand side. This paper proposes a novel hybrid ensemble deep learning (HEDL) approach for deterministic and probabilistic low-voltage load forecasting. The deep belief network (DBN) is applied to low-voltage load point prediction with the strong ability of approximating nonlinear mapping. A series of ensemble learning methods including bagging and boosting variants are introduced to improve the regression ability of DBN. In addition, the differencing transformation technique is utilized to ensure the stationarity of load time series for the application bagging and boosting methods. On the basis of the integrated thought of ensemble learning, a new hybrid ensemble algorithm is developed via integrating multiple separate ensemble methods. Considering the diversity in various ensemble algorithms, an effective K nearest neighbor classification method is utilized to adaptively determine the weights of sub-models. Furthermore, HEDL based probabilistic forecasting is proposed by taking advantage of the inherent resample idea in bagging and boosting. The effectiveness of the HEDL method for both deterministic and probabilistic forecasting has been systematically verified based on realistic load data from East China and Australia, indicating its promising prospective for practical applications in distribution networks. [ABSTRACT FROM AUTHOR]

Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach. [ABSTRACT FROM AUTHOR]

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]

Due to the rapid development of distributed renewable generation, an effective risk assessment and early warning mechanism for active distribution networks is of great significance to maintain the system reliability and enhance energy grid resilience. In this paper, a novel risk assessment model is proposed to assess the probability of potential disturbances to the grid and provide accurate advice for trading prosumers’ renewable energy. The model can compute node failure probability (FP) for transmission networks as well as the area FP for distribution networks, while combining the two perspectives by topology analysis. A weather threshold value is first derived to define the extreme weather condition. Then the FP of transmission lines is calculated by joint probability models under four instances of extreme climate. For distribution networks, the weather influence is obtained by applying the Rare Events Logistic Regression model initially. Then the equipment fault related to the geographical feature is captured using feeder taxonomy and hierarchical clustering. Furthermore, the accidental factor as a new parameter is introduced to evaluate the vandalism, vegetation, and operating fault to the grid. Finally, the warning information and advice for customers will be presented after fault chain analysis. The FP for a specific area in Australia is analyzed in case studies to verify the proposed model. [ABSTRACT FROM AUTHOR]

Contribution: This paper demonstrated on a large scale that explicit articulation of creativity-related learning goals on engineering syllabi is quite limited, and primarily limited to the first year of study. Engineering educators may need to do more to ensure creativity is explicitly addressed as an expected learning outcome within engineering syllabi. Background: Prior studies have shown inclusion of creativity within engineering syllabi and course activities are generally limited. Students may perceive educators do not value creativity, and their studies have limited influence on their creative skills. Studies conflict on whether creativity skills increase over completing an engineering degree. Creativity has been demonstrated to be an important skill for engineering professionals, but one not necessarily appropriately addressed in engineering programs. Few studies have attempted to quantify coverage of creativity material on a nation-wide scale, as opposed to in a single or select few institutions. Research Questions: 1) To what extent do engineering educators explicitly articulate creativity and innovation-related learning goals and material on their course syllabi? and 2) To what extent do engineering educators explicitly articulate exposure to, or instruction in the use of, creativity heuristics within their course syllabi? Methodology: The online publicly accessible course outlines of 1109 compulsory courses from 42 degree programs accredited by two national engineering accreditation bodies were qualitatively analyzed in a two-stage approach using document analysis. Findings: Approximately 2% of compulsory electrical engineering course outlines explicitly articulate creativity-related material; only one course articulated engaging students in using creativity heuristics. [ABSTRACT FROM AUTHOR]

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets. [ABSTRACT FROM AUTHOR]

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]

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]

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]

The tree Acacia mearnsii is native to south-eastern Australia but has become an aggressive invader in many countries. In South Africa, it is a significant threat to the conservation of biomes. Detecting and mapping its early invasion is critical. The current ground-based methods to map A. mearnsii are accurate but are neither economical nor practical. Remote sensing (RS) provides accurate and repeatable spatial information on tree species. The potential of RS technology to map A. mearnsii distributions remains poorly understood, mainly due to a lack of knowledge on the spectral properties of A. mearnsii relative to co-occurring native plants. We investigated the spectral uniqueness of A. mearnsii compared to co-occurring native plant species within the South African landscape. We explored full-range (400–2500 nm), leaf and canopy hyperspectral reflectance of the species. The spectral reflectance was collected biweekly from December 23, 2016 and May 31, 2017. We conducted a time series analysis, to assess the effect of seasonality on species discrimination. For comparison, two classification models were employed: parametric interval extended canonical variate discriminant (iECVA-DA) and nonparametric random forest discriminant classifiers (RF-DA). The results of this paper suggest that phenology plays a crucial role in discriminating between A. mearnsii and sampled species. The RF classifier discriminated A. mearnsii with slightly higher accuracies (from 92% to 100%) when compared with the iECVA-DA (from 85% to 93%). The study showed the potential of RS to discriminate between A. mearnsii and co-occurring plant species. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for 3-D rain field reconstruction using satellite signals. It uses the estimated signal-to-noise ratio (SNR) at the ground receivers for low-earth orbit (LEO) satellites and, thus, indirectly estimates the path-integrated rain attenuation of the microwave communication links. A least-squares algorithm is employed to perform the 3-D tomographic reconstruction of the rain field. The proposed system model consists of an LEO satellite with a realistic overpass trajectory and multiple ground receivers with SNR estimators. Two synthetic rain events near the Great Barrier Reef in Australia are used to test the reconstruction outcome. Simulation results suggest that the reconstructed rain field has close agreement with the synthetic rain field. [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]

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]

Due to the variability and partial unpredictability of renewable energy sources (RES), generation reserve sizing is key for managing operational risks and enhancing grid reliability. Most existing approaches for reserve sizing estimate the forecasting error for RES using either simple point forecasts, or probabilistic forecasts that assume a predefined parametric model of the underlying distribution, such as a Gaussian distribution. However, these approaches are unable to accurately model the probability of critical events that occur in the tails of the distributions, which is vital for operational risk management. Further, they do not account for multiple dynamic factors that may impact operational risk. In this paper, we introduce a data-driven dynamic probabilistic reserve sizing method based on the artificial intelligence technique of dynamic Bayesian belief networks. The method considers the actual underlying distribution of forecasting errors from the availability of conventional generators (forecasting errors of their available generation capacity), demand, RES, and prevailing conditions such as weather and market prices. In addition, we introduce a new dynamic metric for calculating the reliability level of the power grid in order to provide a real-time stochastic decision support tool for power system operators. The proposed method is demonstrated using seven years of real historical data with a granularity of 30 min from the power system in Australia, and has been implemented and is currently used by the Australian system operator. [ABSTRACT FROM AUTHOR]

The aim of this paper was to test the capabilities of the Sentinel-1 radar data in downscaling Soil Moisture Active Passive (SMAP) radiometer data for high-resolution soil moisture estimation. Three different active–passive downscaling algorithms, including the brightness temperature-based downscaling algorithm (BTBDA), the soil moisture-based downscaling algorithm (SMBDA), and a change detection method (CDM), were analyzed using pairs of Sentinel-1 active and SMAP passive observations collected over a semiarid landscape in southeastern Australia from May 2015 to May 2016. While these algorithms have been tested previously, this is the first study to evaluate the three algorithms using real Sentinel-1 radar and SMAP radiometer data. The SMAP passive observations were disaggregated to 9-, 3-, and 1-km scales and then compared with ground soil moisture measurements. The results suggest that the root-mean-square error (RMSE) in downscaled soil moisture at 9-km resolution was 0.057, 0.056, and 0.067 cm3/cm3 for the BTBDA, SMBDA, and CDM, respectively. The accuracy of downscaling methods was generally decreased when applied at the finer spatial resolution. The SMBDA had overall better performance in terms of correctly detecting the soil moisture pattern and relatively lower RMSE values, and is, therefore, recommended for the combined Sentinel-1 radar and SMAP radiometer setup for soil moisture monitoring. The influence of incidence angle normalization of Sentinel-1 SAR data on downscaled soil moisture was also investigated and found to be minimal. [ABSTRACT FROM AUTHOR]

Superconducting devices have emerged in many applications during the last few decades. They offer many advantages, including high efficiency, compact size, and superior performance. However, the main drawback of these devices is the high cost. An option to reduce the high cost and improve the cost-benefit ratio is to integrate two functions into one device. This paper presents the superconducting fault current limiting transformer (SFCLT) as a superior alternative to normal power transformers. The transformer has superconducting windings and also provides fault current limiting capability to reduce high fault currents. The SFCLT is tested in two power system models: a 7 bus wind farm-based model simulated in PSCAD and on the 80 bus simplified Australian power system model simulated in real-time digital simulator. Various conditions were studied to investigate the effectiveness of the fault current limiting transformer. [ABSTRACT FROM AUTHOR]

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel peak load management scheme for rural areas. The scheme transfers certain customers onto local nonembedded generators during peak load periods to alleviate network under voltage problems. This paper develops and presents this system by way of a case study in Central Queensland, Australia. A methodology is presented for determining the best location for the nonembedded generators as well as the number of generators required to alleviate network problems. A control algorithm to transfer and reconnect customers is developed to ensure that the network voltage profile remains within specification under all plausible load conditions. Finally, simulations are presented to show the performance of the system over a typical maximum daily load profile with large stochastic load variations. [ABSTRACT FROM AUTHOR]

Emission trading is widely considered to be the most effective policy to minimize the overall costs for CO2 abatement. However, the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon-intensive industries in order to offset the reduction in profits.This paper aims to analyze these potential profit impacts and the possible compensation to affected generation compaflies through modeling the Australian National Electricity Market under a potential emission trading scheme. Historical emissionbased and historical generation-based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated. Two carbon permit price scenarios are -used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price. [ABSTRACT FROM AUTHOR]

This paper demonstrates how the data-association technique known as the probabilistic multi-hypothesis tracker (PMHT) can be applied to the feature-based simultaneous localization and map building (SLAM) problem. The main advantage of PMHT over other conventional data-association techniques is that it has low computational complexity, while still providing good performance. Low complexity is a particularly desirable feature for the SLAM problem where the estimators used may already be costly to implement. The paper also proposes an estimation approach based on generalized expectation-maximization iterations of the PMHT SLAM problem, which is able to achieve low computation complexity at the expense of local convergence. The performance of the PMHT SLAM algorithm is compared with other approaches, and its output is demonstrated on a benchmark data set recorded in Victoria Park, Sydney, Australia. [ABSTRACT FROM AUTHOR]

This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach—the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output. [ABSTRACT FROM AUTHOR]

This paper presents a multi-nodal intertemporal Cournot gaming model with transmission constraints and uses it to simulate energy-only and capacity-energy market designs in the presence of uncertainties stemming from intermittent renewable power generation. Both market paradigms are compared to a competitive benchmark in order to determine which one performs better in a concentrated market with significant penetration of wind and solar generation. As a specific example, the model is applied to the South Australian zone of the Australian National Electricity Market (NEM). The simulation results for the time interval 2013–2030 show that the capacity-energy market has the potential to induce significant new capacity and push prices much closer to the competitive level in contrast to the current energy-only market design. [ABSTRACT FROM AUTHOR]

This paper proposes a rule-based power management algorithm to enable the dispatch of a utility-scale PV power plant consisting of a hybrid energy storage system, in accordance with the Australian national electricity rules. This algorithm is purposely designed to regulate the instantaneous power of a PV plant with the same level of dispatchability as conventional power plants. Additionally, the proposed algorithm is robust under large forecasting errors (up to 60%) of solar irradiance and it can be easily implemented in practice. Specifically, this algorithm can be executed within seconds even when considering detailed market operation and model nonlinearities. A 30 MW PV plant, including a 7.5 MW/1.25 MWh vanadium redox battery and a 1.5 MW/0.25 MWh suppercapacitors bank, has been modeled using MATLAB/Simulink and PLECS software environment. The simulation results were based on various scenarios and confirmed the effectiveness of the proposed power management algorithm. It has been shown that even in the worst-case scenario of solar forecasting error of 60%, the proposed algorithm still managed to deliver over 94% of the available energy for a given solar irradiance profile of one month. [ABSTRACT FROM AUTHOR]

This paper presents a new method for estimating nearshore wave height from a digital video sequence. The method identifies main wave breaking zones in the video records and estimates the height of breaking waves inside the detected breaking zones. A geometric rectification is applied to the resulting estimation to convert the height measurement from image pixels to meters. The validation of the algorithm was undertaken over three months at Surfers Paradise, Australia. The performance of the algorithm was demonstrated to be comparable with that of buoy-measured wave height, as well as manual estimates of the onshore wave height by a surf reporter. The results indicate that the method can be used as a cost-effective tool for long-term monitoring of nearshore wave conditions. [ABSTRACT FROM PUBLISHER]