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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]

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]

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]

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]

Room-scale 3D position tracking enables users to explore a virtual environment by physically walking, which improves comfort and the level of immersion. However, when users walk with their eyesight blocked by a head-mounted display, they may unexpectedly lose their balance and fall if they bump into real-world obstacles or unintentionally shift their center of mass outside the margin of stability. This paper evaluates balance recovery methods and intervention timing during the use of VR with the assumption that the onset of a fall is given. Our experiment followed the tether-release protocol during clinical research and induced a fall while a subject was engaged in a secondary 3D object selection task. The experiment employed a two-by-two design that evaluated two assistive techniques, i.e., video-see-through and auditory warning at two different timings, i.e., at fall onset and 500ms prior to fall onset. The data from 17 subjects showed that video-see-through triggered 500 ms before the onset of fall can effectively help users recover from falls. Surprisingly, video-see-through at fall onset has a significant negative impact on balance recovery and produces similar results to those of the baseline condition (no intervention). [ABSTRACT FROM AUTHOR]

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]

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]

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance. [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]

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]

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]

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]

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]

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]

In this paper, we revisit a method to produce binary sequences using the most significant bit map from \bf Z4 to the binary field. This method is useful for the construction of binary sequences with low correlation and large family size. There may be more cases where starting with \bf Z4 could help researchers design new low correlation sequences for code-division multiple access application. [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]

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]

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]

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]

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]

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]

Uncertain graph management and analysis have attracted many research attentions. Among them, computing $k$ k -cores in uncertain graphs (aka, $(k,\eta)$ (k , η) -cores) is an important problem and has emerged in many applications such as community detection, protein-protein interaction network analysis and influence maximization. Given an uncertain graph, the $(k,\eta)$ (k , η) -cores can be derived by iteratively removing the vertex with an $\eta$ η -degree of less than $k$ k . However, the results heavily depend on the two input parameters $k$ k and $\eta$ η . The settings for these parameters are unique to the specific graph structure and the user's subjective requirements. In addition, computing and updating the $\eta$ η -degree for each vertex is the most costly component in the algorithm, and the cost is high. To overcome these drawbacks, we propose an index-based solution for computing $(k,\eta)$ (k , η) -cores. The size of the index is well bounded by $O(m)$ O (m) , where $m$ m is the number of edges in the graph. Based on the index, queries for any $k$ k and $\eta$ η can be answered in optimal time. We propose an algorithm for index construction with several different optimizations. We also propose a new algorithm for index construction in external memory. We conduct extensive experiments on eight real-world datasets to practically evaluate the performance of all proposed algorithms. [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]

A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. [ABSTRACT FROM AUTHOR]

A GPS-denied unmanned aerial vehicle (UAV) (Agent B) is localized through inertial navigation system alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals' direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored. [ABSTRACT FROM AUTHOR]

Background: The ability to create a context is essential in writing the introduction of a research article (RA). This study explores the experience of engineering Ph.D. students in Australia, for whom English is an Additional Language (EAL), in using a problem-solution-based writing model to develop context-creating skills in writing RA introductions. Research question: What is the experience of engineering Ph.D. students in creating contexts through explicit learning of a problem-solution-based model for writing RA introductions? Literature review: Genre-based teaching is a common approach in the second language classroom. Recently, a genre-based approach for writing the introduction of engineering RAs has been proposed. The descriptive values of the model, PSP-CaRS, have been shown in corpus studies of published engineering RAs. However, its applicability has not been explored pedagogically. Methodology: Twenty-nine Ph.D. students were asked to respond to a questionnaire nine months after learning the model and reflect on their experience using it. The findings were then corroborated with data obtained from interviews, researcher observation, and writing samples. Results: The findings showed that the participants perceived PSP-CaRS to be useful and they continued using it after nine months despite some difficulties encountered in the writing process. Participants’ responses showed that explicit teaching of PSP-CaRS formed the foundation upon which more competent skills to create contexts were developed through practice and integration of subject knowledge. Discussion: Explicit teaching using a model can impart the basics of genre awareness to students. Once students gained an in-depth understanding of the model by working through their difficulties, they developed better genre awareness, and used the model adaptively to visualize and write their RA introductions. Conclusion: The results confirm the usefulness of the proposed model and reveal how a continuing process of learning and practicing using the model helps students develop their skills to create contexts and enhance their genre awareness. [ABSTRACT FROM AUTHOR]

Poor-quality remains a pervasive challenge for the delivery of large-scale transport infrastructure projects. Typically, it manifests as nonconformance and often requires rework. If, however, a nonconformance goes unidentified, then an asset's integrity can be jeopardized and put people's lives at risk. Hence, governments should shoulder the responsibility for quality. In practice, however, there has been a proclivity for governments to place fixed-price contracts and high-level quality risks on contractors. Yet, there is a dearth of research that has addressed the issue of poor-quality within large-scale transport infrastructure projects. In this article, we attempt to fill this gap and adopt an illustrative case-study approach to garner an understanding as to why poor-quality manifests during the construction of large-scale transport projects. We examine the issue of poor-quality in three high-profile rail transit projects using the gray literature: 1) Delhi Airport Metro Express (India); 2) Honolulu Rail Transit (United States); and 3) Sydney Skytrain (Australia). Then, drawing on empirical observations from two road projects, a highway corridor and a busway, we bring to the fore the poor-quality issue that pervades practice. To curb poor-quality, we suggest that the public and private sectors work collaboratively and share risks to ensure the benefits and value of an asset can be realized. To facilitate the process of collaboration and sharing of quality risks, we propose a Standard Life-cycle Quality Assessment system that can be used to ensure projects conform to standards and requirements. [ABSTRACT FROM AUTHOR]

The planning fallacy is at play in projects when optimism bias and/or strategic misrepresentation are present. We examine the cost performance of approximately US$ 6.5 billion worth of social infrastructure projects that were procured in Hong Kong and specifically the differences between their final accounts and the various types of estimates that were prepared prior to construction. We focus on the (honest) planning fallacy and hence aim to determine whether estimates are more optimistic than actual costs. Our data shows that 43% of projects incurred a cost underrun from their contract award. We therefore infer that at best 57% of projects may be explained by the presence of the planning fallacy. Based on our findings, we argue that optimism and pessimism bias coexist. Likewise, the planning fallacy and competing explanations can simultaneously account for cost deviations in social infrastructure projects. We submit that the prevalence of the planning fallacy has been exaggerated and hence provide an explanation as to why this has occurred. We finally suggest there is a need for additional empirical work to test the claim that the planning fallacy is the underlying cause of cost overruns and thus the best “theory” to explain “how projects work.” [ABSTRACT FROM AUTHOR]

Breast microcalcifications are an important primary radiological indicator of breast cancer. However, microcalcification classification and diagnosis may be still challenging for radiologists due to limitations of the standard 2D mammography technique, including spatial and contrast resolution. In this study, we propose an approach to improve the detection of microcalcifications in propagation-based phase-contrast X-ray computed tomography of breast tissues. Five fresh mastectomies containing microcalcifications were scanned at different X-ray energies and radiation doses using synchrotron radiation. Both bright-field (i.e. conventional phase-retrieved images) and dark-field images were extracted from the same data sets using different image processing methods. A quantitative analysis was performed in terms of visibility and contrast-to-noise ratio of microcalcifications. The results show that while the signal-to-noise and the contrast-to-noise ratios are lower, the visibility of the microcalcifications is more than two times higher in the dark-field images compared to the bright-field images. Dark-field images have also provided more accurate information about the size and shape of the microcalcifications. [ABSTRACT FROM AUTHOR]

In this article, we aim to address the following research question: How can a construction organization reduce and contain errors in its projects and mitigate rework and failures? We adopt an organizing sense-making perspective to acquire a sense of order of quality (e.g., rework) and to understand its relationship with safety (e.g., unsafe behavior) in construction. We undertook semistructured interviews from a range of employees involved with delivering a construction organization's projects. Also, documentary sources were accessed to supplement the rework and safety incidents that were referenced during the interviewing process. We found that the construction organization's prevailing culture focused on error prevention, which stymied its ability to learn and reduce rework in projects. The organization consciously enacted a trade-off between quality and safety. The upshot of this either/or framing of competing values was the suppression by senior management of nonconformances, which then led to deviant behavior manifesting in projects. We also revealed that safety incidents tended to arise when people engaged in unsafe behaviors while performing rework. The empirical evidence supports our call for construction organizations to engage in error management so that they can cultivate mindfulness where individuals and project teams can improvise and better handle errors, so they are not repeated. [ABSTRACT FROM AUTHOR]

Many studies on ISO 9001 have been focused on the motivation, the implementation process, and the outcomes of the implementation of the standard. This article seeks to advance the knowledge on ISO 9001 implementation by examining two research issues. First, we investigate the relationship between external auditors, certification bodies, and the operational and market-oriented outcomes from the ISO 9001 certification. Second, we study the different roles played by external auditors and certification bodies in affecting the outcomes achieved from the quality of implementation of the standard. Using a dataset comprising 537 firms from Australia and New Zealand that are ISO 9001 certified, our findings show that the quality of external auditors enhances operational outcomes as well as strengthens the relationship between ISO 9001 implementation and operational outcomes. This affirms the role of external auditors in the implementation of the standard. On the other hand, the reputation of certification bodies has a positive direct effect on market-oriented outcome, and its effect is strengthened by the quality of ISO 9001 implementation. The findings, therefore, show the contrasting roles between external auditors and certification bodies in enhancing different outcomes of ISO 9001 adoption in terms of operational and market-oriented ones. [ABSTRACT FROM AUTHOR]

It can be expected that the power systems of the future will be significantly different from today’s, especially due to increasing renewable energy sources (RESs), storage systems, and price-responsive users leading to large uncertainty and complexity. The operation of these future grids (FGs) at levels of renewable energy approaching 100% will require all the usual stability analysis along with new issues in a much more complex situation than has been encountered in the past. In fact, how close we can get to this desired level will likely be dependent on the assessed stability limits. Therefore, in this study, we use a novel scenario-sensitivity-contingency based framework to evaluate the system stability along possible evolution pathways towards high renewable FGs. As a case study, we carry out our studies based on proposed future scenarios and sensitivities for the Australian FG. Using a simulation platform that encompasses market simulation, load flow calculation and stability assessment altogether, the impact of grid strength, level of prosumers, and utility storage on the stability of the FG is studied and quantified indices for long term stability have been devised. The results of this study enable us to address the underlying stability issues of the FGs. [ABSTRACT FROM AUTHOR]

Cerebellar ataxia (CA) is concerned with the incoordination of movement caused by cerebellar dysfunction. Movements of the eyes, speech, trunk, and limbs are affected. Conventional machine learning approaches utilizing centralised databases have been used to objectively diagnose and quantify the severity of CA. Although these approaches achieved high accuracy, large scale deployment will require large clinics and raises privacy concerns. In this study, we propose an image transformation-based approach to leverage the advantages of state-of-the-art deep learning with federated learning in diagnosing CA. We use motion capture sensors during the performance of a standard neurological balance test obtained from four geographically separated clinics. The recurrence plot, melspectrogram, and poincaré plot are three transformation techniques explored. Experimental results indicate that the recurrence plot yields the highest validation accuracy (86.69%) with MobileNetV2 model in diagnosing CA. The proposed scheme provides a practical solution with high diagnosis accuracy, removing the need for feature engineering and preserving data privacy for a large-scale deployment. [ABSTRACT FROM AUTHOR]