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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]

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]

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]

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]

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]

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]

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]

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]

This article develops a practical framework for the multiobjective optimal planning of a grid-connected renewable-battery system considering a long-period operation. The capacities of wind turbine, solar photovoltaic (PV), and battery storage are optimized by minimizing three objective functions: cost of electricity (COE), grid dependence (GD), and total curtailed energy (TCE). A new rule-based energy management is developed for the long-period operation, where: 1) the capacity degradations of PV and battery are applied; 2) purchase and sell electricity prices are updated for each year using interest and escalation rates; and 3) the salvation value of the components is considered to achieve a realistic economic analysis of the planning problem. The developed multiobjective optimal planning model is examined using the long-period (ten years) real data of wind speed, solar insolation, ambient temperature, and load consumption for a grid-connected household in Australia. It is found that a household with the minimum GD (0.008%) results in a COE of 116 ¢/kWh with a TCE of 100 MWh in ten years. The proposed optimal planning framework based on the long-period operation is compared with the short-period operation. [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]

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators’ performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just “when” but also “what” to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments. [ABSTRACT FROM AUTHOR]

Enabling global Internet access is challenging for cellular-based Internet of Things (IoT) due to the limited range of terrestrial network services. One viable solution is to deploy IoT over satellite systems for coverage extension. However, operating a hybrid satellite–terrestrial network might incur high satellite bandwidth consumption and excessive service latency. Aiming to reduce the content delivery latency from the Internet-connected gateway to the users, this article proposes a two-tier cache-enabled model with full-duplex transmissions where content caches are deployed at the satellite and ground station. A closed-form solution for the successful delivery probability (SDP) of the files is derived considering the requested content distributions and channel statistics. Then, the SDP performance under common caching policies can be evaluated. The results are also used to optimize cache placement under caching capacity constraints. Numerical results demonstrate the performance improvements of the proposed system over those of single-tier cache-aided and half-duplex transmission systems. [ABSTRACT FROM AUTHOR]

An increasing number of residential customers have installed hybrid rooftop solar battery systems (HRSBSs). Currently, most HRSBSs are installed behind-the-meter (BTM), where only customers’ net load is measured by smart meters. This invisibility poses significant challenges to the system operation. Disaggregating BTM PV generation and battery charging/discharging profile of customers can enhance the grid-edge observability. This article proposes an energy disaggregation method to jointly separate the PV generation and battery charging/discharging power from the net load. First, a Home Smart Battery Management model is built to simulate the battery charging/discharging profile. Second, an optimal disaggregation model is established based on a contextually supervised source separation method. Furthermore, the feature vectors of PV, load, and battery are extracted as the input of the disaggregation model. Case studies on two datasets from Australia and China show that the proposed method has a promising disaggregation performance. [ABSTRACT FROM AUTHOR]

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository1. [ABSTRACT FROM AUTHOR]

As a biomarker for liver disease, bilirubin has been utilized in prognostic scoring systems for cirrhosis. While laboratory-based methods are used to determine bilirubin levels in clinical settings, they do not readily lend themselves to applications outside of hospitals. Consequently, bilirubin monitoring for cirrhotic patients is often performed only intermittently; thus, episodes requiring clinical interventions could be missed. This work investigates the feasibility of measuring bilirubin concentration in whole porcine blood samples using dual-wavelength transmission measurement. A compact and low-cost dual-wavelength transmission measurement setup is developed and optimized to measure whole blood bilirubin concentrations. Using small volumes of whole porcine blood (72 µL), we measured the bilirubin concentration within a range corresponding to healthy individuals and cirrhotic patients (1.2–30 mg/dL). We demonstrate that bilirubin levels can be estimated with a positive correlation (R-square > 0.95) and an accuracy of ±1.7 mg/dL, with higher reliability in cirrhotic bilirubin concentrations (> 4 mg/dL) – critical for high-risk patients. The optical and electronic components utilized are economical and can be readily integrated into a miniature, low-cost, and user-friendly system. This could provide a pathway for point-of-care monitoring of blood bilirubin outside of medical facilities (e.g., patient's home). [ABSTRACT FROM AUTHOR]

This article focuses on the issue of scaled tracking consensus for discrete-time second-order multiagent systems under random packet dropouts, where the cases with a static leader and a dynamic leader are considered, respectively. The scaled tracking consensus means that all agents reach a consensus value determined by the leader but with different scales, and the phenomenon of packet dropout on each communication link is described as a Bernoulli variable independent of other communication links. By virtue of random environment-based scaled consensus algorithms, it is shown how to reconstruct the original system into augmented error systems with random coefficient matrices. With the kind assistance of substochastic matrix and super-stochastic matrix, sufficient conditions for the cases with a static leader and a dynamic leader are derived, respectively. Moreover, computer simulations are performed to demonstrate the dynamics of network agents under random packet dropouts. [ABSTRACT FROM AUTHOR]