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This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks. [ABSTRACT FROM AUTHOR]

Primality generation is the cornerstone of several essential cryptographic systems. The problem has been a subject of deep investigations, but there is still a substantial room for improvements. Typically, the algorithms used have two parts – trial divisions aimed at eliminating numbers with small prime factors and primality tests based on an easy-to-compute statement that is valid for primes and invalid for composites. In this paper, we will showcase a technique that will eliminate the first phase of the primality testing algorithms. The computational simulations show a reduction of the primality generation time by about 30 percent in the case of 1024-bit RSA key pairs. This can be particularly beneficial in the case of decentralized environments for shared RSA keys as the initial trial division part of the key generation algorithms can be avoided at no cost. This also significantly reduces the communication complexity. Another essential contribution of the paper is the introduction of a new one-way function that is computationally simpler than the existing ones used in public-key cryptography. This function can be used to create new random number generators, and it also could be potentially used for designing entirely new public-key encryption systems. [ABSTRACT FROM AUTHOR]

The vision recognition module is one of the very important modules in the normal operation of the intelligent plant cultivation robot. In the original vision module, by improving the original YOLOV3(The algorithm proposed in YOLOv3: An Incremental Improvement), a more excellent YOLOV3 was obtained to recognize plants under normal conditions. However, the improved YOLOV3 does not perform well in the visual recognition process when there is a lot of noise interference. To make the visual recognition module continue to work properly, in this paper, the better Cycle GAN (generative adversarial networks) model is proposed to deal with noise based on three common noises, and finally, through a lot of experiments, it is proved that the improvement has some significance for the vision recognition module, which extends the use of the vision recognition module and makes the intelligent plant cultivation robot avoid the interference of noise. [ABSTRACT FROM AUTHOR]

Time-domain indirect methods based on Lyapunov stability theory and linear matrix inequality techniques (LMIs) have been applied for delay-dependent stability analysis of large-scale load frequency control (LFC) schemes. This paper aims to enhance the numerical tractability of large-scale LMIs by exploiting the special characteristics of the LFC loops. First, in the typical LFC model, only a few delayed states that are directly influenced by transmission delays are distinguished from other normal system states. Hence, an improved reconstruction model is formed, based on which the delay-dependent stability condition is established with the decreased order of the LMIs and decision variables. Then, to further improve the numerical tractability of the developed stability criterion, all weighting matrices required in the augmented Lyapunov functional are enforced to have structural restrictions by proposing an extended symmetry-exploiting technique. Case studies show that the method proposed in this paper significantly improves the calculation efficiency of stability criterion established for multi-area power systems at the cost of only a minor reduction in computational accuracy. [ABSTRACT FROM AUTHOR]

Long period digital random sequence plays an important role in reliable communications and high security scenarios. This paper improved the method of generating long period digital random sequences based on the Residue Number System (RNS) and the Chinese Remainder Theorem (CRT), and a sequence mapping method after CRT extension. This paper proves that the period of sequence after mapping will not degenerate if the modulus used in the mapping stage is coprime with the period of the original sequence. By using the parallelism of RNS, the proposed method can generate sequences at high speed with fewer hardware resources. The NIST test results show that the pass rate of each test item is above 98.40%, which meets the NIST test confidence requirements, confirming the randomness of the generated sequences. An image encryption test is given as one of the example applications of the generated sequences. On the theoretical basis, by jointly optimizing the sequence mapping and iteration procedure, a hardware implementation architecture is also presented in this paper. The implementation is based on Xilinx XC7Z020CLG484-3 FPGA and compared with the implementations of classical chaotic maps. The results show that the proposed architecture has longer sequence period with less hardware resource consumption and higher generation speed and is more general. Meanwhile, the proposed architecture has fast phase switching ability, which is about 10 clock periods. This is one of the key attributes when the sequence is used in communication systems. [ABSTRACT FROM AUTHOR]

Multiple strokes are the main factors affecting the safe and stable operation of power equipment. As the core lightning protection equipment, arresters are also frequently thermally collapsed due to multiple strokes. This paper investigates thermal effects of polymer-housed metal-oxide surge arrester (PMOSA) under multiple strokes. The comparative experiments of PMOSA lightning impulse are carried out through multiple strokes of experiment platform, including continuous lightning stroke and multiple strokes. Furthermore, the temperature distribution of the housing surface is obtained by infrared imager for thermal effect analysis. Subsequently, some typical DC parameters are tested to explore the association of impulse aging and thermal effects of the arrester under multiple strokes. Finally, a thermal-electric coupling model (TECM) is constructed to investigate the influence of some factors on the temperature of PMOSA from the perspective of materials and structure. The research in this paper can provide a theory reference and brief data support for improving the heat dissipation performance of arresters under multiple strokes based on structural optimization design and material modification. [ABSTRACT FROM AUTHOR]

Power system inertia is declining and is increasingly variable and uncertain in regions where the penetration of non-synchronous generation and interconnectors is growing. This presents a challenge to power system operators who must take appropriate actions to ensure the stability and security of power systems relying on short-term forecasts of the system’s inertial response. Existing models to forecast inertia fail to quantify uncertainty, which may prevent their utilization given the risk aversion of the system operators when handling stability issues. This paper is the first to develop a model to produce calibrated, data-driven probabilistic forecasts of the inertia contribution of transmission-connected synchronous generators. The model provides a necessary tool for system operators to quantify forecast uncertainty, allowing them to manage the risk of frequency instability cost-effectively. The paper demonstrates that the assumption of a Gaussian distribution of uncertainty applied in existing models is not acceptable to accurately forecast the inertial response and provides a satisfactory forecast model by combining non-parametric density forecasting with parametric tail distributions. Moreover, the paper shows that satisfactory predictive performance can only be achieved by adopting a rolling horizon forecast approach to deal with the rapidly changing characteristics of the inertial response in power systems. [ABSTRACT FROM AUTHOR]

This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper demonstrates the potential of inverter-based loads to support grid reliability during power system transients thereby enabling reliable integration of renewable energy in power systems. Such loads are referred to in this paper as grid-supportive loads (GSLs). A new GSL model is developed that simulates the transient response capabilities that can be programmed in electronic loads. The model’s design enables it to be easily integrated in widely used commercial power system transient analysis software. Theoretical expressions are derived that explain the workings of the GSL model. The performance, numerical stability, and impact of the GSL model is validated on 9-bus and 2000-bus synthetic power system models using generator tripping and bus fault disturbances. Results on the 2000 bus system show that in the absence of frequency support from wind/solar generation resources, just 20% of loads with grid-supportive capabilities can improve frequency response by up to 2000 MW/0.1 Hz and reduce deviation in frequency at nadir by up to 60% compared to the situation when GSLs are absent. Power system reliability also improves under fault events. It is further shown that GSLs can aid in integrating more renewable generation without degrading the overall transient response of the power system. [ABSTRACT FROM AUTHOR]

Textures contain a wealth of image information and are widely used in various fields such as computer graphics and computer vision. With the development of machine learning, the texture synthesis and generation have been greatly improved. As a very common element in everyday life, wallpapers contain a wealth of texture information, making it difficult to annotate with a simple single label. Moreover, wallpaper designers spend significant time to create different styles of wallpaper. For this purpose, this paper proposes to describe wallpaper texture images by using multi-label semantics. Based on these labels and generative adversarial networks, we present a framework for perception driven wallpaper texture generation and style transfer. In this framework, a perceptual model is trained to recognize whether the wallpapers produced by the generator network are sufficiently realistic and have the attribute designated by given perceptual description; these multi-label semantic attributes are treated as condition variables to generate wallpaper images. The generated wallpaper images can be converted to those with well-known artist styles using CycleGAN. Finally, using the aesthetic evaluation method, the generated wallpaper images are quantitatively measured. The experimental results demonstrate that the proposed method can generate wallpaper textures conforming to human aesthetics and have artistic characteristics. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for synthesizing a novel street-view panorama given a satellite image, as if captured from the geographical location at the center of the satellite image. Existing works approach this as an image generation problem, adopting generative adversarial networks to implicitly learn the cross-view transformations, but ignore the geometric constraints. In this paper, we make the geometric correspondences between the satellite and street-view images explicit so as to facilitate the transfer of information between domains. Specifically, we observe that when a 3D point is visible in both views, and the height of the point relative to the camera is known, there is a deterministic mapping between the projected points in the images. Motivated by this, we develop a novel satellite to street-view projection (S2SP) module which learns the height map and projects the satellite image to the ground-level viewpoint, explicitly connecting corresponding pixels. With these projected satellite images as input, we next employ a generator to synthesize realistic street-view panoramas that are geometrically consistent with the satellite images. Our S2SP module is differentiable and the whole framework is trained in an end-to-end manner. Extensive experimental results on two cross-view benchmark datasets demonstrate that our method generates more accurate and consistent images than existing approaches. [ABSTRACT FROM AUTHOR]

Linear codes achieving or nearly achieving the Singleton bound are interesting in both theory and practice. The objective of this paper is to construct several infinite families of MDS, near MDS and almost MDS codes from some special cyclic subgroups of ${\mathbb {F}}_{q^{2}}^{*}$. To this end, the augmentation and extension techniques are used. The codes in this paper have flexible parameters and their lengths could be large. The minimum linear locality of the codes constructed in this paper is also studied. Some infinite families of optimal linearly locally recoverable codes are obtained. Besides, some codes in this paper are proved to be proper for error detection. [ABSTRACT FROM AUTHOR]

This paper proposed a distributed uniform controlstrategy for parallel bidirectional interlinking converters(BICs) to realize resilient operation of hybrid ac/dc microgrid (HMG). The proposed control strategy can coordinate the control objectives of BICs in different HMG operation modes with a uniform control structure, thus, to eliminate any mode transition detections. In normal condition, the power coordination between ac and dc subgrids can be achieved, while ac or dc voltage regulation can be maintained when main voltage source(MVS) fails in ac or dc subgrid. The coordination between parallel BICs relies on low-bandwidth neighbourhood communication. With the communication planned, accurate power sharing can be achieved. Communication fault ride-through capability is enabled with less power sharing accuracy. The system reliability and resilience of HMG under various fault conditions can be improved by the proposed control strategy. To verify the effectiveness of the proposed control strategy, the controller hardware-in-the-loop experiment results have been provide in this paper. [ABSTRACT FROM AUTHOR]

Human beings tend to learn unknown knowledge in a gradual process, from the basic to the complex. Based on this point, we propose a progressive learning method for producing service strategies according to requests, with a hierarchical priori knowledge and reinforcement learning. Service strategy aims to guide how to perform home services and takes into consideration the relationship between actions and objects in home environment. In this paper, strategy generation is regarded as a text generation problem in question answering (QA). Firstly, a hierarchical priori knowledge with service-object correlation at the bottom and action-object correlation at the top is constructed to assist the understanding on the relationship of objects and actions in service strategies. Service-object correlation guides how to select proper objects with the correct order, while action-object correlation associates actions in strategies according to selected objects. Based on the hierarchical priori knowledge, a progressive learning method is proposed to make the model produce effective strategies with a sequential cognition, from service-object correlation (objects) to action-object correlation (actions). After that, reinforcement learning is employed to enhance the progressive guidance, by designing rewards in terms of the hierarchical priori knowledge. Finally, the proposed method is tested with both comparative experiments and ablation studies, and the experimental results demonstrate the superiority in producing comprehensive and logical strategies, indicating that the progressive learning method in our paper can further improve the QA performance. [ABSTRACT FROM AUTHOR]

After any major blackout, recreating the exact scene is one of the crucial but foundation steps in postmortem analysis. This helps in identifying and understanding the exact causes and sequence of events to avoid such failures in the future. Rather, power system restoration demands critical skills which involves deployment of appropriate strategies based on information about various factors, notably the extent and duration of the blackout, location of black-started units, interconnections with neighbouring systems, generator capabilities, and selecting appropriate restoration paths. The actual execution of the restoration plan consists of many surprises of unique nature and a lot of learning’s to avoid future occurrences of such incidents. The restoration efforts and failure of Mumbai grid failure on October 12, 2020, are highlighted in this paper. The paper first proposes formulating the dynamic models to get a better insight of the restoration process in the event of critical issues during this incident. The various challenges faced in systematic recovery of such a large system and lessons learnt from it forms focus of the paper. The paper also discusses factors not covered in existing literature, such as weather, fuel availability, and water mass oscillations which plays an important role in the restoration. [ABSTRACT FROM AUTHOR]

A functional $k$ -batch code of dimension $s$ consists of $n$ servers storing linear combinations of $s$ linearly independent information bits. Any multiset request of size $k$ of linear combinations (or requests) of the information bits can be recovered by $k$ disjoint subsets of the servers. The goal under this paradigm is to find the minimum number of servers for given values of $s$ and $k$. A recent conjecture states that for any $k=2^{s-1}$ requests the optimal solution requires $2^{s}-1$ servers. This conjecture is verified for $s \leqslant 5$ but previous work could only show that codes with $n=2^{s}-1$ servers can support a solution for $k=2^{s-2} + 2^{s-4} + \left \lfloor{ \frac { 2^{s/2}}{\sqrt {24}} }\right \rfloor $ requests. This paper reduces this gap and shows the existence of codes for $k=\lfloor \frac {5}{6}2^{s-1} \rfloor - s$ requests with the same number of servers. Another construction in the paper provides a code with $n=2^{s+1}-2$ servers and $k=2^{s}$ requests, which is an optimal result. These constructions are mainly based on extended Simplex codes and equivalently provide constructions for parallel Random I/O (RIO) codes. [ABSTRACT FROM AUTHOR]

The energy transition is aimed to take advantage of the operational flexibility of hydropower to extend the integration of intermittent renewable sources. Consequently, the hydrogenerators will have to operate in regimes far away from their designed best-point operation. In order to accurately assess the impact, this paper presents a useful approach to determine the overall operating efficiency of synchronous generators under intermittent operation. An accumulated average efficiency (AAE) model is proposed and compared against an alternative approach; the weighted average efficiency (WAE) model. It is found that the WAE approach produces unrealistic low efficiencies when the generator operates in synchronous condenser mode (SCM) for long periods. In general, the AAE supersedes the WAE for all the different load distributions that were investigated. This was further illustrated by a worked example and by constructing more complex load distributions. A load distribution dominated by SCM yields a difference as high as $33.18 \,\%$ , while an even distribution deviates $1.43 \,\%$ in their respective efficiencies. Finally, a yearly on-site measurement of our studied $103 \,\mathrm{MVA}$ generator’s concentrated load distribution revealed a discrepancy of $0.67 \,\%$ , which is a significant deviation considering what the operating regime would mean in terms of economic implications. [ABSTRACT FROM AUTHOR]

With the development of Internet technology, information has become an increasingly important asset for the operation and planning of power systems. However, the existing studies and practices pay little attention to information value evaluation, i.e., the potential for information to be converted into actual economic benefits. To this end, this paper designs an information market framework and proposes a generalized information valuation model to help price data in smart grids efficiently. Here we analyze the information value of photovoltaic (PV)-related data in a power system operation problem. Specifically, we examine how additional meteorological and PV power data help to improve day-ahead forecasting accuracy, thus enhancing unit commitment (UC). In this paper, information quality is captured by two indices of a set of PV-related data, i.e., Shannon entropy and non-noise ratio. Then a neural network-based engine is employed to predict day-ahead hourly solar power on the premise of input datasets with different information quality. Here we define forecasting accuracy as information utility, and discover an exponential relationship between such utility and information quality. Finally, a two-stage stochastic UC model is formulated to quantify the contributions of different PV-related datasets, in which real-time solar power deviation is penalized. In this instance, the economic value of PV-related data is measured as the operational cost reduction induced by forecasting accuracy improvement, which we find can be estimated by information quality. Case studies based on the IEEE 30- and 118-bus systems validate the effectiveness of the proposed paradigm and method. [ABSTRACT FROM AUTHOR]

Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. Results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration. [ABSTRACT FROM AUTHOR]

This paper proposes a novel inertia-enhanced distributed control method to complement the inertia of microgrids. The rate of change of frequency (RoCoF) and the rate of change of voltage (RoCoV) are employed in this paper to quantify the frequency inertia and voltage inertia, respectively. Then, a fully distributed algorithm with constrained changing rates is proposed. By bounding the changing rates of frequency and voltage during the consensus control, the algorithm can address the consensus problem while enhancing the inertia of microgrids. Compared with most inertia control methods, the proposed method can utilize the reserve power of scattered DGs to supply inertia. Besides, it performs better under disturbances and delays than conventional distributed control methods. The effectiveness of the proposed method is validated by several cases in MATLAB/Simulation and a hardware experiment. [ABSTRACT FROM AUTHOR]

Intelligent false data injection on load measurements can trigger false relay operation (FRO) of frequency-based protection relays, affecting the power system frequency and thus threatening the security of power systems. In this paper, we propose an optimization-based formal model to find the optimal false data injection attack (OFDIA) with the minimum required time leading to an FRO. The proposed model considers the dynamic behavior of the power system in an optimization framework to find the optimal size of attacks over multiple generators’ dispatching cycles to minimize the attack launch time. Using the proposed formal modeling, we study the impact of power system parameters, including inertia, governor’s droop and time constant, and the attacker’s accessibility to loads on the attack success and launch time. The results demonstrate that systems with low inertia are more vulnerable to FDIAs while systems with higher inertia are more secure as fewer generator protection relays are impacted by FRO. In addition, we show that securing more load meters can increase the time for launching an attack in the system. Moreover, our studies show that a combination of large values of the governor’s time constants and small values of the governor’s droops can raise the time of successful attacks, making the system more secure against FDIAs. [ABSTRACT FROM AUTHOR]

This paper proposes a Principal Component Analysis (PCA)-based method to construct composite indices to rapidly assess power system transient stability. By assigning appropriate weights to a few selected sub-indicators, a composite index can be constructed to guarantee a better coverage of worst-case stability scenarios of the power system. This paper provides an automatic method to construct a composite index with four sub-indicators calculated by the integral of acceleration, speed deviation and rotor angle of generators over a certain integration period using PCA. The effectiveness of the sub-indicators is validated first, followed by a comprehensive example of process of composite index construction, and the assessment of the influence of the effect of integration period on the performance of the composite index. High accuracy of classification has been demonstrated using composite index with appropriate settings of the integration periods of the sub-indicators. [ABSTRACT FROM AUTHOR]

With the expansion of system scale and data size in power systems, data-driven methods are gradually becoming widely used. However, compared with traditional methods, data-driven methods face more threats in data security and algorithm security. This paper proposes a black box data injection attack method against data-driven strategies in power systems. To obtain a stealthy attack scheme with the greatest impact, an attack vector generator based on deep convolutional neural networks (DCNN) is designed. The generator includes a node selector, a filter, an encoder, and a decoder. The node selector is used to select the most likely successful attack scheme for the filter, the encoder is used to extract sample features, and the decoder is used to generate disturbances based on the features. The proposed generator is trained with an improved generative adversarial network (GAN) and can generate minimal disturbances in real time based on measurement data from the power grid. The effectiveness of the proposed method is demonstrated with an attack experiment on an online transient stability application. [ABSTRACT FROM AUTHOR]

Over the last decade, deep neural networks (DNNs) are regarded as black-box methods, and their decisions are criticized for the lack of explainability. Existing attempts based on local explanations offer each input a visual saliency map, where the supporting features that contribute to the decision are emphasized with high relevance scores. In this paper, we improve the saliency map based on differentiated explanations, of which the saliency map not only distinguishes the supporting features from backgrounds but also shows the different degrees of importance of the various parts within the supporting features. To do this, we propose to learn a differentiated relevance estimator called DRE, where a carefully-designed distribution controller is introduced to guide the relevance scores towards right-skewed distributions. DRE can be directly optimized under pure classification losses, enabling higher faithfulness of explanations and avoiding non-trivial hyper-parameter tuning. The experimental results on three real-world datasets demonstrate that our differentiated explanations significantly improve the faithfulness with high explainability. Our code and trained models are available at https://github.com/fuweijie/DRE. [ABSTRACT FROM AUTHOR]

This paper addresses the quality control of the photolithography process in the semiconductor industry. Overlay errors in the process seriously affect the wafer yield, and cause the wafer to be forced to rework and affect the production efficiency of the equipment. We examine the current state of its process control, develop a novel overlay predict model, and verify the prediction results. This study proposes a Global Attention Generative Adversarial Networks (GAGAN) model to precisely predict the overlay error for the feed-forward data of the front layer, which is used as the important information and process parameters for the advanced process control of the current layer. Experiment results on a semiconductor shop-floor confirms that our proposed method achieves high predictive performance while maintaining extensibility and visual quality. [ABSTRACT FROM AUTHOR]

How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. However, the large variations in image content and the real-world picture-taking conditions often result in far more complex reflection. In this paper, we introduce a new screen-blur combination based on two important factors, namely the intensity and the blurriness of reflection, to better characterize the reflection formulation in SIRR. Specifically, we present Screen-blur Reflection Networks (SRNet), which executes the screen-blur formulation in its network design and adapts to the complex reflection on real scenes. Technically, SRNet consists of three components: a blended image generator, a reflection estimator and a reflection removal module. The image generator exploits the screen-blur combination to synthesize the training blended images. The reflection estimator learns the reflection layer and a blur degree that measures the level of blurriness for reflection. The reflection removal module further uses the blended image, blur degree and reflection layer to filter out the transmission layer in a cascaded manner. Superior results on three different SIRR methods are reported when generating the training data on the principle of the screen-blur combination. Moreover, extensive experiments on six datasets quantitatively and qualitatively demonstrate the efficacy of SRNet over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the growing use of mobile devices, location-based services (LBS) are becoming increasingly popular. BLS deliver accurate services to individuals according to their geographical locations, but privacy issues have been the primary concerns of users. Privacy-preserving LBS (PPLBS) were proposed to protect location privacy, but there are still some problems: 1) a semi-trusted third party (STTP) is required to blur users’ locations; 2) both the computation and communication costs of generating a query are linear with the size of queried areas; 3) the schemes were not formally treated, in terms of definition, security model, security proof, etc. In this paper, to protect location privacy and improve query efficiency, an oblivious location-based services (OLBS) scheme is proposed. Our scheme captures the following features: 1) an STTP is not required; 2) users can query services without revealing their exact location information; 3) the service provider only knows the size of queried areas and nothing else; and 4) both the computation and communication costs of generating a query is constant, instead of linear with the size of queried areas. We formalise both the definition and security model of our OLBS scheme, and propose a concrete construction. Furthermore, the implementation is conducted to show its efficiency. The security of our scheme is reduced to well-known complexity assumptions. The novelty is to reduce the computation and communication costs of generating a query and enable the service provider to obliviously generate decrypt keys for queried services. This contributes to the growing work of formalising PPLBS schemes and improving query efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a method for controlling the voltage of inverter-based Microgrids by proposing a new scale-free distributed cooperative controller. The main contribution of this paper is that the proposed distributed cooperative controller is scale-free where is independent of any information about the communication system and the number of distributed generators, as such it works for any Microgrids with any size. Moreover, the communication network is modeled by a general time-varying graph which enhances the resilience of the proposed protocol against communication link failure, data packet loss, and arbitrarily fast plug and play operation in the presence of arbitrarily finite communication delays as the protocol does not require the knowledge of the upper bound on the delay. The stability analysis of the proposed protocol is provided. The proposed method is simulated on the CIGRE medium voltage Microgrid test system. The simulation results demonstrate the feasibility of the proposed scale-free distributed nonlinear protocol for regulating voltage of Microgrids in the presence of communication failures, data packet loss, noise, and degradation. [ABSTRACT FROM AUTHOR]

The Bose-Chaudhuri-Hocquenghem (BCH) codes are a well-studied subclass of cyclic codes that have found numerous applications in error correction and notably in quantum information processing. They are widely used in data storage and communication systems. A subclass of attractive BCH codes is the narrow-sense BCH codes over the Galois field ${\mathrm {GF}}(q)$ with length $q+1$ , which are closely related to the action of the projective general linear group of degree two on the projective line. Despite its interest, not much is known about this class of BCH codes. This paper aims to study some of the codes within this class and specifically narrow-sense antiprimitive BCH codes (these codes are also linear complementary duals (LCD) codes that have interesting practical recent applications in cryptography, among other benefits). We shall use tools and combine arguments from algebraic coding theory, combinatorial designs, and group theory (group actions, representation theory of finite groups, etc.) to investigate narrow-sense antiprimitive BCH Codes and extend results from the recent literature. Notably, the dimension, the minimum distance of some $q$ -ary BCH codes with length $q+1$ , and their duals are determined in this paper. The dual codes of the narrow-sense antiprimitive BCH codes derived in this paper include almost MDS codes. Furthermore, the classification of ${\mathrm {PGL}}(2, p^{m})$ -invariant codes over ${\mathrm {GF}}(p^{h})$ is completed. As an application of this result, the $p$ -ranks of all incidence structures invariant under the projective general linear group ${\mathrm {PGL}}(2, p^{m})$ are determined. Furthermore, infinite families of narrow-sense BCH codes admitting a 3-transitive automorphism group are obtained. Via these BCH codes, a coding-theory approach to constructing the Witt spherical geometry designs is presented. The BCH codes proposed in this paper are good candidates for permutation decoding, as they have a relatively large group of automorphisms. [ABSTRACT FROM AUTHOR]

Map inference algorithm aims to construct a digital map from other data sources automatically. Due to the labour intensity of traditional map creation and the frequent road change nowadays, map inference is deemed to be a promising solution to automatic map construction and update. However, existing map inference from GPS trajectories suffers from low GPS data quality, which makes the quality of the constructed map unsatisfactory. In this paper, we study the existing map inference algorithms using GPS trajectories. Different from previous surveys, we (1) include the most recent solutions and propose a new categorisation of method; (2) study how different types of GPS errors affect the quality of inference results; (3) evaluate the existing map inference quality measures regarding their ability to identify map quality issues. To achieve these goals, we conduct a comprehensive experimental study on several representative algorithms using both real-world datasets and synthetic datasets, which are generated from our proposed synthetic trajectory generator and artificial map generator. Overall, our study provides insightful observations regarding (1) which inference method performs better in each working scenario, (2) the general data quality requirements for map inference, (3) the direction of future works for quantitative map quality measures. [ABSTRACT FROM AUTHOR]

Supporting parallel multi-priority data streams is vital for maintaining the quality of service (QoS) in vehicle-to-everything (V2X) communication. Hence, the European telecommunications standard institute (ETSI) has defined four packet types, with varying priority levels, to be used as broadcast packets in such communication. This paper studies the medium access control (MAC) layer performance of IEEE 802.11p and cellular-V2X (C-V2X) Mode 4 using discrete-time Markov chain (DTMC) based models, while considering parallel multi-priority data streams. The overall model consists of four queue models with their respective traffic generators, which are appropriately linked with the DTMCs modeling the MAC layer operations of IEEE 802.11p and C-V2X Mode 4. Closed-form solutions for the steady-state probabilities of the models are obtained, which are then utilized to derive expressions for key performance indicators at the MAC layer. Numerical results are provided to draw insights on the MAC layer performance of the two technologies. IEEE 802.11p is comparatively superior in average delay, and at maintaining fairness among multi-priority data streams, whereas C-V2X Mode 4 exhibits better collision resolution, which leads to its higher throughput. The paper also includes design insights on possible performance enhancements for future releases. [ABSTRACT FROM AUTHOR]

The short-circuit response of inverter-interfaced distributed generators (IIDGs) is not adequately represented in many conventional protection studies. This paper presents an in-depth analysis of IIDG behavior during grid faults and proposes a more accurate reduced-order parameterized short-circuit current (RPSC) model of inverters. Typical inverter components are thoroughly investigated to identify those that play dominant roles during faults. The paper shows that the current limiter is the dominant factor for the steady-state fault-current of inverter; while the inverter filter along with the severity of the voltage disturbance largely determine the initial transient spike of inverter fault current. The proposed model is low-order and can be used in large scale simulations. The parameters of the proposed RPSC model can be extracted from laboratory experiments without requiring proprietary manufacturer information. The proposed fault-current model is analogous to the well-known synchronous machine model that segregates the inverter fault current into subtransient, transient, and steady-state fault currents. Experimental and simulation tests are presented to validate the model. [ABSTRACT FROM AUTHOR]

Autonomous power systems with a single power station and a high-voltage (HV) network may be susceptible to overvoltage instability, especially during light-load conditions. Moreover, in low inertia systems, considerable frequency excursions may be observed after disturbances. To deal with voltage and frequency stability problems in such isolated power systems, several measures should be taken, such as rules for adequate automatic voltage control of the synchronous generators and load-tap-changers (LTCs), and/or adequate design of actions applied during emergency system conditions such as reactors switching, and redesigning underfrequency load shedding taking into account overvoltage problems. The effectiveness of the proposed countermeasures against overvoltage instability in autonomous power systems is illustrated in this paper. In 2016, the autonomous power system of the Greek island of Rhodes experienced similar voltage/frequency problems leading to a blackout. This event has been accurately reconstructed in this paper. The application of the suggested countermeasures during the simulation of the 2016 blackout event in Rhodes, further highlighted the effectiveness of these measures, providing a guideline for similar cases. [ABSTRACT FROM AUTHOR]

Electrical equipment of the floating nuclear power plant operates in a long-term vibration environment. Ground faults occur frequently and are often accompanied by arc, a reliable arc suppression algorithm for the generator stator winding is necessary. In this paper, an active arc suppression method is proposed by connecting a controllable voltage source to the generator neutral point. Through regulating the neutral point voltage to be equal in amplitude and opposite in phase to the fault potential, the fault point voltage can be clamped to a low level to achieve arc suppression. Since the generator's phase potential contains certain harmonic components, the controllable voltage source includes fundamental and third-order harmonic components. To accurately calculate the output voltage, online calculation methods for the fault location, fault potential and grounding capacitance are proposed. The accuracy of the fault location method is verified by simulation and dynamic test. And simulation results based on PSCAD/EMTDC software show that the proposed method can achieve effective arc suppression at different ground fault conditions. [ABSTRACT FROM AUTHOR]

With the increase of photovoltaic penetration, the power-electronic converter (PEC)-interfaced generators with traditional control strategy may cause severe power oscillations after being disturbed due to lack of inertia and damping. The photovoltaic power plant controlled by virtual synchronous generator (VSG) technology, while introducing virtual inertia and virtual damping to improve the stability of the system, will also inherit its oscillation characteristics and cause a new oscillation mode. Aiming at this problem, this paper analyzes the difference between the system low-frequency oscillation (LFO) modes before and after the VSG is connected to the two-area system by the modal analysis. It is pointed out that the PEC-interfaced renewable energy generators based on VSG control technology could change the oscillation mode of the original system. The influence of VSG's main parameters and capacity on system dynamics is analyzed. Finally, a synchrophasor-based inter-area oscillation suppressed comprehensive control strategy is proposed to suppress the LFO of active power on the tie line, and the superiority of the strategy is verified by DIgSILENT /PowerFactory. [ABSTRACT FROM AUTHOR]

The risk of unstable subsynchronous oscillations (SSO) in DFIG-based wind parks radially connected to transmission lines with series capacitive compensation is typically assessed with numerous electromagnetic transient (EMT) simulations, which is a time-consuming process. However, not all operating scenarios present risk of SSO and require such a detailed investigation. This paper proposes two charts based on impedance equivalents of wind generators obtained through frequency scans to quickly map which combinations of parameters can create unstable SSOs and which combinations are risk-free. With the information, engineers can immediately filter out scenarios with no risk of instability and conduct detailed studies on fewer scenarios that are truly critical. These charts can be obtained with simple equations that use only information readily available in practice to engineers, without the need to run any computer simulation. The charts also reveal the key operating conditions that can increase the risk of an unstable SSO and provide real-time quantitative insights into how close a wind park is to an instability. Application examples are provided to illustrate how this approach can help engineers speed up studies to identify the risk of SSO. [ABSTRACT FROM AUTHOR]

Metal-oxide arresters (MOA) are widely used to protect equipment against transient electromagnetic disturbances (TED) like very fast transient overvoltage (VFTO), high-altitude electromagnetic pulse (HEMP), lightning pulse, et al. Researches on the performance of MOA under microsecond level rise-time TED are abundant, such as lightning pulse. However, when MOA is subjected to TED of rise-time at nanosecond level, the voltage/current responses of MOA are not fully studied due to the lack of a standardized and reliable test setup. In this paper, a test setup is proposed for acquiring the response behavior of MOA under nanosecond level pulse. The setup is comprised of a pulse generator, a two-branch transmission line system and a high voltage power divider. The generator can provide stable double exponential pulse with tunable rise-time ranging from 5 ns to 100 ns. The divider ensures an even distribution of voltage/current surge wave along the two-branch line. Through the numerical analysis, both impinging and residual surge waveforms could be retrieved from measurements. The proposed method has been applied to investigate response behavior of MOA subjected to nanosecond level rise-time pulses. [ABSTRACT FROM AUTHOR]

As a new attempt to reject clutter in High Frequency Passive Bistatic Radar (HFPBR), the Cycle-Consistent Adversarial Network (CycleGAN)-based image domain clutter suppression algorithm works successfully by treating the clutter rejection problem as an image-to-image migration between the range-doppler spectrograms without and with clutter suppression. However, this algorithm also has some disadvantages such as too many calculation parameters, difficult model training and low clutter suppression rate. In this paper, a novel clutter suppression method based on an improved DenseNet CycleGAN (D-CycleGAN) is proposed. In this method, the ResNet block of the generator in the original CycleGAN network is replaced with the DenseNet block, to better extract clutter features. To better evaluate the clutter suppression performance of the deep learning network-based graph-relational method, a new index suitable for HF passive radar is introduced. Furthermore, the influence of the number of Dense blocks on the performance of the algorithm is also discussed. Finally, the performance of the proposed method is tested and verified on the measured data, compared with the traditional clutter suppression method and other popular image migration algorithms, such as Pix2pix. The results show that the D-CycleGAN-based method has better clutter suppression performance, stronger generalization ability, and better retained radar targets simultaneously. [ABSTRACT FROM AUTHOR]

Droop control is a viable method for the operation of island DC microgrids in a decentralized architecture. This paper presents a mixed-integer conic optimization formulation for the design of generator droop control, comprising the parameters of a piecewise linear droop curve. The mixed-integer formulation originates from a stochastic optimization framework that considers several operating scenarios for finding the optimal design. The convexity of the mixed-integer problem continuous relaxation gives global optimality guarantees for the design problem. The paper presents computational results using a tight polyhedral approximation of the conic program, leading to a mixed-integer linear programming (MILP) problem that is solved using a state-of-the-art commercial solver. The results from the proposed approach are contrasted with both a classic linear droop control design and a recent piecewise linear formulation. The Monte-Carlo simulation results quantify the extent to which the MILP solution is superior in reducing voltage violations and power loss, and the degree to which the loss is close to that from a conic optimal power flow solution. [ABSTRACT FROM AUTHOR]

Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives of profit-maximizing generators and shows that, under mild conditions, no uniform-pricing scheme for the rolling-window economic dispatch provides dispatch-following incentives that avoid discriminative out-of-the-market uplifts. A nonuniform pricing mechanism, referred to as the temporal locational marginal pricing (TLMP), is proposed. As an extension of the standard locational marginal pricing (LMP), TLMP takes into account both generation and ramping-induced opportunity costs. It eliminates the need for the out-of-the-market uplifts and guarantees full dispatch-following incentives regardless of the accuracy of the demand forecasts used in the dispatch. It is also shown that, under TLMP, a price-taking market participant has incentives to bid truthfully with its marginal cost of generation. Part II of the paper extends the theoretical results developed in Part I to more general network settings. It investigates a broader set of performance measures, including the incentives of the truthful revelation of ramping limits, revenue adequacy of the operator, consumer payments, generator profits, and price volatility under the rolling-window dispatch model with demand forecast errors. [ABSTRACT FROM AUTHOR]

For n ∈ N , let [ n ] = { 1 , 2 , ... , n } be an n - element set. As usual, we denote by I n the symmetric inverse semigroup on [n], i.e. the partial one-to-one transformation semigroup on [n] under composition of mappings. The crown (cycle) C n is an n-ordered set with the partial order ≺ on [n], where the only comparabilities are 1 ≺ 2 ≻ 3 ≺ 4 ≻ ⋯ ≺ n ≻ 1 or 1 ≻ 2 ≺ 3 ≻ 4 ≺ ⋯ ≻ n ≺ 1. We say that a transformation α ∈ I n is order-preserving if x ≺ y implies that x α ≺ y α , for all x, y from the domain of α . In this paper, we study the inverse semigroup I C n of all partial automorphisms on a finite crown C n . We consider the elements, determine a generating set of minimal size and calculate the rank of I C n . [ABSTRACT FROM AUTHOR]

The penetration of inverter-based resources is growing rapidly around the globe. During a fault, the behavior of inverter-based resource is very different from a traditional synchronous machine based generation. As the penetration of inverter-based resources increases, there is an urgency for the industry to understand their behavior so that these resources can be accurately modeled and their impact on various protection schemes can be studied in detail. This path begins with standardizing the response of inverter-based resources to a fault. Additionally, this paper discusses issues with modeling inverter-based resources in a short circuit model which is developed for systems dominated by synchronous machines and finally a need for further research to understand impacts on traditional protection schemes as inverter-based resources continue to displace traditional generation. This paper does not offer solutions however, it is intended to encourage further study by industry professionals toward development of solutions. [ABSTRACT FROM AUTHOR]

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. This paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories. [ABSTRACT FROM AUTHOR]

Unified power flow controller (UPFC) gains growing application in power systems due to its power flow regulation ability. However, there is still a dilemma in the incorporation of UPFC into security-constrained economic dispatch (SCED). The modeling of UPFC involves nonlinear and nonconvex operational constraints, which is difficult to solve efficiently and accurately. Conventional methods adopted Taylor expansion or sensitivity analysis to realize the convexification of UPFC models, which leads to a loop structure and is subject to initial point choice. In this paper, a novel three-terminal injected UPFC model is proposed. In the proposed model, the voltage source variables are eliminated completely. Instead, only power injection variables are introduced to equivalently describe physical characteristics and constraints of UPFC. The nonlinear and nonconvex operational constraints of UPFC are transformed into mixed integer second-order cone form. Moreover, compared with conventional UPFC models, the control characteristic of UPFC is considered in a more accurate and practical manner in the proposed SCED, which helps to prevent potential risks of power flow over-limit under N-1 contingencies. Simulation results show the effectiveness and efficiency of the established solution. [ABSTRACT FROM AUTHOR]

Achieving the low-carbon transition of power systems requires the coordination of renewable energy generation and other emerging electricity technologies. Their technical indicators are changing rapidly since these technologies are in the initial stages of development. Determining the technical and policy targets for achieving the low-carbon transition is critical for regulators and power system planners. This paper defines this problem as a backcasting problem, whose concept is derived from environmental science research. We introduce it into the power system planning field and innovatively model it as a bi-level optimization problem. According to the characteristics of the backcasting problem, an improved barrier method is proposed to solve it efficiently. The effectiveness of the proposed model and algorithm are verified through case studies on a modified Garver’s 6-bus system and a realistic Northwest China power system, and they are compared with state-of-the-art solution techniques. The results of the backcasting problem provide a new perspective on the economic impacts of the low-carbon transition and are helpful in energy policy formulation for power systems. [ABSTRACT FROM AUTHOR]

Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (SNNs) to reproduce spatio-temporal spiking patterns in response to given spiking stimuli. Most existing approaches model the input-output behavior of an SNN in a deterministic fashion by assigning each input to a specific desired output spiking sequence. In contrast, in order to fully leverage the time-encoding capacity of spikes, this work proposes to train SNNs so as to match distributions of spiking signals rather than individual spiking signals. To this end, the paper introduces a novel hybrid architecture comprising a conditional generator, implemented via an SNN, and a discriminator, implemented by a conventional artificial neural network (ANN). The role of the ANN is to provide feedback during training to the SNN within an adversarial iterative learning strategy that follows the principle of generative adversarial network (GANs). In order to better capture multi-modal spatio-temporal distribution, the proposed approach – termed SpikeGAN – is further extended to support Bayesian learning of the generator's weight. Finally, settings with time-varying statistics are addressed by proposing an online meta-learning variant of SpikeGAN. Experiments bring insights into the merits of the proposed approach as compared to existing solutions based on (static) belief networks and maximum likelihood (or empirical risk minimization). In our experiments, handwritten digit images generated by SpikeGAN are observed to train an ANN classifier with $20\%$ 20 % higher accuracy than a comparable belief network. Our experiments also demonstrate the use of SpikeGAN to generate neuromorphic data sets from handwritten digits. It is shown that these data can be used to train an SNN classifier that achieves an accuracy level approaching the baseline accuracy of an SNN classifier trained on rate-encoded real data. [ABSTRACT FROM AUTHOR]

Contrastive self-supervised representation learning methods maximize the similarity between the positive pairs, and at the same time tend to minimize the similarity between the negative pairs. However, in general the interplay between the negative pairs is ignored as they do not put in place special mechanisms to treat negative pairs differently according to their specific differences and similarities. In this paper, we present Extended Momentum Contrast (XMoCo), a self-supervised representation learning method founded upon the legacy of the momentum-encoder unit proposed in the MoCo family configurations. To this end, we introduce a cross consistency regularization loss, with which we extend the transformation consistency to dissimilar images (negative pairs). Under the cross consistency regularization rule, we argue that semantic representations associated with any pair of images (positive or negative) should preserve their cross-similarity under pretext transformations. Moreover, we further regularize the training loss by enforcing a uniform distribution of similarity over the negative pairs across a batch. The proposed regularization can easily be added to existing self-supervised learning algorithms in a plug-and-play fashion. Empirically, we report a competitive performance on the standard Imagenet-1K linear head classification benchmark. In addition, by transferring the learned representations to common downstream tasks, we show that using XMoCo with the prevalently utilized augmentations can lead to improvements in the performance of such tasks. We hope the findings of this paper serve as a motivation for researchers to take into consideration the important interplay among the negative examples in self-supervised learning. [ABSTRACT FROM AUTHOR]