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Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources (RES) and power electronics equipment. Therefore, fast and accurate corrective control actions in real-time are needed to ensure system security and economics. This paper presents a novel method to derive real-time AC optimal power flow (OPF) solutions considering uncertainties including varying renewable energy and topology changes by using state-of-the-art deep reinforcement learning (DRL) algorithm, which can effectively assist grid operators in making rapid and effective real-time decisions. The presented DRL-based approach first adopts a supervised-learning method from deep learning to generate good initial weights for neural networks, and then the proximal policy optimization (PPO) algorithm is applied to train and test the AI agents for stable and robust performance. An ancillary classifier is designed to identify the feasibility of the AC OPF problem. Case studies conducted on the Illinois 200-bus system with wind generation variation and N1 topology changes validate the effectiveness of the proposed approach and demonstrate its great potential in promoting sustainable energy integration into the power grid.

Due to environmental concerns, renewable energy sources (RESs) gradually replace conventional power generations. In the last decade, wind and solar electricity generation have increased from 55 million MWh to 275 million MWh and from 2 million MWh to 96 Million MWh, respectively, in the US. Recently, FERC Order 2222 allows distributed energy resources, including distributed renewable generation and energy storage, to participate in the electricity wholesale market through aggregations. Limited rotating inertia of renewable generation resources results in reduced system inertia, which will increase the rate of change of frequency (ROCOF) due to sudden mismatches of generation and demand. During the high visibility national social event, the brightness of LED TV display put simultaneous demand change in power systems. With the development of slim, tightly packaged, and yearly update features of Smart TV, the penetration of different LED displays climbed rapidly from 9% to 60% in the US during 2012–2019. In this paper, the impact of societal events on frequency stability caused by large viewership on LED TV s is discussed. Increasing RESs penetration and FERC order 2222 are considered in this study. The power demand data of a 55-inch LED TV is collected and analyzed to address the problem. This study discusses the frequency stability issue caused by LED TV s in a low inertia power system. It can help system operators to develop frequency control practices accordingly.

Chronic intermittent hypoxia (CIH) plays a key role in the complications of obstructive sleep apnea (OSA), which is strongly associated with retinal and optic nerve diseases. Additionally, the brain-derived neurotrophic factor (BDNF)/tropomyosin receptor kinase B (TrkB) signaling pathway plays an important protective role in neuronal injury. In the present study, we investigated the role of 7,8-dihydroxyflavone (7,8-DHF) in regulating CIH-induced injury in mice retinas and rat primary retinal ganglion cells (RGCs). C57BL/6 mice and in vitro primary RGCs were exposed to CIH or normoxia and treated with or without 7,8-DHF. The mice eyeballs or cultured cells were then taken for histochemistry, immunofluorescence or biochemistry, and the protein expression of the BDNF/TrkB signaling pathway analysis. Our results showed that CIH induced oxidative stress (OS) in in vivo and in vitro models and inhibited the conversion of BDNF precursor (pro-BDNF) to a mature form of BDNF, which increased neuronal cell apoptosis. 7,8-DHF reduced the production of reactive oxygen species (ROS) caused by CIH and effectively activated TrkB signals and downstream protein kinase B (Akt) and extracellular signal-regulated kinase (Erk) survival signaling pathways, which upregulated the expression of mature BDNF. ANA-12 (a TrkB specific inhibitor) blocked the protective effect of 7,8-DHF. In short, the activation of the BDNF/TrkB signaling pathway alleviated CIH-induced oxidative stress damage of the optic nerve and retinal ganglion cells. 7,8-DHF may serve as a promising agent for OSA related neuropathy.

Unlike short circuit faults in power systems, arcing faults produce intense light during fault events. Light-sensing technology has been under development to detect arc faults since the 1980s. Currently, optical fiber and point sensors are two types of light sensors applied, along with the simultaneous overcurrent mechanism, for arc-flash relays. Due to the characteristics of light sensors, sensitivity and reliability of the relay may be affected by ambient light. This article proposes a new approach for arc-flash fault detection by using the spectrum of light. Electromagnetic radiation means different elements would emit spectra with unique wavelengths when their atoms are excited. Elements can then be identified if a specific emission spectrum is detected during the excitation period. In general, copper and aluminum are commonly used for conductors. By examining the light spectrum, arc flash can be accurately and quickly detected. In this study, copper and aluminum are applied as conductors. The light spectrum for both materials is measured and recorded by an optic spectrometer during the arcing incidents. The results show that accuracy and reliability of the lightbased arc-flash fault-detection operation can be improved by using the proposed method.

In the past decades, apoptosis has been the most well-studied regulated cell death (RCD) that has essential functions in tissue homeostasis throughout life. However, a novel form of RCD called necroptosis, which requires receptor-interacting protein kinase-3 (RIPK3) and mixed-lineage kinase domain-like pseudokinase (MLKL), has recently been receiving increasing scientific attention. The phosphorylation of RIPK3 enables the recruitment and phosphorylation of MLKL, which oligomerizes and translocates to the plasma membranes, ultimately leading to plasma membrane rupture and cell death. Although apoptosis elicits no inflammatory responses, necroptosis triggers inflammation or causes an innate immune response to protect the body through the release of damage-associated molecular patterns (DAMPs). Increasing evidence now suggests that necroptosis is implicated in the pathogenesis of several human diseases such as systemic inflammation, respiratory diseases, cardiovascular diseases, neurodegenerative diseases, neurological diseases, and cancer. This review summarizes the emerging insights of necroptosis and its contribution toward the pathogenesis of lung diseases.

As one of the most promising renewable energy resources, a large amount of wind energy has been implemented all around the world since 1990s. Increasing the size of the wind turbines and the harsh operating environment lead to the higher failure rates on the wind turbines compared with the other renewable resources. As a result, not only unplanned maintenance and repairments increases but also the downtime of the wind turbine increases the operational cost significantly. In addition, the long-distance transmission lines are required to transfer the wind generation power to the load centers due to the remote operation location of the wind farms. To improve the power transfer capability, the series-compensated lines are adopted in many cases. However, subsynchronous control interactions (SSCI) could happen between the series-compensated transmission lines and the wind turbines. This may damage the wind turbine components severely and cause stability issues in the power grids. Currently, various condition monitoring systems have been developed and applied for the wind turbines. Different SSCI detection mechanisms are also proposed in many studies. This article proposes a field-programmable gate array CPU-based holistic monitoring system which not only provides both condition and SSCI monitoring functions simultaneously in real time but also records the necessary data for the postevent analysis.

With the increasing penetration of renewable energy, power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and accurate control actions derived in real time are vital to ensure system security and economics. To this end, solving alternating current (AC) optimal power flow (OPF) with operational constraints remains an important yet challenging optimization problem for secure and economic operation of the power grid. This paper adopts a novel method to derive fast OPF solutions using state-of-the-art deep reinforcement learning (DRL) algorithm, which can greatly assist power grid operators in making rapid and effective decisions. The presented method adopts imitation learning to generate initial weights for the neural network (NN), and a proximal policy optimization algorithm to train and test stable and robust artificial intelligence (AI) agents. Training and testing procedures are conducted on the IEEE 14-bus and the Illinois 200-bus systems. The results show the effectiveness of the method with significant potential for assisting power grid operators in real-time operations.

The demand side management (DSM) has been conceived to improve the efficiency and flexibility of the power grids. Currently, different types of DSM programs, such as demand response, the efficiency of power delivery, heating, ventilation, and air conditioning (HVAC) improvements and distributed energy storage, have been developed and implemented by different utility companies. Since DSM is not a one-size-fit-all program, detailed consumers’ consumption data are required to both develop and improve effective DSM programs for different consumers. The rapid expansion of the advanced metering infrastructure in recent years has produced an enormous amount of data which can be utilized by utility companies for the DSM applications. Based upon the consumption characteristics of different types of consumers, various DSM programs can be developed and applied accordingly. This article analyzes and studies the practical smart meter data of commercial consumers from New York City considering the potential of commercial consumers’ participation for DSM applications, and it provides a better understanding of the power consumption pattern of commercial consumers in the urban area. Both utility companies and commercial consumers could benefit from this article to develop and participate in DSM programs, respectively.

Unlike rotational symmetric surfaces, free-form surfaces have sophisticated degrees of freedom, therefore, are more difficult to be aligned. In this work, a practical and accurate correction method is proposed for the misalignment removal of the free-form surface in a non-null interferometric testing system based on computer modeling. The misalignment aberrations, introduced by axial location error, rotation error and non-axial attitude error, are modeled and corrected step by step. The axial and non-axial positions of the free-form surface in the computer model are adjusted from the ideal position to the misaligned one, matching the actual positions in experiment for the correction. Since the modeled position are tuned to be consistent with the actual one, all the misalignment aberrations can be removed from the test wavefront with the reconstruction algorithm. Computer simulations are displayed to verify the accuracy of the proposed method. Experimental results after alignment show basic consistency with the results of Taylor Hobson Profilometer, in which the peak-to-valley (PV) value error of the profile line is better than 1/30 λ .

Accurate visual re-localization is very critical to many artificial intelligence applications, such as augmented reality, virtual reality, robotics and autonomous driving. To accomplish this task, we propose an integrated visual re-localization method called RLOCS by combining image retrieval, semantic consistency and geometry verification to achieve accurate estimations. The localization pipeline is designed as a coarse-to-fine paradigm. In the retrieval part, we cascade the architecture of ResNet101-GeM-ArcFace and employ DBSCAN followed by spatial verification to obtain a better initial coarse pose. We design a module called observation constraints, which combines geometry information and semantic consistency for filtering outliers. Comprehensive experiments are conducted on open datasets, including retrieval on R-Oxford5k and R-Paris6k, semantic segmentation on Cityscapes, localization on Aachen Day-Night and InLoc. By creatively modifying separate modules in the total pipeline, our method achieves many performance improvements on the challenging localization benchmarks., Accepted by the 2021 International Conference on Robotics and Automation (ICRA2021)

Objective Von Hippel−Lindau (VHL) syndrome is a rare disease that occurs in an autosomal-dominant genetic pattern. Due to the high genetic variability of VHL diseases, current studies have limited clinical value. Moreover, casual genetic variations in patients with VHL syndrome are still unclear. Methods Here, we performed whole-exome sequencing of 25 individuals to identify reliable disease-related variations. Systemic computational analysis was performed for variant detection, and Sanger sequencing was used to validate detected mutations. Results Most of the known mutations in the VHL gene were observed in the studied population. In addition, a large fragment deletion in VHL exon 2 in the immediate family members of the last family was detected. This had not been reported earlier. Moreover, we identified 3 novel mutation sites in the MAP2K3 gene that may be involved in the occurrence and development of the VHL disease. Conclusions These results demonstrated that the heterogeneous nature of VHL syndrome and novel mutational signatures may help to improve the diagnostic ability of VHL syndrome.

Due to the close coupling between subsystems and complex energy flow, the distributed energy system is different from the traditional power grid in terms of optimal operation. Therefore, it is necessary to study the optimal operation strategy of distributed energy system. Optimization operation Strategy of a distributed energy station in Guangzhou, China was investigated. Ebsilon software was used to model and simulate the main components and auxiliary machines of distributed energy stations. The opening number and specific load of each main equipment in the distributed energy system were investigated under the cooling load demand of 1000~128000kW. According to the optimal operation scheme under different cooling load demand, the revenue of the energy station with peak-flat-valley electricity price were discussed. The results show that there is largest income while the chillers are operated in the generator supply mode and flat electricity price mode under high cooling load range; In the case of moderate cooling load demand, it is profitable to operate gas engine couple with lithium bromide chiller series centrifugal chiller, but it always causes a waste of energy because the supply of frozen water exceeds the demand; It is feasible to buying electricity to supply chiller for refrigeration operation under low cooling load range. This study provides an important reference for mastering the optimization operation strategy of distributed energy system with complex energy flow.

Common microgrid dispatches are mainly based on static model and ignore the dynamic characteristic of the micro-turbine system when dispatching on a short time scale. In this paper, based on an isolated microgrid, an intra-day rolling dynamic dispatch problem considering the dynamic characteristic of micro-turbine on a short dispatch time scale is formulated under the framework of model predictive control (MPC), and then solved by the mixed integer linear programming (MILP) algorithm. In addition, the actual output of the micro-turbine is obtained and compared with its setpoint given by the dispatch results. The simulation results show that the dynamic dispatch strategy has less output power deviation and can make full use of the micro-turbine to formulate a more reasonable dispatch plan compared with the static one.

Cameras are deployed at scale with the purpose of searching and tracking objects of interest (e.g., a suspected person) through the camera network on live videos. Such cross-camera analytics is data and compute intensive, whose costs grow with the number of cameras and time. We present Spatula, a cost-efficient system that enables scaling cross-camera analytics on edge compute boxes to large camera networks by leveraging the spatial and temporal cross-camera correlations. While such correlations have been used in computer vision community, Spatula uses them to drastically reduce the communication and computation costs by pruning search space of a query identity (e.g., ignoring frames not correlated with the query identity’s current position). Spatula provides the first system substrate on which cross-camera analytics applications can be built to efficiently harness the cross-camera correlations that are abundant in large camera deployments. Spatula reduces compute load by $8.3\times$ on an 8-camera dataset, and by $23\times-86\times$ on two datasets with hundreds of cameras (simulated from real vehicle/pedestrian traces). We have also implemented Spatula on a testbed of 5 AWS DeepLens cameras.

Machine learning based transient stability analysis of power system mostly uses feedforward neural network, which does not consider the relationship between data and data in time domain. This paper presents a power system transient stability assessment based on gated recurrent unit. The proposed system manages to extract both spatial and temporal data dependency from the input power system state for security assessment, and output evaluation value faster by controlling stability threshold η.In this paper, the active power and reactive power of the line are input, which are obtained by time-domain simulation. Two different evaluation methods are used to evaluate the experimental results. The simulation results on IEEE 39-bus test system demonstrate accurate assessment with significantly reduced system response time.

Simulation is a crucial component of any robotic system. In order to simulate correctly, we need to write complex rules of the environment: how dynamic agents behave, and how the actions of each of the agents affect the behavior of others. In this paper, we aim to learn a simulator by simply watching an agent interact with an environment. We focus on graphics games as a proxy of the real environment. We introduce GameGAN, a generative model that learns to visually imitate a desired game by ingesting screenplay and keyboard actions during training. Given a key pressed by the agent, GameGAN "renders" the next screen using a carefully designed generative adversarial network. Our approach offers key advantages over existing work: we design a memory module that builds an internal map of the environment, allowing for the agent to return to previously visited locations with high visual consistency. In addition, GameGAN is able to disentangle static and dynamic components within an image making the behavior of the model more interpretable, and relevant for downstream tasks that require explicit reasoning over dynamic elements. This enables many interesting applications such as swapping different components of the game to build new games that do not exist., CVPR 2020

Petrochemical industry not only consumes large amount of electricity every year but also requires high quality power supplies. Any unexpected power failure could shut down the entire production line and cause significant financial loss. Meanwhile, due to the large electricity consumption and intensive power demand, in-house cogeneration plants have been equipped for most petrochemical facilities. Thus, any power interruption or disturbance between petrochemical facility and interconnected power grids could affect both sides of the power systems. Therefore, having high power quality becomes one of the most critical parts for petrochemical facilities. To avoid unexpected power failure and to provide better understanding of the power system at petrochemical facilities, a fast, precise and reliable power monitoring system is required. A novel Real-Time Web-Based Power Monitoring system on field-programmable gate array (FPGA) platform is proposed in this paper for a power substation at the petrochemical facility. Besides the data collecting with event triggering mechanism and measurement data recording functions, Subsynchronous Oscillation (SSO) detection application is also developed in this system. This proposed monitoring system could provide precise data to help engineers with insightful analysis of the electric system to prevent power failure, and it also could help system operators to have a better understanding of the system operation characteristics.

With the wind power energy largely interconnected with power grid, it's necessary to develop a robust dynamic equivalent model of the wind farm for the system stability analysis and control. In this paper, a detailed wind farm model with doubly-fed induction generators (DFIGs) is established according to Western Electricity Coordinating Council (WECC) benchmark test system. Then the parameters identification process based on aggregation technique consisting of a hybrid algorithm combined improved-particle swarm optimization (IPSO) with improved-genetic algorithm (I-GA) is proposed for dynamic equivalent model development by using the measurement data from phasor measurement unit (PMU) at the point of interconnection (POI). Additionally, the robustness of the developed equivalent model is analyzed. The simulation results of the benchmark test system and IEEE-9 bus system demonstrate the effectiveness and robustness of the proposed equivalent modeling and identification methodology.

False data injection attacks (FIDAs) against state estimation in power system are a problem that could not be effectively solved by traditional methods. In this paper, we use four outlier detection methods, namely one-Class SVM, Robust covariance, Isolation forest and Local outlier factor method from machine learning area in IEEE14 simulation platform for test and compare their performance. The accuracy and precision were estimated through simulation to observe the classification effect.

Aims Hepatocyte necroptosis is a critical event in the progression of non-alcoholic fatty liver disease (NAFLD). Obstructive sleep apnea hypopnea syndrome (OSAHS) and chronic intermittent hypoxia (CIH) may be linked with the pathogenesis and the severity of NAFLD. However, the potential role of necroptosis in OSAHS-associated NAFLD has not been evaluated. The present study investigated whether IH could affect NAFLD progression through promoting receptor-interacting protein kinase-3 (RIPK3)-dependent necroptosis, oxidative stress, and inflammatory response, and further elucidated the underlying molecular mechanisms. Main methods LO2 cells were treated with palmitic acid (PA) and subjected to IH, and necroptosis, oxidative stress, and inflammation were assessed. The high-fat choline-deficient (HFCD)-fed mouse model was also used to assess the effects of CIH in experimental NAFLD in vivo. Key findings In this study, we found that RIPK3-mediated necroptosis was activated both in the PA plus IH-treated LO2 cells and liver of HFCD/CIH mice, and which could trigger oxidative stress and inflammatory response by decreasing Nrf2 and increasing p-P65. RIPK3 downregulation significantly reduced hepatocyte necroptosis, and ameliorated oxidative stress and inflammation through modulating Nrf2/NFκB pathway in vitro and vivo. Similarly, pretreatment with TBHQ, an activator of Nrf2, effectively blocked the generation of oxidative productions and inflammatory cytokines. In addition, RIPK3 inhibitor GSK-872 or TBHQ administration obviously alleviated hepatic injury, including histology, transaminase activities, and triglyceride contents in vivo. Significance IH aggravates NAFLD via RIPK3-dependent necroptosis-modulated Nrf2/NFκB signaling pathway, and which should be considered as a potential therapeutic strategy for the treatment of NAFLD with OSASH.

Petabytes of data are generated each day by emerging Internet of Things (IoT), but only few of them can be finally collected and used for Machine Learning (ML) purposes due to the apprehension of data & privacy leakage, which seriously retarding ML's growth. To alleviate this problem, Federated learning is proposed to perform model training by multiple clients' combined data without the dataset sharing within the cluster. Nevertheless, federated learning introduces massive communication overhead as the synchronized data in each epoch is of the same size as the model, and thereby leading to a low communication efficiency. Consequently, variant methods mainly focusing on the communication rounds reduction and data compression are proposed to reduce the communication overhead of federated learning. In this paper, we propose Overlap-FedAvg, a framework that parallels the model training phase with model uploading & downloading phase, so that the latter phase can be totally covered by the former phase. Compared to vanilla FedAvg, Overlap-FedAvg is further developed with a hierarchical computing strategy, a data compensation mechanism and a nesterov accelerated gradients~(NAG) algorithm. Besides, Overlap-FedAvg is orthogonal to many other compression methods so that they can be applied together to maximize the utilization of the cluster. Furthermore, the theoretical analysis is provided to prove the convergence of the proposed Overlap-FedAvg framework. Extensive experiments on both conventional and recurrent tasks with multiple models and datasets also demonstrate that the proposed Overlap-FedAvg framework substantially boosts the federated learning process., 15 pages, 9 figures

The increasing complexity of deep learning and massive deployment of cameras at the edge have drastically increased the resource demand of edge data analytics. Compared to traditional Internet web applications, such resource demand (in computing, storage and networking) is not limited by millions of human users, but rather the continuous activities of billions of sensors. This paper presents the abstraction of camera cluster as an attempt to address this challenge in the context of video analytics. We envision a novel analytics stack that orchestrates the computing resource of massive networked cameras to enable efficient edge video analytics.

With the increasing demand for energy resource, the development of marine energy harvesting technology has attracted widespread attention. Herein, a spherical electret wave power generator is designed and constructed for ocean wave energy harvesting. Based on electrostatic induction from electret material, the generator converts wave energy into electric energy through the bias voltage of the folded electret power generation unit. With ultra-low excitation frequency at 2Hz, the peak-to-peak output voltage and output power can be reached up to around 300 V and 20 mW, respectively. Compared with other traditional wave energy generators, the utilization of space in the sphere is improved by the folded structure, which also has the advantages in terms of simple structure, high-energy conversion efficiency, light weight and small size. The generator is also capable of collecting low-frequency vibration energy in random directions, as well as realizing large-scale power supply after deploying an array of structures.

In recent years, the MICS band has become more and more versatile, and at the same time, the power consumption issue of the transmitter has been plaguing people. We designed a highly integrated 433 MHz MICS band transmitter which is based on cascaded multi-phase injection locking and frequency multiplication to enable low power operation. The transmitter is designed with the TSMC 180-nm CMOS process. The core area of the transmitter is only 0.0018 mm2. This structure consumes a total power of 0.58mW and outputs -28dBm power on 50 Ω load. It is fully integrated except for the crystal and the matching network.

Research communities have made significant efforts to derive dynamic equivalent models (DEM) for wind farms due to the high penetration of wind energy. The whole development can be divided into two categorical approaches: aggregated DEM and multi-machine pre-clustered DEM. However, with the stochastic characteristics of wind speeds and different crowbar activation patterns inside the wind farm resulted from different fault types, conventional DEMs may not be accurate for all kinds of faults, therefor are not suitable for post-fault analysis. This paper focuses on developing a DEM for LVRT studies. Firstly, a two-level clustering algorithm was proposed to build dynamic clusters based on the crowbar triggering waveforms and the grouping indicators' data, where Gaussian density distance clustering algorithm was applied. Then the established aggregated DEM was updated according to the previous clustering results. Different fault scenarios were tested. Compared with conventional methods, the simulation results showed that the proposed method could be more accurate to represent the dynamic response of the wind farm when there were several different triggering patterns from crowbar systems for post fault analysis.

With high penetration level of wind generation, it's critical to establish a robust dynamic equivalent model of the wind farm for system's stability analysis and smart grid development. Since wind farms may have a multi-stage development and are installed with different wind turbines from different technologies and/or venders, in this paper, a wind farm model with different types of wind generators is constructed according to Western Electricity Coordinating Council (WECC) generic wind generator models. In order to describe the dynamic behavior of the wind farm during system disturbances, by applying the data from phasor measurement units (PMUs), a multi-stage hierarchical parameters identification process based on heuristic algorithms is proposed to develop a dynamic equivalent model for the wind farm. Different scenarios are simulated to validate the effectiveness and robustness of the proposed equivalent model. In addition, the modal analysis is performed to further validate the proposed approach by comparing the eigenvalues between the detailed wind farm model and the equivalent model.

The Internet of Things (IoT) concept allows objects to share data through wired or wireless connections for communication purposes. The industrial Internet of Things (IIoT) is an extended concept of IoT that refers to an integration of data acquisition, communication, and processing on a real-time network. Currently, IIoT has been involved with the development of smart grids in many applications. As the operation of power systems is extremely time-critical, low-latency communication needs to be considered for most control and monitoring applications. Real-time capability of IoT is considered as a key feature for monitoring and control applications of power systems. Therefore, system operators can use the real-time monitoring system to provide better decisions for both technical- and financial-related matters. In this article, a high-speed IIoT-based monitoring system with recording functions is developed and implemented for a power system substation. Due to the high reliability and processing speed of field-programmable gate arrays (FPGAs), an FPGA-embedded controller is adopted in this system. The IoT platform also provides remote visualization for system operators in real-time. This article mainly aims to provide a practical application that was implemented and tested in a real-power substation. The system incorporates the features of an IoT platform with the needs of high-speed real-time applications while using a single high-resolution time source as the reference for both steady-state and transient conditions.

As one of the most promising renewable energy resources, a large amount of wind energy has been implemented all around the world since 1990s. Increasing the size of the wind turbines and the harsh operating environment lead to higher failure rates on the wind turbines compared with other renewable resources. As the result, not only unplanned maintenance and repairments but also downtime of the wind turbine increases the operation cost significantly. In addition, the long-distance transmission lines are required to transfer the wind generation power to the load centers due to the remote operation location of the wind farms. To improve the power transfer capability, series compensated lines are adopted in many cases. However, subsynchronous control interactions (SSCI) could happen between series compensated transmission lines and wind turbines. This may damage wind turbine components severely and cause stability issues in the power grids. Currently, various condition monitoring systems have been developed and applied for wind turbines. Different SSCI detection mechanisms are also proposed in many studies. This paper proposes an FPGA-CPU based holistic monitoring system which not only provides both condition and SSCI monitoring functions simultaneously in real-time but also records necessary data for post-event analysis.

The electronic structures of phosphorene-like SnS/SnSe nanoribbons and the transport properties of a SnS-SnSe nanoribbon lateral heterostructure are investigated by using first-principles calculations combined with nonequilibrium Green's function (NEGF) theory. It is demonstrated that SnS and SnSe nanoribbons with armchair edges (A-SnSNRs and A-SnSeNRs) are semiconductors, independent of the width of the ribbon. Their bandgaps have an indirect-to-direct transition, which varies with the ribbon width. In contrast, Z-SnSNRs and Z-SnSeNRs are metals. The transmission gap of armchair SnSNR-SnSeNR is different from the potential barrier of SnSNR and SnSeNR. The I-V curves of zigzag SnSNR-SnSeNR exhibit a negative differential resistive (NDR) effect due to the bias-dependent transmission in the voltage window and are independent of the ribbon width. However, for armchair SnSNR-SnSeNR, which has a low current under low biases, it is only about 10-6 μA. All the results suggest that phosphorene-like MX (M = Sn/Ge, X = S/Se) materials are promising candidates for next-generation nanodevices.

Currently, most modern Arc Flash relays use light detection to detect an arcing event, optionally they add current detection for security. Traditional scheme relies on the current for fault classification. This paper proposes an approach to identify arcing fault types by using the light spectrum emission from the materials or coating of the materials during the incident. This is based upon the distinct spectra that is emitted from excited material that can be used as signatures to identify the fault type. As one of the characteristics of arcing faults, intensive lights would be produced. Therefore, using the different type of conductors and coatings on different phases has the potential to be used to identify arcing fault types by analyzing the light spectrum during the event. In this study, the conductors made of copper and aluminum and the copper conductor with coating Tin are used as the example to test the effectiveness of the proposed approach. Light spectrum of each case during the arc flash will be measured and recorded by a spectrometer. By using the light spectrum of arcing fault conductors, types of arcing fault could be identified by using General Regression Neural Network (GRNN). The application of this study could improve the speed to identify the fault type and reduce the downtime.

Steam flooding and chemical flooding have been widely proven to be the most promising methods to facilitate the development of heavy oil reservoirs. However, the remaining oil reserves after steam flooding or chemical flooding still account for the vast majority, and it is challenging and necessary to study the occurrence state and formation mechanism of the remaining oil. In this paper, an anionic Gemini surfactant and an amphoteric surfactant are synthesized and combined with a hydrophobic modified polyacrylamide-based polymer to form the surfactant-polymer (SP) flooding system. Then, the development effect of the system is investigated by the visualization experiment, and high-resolution images of the remaining oil are obtained. Finally, Mask R-CNN, an intelligent image recognition technology based on deep learning, is introduced to more accurately and quickly study the microscopic remaining oil occurrence state and macroscopic remaining oil distribution. The results show that the SP system has excellent synergistic oil displacement effect and can increase the oil recovery by 47.2% in visualization experiments. Remaining oil is classified into four categories: flake oil, columnar oil, droplet oil and membranous oil. The algorithm achieves an accuracy of 93.83% on the identification task and an intersection over union (IOU) of 91.5% for the instance segmentation. The oil remaining after steam and chemical flooding is mainly flake oil and dispersed column oil, droplet oil and film oil, respectively. This algorithm lays the foundation for the study of the microscopic occurrence state and macroscopic statistical distribution of remaining oil under different displacement methods.

Steam-assisted gravity drainage (SAGD) has been widely proven to enhance heavy oil recovery effectively. However, the performance evaluation of SAGD in heavy oil reservoirs usually involves time-consuming and tedious static and dynamic numerical simulations, which is not suitable for practical decision-making and prediction. As a reliable alternative modeling tool, the deep learning-based model has great potential for comprehensive data analysis and system prediction of machine learning methods. Still, it has not been widely used in SAGD development planning. In this paper, a recurrent neural network model is applied to the prediction of SAGD production information. A large number of simulation results are collected using CMG software to build a training database, which includes static parameters describing various characteristics of the reservoir and dynamic parameters for production control. The deep learning-based model is divided into two parts. One part is responsible for predicting the thermal communication in the preheating stage, and the other part is responsible for predicting the production information in the production stage, using the principal component analysis method to reduce the dimension of the input vector, limiting the overfitting and improving the quality of the prediction. Besides, an intelligent history matching technology for oilfield SAGD production is proposed. The model is designed to be controlled by a user-friendly graphical interface for user convenience and can be directly integrated into reservoir management programs. This research highlights the enormous potential of replacing traditional modeling tools with neural network models for decision-making and predicting SAGD production projects in oilfields.

This paper applies cluster analysis to eleven (continuous) years' worth of responses to the Canadian Payroll Association (CPA) survey of employed Canadians. The clustering algorithm clearly identifies three distinct groups of respondents. Between-group comparison of response patterns reveals that two of the groups lie on opposite sides of the financial health spectrum, and leads us to label their members "financially stressed" and "financially capable", respectively. The third group shares characteristics with both the stressed and capable groups, and we label its members as "financially coping". We find that financial stress is both widespread (one third of all respondents are identified as stressed) and complex (stress is only weakly related to simple demographics such as age or income). From a methodological perspective, an important point is that our use of cluster analysis allows us to generate rigorous insights into financial well-being, without having to measure it directly.

This paper demonstrated such real attacks on distributed energy systems. We assume hackers have ability to connect the IEC61850 communication net. The attacks include four types man in the middle attack, flag attack, packet length attack and function attack. We found the IEC61850 vulnerability which cause the serious damage to distributed energy test bed and would bring potential risk to the substations and power system.

An iterative algorithm based on optical path difference (OPD) and ray deflection is proposed to obtain the DT (deuterium-tritium)-layer refractive index and thickness of the ICF (inertial confinement fusion) target simultaneously. Starting from an assumed initial value, the refractive index and thickness are solved back and forth until the iteration stopping criterion is reached. Simulations show that the relative retrieval error of the DT-layer refractive index is better than 0.05% after finite iterations, and that of the thickness is better than 0.1%. Experiments show that the target shell refractive index and thickness can be retrieved with a relative error below ±2%. The test uncertainties from experiments were also analyzed.

The intra-uterine and external environmental factors not only affect the early development of fetuses, their interaction with genesis will also substantially program the physiological functions of offspring throughout life. Synthetic glucocorticoid (GC) is widely used for the management of women at risk of preterm birth or undergone autoimmune diseases. However, excess GC might cause a number of chronic diseases in later life. In the present study, we set up a programming rat model by daily injection of dexamethasone (DEX) since 14.5 dpc until labor, and found that the cardiac functions were significantly compromised in the male offspring compared with that exposed to NS, especially after ischemia/reperfusion (I/R), due to the increased infarction and apoptosis of myocardium. Using MeDIP sequencing, we identified four genes involved in the cardiac muscle cell differentiation and development pathway exhibited increased methylation in their promoter regions, among which, bone morphogenetic protein-4 (BMP4) expression is coordinately decreased in myocardium from male mice prenatally exposed to DEX. The programming effect of DEX on cardiomyocytes apoptosis was found to be dependent on mitochondria dysfunction, whereas the breakdown of mitochondrial membrane potential (ΔΨm) and the decrease of ATP production from mitochondria caused by prenatal DEX exposure both can be restored by BMP4 predisposing on neonatal cardiomyocytes 24 h prior to I/R. Inversely consistent with ΔΨm and ATP production, the release of reactive oxygen species was dramatically elevated in cardiomyocytes, which was significantly inhibited in the presence of BMP4 prior to I/R. These findings suggested that the excess GC exposure during pregnancy increases the susceptibility of male offspring’s heart to “second strike”, due to the decrease of BMP4 expression caused by the hypermethylation on Bmp4 promoter and the absence of BMP4 protective effect in cardiomyocytes, making the addition of BMP4 a promising treatment for the congenital heart disease under such circumstances.

Irregular subaperture stitching interferometry (ISSI) is proposed for non-null optical freeform surface testing. Compared with circular and annular subapertures, irregular subapertures have a better performance in matching the locally resolvable interferograms due to the rotational asymmetry of freeform surfaces. A modified multi-aperture simultaneous reverse optimization reconstruction algorithm is employed for the subaperture stitching, allowing not only non-complementary subapertures but also no subaperture overlaps. Numerical simulations with error analyses display the accuracy of our method. Experiments showing the effectiveness of the ISSI are presented by testing a Φ20 mm biconic surface with cross-validation.

The discrete element method (DEM) has recently been widely used to analyze the micromechanics behavior of asphalt mixtures. The objective of this study is to present a more accurate image-processing technique to perform an indirect tensile test of asphalt mixture. Cross-sectional images of the specimen were obtained by an X-ray computed tomography (CT) technique. These CT images were processed by annular segmentation combined with bimodal threshold. The image information was transformed to coordinate information by the user-defined program in Matrix Laboratory (MATLAB). Then, a two-dimensional discrete element model of the indirect tensile test specimen was reconstructed. The indirect tensile test was simulated by the DEM and verified in the laboratory. It was found that this image-processing technology could separate connected or overlapping aggregates and better avoid defects among the aggregates. During the simulation test, the distributed internal forces were gradually concentrated along the loading axis. Four-stage crack initially appeared directly under the loading area and quickly developed along the interface between the aggregate and mortar, mainly along the loading axis. In view of the consistency of the load–displacement curves and crack distribution characteristics in both the simulation and laboratory tests, this numerical method is able to simulate the indirect tensile test.

A free-form surface sub-aperture stitching interferometry (FSSI) is proposed for aspheric and free-form surfaces testing. Different from normal annular and circular sub-aperture stitching method, non-null interferometric configuration and irregular sub-apertures are employed to make the best use of the resolved fringes. Meanwhile, a multi-aperture simultaneous reverse optimizing reconstruction (MSROR) process is proposed for full aperture figure error reconstruction. With the multi-configuration model, full aperture figure error would be extracted in form of Zernike polynomials from sub-apertures wavefront data by the MSROR method. This method concurrently accomplishes sub-aperture retrace error and misalignment correction, requiring neither complex mathematical algorithms, nor sub-aperture overlaps. The experiments show that, the aspheric annular sub-aperture stitching accuracy with the proposed FSSI is more than 1/50λ (rms) comparing with the result of ZYGO Verifier Asphere interferometer. The bi-conic surface sector sub-aperture stitching accuracy is about 1/ 50λ (rms) comparing with result of Taylor Hobson contourgraph in longitudinal section contour.

Purpose: Published data have shown conflicting results about the relationship between X-ray repair cross-complementing group 1 (XRCC1) gene polymorphisms (Arg399Gln and Arg194Trp) and clinical outcome of platinum-based chemotherapy in patients with advanced non–small cell lung cancer (NSCLC). A meta-analysis is needed to provide a systematic review of the published findings. Experimental Design: We conducted a systematic review and meta-analysis to evaluate the predictive value of XRCC1 gene polymorphisms on clinical outcome up to October 1, 2010. The quality of each study was scored on the basis of predefined criteria. Results: A total of 13 eligible follow-up studies met all the inclusion criteria. The XRCC1194Trp allele was found to be significantly associated with a favorable response rate relative to 194Arg [Trp vs. Arg: OR, 1.88; 95% confidence interval (CI), 1.48–2.38]. XRCC1399Gln was less favorably associated with both response rate (Gln vs. Arg: OR, 0.67; 95% CI, 0.52–0.87) and overall survival (Gln vs. Arg: HR, 1.30; 95% CI, 1.04–1.63) than 399Arg in analyses using all available studies; but these associations became insignificant when only high-quality studies were used. Conclusion: These findings suggest a predictive role for XRCC1 gene polymorphisms in clinical outcome. However, the role of 399Gln could be considered controversial because its impact on clinical outcome was insignificant in high-quality studies. These findings show the importance of establishing suitable criteria, including genetic epidemiologic, phenotypic, and clinical criteria, to improve quality control of study design and methods in pharmacogenomic studies related to XRCC1 gene polymorphism. Clin Cancer Res; 18(14); 3972–81. ©2012 AACR.

With only 30 years development of Chinese capital market, especially the listed companies and audit profession, there are still some deficiencies compared to the UK which limits the role of external auditors. With the comparisons in the legal and regulatory framework governing external auditors and the responsibilities of external auditors in detecting and reporting corporate fraud between the UK and China, these differences can reflect some difficulties for Chinese auditors in detecting and reporting corporate fraud. Hence, semi-structured interviews are conducted to collect information about the practical difficulties in their audits of listed companies in detecting and reporting fraud in China. In order to strengthen the role of Chinese auditors in detecting and reporting corporate fraud, some recommendations are made finally.

In modern power system, increasing pressures of system stability have become more and more serious. Because ex citation systems are one of the most effective and economical ways contribute to effective control of voltage, increasing attention has been focused on the effects of excitation control. To satisfy the needs of modern power system, excitation system based on full-controlled devices is proposed to be used in synchronous generator excitation, and a new decoupled control technique is adopted to design a controller for current source converter based ex citation system in this paper. Then, the modified Heffron-Phllips mathematical model of synchronous generator based on this new type of excitation system is discussed, which helps to explicate the principle of reactive power injector in the new excitation system and its design strategy. Also, the crucial function of reactive power injector that could provide more damping to the system is explained. The simulation results are presented to demonstrate the correctness and effectiveness of the proposed approaches, and its advantage over the conventional excitation system.

This paper presents the design of the voltage source converter(VSC) excitation system and an integrated prototype using digital signal processor(DSP) as the core of the excitation controller is developed. Compared with the static excitation systems with thyristor, the VSC excitation system owns one more channel offering positive damping to power system by controlling reactive power of VSC on its ac side, and this approach is approved much faster than field voltage regulation. To design the prototype, the VSC excitation system has to meet the practical condition of the selected synchronous generator(SG) set such as generator capacity, terminal voltage, field voltage and field current, etc. The designed VSC excitation system consists of converters containing a front-end three-phase VSC and a back-end H-bridge chopper, a step-down transformer, and a coordinate excitation controller. Through a series of experiments, it is demonstrated that the VSC excitation system is able to improve both transient stability and steady-state stability limit of power systems. It is a prospective substitute to the conventional thyristor based static excitation system and an attractive solution to strengthen power system stability as well as increase the capability of long-distance transmission systems.

In this study, a measurement system is developed for obtaining continuous piston temperatures in working engine by using a voltage recorder. In this paper, authors discuss the sensor package impact of test results in temperature measurement, and measured the piston temperature when the CNG engine was at speed of 1100rpm, 1200rpm, 1300rpm, 1500rpm, 1700rpm and 1900rpm, the oil/water temperature steady.

This paper proposes an optimized and efficient matching method based on Particle Swarm Optimization (PSO) for image matching. PSO is an efficient intelligent algorithm in image matching. It is a kind of stochastic optimized algorithm developed by Eberhart and Kennedy in 1995. In this paper, the application of PSO is focused on image matching in 3 dimensions with variant angles. The ordinary template matching for the 3 dimensions image matching involves large computational complexity. PSO has been improved in the aspect of self-adaption for convergence. Combining PSO with the individual intelligence, the computation and error rate have been significantly reduced. An extended part of PSO algorithm called multi-swarms is introduced. The multi-swarms PSO (MPSO) is applied to the multi-targets matching in the high dimension space. The performance of MPSO is satisfactory due to the interaction between different swarms such as repulsion and convergence. The Experiments results show that Particle Swarm Optimization Algorithm is much faster in the image matching tasks. MPSO has a good performance in multi-targets matching which involves huge computation complexity.

For remote sensing applications, automatic image registration is an essential part for further image processing such as image fusion, change detection and so on. In this paper, we propose an effective and fast automatic image registration based on both global and local method. In the first stage, we extract SIFT features and make the affine transformation with RANSAC, a robust outlier removal method. After the image globally registered, uniform spacing control points are selected in the common area. Through template matching, control points are refined and more local to the image. At last thin-plate spline, a nonrigid image registration transformation function is applied to achieve local matching. The experiment results indicate that this method not only distinctive and robust in overall registration but also significantly improve matching performance in local area1.

In this paper, we propose a coarse-to-fine framework design and implementation for oil tank detection in optical satellite imagery. The framework is mainly composed of two operations: 1) from the whole scene imagery, extraction of patches with oil tanks based on the probabilistic latent semantic analysis model; 2) in the relatively small size patches, detection of the oil tanks with Hough transform and template matching. Experiments show that the framework provides a promising solution for oil tank detection in optical satellite imagery.

A link quality assessment model named BDCT-SVM based on support vector machine and decision tree is proposed for wireless sensor networks in this paper. The communication area is divided into effective region, transitional region and clear region by experiments in the first place. Then a 4 level link quality decision tree is made to have five grades of link quality. The radial basis function is chosen as the kernel function in BDCT-SVM model and the k-fold cross validation method is used to optimize parameters according to the CCI, RSSI and PRR value by statistics. Experimental results show that the BDCT-SVM model proposed in this paper is reasonable and more accurate.