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This article investigates the voltage monitoring and control feature for smart meters and identifies the impact of this feature on both power distribution and communication systems. Regarding the voltage monitoring, a cosimulation platform is developed using GridLAB-D and ns-3 to analyze the impact of adding voltage measurements to smart meter readings and assess the mitigation strategies for reducing timeout errors and packet drops of smart meter data. Regarding the voltage control, a new voltage stability control scheme is developed, which applies the voltage stability margin as the control objective, instead of the traditional voltage magnitude. The proposed control scheme makes use of existing advanced metering infrastructure and distributed energy resources (DERs), requiring small marginal costs. It is indicated that integrating the voltage monitoring and control feature, smart meters could enable the voltage stability issues being solved at end-user sides, i.e., the “last-mile” segment. It is also implied that the new feature could support the coordination of the local and system-level voltage controls using both customer-owned and utility-scale DERs.

Existing multi-orientation scene text detection methods generally contain two crucial components: regression prediction for text bounding boxes and classification prediction for text/non-text. However, these methods always regard classification prediction and regression prediction as two independent procedures, neglecting fully exploring their mutual relations. Based on this key observation, we propose an innovative Scale-Guided Regression Module (SRM), specially for multi-orientation scene text detection. Equipped with width-guided kernels and height-guided kernels of different sizes, our SRM can generate a series of scale feature maps of candidate texts by capturing their shape information in classification prediction. The scale feature maps are used to predict the width and height of candidate texts, which can serve as guides for regressing bounding boxes. In this way, the procedures of classification and regression can be coherently integrated. In addition, we adopt IoU loss to train our network and then integrate IoU loss and l 1 -smooth loss for fine-tuning. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. Notably, our method achieves significant improvement of performance on long texts, e.g., on MSRA-TD500, our method outperforms Basemodel with a great margin (4.86 % in terms of Recall).

This short paper investigates distribution-level synchrophasor measurement errors with online and offline tests, and mathematically and systematically identifies the actual distribution of the measurement errors through graphical and numerical analysis. It is observed that the measurement errors in both online and offline case studies follow a non-Gaussian distribution, instead of the traditionally assumed Gaussian distribution. It suggests the use of non-Gaussian models, such as Gaussian mixture models, for representing the measurement errors more accurately and realistically. The presented tests and analysis are helpful for the understanding of distribution-level measurement characteristics, and for the modeling and simulation of distribution system applications, such as state estimation.

In this work, we develop a polynomial-chaos-expa- nsion (PCE)-based approach for decentralized dynamic parameter estimation through Bayesian inference. Using this approach, the non-Gaussian distribution of the inverted parameters is obtained. More specifically, we first represent the decentralized generator model with the PCE-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through Metropolis-Hastings (M-H)-based Markov chain Monte Carlo (MCMC). Then, we propose a two-stage hybrid Markov chain Monte Carlo (MCMC) to recover a posteriori distribution of the decentralized generator model parameters. In the first stage, we use the gradient-enhanced Langevin MCMC algorithm to characterize an intermediate posterior parameter distribution. This algorithm is computationally scalable to the high-dimensional parameter space. Based on the intermediate posterior distribution, during the second stage, we use the adaptive MCMC algorithm to fine-tune the strong correlations between the parameters. Finally, the fully recovered a posterior distribution is obtained in the end. The simulation results show that the proposed PCE-based hybrid MCMC algorithm can accurately and efficiently estimate the high-dimensional generator dynamic model parameters with full probabilistic distribution provided.

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

The emerging distribution-level phasor measurement unit (D-PMU) is expected to play an important role in enhancing distribution system observability and situational-awareness. It is a demanding yet challenging task to develop advanced D-PMU data management and analytics tools to improve D-PMU data usability and further promote D-PMU projects deployment. This paper focuses on D-PMU data processing and storage. It presents a brief review of existing D-PMU data storage systems and points out their limitations on high-performance, flexibility, and scalability. To overcome the limitations, a unified PMU-data storage system (UPS) is proposed. Specifically, a unified I/O interface between storage servers and computing jobs is developed to effectively reduce the overhead of managing various computing jobs and data analytics over multiple storage infrastructures; and PMUCache with PMUCache partitioning and PMUCache replacement algorithms are designed to support in-situ data processing and shared distributed data storage and further serve the computing jobs/queries with different quality of service (QoS) requirements. Through a series of experiments, it is demonstrated that UPS achieves high performance on fast and big data processing and storage, and efficiently increases the flexibility and scalability of PMU data management systems.

This paper develops a new response-surface-based Bayesian inference approach for power system dynamic parameter estimation of a decentralized generator using phasor-measurement-unit measurement. The response surface for the decentralized generator model is formulated through a polynomial-chaos-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through a polynomial-based reduced-order representation. In addition, a polynomial-chaos-based analysis of variance is performed to screen out model parameters while ensuring system observability. In dealing with sampling the non-Gaussian posterior distribution for the parameters, the Metropolis-Hastings sampler is adopted. The simulations conducted in the New England system under different system events show that the proposed method can achieve a speedup factor of two orders or magnitude compared with the traditional method while providing full probabilistic distribution of model parameters and achieving the same level of accuracy.

With the development and popularization of big data, artificial intelligence, and cloud computing, the digital and information development of campuses and education has become a common phenomenon. Smart campus is the development of traditional digital campus. At present, many colleges and universities are constructing and exploring smart campus. The lightweight “smart campus” based on LAPP is a unique campus system that combines manpower, equipment, environment, resources, and social factors on the basis of informatization to form a unique campus system. All information resources related to life services are fully integrated, interconnected and integrated, based on in-depth data mining and analysis, to provide comprehensive, effective and intelligent management and service process support for school work. Based on in-depth data mining and analysis, this paper analyzes and studies the lightweight “smart campus” system of LAPP from the perspective of construction and technology.

With Electric Vehicles (EVs) Rapidly becoming more popular, along with a need for flexible energy storage resources in the electric grid, the development of vehiclegrid integration (VGI) technology has taken on a new urgency. VGI encompasses any technology that helps EVs better integrate with the grid, including management of charging and control of bidirectional charging and discharging. The practice of managing the one-directional flow of power from the grid to vehicles (V1G) is currently being rolled out across California and in many places where EVs have become popular. V1G programs in California include San Diego Gas and Electric's Power Your Drive, EV-specific retail tariffs from Southern California Edison, and the Charge Forward smart charging pilot program from Pacific Gas and Electric in collaboration with automaker BMW.

In this work, we develop a polynomial-chaos-expansion (PCE)-based approach for decentralized dynamic parameter estimation through Bayesian inference. Using this approach, the non-Gaussian distribution of the inverted parameters is obtained. More specifically, we first represent the decentralized generator model with the PCE-based surrogate. This surrogate allows us to efficiently evaluate the time-consuming dynamic solver at parameter values through Metropolis-Hastings (M-H)-based Markov chain Monte Carlo (MCMC). Then, we propose a two-stage hybrid Markov chain Monte Carlo (MCMC) to recover a posteriori distribution of the decentralized generator model parameters. In the first stage, we use the gradient-enhanced Langevin MCMC algorithm to characterize an intermediate posterior parameter distribution. This algorithm is computationally scalable to the high-dimensional parameter space. Based on the intermediate posterior distribution, during the second stage, we use the adaptive MCMC algorithm to fine-tune the strong correlations between the parameters. Finally, the fully recovered a posterior distribution is obtained in the end. The simulation results show that the proposed PCE-based hybrid MCMC algorithm can accurately and efficiently estimate the high-dimensional generator dynamic model parameters with full probabilistic distribution provided

The fault diagnosis technology of rubber extruder is particularly important in the rubber production industry. The normal operation of the equipment is related to the quality of the extruded product, and the vibration detection technology can detect hidden troubles in time and maintain the normal operation of the equipment. This article analyzes some common failures of rubber extruder in use, and analyzes how to diagnose equipment failures through vibration signals. Finally, a rubber extruder fault monitoring system based on vibration detection technology was established.

As SDN-based networking infrastructure continues to evolve, an increasing number of traditional network functions are deployed over virtualized network. Like traditional networks, traffic monitoring in a Software Defined Network is critical in order to ensure security and performance of the underlying infrastructure. In the context of virtualized networks, deployment of a virtualized TAP service has been reported as an effective VNF that can provide the same monitoring capabilities as a physical TAP. Unfortunately, over a para-virtualization environment, e.g. OVS, where inter-VM communication is expensive it has been observed that virtual TAPs can contribute up to 70% performance degradation. In this paper, we present a hybrid approach that allows network administrators to mirror VIRTIO port traffic to another VF (SR-IOV) via NIC hardware offloading. As a result, the mirrored traffic can be viewed through a monitoring VNF in a separate VM. Through this approach, the throughput overhead can be reduced by as much as 50%.

The P4 language was originally proposed as a means of providing programmable network data plane functions with the potential to implement, understand, and interpret custom protocols. However, the recompilation process and the re-instantiation of network functions on a target device introduces measurable downtime for network services. In this work, we present Ripple, an efficient, runtime reconfigurable P4 engine on multicore systems to eliminate the recompilation process. The Ripple framework adopts the "generic and static" philosophy, and uses a highly optimized universal binary executable to avoid the lengthy recompilation and reconfiguration process, while supporting packet processing at line rates. We leverage advanced I/O and vectorization techniques to take advantage of packet level parallelism and processor architectural features, thus significantly improve the performance of P4 data plane at runtime. Our in-depth evaluation on multicore architecture shows that Ripple reaches comparable throughput with one of the state-of-art frameworks that requires recompilation and reconfiguration.

With increasing penetration of intermittent generation in power systems, it becomes essential to develop an integrated component-oriented modeling, simulation, and optimization tool for renewable energy resources to accurately capture the dynamics of large-scale power systems. Being an open, straightforward specification of differential-algebraic equations and discrete variables for component-oriented modeling and simulation of complex system, Modelica has been successfully used in robotics, automotive, aircraft, and communication systems, and is now being considered for use in electric power systems. In this paper, we show the initial efforts in the creation of OpenRES—an open-source JModelica.org library for renewable energy resources with their control blocks, and demonstrate the use of wind-farm frequency and voltage control functions in power grid dynamic simulation that is performed on the New England IEEE 39-bus test system.

In this paper, a model-based fault diagnosis method is investigated for gyro. An adaptive observer is designed to detect the occurrence of gyro fault. And then, the size of fault is estimated accurately according to our designed observer. The simulation on a satellite control system is performed. The results show the effectiveness of the proposed method.

Scott connection transformer can be used as traction transformer, which is an important part of electrified railway traction power supply system. The application of Scott connection transformer can solve current unbalance problem of three phase power source and realize bilateral power supply of the contact network. Scott transformer and back-to-back converter can be joined together to form the railway co-phase power supply system, which will not only provide the traction load with all needed active power, but also compensate negative sequence components, harmonics and reactive power. This provides a new idea for the development of traction power supply. A simulation with Matlab/Simulink is performed to validate the feasibility of this proposed control strategy.

Aspect sentiment analysis of online course reviews is of great significance in helping users choose courses and improve course quality. Review target extraction is particularly important as the basis of aspect sentiment analysis. Because the current models mostly rely on a large amount of annotation data, there is fewer relevant research on the extraction of online course review targets with higher annotation costs. This paper proposes an ALBERT-IDCNN-CRF review target extraction model for a small amount of labeled data. First, using ALBERT pre-trained sentences obtained dynamic model Chinese word vector coding; Simultaneously, using ALBERT pre-trained model of Transformer obtain sentence abstract features. Then, abstract features are input into the dilated convolutional neural network (IDCNN) to reduce the number of neuron layers and parameters. Finally, conditional random field (CRF) is used to decode and annotate the review sentences to extract the appropriate review objectives. The experimental results on the school online real Chinese online course review data set show that our model has achieved better results than existing models.

In this paper, we proposed a novel method to embed a series of ternary secret data into a cover image based on an improved Least-Significant-Bit (LSB) scheme using the modulo three strategy. Our new method can hide two ternary numbers into each grayscale pixel, normally only modify the two LSBs of the pixel, while it may cause overflow/underflow and a carry/borrow. We solve these problems by adding 1 to the pixel or subtracting 1 from the pixel before embedding. The embedding capacity of our method can be 3.1699 bpp. At the same time, the quality of the stego image of our new method also is better than traditional LSB scheme when the embedding capacity is greater than 3 bpp with a Peak Signal-to-Noise Ratio (PSNR) greater than 37 dB. Extensive experimental results indicated that our new method is capable of getting a higher PSNR than traditional LSB scheme when the embedding capacity is greater than 3 bpp, and it has higher resistance ability against the chosen steganalysis algorithm when the embedding capacity is low.

The injection of emergency capacity may lead to Locational Marginal Price (LMP) sag, and the uneconomic LMP signals may further result in dangerous actions for reliable system operations from participants. Moreover, it has been a challenge to value emergency resources since they cannot set LMP under the current pricing and their offers may be even cheaper than regular energy resources. In this paper, an innovative transactive energy market is presented to quantify the value of emergency energy transactions in microgrid (MG) based distribution systems. For clearing the market with different transactions, the distribution system operator (DSO) considers bids/offers of MGs as well as system constraints. In addition, a straightforward method is introduced to determine MGs’ bids/offers considering available resources and flexible demands. Market clearing process and MGs bidding procedure are modeled with Mixed-integer linear programming (MILP). Numerical case studies on an MG based system illustrate the effectiveness of the proposed energy management model for the emergency conditions.

Network measurement is critical in network management such as performance monitoring, diagnosis, and traffic engineering. However, conventional network measurement solutions are limited by simple and fixed functionalities as well as coarse-grained statistics which often fail to precisely illustrate network conditions. In this paper, we propose an event-driven declarative query language, EQuery, for programmable network management in order to design sophisticated measurement tasks and enable event mechanism to avoid human intervene. Furthermore, we design a compiler to support the query language on the EQuery Controller, which drives the chaining query workflow with nondeterministic finite automaton (NFA), and translates measurement jobs into low-level rules/states on the physical devices. Finally, we evaluate the effectiveness of our EQuery framework on a nation-wide operational network with real-time network statistics.

In a public cloud, a data owner outsources encrypted data in order to protect its privacy. However, when sharing data with particular users, it is neither willing to share the decryption key nor reluctant to download and decrypt data. The former may reveal its identity and the latter can cause high computing and communication cost. In this paper, we proposed a half-decryption scheme (SDS2) for crowd owners to share privacy data with authorized users securely in a public cloud. Our scheme allows the owner to upload confidential data to a cloud, and deliver two half-decryption keys to a trusted authority and a user respectively according to our protocol. The trusted authority transfers the half-decryption key to the cloud after receiving a data sharing request. Then the user gets half-decrypted data from the cloud for further decryption. Experimental results show that computing and communication cost of our scheme is lower than other schemes with the increasing data and sharing requests.

Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

Medium Access Control (MAC) protocols of Wireless Sensor Network (WSN) are often required to have special characteristics in concrete applications. Related and Urgent message should be sent First (RUF) is a typical scenario in many applications of WSN. In this paper, we focus on presenting a MAC protocol for RUF applications in WSN. First of all, through analysis, some concrete cases of RUF scenario are listed, from which 4 common requirements of MAC protocols are summed up. Then a novel RUF-MAC is devised to meet these requirements. By modifying the frame structure of IEEE 802.15.4 standards, new algorithms are presented for the assignment and adjustment of time slots, of which some measures are introduced into RUF-MAC, such as sub-frames competition, priority assignment and adaptive period adjustment. The simulation results of data from RUF scenario show that the performance of RUF-MAC is better than that of other MAC protocols for WSN in RUF scenario.

It is an important topic to find and identify molecular drug targets for modern drug research. Naturally, to find new or potential drug-GPCRs couples is very useful for determining the new effective medicine target, because GPCRs are among the most frequent targets of therapeutic drugs and proven evolutionary pharmacology value. To realize this, this paper incorporates chemical functional groups of drugs and grey model GM(1,1) based on digital coding of amino acids to formulate the drug-GPCRs couples for statistical prediction. The overall success rate using the fuzzy K-Nearest Neighbor algorithm by the jackknife test is about 81%. This novel approach will further stimulate the development of scanning target via a computational approach.

Modelica is an object oriented modeling language that allows straightforward specification of differential,algebraic and discrete equations in a standardized format. It has been used in a number of applications to support multi-domain system simulations such as are found in the automotive and aerospace industries. More recently it has begun to be used in the power systems industry. Modelica models can be compiled into functional mock-up interfaces(FMI) which contain standardized interfaces to the modeling equations and dynamically linked libraries which allow the coupling of models with each other or into other simulation software. The interface allows the decoupling of the model from the mathematical solver used for the simulation. In this document we describe in detail the coupling between functional mock-up units and GridDyn, a dynamic power system simulation tool, which incorporates a DAE solver and variable time step methods.

With the popular development of smart phones and social networks, a large quantity of location-embedded data is collected in mobile cloud. By utilizing these data, the service providers of mobile cloud (MCSP) can provide users with great convenience by location-based queries. Thus the mobile cloud service system (MCSS) comes into being. However, the MCSP are untrustworthy, and the location itself contains much privacy information. Hence, the data publishers always encrypt the location information before publishing it. But it brings a great challenge, that how to utilize the encrypted information efficiently. To solve this problem, this paper designs an efficient privacy-preserving processing scheme for location-based queries in mobile cloud. We adapt the RSA algorithm, and combine it with ciphertext policy attribute-based encryption (CP-ABE) approach. The proposed scheme achieves fine-grained access control over location information, efficient and privacy-preserving location distance computation and comparison. The performance evaluation shows that our scheme is efficient enough to be applied into MCSS.

Dark areas often exist in gray image as light and other reasons,hence,in these areas the image detail can not be directly identified.So an improved image enhancement algorithm based on local statistics and partially overlapped sub-block ideas is proposed in this paper.The proposed algorithm can achieve real-time enhancement of low gray image under dark background.Compared with traditional histogram equalization method,the new algorithm not only reserves the visual characteristics of bright region but also brings relatively weak noise enhancement.Experimental results tested on TMS320DM642 real-time image processing system show that the proposed algorithm obtains excellent display performance,and the processing time for each frame is around 8 ms,which satisfies the real-time requirement completely.

In view of the problems of limited energy of the nodes and threat to routing security in wireless sensor networks, Energy-efficient Secure Routing Protocol (ESRP), an energy-efficient secure routing protocol for wireless sensor networks, is proposed. According to the structured topology of hexagonal mesh, hops at different directions are calculated on the optimal route for transmitting data packets in ESRP. With the help of characters of hops, the nodes can rapidly find a route among multiple optimal routes by the policy of the twice probability routing selection. Data breach is prevented by data encryption and data security is realized by one-way hash key chain and symmetric key authentication. Analysis and simulation results show that ESRP has better performance on security and saving energy.

This paper introduces a federated simulation toolkit (FSKIT) that couples continuous time and discrete event simula- tions (DES) to perform the co-simulation of electric power grids and communication networks. A High Performance Computing (HPC) oriented power system dynamic simulator, GridDyn, was used for the electric power grid simulation. GridDyn is coupled to the open-source network simulator, ns-3, through FSKIT. FSKIT provides time control for advancing the state of federated simulators, and facilitates communication among objects in the federate. A wide-area communication-based electric transmission protection scheme is simulated with FSKIT, using the IEEE 39- bus test system. A communication network for the 39-bus system is built in ns-3, and basic protection relay logic is added to the power system model in order to perform the co-simulation.

Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug-protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug-protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug-protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users' convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/ , by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.

This paper presents an overview of the research project “High-Performance Hybrid Simulation/Measurement-Based Tools for Proactive Operator Decision-Support”, performed under the auspices of the U.S. Department of Energy grant DE-OE0000628. The objective of this project is to develop software tools to provide enhanced real-time situational awareness to support the decision making and system control actions of transmission operators. The integrated tool will combine high-performance dynamic simulation with synchrophasor measurement data to assess in real time system dynamic performance and operation security risk. The project includes: (i) The development of high-performance dynamic simulation software; (ii) the development of new computationally effective measurement-based tools to estimate operating margins of a power system in real time using measurement data from synchrophasors and SCADA; (iii) the development a hybrid framework integrating measurement-based and simulation-based approaches, and (iv) the use of cutting-edge visualization technology to display various system quantities and to visually process the results of the hybrid measurement-base/simulation-based security-assessment tool. Parallelization and high performance computing are utilized to enable ultrafast transient stability analysis that can be used in a real-time environment to quickly perform “what-if” simulations involving system dynamics phenomena. EPRI's Extended Transient Midterm Simulation Program (ETMSP) is modified and enhanced for this work. The contingency analysis is scaled for large-scale contingency analysis using MPI-based parallelization. Simulations of thousands of contingencies on a high performance computing machine are performed, and results show that parallelization over contingencies with MPI provides good scalability and computational gains. Different ways to reduce the I/O bottleneck have been also exprored. Thread-parallelization of the sparse linear solve is explored also through use of the SuperLU_MT library. Based on performance profiling results for the implicit method, the majority of CPU time is spent on the integration steps. Hence, in order to further improve the ETMSP performance, a variable time step control scheme for the original trapezoidal integration method has been developed and implemented. The Adams-Bashforth-Moulton predictor-corrector method was introduced and designed for ETMSP. Test results show superior performance with this method.

In this paper, parallelization and high performance computing are utilized to enable ultrafast transient stability analysis that can be used in a real-time environment to quickly perform “what-if” simulations involving system dynamics phenomena. EPRI's Extended Transient Midterm Simulation Program (ETMSP) is modified and enhanced for this work. The contingency analysis is scaled for large-scale contingency analysis using Message Passing Interface (MPI) based parallelization. Simulations of thousands of contingencies on a high performance computing machine are performed, and results show that parallelization over contingencies with MPI provides good scalability and computational gains. Different ways to reduce the Input/Output (I/O) bottleneck are explored, and findings indicate that architecting a machine with a larger local disk and maintaining a local file system significantly improve the scaling results. Thread-parallelization of the sparse linear solve is explored also through use of the SuperLU_MT library.

To solve the problems of limited energy of the nodes and security of routing in wireless sensor networks, load-balanced secure routing protocol (LSRP), a load-balanced secure routing protocol for wireless sensor networks, is proposed. Based on structured topology of hexagonal mesh, hops at different directions are calculated on the optimal route for transmitting data packets in LSRP. Depending on characters of hops, the nodes can rapidly find a route among multiple optimal routes by the policy of the twice probability routing selection. Data breach is prevented by data encryption, and data security is realized by one-way hash key chain and symmetric key authentication. LSRP offers preventions against usual attacks, and it also takes into account traffic load balance. Analysis and simulation results show that LSRP has better performance on traffic load balance and security.

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called "iGPCR-drug", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

One of the major drawbacks of ultrasonic Doppler instruments in measuring blood flow is their inability to measure the velocity perpendicular to the beam. Time domain RF echo or speckle tracking has been studied as an alternative to overcome this problem. By acquiring two-dimensional (2-D) echo signals, both lateral (perpendicular to the beam) and axial (parallel to the beam) velocities can be calculated with 2-D pattern correlation algorithms. One of the disadvantages of the current 2-D pattern correlation algorithms is the extensive computation time involved in computing the 2-D cross-correlation function. In this paper, we present several time-efficient bit-pattern correlation algorithms to execute 2-D speckle tracking. The proposed algorithms first estimate the noise level from the acquired signals and use it as a priori knowledge to minimize computation time. The reduction of computation time may make it more feasible for real-time measurements of flow velocities in two dimensions. Radio frequency and video data collected from two commercial scanners are used to validate the feasibility of these proposed algorithms with porcine blood as the flowing medium in in vitro experiments. The results obtained by the proposed algorithms are in good agreement with those computed from the cross-correlation function.

Time delay estimation is a very important operation in ultrasound time-domain flow mapping and correction of phase aberration of an array transducer. As the interest increases in the application of one and a half-dimensional (1.5-D) and two-dimensional (2-D) array transducers to improving image quality and three-dimensional (3-D) imaging, the need of simple, fast, and sufficiently accurate algorithms for real-time time delay estimation becomes exceedingly crucial. In this paper, we present an adaptive time-delay estimation algorithm which minimizes the problem of noise sensitivity associated with the one bit correlation while retaining simplicity in implementation. This algorithm converts each sample datum into a two bit representation including the sign of the sample and an adaptively selected threshold. A bit pattern correlation operation is applied to find the time delay between two engaged signals. By using the criterion of misregistration as an indicator, we are able to show that the proposed algorithm is better than one bit correlation in susceptibility to noise level. Analytical results show that the improvement in reducing misregistration of the two bit correlation over its counterpart is consistent over a wide range of noise level. This is achieved by an adaptive adjustment of the threshold to accommodate signal corruption due to noise. The analytical results are corroborated by results from simulating the blood as a random distribution of red blood cells. Finally, we also present a memory-based architecture to implement the two bit correlation algorithm whose computation time does not depend upon the time delay of the signals to be correlated.

Control center operation is becoming more complex as new and often-conflicting reliability, economics, and public policy issues emerge. Computer simulations will be required to analyze larger and larger amounts of system data (of different types) and what-if-scenarios to derive succinct information for operators to make informed decisions. Existing control center systems are primarily based on the original digital computing infrastructure first designed in the 1960-70's. Incremental changes have occurred since then addressing issues in a piecemeal fashion. Thus, today's systems do not take full advantage of computing power in their existing infrastructure or in the computing industry in general. Researchers should also scrutinize the existing fundamental power system algorithms with advancements in numerical algorithms and simulation techniques with the availability of new system data and additional demands and uncertainties exerted on the system. The Electric Power Research Institute (EPRI) is exploring advancements in computing technologies and power system problem solution methodologies to provide more timely and accurate information to control center operations. The authors have identified some long-term research goals and near-term research efforts to advance the adoption new computational technologies and simulation techniques in control center operations. This work considers both operations and operations planning (day-ahead) needs. A preliminary study of the use of Graphics Processing Units (GPU) for speeding up AC power flow is given as an example application.

Reversible logic circuits are emerging as a promising technology for power minimization. Parity preserving reversible circuits design will be very important for development of fault tolerant reversible systems in nanotechnology. In this paper, a fast fault tolerant reversible signed multiplier is proposed. In order to construct the multiplier, five variables parity preserving gate (F2PG) and modified new fault tolerant (MNFT) are designed, which are parity preserving reversible gates. The most significant aspect of F2PG is that it can work independently as a reversible fault tolerant full adder. Meanwhile, it can implement all Boolean functions. MNFT can reduce cross redundancy. The quantum implementations of F2PG and MNFT are also given. Otherwise, a new quantum implementation of the modified Islam gate (MIG) is presented. The Wallace tree is applied to improve the operating speed of the multiplier, which can implement the multiplication of two 5-bit binary signed numbers. Simulation and evaluation results indicate that the multiplier logic structure is correct with excellent performance. Key words: Parity preserving reversible gate, fault tolerant, five variables parity preserving gate (F2PG), modified new fault tolerant (MNFT), reversible signed multiplier.