This paper presents a procedure for designing a supplementary damping stabilizer for a static synchronous series compensator (SSSC) in multi-machine power systems. The objective is to shift the lightly damped inter-area modes toward the prescribed stability region. A lead-lag stabilizer is used to demonstrate this technique, in which a particular measure of stabilizer gain is considered as an objective function. Constraints of the problem for phase-lead and lag structures are derived. The objective function with the constraints is formed as a quadratic mathematical programming problem. For robust design, the parameters of the stabilizer are calculated under various operating conditions. Two types of SSSC-based stabilizer have been presented and designed. Numerical results including eigenvalue analysis and the nonlinear simulations on the 4- and 50-machine power systems are presented to show the effectiveness of the proposed method.

Image resizing is a key technique for displaying images on different devices, and has attracted much attention in the past few years. This paper reviews the image resizing methods proposed in recent years, gives a detailed comparison on their performance, and reveals the main challenges raised in several important issues such as preserving an important region, minimizing distortions, and improving efficiency. Furthermore, this paper discusses the research trends and points out the possible hotspots in this field. We believe this survey can give some guidance for researchers from relevant research areas, offering them an overall and novel view.

Since Service-Oriented Architecture (SOA) reveals the black box nature of services, heterogeneity, service dynamism, and service evolvability, managing services is known to be a challenging problem. Autonomic computing (AC) is a way of designing systems that can manage themselves without direct human intervention. Hence, applying the key disciplines of AC to service management is appealing. A key task of service management is to identify probable causes for symptoms detected and to devise actuation methods that can remedy the causes. In SOA, there are a number of target elements for service remedies, and there can be a number of causes associated with each target element. However, there is not yet a comprehensive taxonomy of causes that is widely accepted. The lack of cause taxonomy results in the limited possibility of remedying the problems in an autonomic way. In this paper, we first present a meta-model, extract all target elements for service fault management, and present a computing model for autonomously managing service faults. Then we define fault taxonomy for each target element and inter-relationships among the elements. Finally, we show prototype implementation using cause taxonomy and conduct experiments with the prototype for validating its applicability and effectiveness.

Proportional integrator (PI) is always adopted in the resonant frequency servo loop in a resonator micro optic gyro (RMOG). The oscillation phenomenon is observed when adjusting the loop gain surpassing a threshold. This phenomenon limits system performance on step response speed and residual error. Based on the experiment system, a simulation model was set up. Further analysis shows that the threshold gain is related to the system loop filter setting and the loop delay. The traditional PI frequency servo loop technique in the RMOG system cannot keep up with the environment’s disturbance quickly enough, which leads to a large residual error. A compensating method is proposed to optimize the tracking performance, solve the oscillation problem, and speed up the system response. Simulation and experiment results show that the compensated system is superior in performance. It has less residual error in the stable state and is 10 times quicker than the uncompensated system on the step response.

We develop a new kind of underwater inductive coupling power transfer (ICPT) system to evaluate wireless power transfer in autonomous underwater vehicle (AUV) docking applications. Parameters that determine the performance of the system are systematically analyzed through mathematical methods. A circuit simulation model and a finite element analysis (FEA) simulation model are developed to study the power losses of the system, including copper loss in coils, semiconductor loss in circuits, and eddy current loss in transmission media. The characteristics of the power losses can provide guidelines to improve the efficiency of ICPT systems. Calculation results and simulation results are validated by relevant experiments of the prototype system. The output power of the prototype system is up to 45 W and the efficiency is up to 0.84. The preliminary results indicate that the efficiency will increase as the transmission power is raised by increasing the input voltage. When the output power reaches 500 W, the efficiency is expected to exceed 0.94. The efficiency can be further improved by choosing proper semiconductors and coils. The analysis methods prove effective in predicting the performance of similar ICPT systems and should be useful in designing new systems.

Anti-aliasing is a well-established technique in computer graphics that reduces the blocky or stair-wise appearance of pixels. This paper provides a comprehensive overview of the anti-aliasing techniques used in computer graphics, which can be classified into two categories: post-filtering based anti-aliasing and pre-filtering based anti-aliasing. We discuss post-filtering based anti-aliasing algorithms through classifying them into hardware anti-aliasing techniques and post-process techniques for deferred rendering. Comparisons are made among different methods to illustrate the strengths and weaknesses of every category. We also review the utilization of anti-aliasing techniques from the first category in different graphic processing units, i.e., different NVIDIA and AMD series. This review provides a guide that should allow researchers to position their work in this important research area, and new research problems are identified.

An IEEE 1588 based application scheme was proposed to achieve accurate time synchronization for a deep seafloor observatory network based on the communication topological structure of the Zhejiang University Experimental and Research Observatory. The principles of the network time protocol (NTP) and precision time protocol (PTP) were analyzed. The framework for time synchronization of the shore station, undersea junction box layer, and submarine science instrument layer was designed. NTP and PTP network signals were decoded by a PTP master clock on a shore station that receives signals from the Global Positioning System and the BeiDou Navigation Satellite System as reference time sources. These signals were remotely transmitted by a subsea optical–electrical composite cable through an Ethernet passive optical network. Accurate time was determined by time synchronization devices in each layer. Synchronization monitoring experiments performed within a laboratory environment indicated that the proposed system is valid and has the potential to realize microsecond accuracy to satisfy the time synchronization requirements of a high-precision seafloor observatory network.

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.

A local discriminant regularized soft k-means (LDRSKM) method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description (KSVDD) is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

With the deployment of heterogeneous networks, mobile users are expecting ubiquitous connectivity when using applications. For bandwidth-intensive applications such as Internet Protocol Television (IPTV), multimedia contents are typically transmitted using a multicast delivery method due to its bandwidth efficiency. However, not all networks support multicasting. Multicasting alone could lead to service disruption when the users move from a multicast-capable network to a non-multicast network. In this paper, we propose a handover scheme called application layer seamless switching (ALSS) to provide smooth real-time multimedia delivery across unicast and multicast networks. ALSS adopts a soft handover to achieve seamless playback during the handover period. A real-time streaming testbed is implemented to investigate the overall handover performance, especially the overlapping period where both network interfaces are receiving audio and video packets. Both the quality of service (QoS) and objective-mapped quality of experience (QoE) metrics are measured. Experimental results show that the overlapping period takes a minimum of 56 and 4 ms for multicast-to-unicast (M2U) and unicast-to-multicast (U2M) handover, respectively. The measured peak signal-to-noise ratio (PSNR) confirms that the frame-by-frame quality of the streamed video during the handover is at least 33 dB, which is categorized as good based on ITU-T recommendations. The estimated mean opinion score (MOS) in terms of video playback smoothness is also at a satisfactory level.

In the area of 3D digital engineering and 3D digital geometry processing, shape simplification is an important task to reduce their requirement of large memory and high time complexity. By incorporating the content-aware visual salience measure of a polygonal mesh into simplification operation, a novel feature-aware shape simplification approach is presented in this paper. Owing to the robust extraction of relief heights on 3D highly detailed meshes, our visual salience measure is defined by a center-surround operator on Gaussian-weighted relief heights in a scale-dependent manner. Guided by our visual salience map, the feature-aware shape simplification algorithm can be performed by weighting the high-dimensional feature space quadric error metric of vertex pair contractions with the weight map derived from our visual salience map. The weighted quadric error metric is calculated in a six-dimensional feature space by combining the position and normal information of mesh vertices. Experimental results demonstrate that our visual salience guided shape simplification scheme can adaptively and effectively re-sample the underlying models in a feature-aware manner, which can account for the visually salient features of the complex shapes and thus yield better visual fidelity.

Resampling is a critical procedure that is of both theoretical and practical significance for efficient implementation of the particle filter. To gain an insight of the resampling process and the filter, this paper contributes in three further respects as a sequel to the tutorial (Li et al., 2015). First, identical distribution (ID) is established as a general principle for the resampling design, which requires the distribution of particles before and after resampling to be statistically identical. Three consistent metrics including the (symmetrical) Kullback-Leibler divergence, Kolmogorov-Smirnov statistic, and the sampling variance are introduced for assessment of the ID attribute of resampling, and a corresponding, qualitative ID analysis of representative resampling methods is given. Second, a novel resampling scheme that obtains the optimal ID attribute in the sense of minimum sampling variance is proposed. Third, more than a dozen typical resampling methods are compared via simulations in terms of sample size variation, sampling variance, computing speed, and estimation accuracy. These form a more comprehensive understanding of the algorithm, providing solid guidelines for either selection of existing resampling methods or new implementations.

The gradients of a quaternion-valued function are often required for quaternionic signal processing algorithms. The HR gradient operator provides a viable framework and has found a number of applications. However, the applications so far have been limited to mainly real-valued quaternion functions and linear quaternion-valued functions. To generalize the operator to nonlinear quaternion functions, we define a restricted version of the HR operator, which comes in two versions, the left and the right ones. We then present a detailed analysis of the properties of the operators, including several different product rules and chain rules. Using the new rules, we derive explicit expressions for the derivatives of a class of regular nonlinear quaternion-valued functions, and prove that the restricted HR gradients are consistent with the gradients in the real domain. As an application, the derivation of the least mean square algorithm and a nonlinear adaptive algorithm is provided. Simulation results based on vector sensor arrays are presented as an example to demonstrate the effectiveness of the quaternion-valued signal model and the derived signal processing algorithm.

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

It is universally acknowledged by network security experts that proactive peer-to-peer (P2P) worms may soon engender serious threats to the Internet infrastructures. These latent threats stimulate activities of modeling and analysis of the proactive P2P worm propagation. Based on the classical two-factor model, in this paper, we propose a novel proactive worm propagation model in unstructured P2P networks (called the four-factor model) by considering four factors: (1) network topology, (2) countermeasures taken by Internet service providers (ISPs) and users, (3) configuration diversity of nodes in the P2P network, and (4) attack and defense strategies. Simulations and experiments show that proactive P2P worms can be slowed down by two ways: improvement of the configuration diversity of the P2P network and using powerful rules to reinforce the most connected nodes from being compromised. The four-factor model provides a better description and prediction of the proactive P2P worm propagation.

We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F (FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F (KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means (FCM): sensitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the computational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.

Global demand for power has significantly increased, but power generation and transmission capacities have not increased proportionally with this demand. As a result, power consumers suffer from various problems, such as voltage and frequency instability and power quality issues. To overcome these problems, the capacity for available power transfer of a transmission network should be enhanced. Researchers worldwide have addressed this issue by using flexible AC transmission system (FACTS) devices. We have conducted a comprehensive review of how FACTS controllers are used to enhance the available transfer capability (ATC) and power transfer capability (PTC) of power system networks. This review includes a discussion of the classification of different FACTS devices according to different factors. The popularity and applications of these devices are discussed together with relevant statistics. The operating principles of six major FACTS devices and their application in increasing ATC and PTC are also presented. Finally, we evaluate the performance of FACTS devices in ATC and PTC improvement with respect to different control algorithms.

We develop a numerical solution algorithm of the nonlinear potential flow equations with the nonlinear free surface boundary condition. A finite difference method with a predictor-corrector method is applied to solve the nonlinear potential flow equations in a two-dimensional (2D) tank. The irregular tank is mapped onto a fixed square domain with rectangular cells through a proper mapping function. A staggered mesh system is adopted in a 2D tank to capture the wave elevation of the transient fluid. The finite difference method with a predictor-corrector scheme is applied to discretize the nonlinear dynamic boundary condition and nonlinear kinematic boundary condition. We present the numerical results of wave elevations from small to large amplitude waves with free oscillation motion, and the numerical solutions of wave elevation with horizontal excited motion. The beating period and the nonlinear phenomenon are very clear. The numerical solutions agree well with the analytical solutions and previously published results.

We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers (consumer influence) and among the shops (competitions or collaborations). Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.

IPv6 has been an inevitable trend with the depletion of the global IPv4 address space. However, new IPv6 users still need public IPv4 addresses to access global IPv4 users/resources, making it important for providers to share scarce global IPv4 addresses effectively. There are two categories of solutions to the problem, carrier-grade NAT (CGN) and ‘A+P’ (each customer sharing the same IPv4 address is assigned an excluded port range). However, both of them have limitations. Specifically, CGN solutions are not scalable and can bring much complexity in managing customers in large-scale deployments, while A+P solutions are not flexible enough to meet dynamic port requirements. In this paper, we propose a hybrid mechanism to improve current solutions and have deployed it in the Tsinghua University Campus Network. The real traffic data shows that our mechanism can utilize limited IPv4 addresses efficiently without degrading the performance of applications on end hosts. Based on the enhanced mechanism, we propose a method to help service providers make address plans based on their own traffic patterns and actual requirements.

Single-channel separation (SCS) is a challenging scenario where the objective is to segregate speaker signals from their mixture with high accuracy. In this research a novel framework called subband perceptually weighted transformation (SPWT) is developed to offer a perceptually relevant feature to replace the commonly used magnitude of the short-time Fourier transform (STFT). The main objectives of the proposed SPWT are to lower the spectral distortion (SD) and to improve the ideal separation quality. The performance of the SPWT is compared to those obtained using mixmax and Wiener filter methods. A comprehensive statistical analysis is conducted to compare the SPWT quantization performance as well as the ideal separation quality with other features of log-spectrum and magnitude spectrum. Our evaluations show that the SPWT provides lower SD values and a more compact distribution of SD, leading to more acceptable subjective separation quality as evaluated using the mean opinion score.

The data center network (DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.

The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

We present an algorithm for image compression based on an image inpainting method. First the image regions that can be accurately recovered are located. Then, to reduce the data, information of such regions is removed. The remaining data besides essential details for recovering the removed regions are encoded to produce output data. At the decoder, an inpainting method is applied to retrieve removed regions using information extracted at the encoder. The image inpainting technique utilizes partial differential equations (PDEs) for recovering information. It is designed to achieve high performance in terms of image compression criteria. This algorithm was examined for various images. A high compression ratio of 1:40 was achieved at an acceptable quality. Experimental results showed attainable visible quality improvement at a high compression ratio compared with JPEG.

Automatic pectoral muscle removal on medio-lateral oblique (MLO) view of mammogram is an essential step for many mammographic processing algorithms. However, it is still a very difficult task since the sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. In this paper, we propose a novel method based on a discrete time Markov chain (DTMC) and an active contour model to automatically detect the pectoral muscle boundary. DTMC is used to model two important characteristics of the pectoral muscle edge, i.e., continuity and uncertainty. After obtaining a rough boundary, an active contour model is applied to refine the detection results. The experimental results on images from the Digital Database for Screening Mammography (DDSM) showed that our method can overcome many limitations of existing algorithms. The false positive (FP) and false negative (FN) pixel percentages are less than 5% in 77.5% mammograms. The detection precision of 91% meets the clinical requirement.

Any node in a wireless sensor network is a resource constrained device in terms of memory, bandwidth, and energy, which leads to a large number of packet drops, low throughput, and significant waste of energy due to retransmission. This paper presents a new approach for predicting congestion using a probabilistic method and controlling congestion using new rate control methods. The probabilistic approach used for prediction of the occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses a back-off selection scheme and also rate allocation schemes, namely rate regulation (RRG) and split protocol (SP), to improve throughput and reduce packet drop. A back-off interval selection scheme is introduced in combination with rate reduction (RR) and RRG. The back-off interval selection scheme considers channel state and collision-free transmission to prevent congestion. Simulations were conducted and the results were compared with those of decentralized predictive congestion control (DPCC) and adaptive duty-cycle based congestion control (ADCC). The results showed that the proposed method reduces congestion and improves performance.

An appropriate mathematical model can help researchers to simulate, evaluate, and control a proton exchange membrane fuel cell (PEMFC) stack system. Because a PEMFC is a nonlinear and strongly coupled system, many assumptions and approximations are considered during modeling. Therefore, some differences are found between model results and the real performance of PEMFCs. To increase the precision of the models so that they can describe better the actual performance, optimization of PEMFC model parameters is essential. In this paper, an artificial bee swarm optimization algorithm, called ABSO, is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications. For studying the usefulness of the proposed algorithm, ABSO-based results are compared with the results from a genetic algorithm (GA) and particle swarm optimization (PSO). The results show that the ABSO algorithm outperforms the other algorithms.

We use a domain ontology to construct a Semantic Web environment to unify and link the legacy databases, which typically have heterogeneous logic structures and physical properties. Users need only to interact with the Semantic Web environment, and perform searching, querying, and navigating around an extensible set of databases without the awareness of the database boundaries. Additional deductive capabilities can then be implemented to increase the usability and re-usability of data.
In the DartGrid project, we focus on three major TCM requirements, including academic virtual organization, personalized healthcare, and drug discovery and safety. Here we present a brief overview of the major applications that we have developed to satisfy the above requirements.

This paper presents a hybrid brain-computer interface (BCI) control strategy, the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment. The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world. The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states, and state switch is achieved in a sequential manner. In the current system, imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously, while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm. The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI. Subjects obtained similar performances in the hybrid and single control tasks, which indicates the hybrid control strategy works well in the virtual environment.