Sinusoidal oscillators, as excitation sources for impedance measurements, require high signal quality for accurate measurements. However, traditional amplitude control methods, such as automatic gain control (AGC) or Zener clipping amplitude control, introduce either system complexity or additional total harmonic distortion (THD). In this paper, a sinusoidal oscillator topology based on multiple feedback band-pass filter is proposed, which employs antiparallel diode pairs that exploit diodes’ forward characteristics to efficiently generate low-THD, amplitude-tunable sinusoidal signals at low amplitudes. The proposed topology is capable of generating low-distortion sinusoidal signals with specified amplitudes across a frequency range of 10Hz to 1MHz. Within the range of 1kHz to 1MHz, the THD is less than 0.0900%, and below 1kHz, the THD is less than 0.1694%. Methods for generating low-distortion sinusoidal signals are also analyzed. In complex impedance demodulation applications, compared to DDS solution, the improved demodulation accuracy of the proposed topology highlights the indispensable role of high-precision single-frequency excitation sources.

The extraction and filtering of electric network frequency (ENF) is significant for verifying the authenticity of digital audio. However, there are many challenges in accurately extracting ENF from digital audio, which makes it difficult to establish an effective matching relationship with the database. To address this problem, an improved matrix pencil (IMP) method is presented to extract ENF signals for phase measuring units. The power grid signal is constructed into a Hankel matrix, which is decomposed into singular values and filtered out the harmonics of the power grid using an adaptive order determination method. By embedding ENF as a watermark into digital audio through encryption technology, a quadratic box plot (QBP) is proposed to detect potential outliers caused by the bit error rate. Next, the empirical wavelet transform (EWT) is used to filter out Gaussian white noise between different power equipment to improve the similarity of database matching. Integrating the IMP and QBP-EWT, examples from the dataset demonstrate that the proposed ENF extraction and filtering framework has a higher assessment performance. Compared with several commonly used methods, our framework has profound outlier identification ability and effectively improves the accuracy of database matching.

Operational status evaluation (OSE) is essential for the health management of smart electricity meters (SEM). This article develops a machine learning-enabled OSE method for SEM. Specifically, the Gaussian process regression (GPR) is developed for modeling analysis, where an optimized automatic relevance determination (OARD) kernel is first used. Though the conventional ARD kernel structure can capture the potential mapping relationship between running time, temperature, humidity, and measurement error (ME), it cannot extract the influence degree of different stresses on the ME. To address this problem, an OARD kernel structure is proposed to identify the contribution of each part in ARD structure to the target data. Furthermore, the quartiles line instead of 95% confidence interval is exploited to enhance the performance of GPR for long-term prediction. Combining the two above improvements, a novel OSE model is established for the future-oriented long-term operation of the SEM. It is the first-known data-driven application that utilizes the GPR with OARD kernel to perform OSE for SEM. Actual SEM datasets collected from both dry and hot region are used for model validation and prediction. The results demonstrate that the proposed GPR model with OARD Matern52 kernel outperforms other conventional kernel approaches in the aspect of interpretability. More importantly, the operational status of the SEM in future can be assessed via the proposed OSE framework.

Intelligent classification of multiple power quality disturbances (PQDs) is a top priority in pollution control of the power grid. However, the large-scale application of renewable energy introduces lots of nonlinear and impact loads, which makes the PQDs more complex and challenges the effectiveness of conventional detection frameworks. In this article, a novel framework based on optimized Kaiser-window-based$S$-transform (OKST) and multiple fusion convolutional neural network (MFCNN) is proposed to identify multiple complex PQDs. First, the OKST is used for the time–frequency positioning of PQDs, where an improved control function is proposed to meet different detection requirements of time–frequency. Additionally, the parameters of the control function are adjusted automatically using maximum energy concentration. Then, the MFCNN based on residual networks (ResNets) is further proposed to extract and classify these time–frequency features automatically. In MFCNN, feature information is fused using different convolution kernels at a two-dimensional level, which can effectively reduce information loss and improve classification performance. The network model is set up using the Pytorch platform, and the dataset containing 28 types of PQDs and 2 types of nonlinearly mixed PQDs is built to test our framework. The result shows that the proposed OKST-MFCNN obtains an average accuracy of 99.38% under the 20-dB noise level, which is more accurate and robust than some advanced PQDs detection frameworks. Moreover, the accuracy of 97.94% is achieved with satisfactory real-time performance in hardware platform experiments, proving its superior identification performance for complex PQDs.

Accurate assessment of the operation state for smart electricity meters (SEMs) is crucial for electrical metering and service. Nevertheless, the real operation state estimation often ignores the impacts of multiple environmental stresses. In this article, a novel model based on multiple fusion support vector regression (MFSVR) and improved simulated annealing (ISA) is proposed to assess the operation state of SEMs under multiple environmental stresses. First, the MFSVR is used to integrate different input information, in which different types of kernel functions are weighted for emphasizing different feature attributes including time, temperature, and humidity. Then, the ISA is further presented to optimize the model parameters in MFSVR, which can contribute to improving the assessment accuracy. In ISA, the adaptive temperature control strategy and modified Metropolis-based criteria are carried out to enhance the parameter search efficiency. The MFSVR model is set up using the libSVM platform in MATLAB, and the dataset of SEMs is collected from the actual high-cold region in China to test our model. Extensive experiments are conducted for verification analysis from the aspects of accuracy, robustness, and sensitivity. The result shows that the proposed model has the lowest average RMSE of 3.40$\times\; 10^{-2}$and MAE of 2.14$\times\; 10^{-2}$, respectively. Besides, the proposed MFSVR-ISA obtains an average R$^{2}$of 97.8% under the 20 dB noise level, which is more accurate and robust than some popular data-driven prediction methods.

Electrical impedance spectroscopy (EIS) is a noninvasive, inexpensive, and rapid detection technique that is widely used for industrial troubleshooting and medical disease diagnosis. Accurate impedance demodulation is essential to obtain reliable EIS information. This article proposes a general and accurate impedance demodulator based on the improved discrete Fourier transform (DFT), which overcomes the inherent drawbacks of the traditional DFT-based demodulator operating under noninteger period sampling condition. First, the sampled sequence is truncated by the user-selected window function. Second, the discrete spectral line with the largest amplitude is found and its spectral value is calculated by the DFT operation. Finally, a general formula for calculating impedance that is independent of sampling conditions is derived. The effects of the proposed method on the impedance demodulation accuracy with different sampling lengths, window functions, analog-to-digital converter quantization bits, and signal-to-noise ratios are investigated by simulations. Moreover, several passive two-resistors and one-capacitor circuits and fresh ex-vivo biological tissues, including carrot, pumpkin, and beef, are measured using a custom-built EIS meter based on the NI-PXI (PCI eXtensions for Instrumentation) platform. Simulation and experimental results demonstrate that the proposed method can not only achieve accurate impedance demodulation at arbitrary measurement frequencies but also has excellent noise immunity. Importantly, this method has the potential to be applied directly in numerous industrial and medical applications.

In recent years, owing to the penetration of renewable energy and the widespread use of power electronic equipment, power quality disturbances (PQDs) have become more complex and hazardous. As the premise of power quality control, complex PQDs require more accurate and efficient detection. To address this issue, this article proposes a novel automatic method for detecting complex PQDs based on integrated intrinsic variable time-scale decomposition (I-IVTD) and weighted recurrent layer aggregation (WRLA) network. The proposed I-IVTD method reduces aliasing and endpoint effects and improves antinoise performance by innovative use of variable time scales and multiple integrations. The improved WRLA network enhances learning ability and accelerates convergence by adding three weights to each unit. The proposed framework can effectively detect 27 complex disturbances automatically and does not require manual feature design. Finally, a large number of experiments are conducted, including simulation experiments and tests on a PQD analysis platform. The test results based on the analysis platform indicate that the accuracy for complex disturbances is higher than 98%, which demonstrates the superior performance of the proposed framework. Notably, it is effective for detecting nonlinear disturbances as well.

In zero-shot cross-lingual event argument extraction(EAE) task, a model is typically trained on source language datasets and then applied on task language datasets. There is a trend to regard the zero-shot cross-lingual EAE task as a sequence generation task with manual prompts or discrete prompts. However, there are some problems with these prompts, including using suboptimal prompts and difficult to transfer from source language to target language. To overcome these issues, we propose a method called SPTEAE(A Soft Prompt Transfer model for zero-shot cross-lingual Event Argument Extraction). SPTEAE utilizes a sequence of tunable vectors which are tuned in source language as event type prompts. These source language event type prompts can be transferred as target prompts to perform target EAE task by key-value selection mechanism. For each event type, SPTEAE learns a special target prompt by attending to highly relevant source prompts. Experiment results show that the average performance of SPTEAE with soft prompt transfer is 2.6% higher than the current state-of-the-art model on the ACE2005 dataset.

Multi variables, complex correlation and nonlinear characteristic bring challenge to plant-wide process monitoring. In this study, a distributed kernel-global and local preserving projection (distributed KGLPP) algorithm is proposed for distributed process monitoring. First, large-scale process variables are decomposed into different blocks with mutual information. Secondly, the kernel global and local preserving projection (KGLPP) algorithm is applied into every block to detect the fault. Third, support vector data description (SVDD) is introduced to integrate the local detection results of every block and provide the global detection result. The proposed method considers nonlinear relationship of variables and simplified the calculation of fault detection. The feasibility and performance of proposed method are verified with Tennessee Eastman benchmark.

The measurement error assessment for smart electricity meters consists of the measurement error prediction and the stress factors evaluation, which can be used for improving equipment quality and saving power grid costs, especially under extreme natural environmental stresses. However, actual measurement error assessment suffers from the environmental noise and insufficient feature information. To tackle this problem, in this article, an optimized kernel density estimation (OKDE) is first proposed to identify potential outliers, where a modified distance function and adaptive kernel bandwidth are used to obtain the outlier score. Next, a measurement error assessment method, namely the modified double-kernel support vector regression (MKSVR), is proposed to fuse measurement error and multiple stress features using the modified double-kernel function. Combining the OKDE and MKSVR, actual dataset from the high dry heat region shows that the proposed assessment framework has better evaluation performance. Compared with some classical prediction methods, the OKDE–MKSVR framework has profound outlier detection and measurement error assessment performance under the small sample conditions.

The high voltage direct current (HVdc) intertie has been applied to provide ancillary-services for ac grids, utilizing the real-time feedback from phasor measurement units (PMUs). However, PMU data communication is vulnerable to false data injection attacks (FDIA) due to protocol defects, thus the HVdc ancillary control and system stability will be threatened. To address this issue, this article proposes a novel HVdc control strategy based on a hybrid data-driven (HDD) methodology. The HDD methodology is first proposed to detect the types and duration time of multiple frequency attacks. Specifically, the Hilbert Huang transform (HHT) is used to decompose the frequency data, using variational mode decomposition instead of the traditional empirical mode decomposition, to extract data features. Second, a multikernel support vector machine is proposed to classify the attacked data based on the designed distinctive features from HHT. Meanwhile, the attacking duration time is decided using an unsupervised technique. Third, an HDD-based HVdc ancillary control strategy is established to eliminate the effect of FDIAs on the HVdc frequency response. Comprehensive experiments of HDD-based HVdc ancillary controls under different FDIAs suggest that the proposed HDD could fast and accurately classify the FDIAs, and the HDD-based HVdc ancillary control strategy could significantly suppress the impact of the FDIAs.

The failure evaluation of electric energy metering equipment is essential for the equipment design and accurate measurement of electric energy, especially in extreme environmental stress. However, actual failure assessment is often affected by the environmental noise and insufficient interpretability. To address this problem, this article first proposes an improved k-nearest neighbor (IkNN) to identify potential outliers. In addition, an optimized distance function is used to obtain the score for each outlier. Next, a probability analysis method, namely, the weighted fusion Bayesian (WFB), is proposed to fuse multiple extreme environmental stresses and failure rate using the proposed nonlinear fusion function. Combining the WFB and the IkNN, examples from three extreme environmental regions show that the proposed evaluation framework has a higher assessment performance and less uncertainty. Compared with the classical prediction methods, our framework has profound outlier detection and failure prediction performance ever under the condition of small samples. More importantly, the parameters of this model are interpretable compared to some conventional approaches.

Intelligent identification of multiple power quality (PQ) disturbances is very useful for pollution control of power systems. In this paper, we propose a novel detection framework for complex PQ disturbances based on multifusion convolutional neural network (MFCNN). Our contributions focus on automatic extraction and fusion of features from multiple sources. First, an information fusion structure is introduced in which the time domain and frequency domain information of the PQ disturbance signal are used as inputs. Additionally, the one-dimensional composite convolution is proposed to improve the diversity of network features based on the standard convolution and dilated convolution. Then, to speed up the training and prevent overfitting, batch normalization is used to adjust the distribution of features. Second, we use several visualization methods to resolve the internal mode of MFCNN, and demonstrate the working mechanism of the proposed method. Finally, we conduct various experiments to verify the effectiveness of the MFCNN. Compared with the handcrafted feature design methods and the general convolutional neural network models, the simulation under different noises and hardware platform-based experiments verify the effectiveness of noise immunity, higher training speed, and better accuracy of the method.

Summary Regular expression matching as a core component of deep packet inspection is widely used in various kinds of modern network intrusion detection system, traffic classification system, network monitoring system, and so on. In these systems, regular expressions are typically converted to a deterministic finite automaton (DFA), which takes O(1) to scan each input character. However, DFA generally consumes a large amount of memory. This paper proposes a novel, space‐efficient and time‐efficient DFA presentation, called reduced input character set DFA (RICS‐DFA). A character escaping and replacing scheme is first introduced to decrease the size of DFA's character set and then to reduce DFA's space requirement with a series of optimization techniques. Based on transition rewriting, a RICS‐DFA constructing algorithm with time complexity of O(n) is presented in this paper. For real rule‐sets, RICS‐DFA reduces the memory consumption by 68–92%, compared with the original DFA. Finally, this paper designs a scalable RICS‐DFA matching engine on field‐programmable gate array platform in which the reduced state transition matrix is mapped to on‐chip memories. The throughput of executing deep packet inspection for real rule‐sets can achieve 7–50.5 Gbps. Copyright © 2016 John Wiley & Sons, Ltd.

In this study, for the problem of estimating the location and speed of a moving target in the non‐coherent multiple‐input multiple‐output (MIMO) radar systems with widely separated antennas, the authors propose two new methods, in which the parameters used are the joint of bearing, elevation, frequency‐of‐arrival (FOA) and time‐of‐arrival (TOA). The two proposed methods are based on non‐coherent MIMO radar systems, but method 1 centralises all measuring parameters in one linear equation and processes together, while method 2 divides the measurements into several groups according to the different transmitter–receiver pairs. In this study, the authors assume that bearing, elevation, FOA and TOA parameters have already been measured by a preprocessing algorithm. For the both methods, an initial guess is acquired through a best linear unbiased estimator. Then for method 1, a more explicit solution can be acquired by employing a maximum likelihood estimator for decorrelation, while the method 2 applies maximum likelihood estimation of a first‐order Taylor expansion for a better solution. The simulations show that these two methods are effective and both of them can attain to Cramér–Rao lower bound at sufficiently moderate noise conditions.

DPI technology has been widely deployed in networking intrusion detection system (NIDS) to detect attacks or viruses. State-of-the-art NIDS uses deterministic finite automata (DFA) algorithms to perform regular expression matching for its stable matching speed. However, traditional DFA algorithm's throughput is limited by the input character's width (usually one character per time). Although the multi-stride method (process multiple characters per time) can increase the throughput, it leads the DFA transition table to an exponentially increased memory consumption. In this paper, we propose a novel multi-stride regular expression matching engine called PiDFA based on Field-Programmable Gate Array (FPGA). It applies two methods to solve traditional multi-stride algorithms' memory explosion problem: DFA Transition Merging method and top-k state extraction method. Experiment results show that PiDFA achieves more than 30-fold better performance than original DFA algorithm. Whats more, PiDFA is orthogonal to existing transition table compression algorithms. Implemented with PiDFA algorithm, ClusterFA's matching speed is increased by 6-50 times while maintaining ClusterFA's low memory consumption.

The ability of ultra-low latency to process market data feed is the premise and foundation for a today's trading system to grab the instant trading profits. The market data feed containing up-to-date information on market changes is multicasted real-timely from financial exchanges to market participants, usually in the form of financial information exchange (FIX) Adapted for STreaming (FAST) protocol. FAST is a differential compression protocol which significantly reduces the bandwidth requirement to transmit market data. However, it also increases the complexity and latency of market data processing. This paper describes a customized architecture for ultra-low latency of market-data processing. Firstly, we propose a bus-based architecture of market-data decoding on Field Programmable Gate Array (FPGA). Our design is a loose-coupled and scalable architecture which is easy to adapt to different FAST templates by connecting different decoders to the main bus. Then we further exploit a dedicated pipelined design to improve the architecture. The pipelined architecture decompresses multiple messages in parallel, overcoming the challenge of data dependency between consecutive differential encoded (FAST) messages. Finally, we implement two prototypes in RTL code and evaluate them on a Xilinx Kintex-7 FPGA. Real test results show that 1) the pipelined processor gains 180% speedup compared with the non-pipelined processor; 2) it achieves an ultra-low decoding latency of 307 ns per message, which is 2 orders of magnitude faster than the software solution.

The speed of market data processing is a key factor to grab the gains and losses of instant trading profits. Typically, the market data processing systems are deployed on software platforms, which introduce high and unpredictable processing latencies. In this paper, we propose a scalable architecture for low-latency market-data processing on Field Programmable Gate Array (FPGA). A market-data processing IP library is implemented by the high-level synthesis (HLS) which automatically translates the C-coded market-data decoders to logic-coded ones. Based on the IP library, we propose a bus-based architecture of market-data decoding engine. A constructor is proposed to automatically build the decoding engines for different market-data templates. We demonstrate our design within a Xilinx Kintex-7 FPGA using three Chinese A-share templates and multiple history market-data sets. Our implementation achieves an ultra-low latency of market data processing, 0.5~1.3us per message on average, 1~2 orders of magnitude faster than a comparable software implementation.

Regular expression matching is widely used in many programming languages and applications. A regular expression is transformed into a deterministic finite automata DFA for processing. However, the DFA requires large memory resources because of the state blowup problem. Many algorithms have been proposed to compress the DFA storage and generally store the compressed DFA in sparse matrix format. For field-programmable gate array FPGA-based implementations, operations on sparse matrix consume multiple clock cycles, thus reducing the flexibility and performance of applications. To accelerate the regular expression matching, we present a compact sparse matrix format for storing the compressed DFA transition table on the FPGA. Taking advantage of the special properties of sparse matrices generated by DFAs, we can accomplish one access within a single clock cycle. Furthermore, we develop a regular expression matching engine on a Xilinx Xilinx Inc. Location: 2100 Logic Dr, San Jose, CA 95124-3400, USA Virtex-6 FPGA chip using this sparse matrix format. Compared with previous solutions, this regular expression matching engine has more flexibility while keeping high compression ratio. The results show that this regular expression matching engine saves 94% of memory space compared with the original DFA structure while keeping a fast matching speed. By running multiple engines in parallel, our design achieves a throughput up to 29Gbps. Copyright ©2013 John Wiley & Sons, Ltd.

Regular expression matching is considered important as it lies at the heart of many networking applications using deep packet inspection (DPI) techniques. For example, modern networking intrusion detection systems (NIDSs) typically accomplish regular expression matching using deterministic finite automata (DFA) algorithm. However, DFA suffers from the high memory consumption for the state blowup problem. Many algorithms have been proposed to compress the DFA memory storage space, meanwhile, they usually pay the price of low matching speed and high memory bandwidth. In this paper, we first propose an effective DFA compression algorithm by exploiting the similarity between DFA states. Then, we apply a next-state prediction strategy and present a fast DFA matching engine. Carefully designing the DFA matching circuit, we keep the prediction success rate by more than 99,5%, thus get a comparable matching speed with original DFA algorithm. On the side of memory consumption, experimental results show that with typical NIDS rule sets, our algorithm compressed the original DFA by more than 99%. Mapping this algorithm on Xilinx Virtex-7 FPGA chip, we get a throughput of more than 200Gbps.

The S transform is widely used in time‐frequency analysis (TFA). However, the standard S transform suffers from the poor energy concentration in time‐frequency distribution (TFD). In this study, an adaptive Dolph–Chebyshev window instead of the Gaussian window‐based S transform and its fast realisation strategy are introduced for non‐stationary signal TFA. By controlling the shape of the Dolph–Chebyshev window adaptively to signal, the new TFA method is able to achieve a high energy concentration in TFD. In addition, the new method is superior to other classical methods for instantaneous frequency estimation. Several examples are presented to illustrate its behaviour on different signals and demonstrate its validity.