An NT-MT Combined Method for Gross Error Detection and Data Reconciliation

An NT-MT combined method based on nodal test (NT) and measurement test (MT) is developed for gross error detection and data reconciliation for industrial application. The NT-MT combined method makes use of both NT and MT tests and this combination helps to overcome the defects in the respective methods. It also avoids any artificial manipulation and eliminates the huge combinatorial problem that is created in the combined method based on the nodal test in the case of more than one gross error for a large process system. Serial compensation strategy is also used to avoid the decrease of the coefficient matrix rank during the computation of the proposed method. Simulation results show that the proposed method is very effective and possesses good performance.

Single Epoch GPS Deformation Signals Extraction and Gross Error Detection Technique Based on Wavelet Transform

Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

Gross Error Detection and Identification Based on Parameter Estimation for Dynamic Systems

The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.

Robust Principal Component Test in Gross Error Detection and Identification

Principle component analysis （PCA） based chi-square test is more sensitive to subtle gross errors and has greater power to correctly detect gross errors than classical chi-square test. However, classical principal com- ponent test （PCT） is non-robust and can be very sensitive to one or more outliers. In this paper, a Huber function liked robust weight factor was added in the collective chi-square test to eliminate the influence of gross errors on the PCT. Meanwhile, robust chi-square test was applied to modified simultaneous estimation of gross error （MSEGE） strategy to detect and identify multiple gross errors. Simulation results show that the proposed robust test can reduce the possibility of type Ⅱ errors effectively. Adding robust chi-square test into MSEGE does not obviously improve the power of multiple gross error identification, the proposed approach considers the influence of outliers on hypothesis statistic test and is more reasonable.

A Practical Comparison between Signature Approach and Other Existing Approaches in Error Detection over TCP

Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time.

Biomedical Event Extraction Using a New Error Detection Learning Approach Based on Neural Network

Supervised machine learning approaches are effective in text mining,but their success relies heavily on manually annotated corpora.However,there are limited numbers of annotated biomedical event corpora,and the available datasets contain insufficient examples for training classifiers;the common cure is to seek large amounts of training samples from unlabeled data,but such data sets often contain many mislabeled samples,which will degrade the performance of classifiers.Therefore,this study proposes a novel error data detection approach suitable for reducing noise in unlabeled biomedical event data.First,we construct the mislabeled dataset through error data analysis with the development dataset.The sample pairs’vector representations are then obtained by the means of sequence patterns and the joint model of convolutional neural network and long short-term memory recurrent neural network.Following this,the sample identification strategy is proposed,using error detection based on pair representation for unlabeled data.With the latter,the selected samples are added to enrich the training dataset and improve the classification performance.In the BioNLP Shared Task GENIA,the experiments results indicate that the proposed approach is competent in extract the biomedical event from biomedical literature.Our approach can effectively filter some noisy examples and build a satisfactory prediction model.

Exploiting Unlabeled Data for Neural Grammatical Error Detection

Identifying and correcting grammatical errors in the text written by non-native writers have received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVM and convolutional networks with fixed-size context window.

Error detection using mode information of macroblocks for video transmission

A new method for error detection using mode information of macroblocks （MBs） is proposed. For decodable inter MBs, inter residues are calculated by adding up absolute values of received residual pixels and intra complexities are estimated by that of motion compensated reference blocks. If inter residues are larger than intra complexities by a predefined quantity, MBs are considered to be erroneous. For decodable intra MBs, the connective smoothness of the current MB with correctly decoded neighboring MBs is tested to find erroneous MBs. Combined with error concealment, the new method improves the quality of reconstructed images by about 0.5-1 dB in peak signal-noise ratio （PSNR）.

An advanced SEU tolerant latch based on error detection

This paper proposes a latch that can mitigate SEUs via an error detection circuit.The error detection circuit is hardened by a C-element and a stacked PMOS.In the hold state,a particle strikes the latch or the error detection circuit may cause a fault logic state of the circuit.The error detection circuit can detect the upset node in the latch and the fault output will be corrected.The upset node in the error detection circuit can be corrected by the C-element.The power dissipation and propagation delay of the proposed latch are analyzed by HSPICE simulations.The proposed latch consumes about 77.5%less energy and 33.1%less propagation delay than the triple modular redundancy（TMR）latch.Simulation results demonstrate that the proposed latch can mitigate SEU effectively.

A Low Power Error Detection in the Syndrome Calculator Block for Reed-Solomon Codes: RS(204,188)

Reed-Solomon （RS） codes have been widely adopted in many modern communication systems. This paper describes a new method for error detection in the syndrome calculator block of RS decoders. The main feature of this method is to prove that it is possible to compute only a few syndrome coeffi- cients -- less than half-- to detect whether the codeword is correct. The theoretical estimate of the prob- ability that the new algorithm failed is shown to depend on the number of syndrome coefficients computed. The algorithm is tested using the RS（204,188） code with the first four coefficients. With a bit error rate of 1 ~ 104, this method reduces the power consumption by 6% compared to the basic RS（204,188） decoder. The error detection algorithm for the syndrome calculator block does not require modification of the basic hardware implementation of the syndrome coefficients computation. The algorithm significantly reduces the computation complexity of the syndrome calculator block, thus lowering the power needed.

On Error Detection of Intermediate Nodes Offour-Node Network with Small Downstream Links

We investigate the error detection ability of intermedi- ate nodes of zig-zag network with small downstream link. We show that the error detection capability of zig-zag networks in the presence of z malicious edges and 2z-1 limited links from node B to node u. Also, at last we analyze a family of networks, which shows that the upper bound of network is smaller than the bound we expected, indicating that Singleton bound is not the tightest bound in the particular condition of network. According to this result, we design corresponding encode and decode strategy to reach the bound we propose in this paper.

Multi-Level Error Detection and Concealment Algorithm to Improve Speech Quality in GSM Full Rate Speech Codecs

Digital mobile telecommunication systems, such as the global system for mobile （GSM） system, want to further improve speech communication quality without changing the channel encoders and decoders. Speech quality is most affected by residual bit errors in received speech frames. Conventional methods use binary decision strategies for error detection and concealment in frames. This paper presents a multi-level error detection and concealment algorithm for GSM full rate speech codec systems. The algorithm uses multi-source knowledge to detect and conceal speech frame errors at the frame, parameter, and even bit levels. Tests show that most corrupted frames can be appropriately concealed by this algorithm, resulting in MOS gains of more than 50% for real-world data tests.

Error detection based on MB types

This paper proposes a method of error detection based on macroblock （MB） types for video transmission. For decoded inter MBs, the absolute values of received residues are accumulated. At the same time, the intra textural complexity of the current MB is estimated by that of the motion compensated reference block. We compare the inter residue with the intra textural complexity. If the inter residue is larger than the intra textural complexity by a predefined threshold, the MB is considered to be erroneous and errors are concealed. For decoded intra MBs, the connective smoothness of the current MB with neighboring MBs is tested to find erroneous MBs. Simulation results show that the new method can remove those seriously-corrupted MBs efficiently. Combined with error concealment, the new method improves the recovered quality at the decoder by about 0.5--1 dB.

ERROR DETECTION CAPABILITY OF SHORTENED HAMMING CODES AND THEIR DUAL CODES

The undetected error probability and error detection capability of shortened Hamming codes and their dual codes are studied in this paper. We also obtain some interesting properties for the shortened Simplex codes.

Detection of gross errors using mixed integer optimization approach in process industry

A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.

Improved Mixed Integer Optimization Approach for Data Rectification with Gross Error Candidates

Mixed integer linear programming （MILP） approach for simultaneous gross error detection and data reconciliation has been proved as an efficient way to adjust process data with material, energy, and other balance constrains. But the efficiency will decrease significantly when this method is applled in a large-scale problem because there are too many binary variables involved. In this article, an improved method is proposed in order to gen- erate gross error candidates with reliability factors before data rectification. Candidates are used in the MILP objec- tive function to improve the efficiency and accuracy by reducing the number of binary variables and giving accurate weights for suspected gross errors candidates. Performance of this improved method is compared and discussed by applying the algorithm in a widely used industrial example.

Error concealment in pixel based Wyner-Ziv video coding

In the Wyner-Ziv(WZ) video coding paradigm, a virtual correlation channel is assumed between the quantized source and the side information(SI) at the decoder, and channel coding is applied to achieve compression. In this paper, errors caused by the virtual correlation channel are addressed and an error concealment approach is proposed for pixel-based WZ video coding. In the approach, errors after decoding are classified into two types. Type 1 errors are caused by residual bit errors after channel decoding, while type 2 errors are due to low quality of SI in part of a frame which causes SI not lying within the quantization bin of a decoded quantized pixel value. Two separate strategies are respectively designed to detect and conceal the two types of errors. Simulations are carried out and results are presented to demonstrate the effectiveness of the proposed approach.

AN ERROR-RESILIENT H.263+ CODING SCHEME FOR VIDEO TRANSMISSION OVER WIRELESS NETWORKS

Video transmission over wireless networks has received much attention recently for its restricted bandwidth and high bit-error rate, Based on H.263＋, by reversing part stream sequences of each Group Of Block （GOB）, an error resilient scheme is presented to improve video robustness without additional bandwidth burden. Error patterns are employed to simulate Widcband Code Division Multiple Acccss,（WCDMA） channels to check out error resilience performances. Simulation results show that both subjective and objective qualities of the reconstructed images are improved remarkably. The mean Peak Signal to Noise Ratio （PSNR） is increased by 0.5dB, and the highest increment is 2dB.

Joint optimization algorithm of sensing thresholds and cooperative users in cooperative detection

Cooperative detection is an effective method to improve the spectrum sensing of Cognitive Radio (CR), and its detection performance can be improved through optimization. An optimization algorithm for cooperative detection based on "OR Rule" which can optimize the detection threshold of each user and the number of cooperative users simultaneously is proposed in this paper. The algorithm, which is based on minimizing the error detection probability, adopts partial fusion to improve the detection performance effectively. The simulation results show that the error detection probability of the proposed algorithm is lower than that of the cooperative detection algorithm with the settled threshold, and the better performance can be achieved through choosing fewer users.